Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories.
Hippocampus
Wuestefeld, A., Baumeister, H., Adams, J. N., de Flores, R., Hodgetts, C. J., Mazloum-Farzaghi, N., Olsen, R. K., Puliyadi, V., Tran, T. T., Bakker, A., Canada, K. L., Dalton, M. A., Daugherty, A. M., La Joie, R., Wang, L., Bedard, M. L., Buendia, E., Chung, E., Denning, A., Del Mar Arroyo-Jiménez, M., Artacho-Pérula, E., Irwin, D. J., Ittyerah, R., Lee, E. B., Lim, S., Del Pilar Marcos-Rabal, M., Iñiguez de Onzoño Martin, M. M., Lopez, M. M., de la Rosa Prieto, C., Schuck, T., Trotman, W., Vela, A., Yushkevich, P., Amunts, K., Augustinack, J. C., Ding, S. L., Insausti, R., Kedo, O., Berron, D., Wisse, L. E.
2024
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the entorhinal and parahippocampal cortices as well as Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 μm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized slices spaced 5 mm apart (pixel size 0.4 μm at 20× magnification). Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while the definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed less saliently. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed neuroimaging research on the human MTL cortex.
View details for DOI 10.1002/hipo.23602
View details for PubMedID 38415962
iSPAN: Explainable prediction of outcomes post thrombectomy with Machine Learning.
European journal of radiology
Kelly, B. S., Mathur, P., Vaca, S. D., Duignan, J., Power, S., Lee, E. H., Huang, Y., Prolo, L. M., Yeom, K. W., Lawlor, A., Killeen, R. P., Thornton, J.
2024; 173: 111357
Abstract
PURPOSE: This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy.MATERIALS AND METHODS: This retrospective study included all patients aged over 18years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020 with external validation. The primary outcome was day 90 mRS ≥3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN.RESULTS: 812 patients were initially included (397 female, average age 73), 63 for external validation. The best performing clinical score and ML model were SPAN and XGB (sensitivity, specificity and accuracy 0.290, 0.967, 0.628 and 0.693, 0.783, 0.738 respectively). A significant difference was found overall and our XGB model was more accurate than SPAN (p<0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of ≤2B and 3 points for ≥3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than our XGB model (p>0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67) respectively.CONCLUSION: iSPAN incorporates machine-derived features to achieve better predictions compared to existing clinical scores. It is not inferior to our XGB model and is externally generalisable.
View details for DOI 10.1016/j.ejrad.2024.111357
View details for PubMedID 38401408
A Radiomic "Warning-Sign" of Progression on Brain MRI in Individuals with MS.
AJNR. American journal of neuroradiology
Kelly, B. S., Mathur, P., McGuinness, G., Dillon, H., Lee, E. H., Yeom, K. W., Lawlor, A., Killeen, R. P.
2024
Abstract
BACKGROUND AND PURPOSE: MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS.MATERIALS AND METHODS: This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model.RESULTS: The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications.CONCLUSIONS: Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.
View details for DOI 10.3174/ajnr.A8104
View details for PubMedID 38216299
IgM N-glycosylation correlates with COVID-19 severity and rate of complement deposition.
Nature communications
Haslund-Gourley, B. S., Woloszczuk, K., Hou, J., Connors, J., Cusimano, G., Bell, M., Taramangalam, B., Fourati, S., Mege, N., Bernui, M., Altman, M. C., Krammer, F., van Bakel, H., IMPACC Network, Maecker, H. T., Rouphael, N., Diray-Arce, J., Wigdahl, B., Kutzler, M. A., Cairns, C. B., Haddad, E. K., Comunale, M. A., Ozonoff, A., Ehrlich, L. I., Melamed, E., Sesma, A. F., Simon, V., Pulendran, B., Nadeau, K. C., Davis, M. M., McCoey, G. A., Sekaly, R., Baden, L. R., Levy, O., Schaenman, J., Reed, E. F., Shaw, A. C., Hafler, D. A., Montgomery, R. R., Kleinstein, S. H., Becker, P. M., Augustine, A. D., Calfee, C. S., Erle, D. J., DeBakey, M. E., Corry, D. B., Kheradmand, F., Atkinson, M. A., Brakenridge, S. C., Higuita, N. I., Metcalf, J. P., Hough, C. L., Messer, W. B., Kraft, M., Bime, C., Peters, B., Milliren, C. E., Syphurs, C., McEnaney, K., Barton, B., Lentucci, C., Saluvan, M., Chang, A. C., Hoch, A., Albert, M., Shaheen, T., Kho, A. T., Liu, S., Thomas, S., Chen, J., Murphy, M. D., Cooney, M., Hayati, A. N., Bryant, R., Abraham, J., Jayavelu, N. D., Presnell, S., Jancsyk, T., Maguire, C., Qi, J., Lee, B., Fourati, S., Esserman, D. A., Guan, L., Gygi, J., Pawar, S., Brito, A., Fragiadakis, G. K., Patel, R., Overton, J. A., Vita, R., Westendorf, K., Shannon, C. P., Tebbutt, S. J., Thyagarajan, R. V., Rousseau, J. F., Wylie, D., Triplett, T. A., Kojic, E., Chinthrajah, S., Ahuja, N., Rogers, A. J., Artandi, M., Geng, L., Yendewa, G., Powell, D. L., Kim, J. N., Simmons, B., Goonewardene, I. M., Smith, C. M., Martens, M., Sherman, A. C., Walsh, S. R., Issa, N. C., Salehi-Rad, R., Dela Cruz, C., Farhadian, S., Iwasaki, A., Ko, A. I., Anderson, E. J., Mehta, A. K., Sevransky, J. E., Seyfert-Margolis, V., Leligdowicz, A., Matthay, M. A., Singer, J. P., Kangelaris, K. N., Hendrickson, C. M., Krummel, M. F., Langelier, C. R., Woodruff, P. G., Corry, D. B., Kheradmand, F., Anderson, M. L., Guirgis, F. W., Drevets, D. A., Brown, B. R., Siegel, S. A., Lu, Z., Mosier, J., Kimura, H., Khor, B., van Bakel, H., Rahman, A., Stadlbauer, D., Dutta, J., Xie, H., Kim-Schulze, S., Gonzalez-Reiche, A. S., van de Guchte, A., Carreno, J. M., Singh, G., Raskin, A., Tcheou, J., Bielak, D., Kawabata, H., Kelly, G., Patel, M., Nie, K., Yellin, T., Fried, M., Sullivan, L., Morris, S., Sieg, S., Steen, H., van Zalm, P., Fatou, B., Mendez, K., Lasky-Su, J., Hutton, S. R., Michelotti, G., Wong, K., Jha, M., Viode, A., Kanarek, N., Petrova, B., Zhao, Y., Bosinger, S. E., Boddapati, A. K., Tharp, G. K., Pellegrini, K. L., Beagle, E., Cowan, D., Hamilton, S., Ribeiro, S. P., Hodder, T., Rosen, L. B., Lee, S., Wilson, M. R., Dandekar, R., Alvarenga, B., Rajan, J., Eckalbar, W., Schroeder, A. W., Tsitsiklis, A., Mick, E., Guerrero, Y. S., Love, C., Maliskova, L., Adkisson, M., Siles, N., Geltman, J., Hurley, K., Saksena, M., Altman, D., Srivastava, K., Eaker, L. Q., Bermudez-Gonzalez, M. C., Beach, K. F., Sominsky, L. A., Azad, A. R., Mulder, L. C., Kleiner, G., Lee, A. S., Do, E., Fernandes, A., Manohar, M., Hagan, T., Blish, C. A., Din, H. N., Roque, J., Yang, S., Sigal, N., Chang, I., Tribout, H., Harris, P., Consolo, M., Edwards, C., Lee, E., Lin, E., Croen, B., Semenza, N. C., Rogowski, B., Melnyk, N., Bell, M. R., Furukawa, S., McLin, R., Schearer, P., Sheidy, J., Tegos, G. P., Nagle, C., Smolen, K., Desjardins, M., van Haren, S., Mitre, X., Cauley, J., Li, X., Tong, A., Evans, B., Montesano, C., Licona, J. H., Krauss, J., Chang, J. B., Izaguirre, N., Rooks, R., Elashoff, D., Brook, J., Ramires-Sanchez, E., Llamas, M., Rivera, A., Perdomo, C., Ward, D. C., Magyar, C. E., Fulcher, J. A., Pickering, H. C., Sen, S., Chaudhary, O., Coppi, A., Fournier, J., Mohanty, S., Muenker, C., Nelson, A., Raddassi, K., Rainone, M., Ruff, W. E., Salahuddin, S., Schulz, W. L., Vijayakumar, P., Wang, H., Wunder, E. J., Young, H. P., Rothman, J., Konstorum, A., Chen, E., Cotsapas, C., Grubaugh, N. D., Wang, X., Xu, L., Asashima, H., Bristow, L., Hussaini, L., Hellmeister, K., Samaha, H., Wimalasena, S. T., Cheng, A., Spainhour, C., Scherer, E. M., Johnson, B., Bechnak, A., Ciric, C. R., Hewitt, L., Carter, E., Mcnair, N., Panganiban, B., Huerta, C., Usher, J., Vaysman, T., Holland, S. M., Abe-Jones, Y., Asthana, S., Beagle, A., Bhide, S., Carrillo, S. A., Chak, S., Ghale, R., Gonzalez, A., Jauregui, A., Jones, N., Lea, T., Lee, D., Lota, R., Milush, J., Nguyen, V., Pierce, L., Prasad, P. A., Rao, A., Samad, B., Shaw, C., Sigman, A., Sinha, P., Ward, A., Willmore, A., Zhan, J., Rashid, S., Rodriguez, N., Tang, K., Altamirano, L. T., Betancourt, L., Curiel, C., Sutter, N., Paz, M. T., Tietje-Ulrich, G., Leroux, C., Thakur, N., Vasquez, J. J., Santhosh, L., Song, L., Nelson, E., Moldawer, L. L., Borresen, B., Roth-Manning, B., Ungaro, R. F., Oberhaus, J., Booth, J. L., Sinko, L. A., Brunton, A., Sullivan, P. E., Strnad, M., Lyski, Z. L., Coulter, F. J., Micheleti, C., Conway, M., Francisco, D., Molzahn, A., Erickson, H., Wilson, C. C., Schunk, R., Sierra, B., Hughes, T.
2024; 15 (1): 404
Abstract
The glycosylation of IgG plays a critical role during human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during human acute viral infection. The analysis of IgM N-glycosylation from healthy controls and hospitalized coronavirus disease 2019 (COVID-19) patients reveals increased high-mannose and sialylation that correlates with COVID-19 severity. These trends are confirmed within SARS-CoV-2-specific immunoglobulin N-glycan profiles. Moreover, the degree of total IgM mannosylation and sialylation correlate significantly with markers of disease severity. We link the changes of IgM N-glycosylation with the expression of Golgi glycosyltransferases. Lastly, we observe antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients and modulated by exoglycosidase digestion. Taken together, this work links the IgM N-glycosylation with COVID-19 severity and highlights the need to understand IgM glycosylation and downstream immune function during human disease.
View details for DOI 10.1038/s41467-023-44211-0
View details for PubMedID 38195739
Features of acute COVID-19 associated with post-acute sequelae of SARS-CoV-2 phenotypes: results from the IMPACC study.
Nature communications
Ozonoff, A., Jayavelu, N. D., Liu, S., Melamed, E., Milliren, C. E., Qi, J., Geng, L. N., McComsey, G. A., Cairns, C. B., Baden, L. R., Schaenman, J., Shaw, A. C., Samaha, H., Seyfert-Margolis, V., Krammer, F., Rosen, L. B., Steen, H., Syphurs, C., Dandekar, R., Shannon, C. P., Sekaly, R. P., Ehrlich, L. I., Corry, D. B., Kheradmand, F., Atkinson, M. A., Brakenridge, S. C., Higuita, N. I., Metcalf, J. P., Hough, C. L., Messer, W. B., Pulendran, B., Nadeau, K. C., Davis, M. M., Sesma, A. F., Simon, V., van Bakel, H., Kim-Schulze, S., Hafler, D. A., Levy, O., Kraft, M., Bime, C., Haddad, E. K., Calfee, C. S., Erle, D. J., Langelier, C. R., Eckalbar, W., Bosinger, S. E., Peters, B., Kleinstein, S. H., Reed, E. F., Augustine, A. D., Diray-Arce, J., Maecker, H. T., Altman, M. C., Montgomery, R. R., Becker, P. M., Rouphael, N.
2024; 15 (1): 216
Abstract
Post-acute sequelae of SARS-CoV-2 (PASC) is a significant public health concern. We describe Patient Reported Outcomes (PROs) on 590 participants prospectively assessed from hospital admission for COVID-19 through one year after discharge. Modeling identified 4 PRO clusters based on reported deficits (minimal, physical, mental/cognitive, and multidomain), supporting heterogenous clinical presentations in PASC, with sub-phenotypes associated with female sex and distinctive comorbidities. During the acute phase of disease, a higher respiratory SARS-CoV-2 viral burden and lower Receptor Binding Domain and Spike antibody titers were associated with both the physical predominant and the multidomain deficit clusters. A lower frequency of circulating B lymphocytes by mass cytometry (CyTOF) was observed in the multidomain deficit cluster. Circulating fibroblast growth factor 21 (FGF21) was significantly elevated in the mental/cognitive predominant and the multidomain clusters. Future efforts to link PASC to acute anti-viral host responses may help to better target treatment and prevention of PASC.
View details for DOI 10.1038/s41467-023-44090-5
View details for PubMedID 38172101
View details for PubMedCentralID PMC10764789
Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories.
bioRxiv : the preprint server for biology
Wuestefeld, A., Baumeister, H., Adams, J. N., de Flores, R., Hodgetts, C., Mazloum-Farzaghi, N., Olsen, R. K., Puliyadi, V., Tran, T. T., Bakker, A., Canada, K. L., Dalton, M. A., Daugherty, A. M., La Joie, R., Wang, L., Bedard, M., Buendia, E., Denning, A., Del Mar Arroyo-Jiménez, M., Artacho-Pérula, E., Irwin, D. J., Ittyerah, R., Lee, E. B., Lim, S., Del Pilar Marcos-Rabal, M., de Onzoño Martin, M. M., Lopez, M. M., de la Rosa Prieto, C., Schuck, T., Trotman, W., Vela, A., Yushkevich, P., Amunts, K., Augustinack, J. C., Ding, S. L., Insausti, R., Kedo, O., Berron, D., Wisse, L. E.
2023
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the cortices that make up the parahippocampal gyrus (entorhinal and parahippocampal cortices) and the adjacent Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 μm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized (20X resolution) slices with 5 mm spacing. Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed more gradually. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed human neuroimaging research on the MTL cortex.
View details for DOI 10.1101/2023.05.24.542054
View details for PubMedID 37292729
View details for PubMedCentralID PMC10245880
DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy.
European radiology
Kelly, B., Martinez, M., Do, H., Hayden, J., Huang, Y., Yedavalli, V., Ho, C., Keane, P. A., Killeen, R., Lawlor, A., Moseley, M. E., Yeom, K. W., Lee, E. H.
2023
Abstract
OBJECTIVES: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion.METHODS: All patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy.RESULTS: In total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n=194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI<3 with an AUC of 0.71.CONCLUSIONS: Our model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention).KEY POINTS: DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention. The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy. Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy).
View details for DOI 10.1007/s00330-023-09478-3
View details for PubMedID 36847835
LATE-NC staging in routine neuropathologic diagnosis: an update.
Acta neuropathologica
Nelson, P. T., Lee, E. B., Cykowski, M. D., Alafuzoff, I., Arfanakis, K., Attems, J., Brayne, C., Corrada, M. M., Dugger, B. N., Flanagan, M. E., Ghetti, B., Grinberg, L. T., Grossman, M., Grothe, M. J., Halliday, G. M., Hasegawa, M., Hokkanen, S. R., Hunter, S., Jellinger, K., Kawas, C. H., Keene, C. D., Kouri, N., Kovacs, G. G., Leverenz, J. B., Latimer, C. S., Mackenzie, I. R., Mao, Q., McAleese, K. E., Merrick, R., Montine, T. J., Murray, M. E., Myllykangas, L., Nag, S., Neltner, J. H., Newell, K. L., Rissman, R. A., Saito, Y., Sajjadi, S. A., Schwetye, K. E., Teich, A. F., Thal, D. R., Tome, S. O., Troncoso, J. C., Wang, S. J., White, C. L., Wisniewski, T., Yang, H., Schneider, J. A., Dickson, D. W., Neumann, M.
2022
Abstract
An international consensus report in 2019 recommended a classification system for limbic-predominant age-related TDP-43 encephalopathy neuropathologic changes (LATE-NC). The suggested neuropathologic staging system and nomenclature have proven useful for autopsy practice and dementia research. However, some issues remain unresolved, such as cases with unusual features that do not fit with current diagnostic categories. The goal of this report is to update the neuropathologic criteria for the diagnosis and staging of LATE-NC, based primarily on published data. We provide practical suggestions about how to integrate available genetic information and comorbid pathologies [e.g., Alzheimer's disease neuropathologic changes (ADNC) and Lewy body disease]. We also describe recent research findings that have enabled more precise guidance on how to differentiate LATE-NC from other subtypes of TDP-43 pathology [e.g., frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS)], and how to render diagnoses in unusual situations in which TDP-43 pathology does not follow the staging scheme proposed in 2019. Specific recommendations are also made on when not to apply this diagnostic term based on current knowledge. Neuroanatomical regions of interest in LATE-NC are described in detail and the implications for TDP-43 immunohistochemical results are specified more precisely. We also highlight questions that remain unresolved and areas needing additional study. In summary, the current work lays out a number of recommendations to improve the precision of LATE-NC staging based on published reports and diagnostic experience.
View details for DOI 10.1007/s00401-022-02524-2
View details for PubMedID 36512061
Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: Results from the IMPACC study.
EBioMedicine
Ozonoff, A., Schaenman, J., Jayavelu, N. D., Milliren, C. E., Calfee, C. S., Cairns, C. B., Kraft, M., Baden, L. R., Shaw, A. C., Krammer, F., van Bakel, H., Esserman, D. A., Liu, S., Sesma, A. F., Simon, V., Hafler, D. A., Montgomery, R. R., Kleinstein, S. H., Levy, O., Bime, C., Haddad, E. K., Erle, D. J., Pulendran, B., Nadeau, K. C., Davis, M. M., Hough, C. L., Messer, W. B., Higuita, N. I., Metcalf, J. P., Atkinson, M. A., Brakenridge, S. C., Corry, D., Kheradmand, F., Ehrlich, L. I., Melamed, E., McComsey, G. A., Sekaly, R., Diray-Arce, J., Peters, B., Augustine, A. D., Reed, E. F., Altman, M. C., Becker, P. M., Rouphael, N.
2022; 83: 104208
Abstract
Better understanding of the association between characteristics of patients hospitalized with coronavirus disease 2019 (COVID-19) and outcome is needed to further improve upon patient management.Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective, observational study of 1164 patients from 20 hospitals across the United States. Disease severity was assessed using a 7-point ordinal scale based on degree of respiratory illness. Patients were prospectively surveyed for 1 year after discharge for post-acute sequalae of COVID-19 (PASC) through quarterly surveys. Demographics, comorbidities, radiographic findings, clinical laboratory values, SARS-CoV-2 PCR and serology were captured over a 28-day period. Multivariable logistic regression was performed.The median age was 59 years (interquartile range [IQR] 20); 711 (61%) were men; overall mortality was 14%, and 228 (20%) required invasive mechanical ventilation. Unsupervised clustering of ordinal score over time revealed distinct disease course trajectories. Risk factors associated with prolonged hospitalization or death by day 28 included age ≥ 65 years (odds ratio [OR], 2.01; 95% CI 1.28-3.17), Hispanic ethnicity (OR, 1.71; 95% CI 1.13-2.57), elevated baseline creatinine (OR 2.80; 95% CI 1.63- 4.80) or troponin (OR 1.89; 95% 1.03-3.47), baseline lymphopenia (OR 2.19; 95% CI 1.61-2.97), presence of infiltrate by chest imaging (OR 3.16; 95% CI 1.96-5.10), and high SARS-CoV2 viral load (OR 1.53; 95% CI 1.17-2.00). Fatal cases had the lowest ratio of SARS-CoV-2 antibody to viral load levels compared to other trajectories over time (p=0.001). 589 survivors (51%) completed at least one survey at follow-up with 305 (52%) having at least one symptom consistent with PASC, most commonly dyspnea (56% among symptomatic patients). Female sex was the only associated risk factor for PASC.Integration of PCR cycle threshold, and antibody values with demographics, comorbidities, and laboratory/radiographic findings identified risk factors for 28-day outcome severity, though only female sex was associated with PASC. Longitudinal clinical phenotyping offers important insights, and provides a framework for immunophenotyping for acute and long COVID-19.NIH.
View details for DOI 10.1016/j.ebiom.2022.104208
View details for PubMedID 35952496
Attention-guided deep learning for gestational age prediction using fetal brain MRI.
Scientific reports
Shen, L., Zheng, J., Lee, E. H., Shpanskaya, K., McKenna, E. S., Atluri, M. G., Plasto, D., Mitchell, C., Lai, L. M., Guimaraes, C. V., Dahmoush, H., Chueh, J., Halabi, S. S., Pauly, J. M., Xing, L., Lu, Q., Oztekin, O., Kline-Fath, B. M., Yeom, K. W.
1800; 12 (1): 1408
Abstract
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81-0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.
View details for DOI 10.1038/s41598-022-05468-5
View details for PubMedID 35082346
Look youse guys and gals, dat just ain't right
AUTOPHAGY
Klionsky, D. J.
2021
Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.
Neurosurgery
Zhang, M., Tong, E., Hamrick, F., Lee, E. H., Tam, L. T., Pendleton, C., Smith, B. W., Hug, N. F., Biswal, S., Seekins, J., Mattonen, S. A., Napel, S., Campen, C. J., Spinner, R. J., Yeom, K. W., Wilson, T. J., Mahan, M. A.
2021
Abstract
BACKGROUND: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications.OBJECTIVE: To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs.METHODS: We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers.RESULTS: A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P=.002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P=.001).CONCLUSION: Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.
View details for DOI 10.1093/neuros/nyab212
View details for PubMedID 34131749
Correction to: Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.
European journal of nuclear medicine and molecular imaging
Wang, H., Wang, L., Lee, E. H., Zheng, J., Zhang, W., Halabi, S., Liu, C., Deng, K., Song, J., Yeom, K. W.
2021
COllaborative Neuropathology NEtwork Characterizing ouTcomes of TBI (CONNECT-TBI).
Acta neuropathologica communications
Smith, D. H., Dolle, J., Ameen-Ali, K. E., Bretzin, A., Cortes, E., Crary, J. F., Dams-O'Connor, K., Diaz-Arrastia, R., Edlow, B. L., Folkerth, R., Hazrati, L., Hinds, S. R., Iacono, D., Johnson, V. E., Keene, C. D., Kofler, J., Kovacs, G. G., Lee, E. B., Manley, G., Meaney, D., Montine, T., Okonkwo, D. O., Perl, D. P., Trojanowski, J. Q., Wiebe, D. J., Yaffe, K., McCabe, T., Stewart, W.
2021; 9 (1): 32
Abstract
Efforts to characterize the late effects of traumatic brain injury (TBI) have been in progress for some time. In recent years much of this activity has been directed towards reporting of chronic traumatic encephalopathy (CTE) in former contact sports athletes and others exposed to repetitive head impacts. However, the association between TBI and dementia risk has long been acknowledged outside of contact sports. Further, growing experience suggests a complex of neurodegenerative pathologies in those surviving TBI, which extends beyond CTE. Nevertheless, despite extensive research, we have scant knowledge of the mechanisms underlying TBI-related neurodegeneration (TReND) and its link to dementia. In part, this is due to the limited number of human brain samples linked to robust demographic and clinical information available for research. Here we detail a National Institutes for Neurological Disease and Stroke Center Without Walls project, the COllaborative Neuropathology NEtwork Characterizing ouTcomes of TBI (CONNECT-TBI), designed to address current limitations in tissue and research access and to advance understanding of the neuropathologies of TReND. As an international, multidisciplinary collaboration CONNECT-TBI brings together multiple experts across 13 institutions. In so doing, CONNECT-TBI unites the existing, comprehensive clinical and neuropathological datasets of multiple established research brain archives in TBI, with survivals ranging minutes to many decades and spanning diverse injury exposures. These existing tissue specimens will be supplemented by prospective brain banking and contribute to a centralized route of access to human tissue for research for investigators. Importantly, each new case will be subject to consensus neuropathology review by the CONNECT-TBI Expert Pathology Group. Herein we set out the CONNECT-TBI program structure and aims and, by way of an illustrative case, the approach to consensus evaluation of new case donations.
View details for DOI 10.1186/s40478-021-01122-9
View details for PubMedID 33648593
Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis
EMBO MOLECULAR MEDICINE
Placek, K., Benatar, M., Wuu, J., Rampersaud, E., Hennessy, L., Van Deerlin, V. M., Grossman, M., Irwin, D. J., Elman, L., McCluskey, L., Quinn, C., Granit, V., Statland, J. M., Burns, T. M., Ravits, J., Swenson, A., Katz, J., Pioro, E. P., Jackson, C., Caress, J., So, Y., Maiser, S., Walk, D., Lee, E. B., Trojanowski, J. Q., Cook, P., Gee, J., Sha, J., Naj, A. C., Rademakers, R., Chen, W., Wu, G., Paul Taylor, J., McMillan, C. T., CReATe Consortium
2021; 13 (1)
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition).
Autophagy
Klionsky, D. J., Abdel-Aziz, A. K., Abdelfatah, S. n., Abdellatif, M. n., Abdoli, A. n., Abel, S. n., Abeliovich, H. n., Abildgaard, M. H., Abudu, Y. P., Acevedo-Arozena, A. n., Adamopoulos, I. E., Adeli, K. n., Adolph, T. E., Adornetto, A. n., Aflaki, E. n., Agam, G. n., Agarwal, A. n., Aggarwal, B. B., Agnello, M. n., Agostinis, P. n., Agrewala, J. N., Agrotis, A. n., Aguilar, P. V., Ahmad, S. T., Ahmed, Z. M., Ahumada-Castro, U. n., Aits, S. n., Aizawa, S. n., Akkoc, Y. n., Akoumianaki, T. n., Akpinar, H. A., Al-Abd, A. M., Al-Akra, L. n., Al-Gharaibeh, A. n., Alaoui-Jamali, M. A., Alberti, S. n., Alcocer-Gómez, E. n., Alessandri, C. n., Ali, M. n., Alim Al-Bari, M. A., Aliwaini, S. n., Alizadeh, J. n., Almacellas, E. n., Almasan, A. n., Alonso, A. n., Alonso, G. D., Altan-Bonnet, N. n., Altieri, D. C., Álvarez, É. M., Alves, S. n., Alves da Costa, C. n., Alzaharna, M. M., Amadio, M. n., Amantini, C. n., Amaral, C. n., Ambrosio, S. n., Amer, A. O., Ammanathan, V. n., An, Z. n., Andersen, S. U., Andrabi, S. A., Andrade-Silva, M. n., Andres, A. M., Angelini, S. n., Ann, D. n., Anozie, U. C., Ansari, M. Y., Antas, P. n., Antebi, A. n., Antón, Z. n., Anwar, T. n., Apetoh, L. n., Apostolova, N. n., Araki, T. n., Araki, Y. n., Arasaki, K. n., Araújo, W. L., Araya, J. n., Arden, C. n., Arévalo, M. A., Arguelles, S. n., Arias, E. n., Arikkath, J. n., Arimoto, H. n., Ariosa, A. R., Armstrong-James, D. n., Arnauné-Pelloquin, L. n., Aroca, A. n., Arroyo, D. S., Arsov, I. n., Artero, R. n., Asaro, D. M., Aschner, M. n., Ashrafizadeh, M. n., Ashur-Fabian, O. n., Atanasov, A. G., Au, A. K., Auberger, P. n., Auner, H. W., Aurelian, L. n., Autelli, R. n., Avagliano, L. n., Ávalos, Y. n., Aveic, S. n., Aveleira, C. A., Avin-Wittenberg, T. n., Aydin, Y. n., Ayton, S. n., Ayyadevara, S. n., Azzopardi, M. n., Baba, M. n., Backer, J. M., Backues, S. K., Bae, D. H., Bae, O. N., Bae, S. H., Baehrecke, E. H., Baek, A. n., Baek, S. H., Baek, S. H., Bagetta, G. n., Bagniewska-Zadworna, A. n., Bai, H. n., Bai, J. n., Bai, X. n., Bai, Y. n., Bairagi, N. n., Baksi, S. n., Balbi, T. n., Baldari, C. T., Balduini, W. n., Ballabio, A. n., Ballester, M. n., Balazadeh, S. n., Balzan, R. n., Bandopadhyay, R. n., Banerjee, S. n., Banerjee, S. n., Bánréti, Á. n., Bao, Y. n., Baptista, M. S., Baracca, A. n., Barbati, C. n., Bargiela, A. n., Barilà, D. n., Barlow, P. G., Barmada, S. J., Barreiro, E. n., Barreto, G. E., Bartek, J. n., Bartel, B. n., Bartolome, A. n., Barve, G. R., Basagoudanavar, S. H., Bassham, D. C., Bast, R. C., Basu, A. n., Batoko, H. n., Batten, I. n., Baulieu, E. E., Baumgarner, B. L., Bayry, J. n., Beale, R. n., Beau, I. n., Beaumatin, F. n., Bechara, L. R., Beck, G. R., Beers, M. F., Begun, J. n., Behrends, C. n., Behrens, G. M., Bei, R. n., Bejarano, E. n., Bel, S. n., Behl, C. n., Belaid, A. n., Belgareh-Touzé, N. n., Bellarosa, C. n., Belleudi, F. n., Belló Pérez, M. n., Bello-Morales, R. n., Beltran, J. a., Beltran, S. n., Benbrook, D. M., Bendorius, M. n., Benitez, B. A., Benito-Cuesta, I. n., Bensalem, J. n., Berchtold, M. W., Berezowska, S. n., Bergamaschi, D. n., Bergami, M. n., Bergmann, A. n., Berliocchi, L. n., Berlioz-Torrent, C. n., Bernard, A. n., Berthoux, L. n., Besirli, C. G., Besteiro, S. n., Betin, V. M., Beyaert, R. n., Bezbradica, J. S., Bhaskar, K. n., Bhatia-Kissova, I. n., Bhattacharya, R. n., Bhattacharya, S. n., Bhattacharyya, S. n., Bhuiyan, M. S., Bhutia, S. K., Bi, L. n., Bi, X. n., Biden, T. J., Bijian, K. n., Billes, V. A., Binart, N. n., Bincoletto, C. n., Birgisdottir, A. B., Bjorkoy, G. n., Blanco, G. n., Blas-Garcia, A. n., Blasiak, J. n., Blomgran, R. n., Blomgren, K. n., Blum, J. S., Boada-Romero, E. n., Boban, M. n., Boesze-Battaglia, K. n., Boeuf, P. n., Boland, B. n., Bomont, P. n., Bonaldo, P. n., Bonam, S. R., Bonfili, L. n., Bonifacino, J. S., Boone, B. A., Bootman, M. D., Bordi, M. n., Borner, C. n., Bornhauser, B. C., Borthakur, G. n., Bosch, J. n., Bose, S. n., Botana, L. M., Botas, J. n., Boulanger, C. M., Boulton, M. E., Bourdenx, M. n., Bourgeois, B. n., Bourke, N. M., Bousquet, G. n., Boya, P. n., Bozhkov, P. V., Bozi, L. H., Bozkurt, T. O., Brackney, D. E., Brandts, C. H., Braun, R. J., Braus, G. H., Bravo-Sagua, R. n., Bravo-San Pedro, J. M., Brest, P. n., Bringer, M. A., Briones-Herrera, A. n., Broaddus, V. C., Brodersen, P. n., Brodsky, J. L., Brody, S. L., Bronson, P. G., Bronstein, J. M., Brown, C. N., Brown, R. E., Brum, P. C., Brumell, J. H., Brunetti-Pierri, N. n., Bruno, D. n., Bryson-Richardson, R. J., Bucci, C. n., Buchrieser, C. n., Bueno, M. n., Buitrago-Molina, L. E., Buraschi, S. n., Buch, S. n., Buchan, J. R., Buckingham, E. M., Budak, H. n., Budini, M. n., Bultynck, G. n., Burada, F. n., Burgoyne, J. R., Burón, M. I., Bustos, V. n., Büttner, S. n., Butturini, E. n., Byrd, A. n., Cabas, I. n., Cabrera-Benitez, S. n., Cadwell, K. n., Cai, J. n., Cai, L. n., Cai, Q. n., Cairó, M. n., Calbet, J. A., Caldwell, G. A., Caldwell, K. A., Call, J. A., Calvani, R. n., Calvo, A. C., Calvo-Rubio Barrera, M. n., Camara, N. O., Camonis, J. H., Camougrand, N. n., Campanella, M. n., Campbell, E. M., Campbell-Valois, F. X., Campello, S. n., Campesi, I. n., Campos, J. C., Camuzard, O. n., Cancino, J. n., Candido de Almeida, D. n., Canesi, L. n., Caniggia, I. n., Canonico, B. n., Cantí, C. n., Cao, B. n., Caraglia, M. n., Caramés, B. n., Carchman, E. H., Cardenal-Muñoz, E. n., Cardenas, C. n., Cardenas, L. n., Cardoso, S. M., Carew, J. S., Carle, G. F., Carleton, G. n., Carloni, S. n., Carmona-Gutierrez, D. n., Carneiro, L. A., Carnevali, O. n., Carosi, J. M., Carra, S. n., Carrier, A. n., Carrier, L. n., Carroll, B. n., Carter, A. B., Carvalho, A. N., Casanova, M. n., Casas, C. n., Casas, J. n., Cassioli, C. n., Castillo, E. F., Castillo, K. n., Castillo-Lluva, S. n., Castoldi, F. n., Castori, M. n., Castro, A. F., Castro-Caldas, M. n., Castro-Hernandez, J. n., Castro-Obregon, S. n., Catz, S. D., Cavadas, C. n., Cavaliere, F. n., Cavallini, G. n., Cavinato, M. n., Cayuela, M. L., Cebollada Rica, P. n., Cecarini, V. n., Cecconi, F. n., Cechowska-Pasko, M. n., Cenci, S. n., Ceperuelo-Mallafré, V. n., Cerqueira, J. J., Cerutti, J. M., Cervia, D. n., Cetintas, V. B., Cetrullo, S. n., Chae, H. J., Chagin, A. S., Chai, C. Y., Chakrabarti, G. n., Chakrabarti, O. n., Chakraborty, T. n., Chakraborty, T. n., Chami, M. n., Chamilos, G. n., Chan, D. W., Chan, E. Y., Chan, E. D., Chan, H. Y., Chan, H. H., Chan, H. n., Chan, M. T., Chan, Y. S., Chandra, P. K., Chang, C. P., Chang, C. n., Chang, H. C., Chang, K. n., Chao, J. n., Chapman, T. n., Charlet-Berguerand, N. n., Chatterjee, S. n., Chaube, S. K., Chaudhary, A. n., Chauhan, S. n., Chaum, E. n., Checler, F. n., Cheetham, M. E., Chen, C. S., Chen, G. C., Chen, J. F., Chen, L. L., Chen, L. n., Chen, L. n., Chen, M. n., Chen, M. K., Chen, N. n., Chen, Q. n., Chen, R. H., Chen, S. n., Chen, W. n., Chen, W. n., Chen, X. M., Chen, X. W., Chen, X. n., Chen, Y. n., Chen, Y. G., Chen, Y. n., Chen, Y. n., Chen, Y. J., Chen, Y. Q., Chen, Z. S., Chen, Z. n., Chen, Z. H., Chen, Z. J., Chen, Z. n., Cheng, H. n., Cheng, J. n., Cheng, S. Y., Cheng, W. n., Cheng, X. n., Cheng, X. T., Cheng, Y. n., Cheng, Z. n., Chen, Z. n., Cheong, H. n., Cheong, J. K., Chernyak, B. V., Cherry, S. n., Cheung, C. F., Cheung, C. H., Cheung, K. H., Chevet, E. n., Chi, R. J., Chiang, A. K., Chiaradonna, F. n., Chiarelli, R. n., Chiariello, M. n., Chica, N. n., Chiocca, S. n., Chiong, M. n., Chiou, S. H., Chiramel, A. I., Chiurchiù, V. n., Cho, D. H., Choe, S. K., Choi, A. M., Choi, M. E., Choudhury, K. R., Chow, N. S., Chu, C. T., Chua, J. P., Chua, J. J., Chung, H. n., Chung, K. P., Chung, S. n., Chung, S. H., Chung, Y. L., Cianfanelli, V. n., Ciechomska, I. A., Cifuentes, M. n., Cinque, L. n., Cirak, S. n., Cirone, M. n., Clague, M. J., Clarke, R. n., Clementi, E. n., Coccia, E. M., Codogno, P. n., Cohen, E. n., Cohen, M. M., Colasanti, T. n., Colasuonno, F. n., Colbert, R. A., Colell, A. n., Čolić, M. n., Coll, N. S., Collins, M. O., Colombo, M. I., Colón-Ramos, D. A., Combaret, L. n., Comincini, S. n., Cominetti, M. R., Consiglio, A. n., Conte, A. n., Conti, F. n., Contu, V. R., Cookson, M. R., Coombs, K. M., Coppens, I. n., Corasaniti, M. T., Corkery, D. P., Cordes, N. n., Cortese, K. n., Costa, M. d., Costantino, S. n., Costelli, P. n., Coto-Montes, A. n., Crack, P. J., Crespo, J. L., Criollo, A. n., Crippa, V. n., Cristofani, R. n., Csizmadia, T. n., Cuadrado, A. n., Cui, B. n., Cui, J. n., Cui, Y. n., Cui, Y. n., Culetto, E. n., Cumino, A. C., Cybulsky, A. V., Czaja, M. J., Czuczwar, S. J., D'Adamo, S. n., D'Amelio, M. n., D'Arcangelo, D. n., D'Lugos, A. C., D'Orazi, G. n., da Silva, J. A., Dafsari, H. S., Dagda, R. K., Dagdas, Y. n., Daglia, M. n., Dai, X. n., Dai, Y. n., Dai, Y. n., Dal Col, J. n., Dalhaimer, P. n., Dalla Valle, L. n., Dallenga, T. n., Dalmasso, G. n., Damme, M. n., Dando, I. n., Dantuma, N. P., Darling, A. L., Das, H. n., Dasarathy, S. n., Dasari, S. K., Dash, S. n., Daumke, O. n., Dauphinee, A. N., Davies, J. S., Dávila, V. A., Davis, R. J., Davis, T. n., Dayalan Naidu, S. n., De Amicis, F. n., De Bosscher, K. n., De Felice, F. n., De Franceschi, L. n., De Leonibus, C. n., de Mattos Barbosa, M. G., De Meyer, G. R., De Milito, A. n., De Nunzio, C. n., De Palma, C. n., De Santi, M. n., De Virgilio, C. n., De Zio, D. n., Debnath, J. n., DeBosch, B. J., Decuypere, J. P., Deehan, M. A., Deflorian, G. n., DeGregori, J. n., Dehay, B. n., Del Rio, G. n., Delaney, J. R., Delbridge, L. M., Delorme-Axford, E. n., Delpino, M. V., Demarchi, F. n., Dembitz, V. n., Demers, N. D., Deng, H. n., Deng, Z. n., Dengjel, J. n., Dent, P. n., Denton, D. n., DePamphilis, M. L., Der, C. J., Deretic, V. n., Descoteaux, A. n., Devis, L. n., Devkota, S. n., Devuyst, O. n., Dewson, G. n., Dharmasivam, M. n., Dhiman, R. n., di Bernardo, D. n., Di Cristina, M. n., Di Domenico, F. n., Di Fazio, P. n., Di Fonzo, A. n., Di Guardo, G. n., Di Guglielmo, G. M., Di Leo, L. n., Di Malta, C. n., Di Nardo, A. n., Di Rienzo, M. n., Di Sano, F. n., Diallinas, G. n., Diao, J. n., Diaz-Araya, G. n., Díaz-Laviada, I. n., Dickinson, J. M., Diederich, M. n., Dieudé, M. n., Dikic, I. n., Ding, S. n., Ding, W. X., Dini, L. n., Dinić, J. n., Dinic, M. n., Dinkova-Kostova, A. T., Dionne, M. S., Distler, J. H., Diwan, A. n., Dixon, I. M., Djavaheri-Mergny, M. n., Dobrinski, I. n., Dobrovinskaya, O. n., Dobrowolski, R. n., Dobson, R. C., Đokić, J. n., Dokmeci Emre, S. n., Donadelli, M. n., Dong, B. n., Dong, X. n., Dong, Z. n., Dorn Ii, G. W., Dotsch, V. n., Dou, H. n., Dou, J. n., Dowaidar, M. n., Dridi, S. n., Drucker, L. n., Du, A. n., Du, C. n., Du, G. n., Du, H. N., Du, L. L., du Toit, A. n., Duan, S. B., Duan, X. n., Duarte, S. P., Dubrovska, A. n., Dunlop, E. A., Dupont, N. n., Durán, R. V., Dwarakanath, B. S., Dyshlovoy, S. A., Ebrahimi-Fakhari, D. n., Eckhart, L. n., Edelstein, C. L., Efferth, T. n., Eftekharpour, E. n., Eichinger, L. n., Eid, N. n., Eisenberg, T. n., Eissa, N. T., Eissa, S. n., Ejarque, M. n., El Andaloussi, A. n., El-Hage, N. n., El-Naggar, S. n., Eleuteri, A. M., El-Shafey, E. S., Elgendy, M. n., Eliopoulos, A. G., Elizalde, M. M., Elks, P. M., Elsasser, H. P., Elsherbiny, E. S., Emerling, B. M., Emre, N. C., Eng, C. H., Engedal, N. n., Engelbrecht, A. M., Engelsen, A. S., Enserink, J. M., Escalante, R. n., Esclatine, A. n., Escobar-Henriques, M. n., Eskelinen, E. L., Espert, L. n., Eusebio, M. O., Fabrias, G. n., Fabrizi, C. n., Facchiano, A. n., Facchiano, F. n., Fadeel, B. n., Fader, C. n., Faesen, A. C., Fairlie, W. D., Falcó, A. n., Falkenburger, B. H., Fan, D. n., Fan, J. n., Fan, Y. n., Fang, E. F., Fang, Y. n., Fang, Y. n., Fanto, M. n., Farfel-Becker, T. n., Faure, M. n., Fazeli, G. n., Fedele, A. O., Feldman, A. M., Feng, D. n., Feng, J. n., Feng, L. n., Feng, Y. n., Feng, Y. n., Feng, W. n., Fenz Araujo, T. n., Ferguson, T. A., Fernández, Á. F., Fernandez-Checa, J. C., Fernández-Veledo, S. n., Fernie, A. R., Ferrante, A. W., Ferraresi, A. n., Ferrari, M. F., Ferreira, J. C., Ferro-Novick, S. n., Figueras, A. n., Filadi, R. n., Filigheddu, N. n., Filippi-Chiela, E. n., Filomeni, G. n., Fimia, G. M., Fineschi, V. n., Finetti, F. n., Finkbeiner, S. n., Fisher, E. A., Fisher, P. B., Flamigni, F. n., Fliesler, S. J., Flo, T. H., Florance, I. n., Florey, O. n., Florio, T. n., Fodor, E. n., Follo, C. n., Fon, E. A., Forlino, A. n., Fornai, F. n., Fortini, P. n., Fracassi, A. n., Fraldi, A. n., Franco, B. n., Franco, R. n., Franconi, F. n., Frankel, L. B., Friedman, S. L., Fröhlich, L. F., Frühbeck, G. n., Fuentes, J. M., Fujiki, Y. n., Fujita, N. n., Fujiwara, Y. n., Fukuda, M. n., Fulda, S. n., Furic, L. n., Furuya, N. n., Fusco, C. n., Gack, M. U., Gaffke, L. n., Galadari, S. n., Galasso, A. n., Galindo, M. F., Gallolu Kankanamalage, S. n., Galluzzi, L. n., Galy, V. n., Gammoh, N. n., Gan, B. n., Ganley, I. G., Gao, F. n., Gao, H. n., Gao, M. n., Gao, P. n., Gao, S. J., Gao, W. n., Gao, X. n., Garcera, A. n., Garcia, M. N., Garcia, V. 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A., Vannini, N. n., Vanrell, M. C., Vantaggiato, C. n., Varano, G. n., Varela-Nieto, I. n., Varga, M. n., Vasconcelos, M. H., Vats, S. n., Vavvas, D. G., Vega-Naredo, I. n., Vega-Rubin-de-Celis, S. n., Velasco, G. n., Velázquez, A. P., Vellai, T. n., Vellenga, E. n., Velotti, F. n., Verdier, M. n., Verginis, P. n., Vergne, I. n., Verkade, P. n., Verma, M. n., Verstreken, P. n., Vervliet, T. n., Vervoorts, J. n., Vessoni, A. T., Victor, V. M., Vidal, M. n., Vidoni, C. n., Vieira, O. V., Vierstra, R. D., Viganó, S. n., Vihinen, H. n., Vijayan, V. n., Vila, M. n., Vilar, M. n., Villalba, J. M., Villalobo, A. n., Villarejo-Zori, B. n., Villarroya, F. n., Villarroya, J. n., Vincent, O. n., Vindis, C. n., Viret, C. n., Viscomi, M. T., Visnjic, D. n., Vitale, I. n., Vocadlo, D. J., Voitsekhovskaja, O. V., Volonté, C. n., Volta, M. n., Vomero, M. n., Von Haefen, C. n., Vooijs, M. A., Voos, W. n., Vucicevic, L. n., Wade-Martins, R. n., Waguri, S. n., Waite, K. A., Wakatsuki, S. n., Walker, D. W., Walker, M. J., Walker, S. A., Walter, J. n., Wandosell, F. G., Wang, B. n., Wang, C. Y., Wang, C. n., Wang, C. n., Wang, C. n., Wang, C. Y., Wang, D. n., Wang, F. n., Wang, F. n., Wang, F. n., Wang, G. n., Wang, H. n., Wang, H. n., Wang, H. n., Wang, H. G., Wang, J. n., Wang, J. n., Wang, J. n., Wang, J. n., Wang, K. n., Wang, L. n., Wang, L. n., Wang, M. H., Wang, M. n., Wang, N. n., Wang, P. n., Wang, P. n., Wang, P. n., Wang, P. n., Wang, Q. J., Wang, Q. n., Wang, Q. K., Wang, Q. A., Wang, W. T., Wang, W. n., Wang, X. n., Wang, X. n., Wang, Y. n., Wang, Y. n., Wang, Y. n., Wang, Y. Y., Wang, Y. n., Wang, Y. n., Wang, Y. n., Wang, Y. n., Wang, Z. n., Wang, Z. n., Wang, Z. n., Warnes, G. n., Warnsmann, V. n., Watada, H. n., Watanabe, E. n., Watchon, M. n., Wawrzyńska, A. n., Weaver, T. E., Wegrzyn, G. n., Wehman, A. M., Wei, H. n., Wei, L. n., Wei, T. n., Wei, Y. n., Weiergräber, O. H., Weihl, C. C., Weindl, G. n., Weiskirchen, R. n., Wells, A. n., Wen, R. H., Wen, X. n., Werner, A. n., Weykopf, B. n., Wheatley, S. P., Whitton, J. L., Whitworth, A. J., Wiktorska, K. n., Wildenberg, M. E., Wileman, T. n., Wilkinson, S. n., Willbold, D. n., Williams, B. n., Williams, R. S., Williams, R. L., Williamson, P. R., Wilson, R. A., Winner, B. n., Winsor, N. J., Witkin, S. S., Wodrich, H. n., Woehlbier, U. n., Wollert, T. n., Wong, E. n., Wong, J. H., Wong, R. W., Wong, V. K., Wong, W. W., Wu, A. G., Wu, C. n., Wu, J. n., Wu, J. n., Wu, K. K., Wu, M. n., Wu, S. Y., Wu, S. n., Wu, S. Y., Wu, S. n., Wu, W. K., Wu, X. n., Wu, X. n., Wu, Y. W., Wu, Y. n., Xavier, R. J., Xia, H. n., Xia, L. n., Xia, Z. n., Xiang, G. n., Xiang, J. n., Xiang, M. n., Xiang, W. n., Xiao, B. n., Xiao, G. n., Xiao, H. n., Xiao, H. T., Xiao, J. n., Xiao, L. n., Xiao, S. n., Xiao, Y. n., Xie, B. n., Xie, C. M., Xie, M. n., Xie, Y. n., Xie, Z. n., Xie, Z. n., Xilouri, M. n., Xu, C. n., Xu, E. n., Xu, H. n., Xu, J. n., Xu, J. n., Xu, L. n., Xu, W. W., Xu, X. n., Xue, Y. n., Yakhine-Diop, S. M., Yamaguchi, M. n., Yamaguchi, O. n., Yamamoto, A. n., Yamashina, S. n., Yan, S. n., Yan, S. J., Yan, Z. n., Yanagi, Y. n., Yang, C. n., Yang, D. S., Yang, H. n., Yang, H. T., Yang, H. n., Yang, J. M., Yang, J. n., Yang, J. n., Yang, L. n., Yang, L. n., Yang, M. n., Yang, P. M., Yang, Q. n., Yang, S. n., Yang, S. n., Yang, S. F., Yang, W. n., Yang, W. Y., Yang, X. n., Yang, X. n., Yang, Y. n., Yang, Y. n., Yao, H. n., Yao, S. n., Yao, X. n., Yao, Y. G., Yao, Y. M., Yasui, T. n., Yazdankhah, M. n., Yen, P. M., Yi, C. n., Yin, X. M., Yin, Y. n., Yin, Z. n., Yin, Z. n., Ying, M. n., Ying, Z. n., Yip, C. K., Yiu, S. P., Yoo, Y. H., Yoshida, K. n., Yoshii, S. R., Yoshimori, T. n., Yousefi, B. n., Yu, B. n., Yu, H. n., Yu, J. n., Yu, J. n., Yu, L. n., Yu, M. L., Yu, S. W., Yu, V. C., Yu, W. H., Yu, Z. n., Yu, Z. n., Yuan, J. n., Yuan, L. Q., Yuan, S. n., Yuan, S. F., Yuan, Y. n., Yuan, Z. n., Yue, J. n., Yue, Z. n., Yun, J. n., Yung, R. L., Zacks, D. N., Zaffagnini, G. n., Zambelli, V. O., Zanella, I. n., Zang, Q. S., Zanivan, S. n., Zappavigna, S. n., Zaragoza, P. n., Zarbalis, K. S., Zarebkohan, A. n., Zarrouk, A. n., Zeitlin, S. O., Zeng, J. n., Zeng, J. D., Žerovnik, E. n., Zhan, L. n., Zhang, B. n., Zhang, D. D., Zhang, H. n., Zhang, H. n., Zhang, H. n., Zhang, H. n., Zhang, H. n., Zhang, H. n., Zhang, H. n., Zhang, H. L., Zhang, J. n., Zhang, J. n., Zhang, J. P., Zhang, K. Y., Zhang, L. W., Zhang, L. n., Zhang, L. n., Zhang, L. n., Zhang, L. n., Zhang, M. n., Zhang, P. n., Zhang, S. n., Zhang, W. n., Zhang, X. n., Zhang, X. W., Zhang, X. n., Zhang, X. n., Zhang, X. n., Zhang, X. n., Zhang, X. D., Zhang, Y. n., Zhang, Y. n., Zhang, Y. n., Zhang, Y. D., Zhang, Y. n., Zhang, Y. Y., Zhang, Y. n., Zhang, Z. n., Zhang, Z. n., Zhang, Z. n., Zhang, Z. n., Zhang, Z. n., Zhang, Z. n., Zhao, H. n., Zhao, L. n., Zhao, S. n., Zhao, T. n., Zhao, X. F., Zhao, Y. n., Zhao, Y. n., Zhao, Y. n., Zhao, Y. n., Zheng, G. n., Zheng, K. n., Zheng, L. n., Zheng, S. n., Zheng, X. L., Zheng, Y. n., Zheng, Z. G., Zhivotovsky, B. n., Zhong, Q. n., Zhou, A. n., Zhou, B. n., Zhou, C. n., Zhou, G. n., Zhou, H. n., Zhou, H. n., Zhou, H. n., Zhou, J. n., Zhou, J. n., Zhou, J. n., Zhou, J. n., Zhou, K. n., Zhou, R. n., Zhou, X. J., Zhou, Y. n., Zhou, Y. n., Zhou, Y. n., Zhou, Z. Y., Zhou, Z. n., Zhu, B. n., Zhu, C. n., Zhu, G. Q., Zhu, H. n., Zhu, H. n., Zhu, H. n., Zhu, W. G., Zhu, Y. n., Zhu, Y. n., Zhuang, H. n., Zhuang, X. n., Zientara-Rytter, K. n., Zimmermann, C. M., Ziviani, E. n., Zoladek, T. n., Zong, W. X., Zorov, D. B., Zorzano, A. n., Zou, W. n., Zou, Z. n., Zou, Z. n., Zuryn, S. n., Zwerschke, W. n., Brand-Saberi, B. n., Dong, X. C., Kenchappa, C. S., Li, Z. n., Lin, Y. n., Oshima, S. n., Rong, Y. n., Sluimer, J. C., Stallings, C. L., Tong, C. K.
2021: 1–382
Abstract
In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
View details for DOI 10.1080/15548627.2020.1797280
View details for PubMedID 33634751
Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.
European journal of radiology
Wang, L. n., Kelly, B. n., Lee, E. H., Wang, H. n., Zheng, J. n., Zhang, W. n., Halabi, S. n., Liu, J. n., Tian, Y. n., Han, B. n., Huang, C. n., Yeom, K. W., Deng, K. n., Song, J. n.
2021; 136: 109552
Abstract
To investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19.Between 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists. The latest version of Pyradiomics was used for feature extraction. Four classifiers (linear classifier, k-nearest neighbour, least absolute shrinkage and selection operator [LASSO], and random forest) were used to differentiate SARS-CoV-2 positive and SARS-CoV-2 negative patients. Comparison of the performance of the classifiers and radiologists was evaluated by ROC curve and Kappa score.We manually segmented 16,053 CT slices, comprising 32,625 pneumonia lesions, from the CT scans of all patients. Using Pyradiomics, 120 radiomic features were extracted from each image. The key radiomic features screened by different classifiers varied and lead to significant differences in classification accuracy. The LASSO achieved the best performance (sensitivity: 72.2%, specificity: 75.1%, and AUC: 0.81) on the external validation dataset and attained excellent agreement (Kappa score: 0.89) with radiologists (average sensitivity: 75.6%, specificity: 78.2%, and AUC: 0.81). All classifiers indicated that "Original_Firstorder_RootMeanSquared" and "Original_Firstorder_Uniformity" were significant features for this task.We identified radiomic features that were significantly associated with the classification of COVID-19 pneumonia using multiple classifiers. The quantifiable interpretation of the differences in features between the two groups extends our understanding of CT imaging characteristics of COVID-19 pneumonia.
View details for DOI 10.1016/j.ejrad.2021.109552
View details for PubMedID 33497881
Genome-wide association study and functional validation implicates JADE1 in tauopathy.
Acta neuropathologica
Farrell, K., Kim, S., Han, N., Iida, M. A., Gonzalez, E. M., Otero-Garcia, M., Walker, J. M., Richardson, T. E., Renton, A. E., Andrews, S. J., Fulton-Howard, B., Humphrey, J., Vialle, R. A., Bowles, K. R., de Paiva Lopes, K., Whitney, K., Dangoor, D. K., Walsh, H., Marcora, E., Hefti, M. M., Casella, A., Sissoko, C. T., Kapoor, M., Novikova, G., Udine, E., Wong, G., Tang, W., Bhangale, T., Hunkapiller, J., Ayalon, G., Graham, R. R., Cherry, J. D., Cortes, E. P., Borukov, V. Y., McKee, A. C., Stein, T. D., Vonsattel, J. P., Teich, A. F., Gearing, M., Glass, J., Troncoso, J. C., Frosch, M. P., Hyman, B. T., Dickson, D. W., Murray, M. E., Attems, J., Flanagan, M. E., Mao, Q., Mesulam, M. M., Weintraub, S., Woltjer, R. L., Pham, T., Kofler, J., Schneider, J. A., Yu, L., Purohit, D. P., Haroutunian, V., Hof, P. R., Gandy, S., Sano, M., Beach, T. G., Poon, W., Kawas, C. H., Corrada, M. M., Rissman, R. A., Metcalf, J., Shuldberg, S., Salehi, B., Nelson, P. T., Trojanowski, J. Q., Lee, E. B., Wolk, D. A., McMillan, C. T., Keene, C. D., Latimer, C. S., Montine, T. J., Kovacs, G. G., Lutz, M. I., Fischer, P., Perrin, R. J., Cairns, N. J., Franklin, E. E., Cohen, H. T., Raj, T., Cobos, I., Frost, B., Goate, A., White Iii, C. L., Crary, J. F.
2021
Abstract
Primary age-related tauopathy (PART) is a neurodegenerative pathology with features distinct from but also overlapping with Alzheimer disease (AD). While both exhibit Alzheimer-type temporal lobe neurofibrillary degeneration alongside amnestic cognitive impairment, PART develops independently of amyloid-β (Aβ) plaques. The pathogenesis of PART is not known, but evidence suggests an association with genes that promote tau pathology and others that protect from Aβ toxicity. Here, we performed a genetic association study in an autopsy cohort of individuals with PART (n = 647) using Braak neurofibrillary tangle stage as a quantitative trait. We found some significant associations with candidate loci associated with AD (SLC24A4, MS4A6A, HS3ST1) and progressive supranuclear palsy (MAPT and EIF2AK3). Genome-wide association analysis revealed a novel significant association with a single nucleotide polymorphism on chromosome 4 (rs56405341) in a locus containing three genes, including JADE1 which was significantly upregulated in tangle-bearing neurons by single-soma RNA-seq. Immunohistochemical studies using antisera targeting JADE1 protein revealed localization within tau aggregates in autopsy brains with four microtubule-binding domain repeats (4R) isoforms and mixed 3R/4R, but not with 3R exclusively. Co-immunoprecipitation in post-mortem human PART brain tissue revealed a specific binding of JADE1 protein to four repeat tau lacking N-terminal inserts (0N4R). Finally, knockdown of the Drosophila JADE1 homolog rhinoceros (rno) enhanced tau-induced toxicity and apoptosis in vivo in a humanized 0N4R mutant tau knock-in model, as quantified by rough eye phenotype and terminal deoxynucleotidyl transferase dUTP nick end-labeling (TUNEL) in the fly brain. Together, these findings indicate that PART has a genetic architecture that partially overlaps with AD and other tauopathies and suggests a novel role for JADE1 as a modifier of neurofibrillary degeneration.
View details for DOI 10.1007/s00401-021-02379-z
View details for PubMedID 34719765
Consensus Guidelines for the Definition of Time-to-Event End Points in Image-guided Tumor Ablation: Results of the SIO and DATECAN Initiative.
Radiology
Puijk, R. S., Ahmed, M., Adam, A., Arai, Y., Arellano, R., de Baère, T., Bale, R., Bellera, C., Binkert, C. A., Brace, C. L., Breen, D. J., Brountzos, E., Callstrom, M. R., Carrafiello, G., Chapiro, J., de Cobelli, F., Coupé, V. M., Crocetti, L., Denys, A., Dupuy, D. E., Erinjeri, J. P., Filippiadis, D., Gangi, A., Gervais, D. A., Gillams, A. R., Greene, T., Guiu, B., Helmberger, T., Iezzi, R., Kang, T. W., Kelekis, A., Kim, H. S., Kröncke, T., Kwan, S., Lee, M. W., Lee, F. T., Lee, E. W., Liang, P., Lissenberg-Witte, B. I., Lu, D. S., Madoff, D. C., Mauri, G., Meloni, M. F., Morgan, R., Nadolski, G., Narayanan, G., Newton, I., Nikolic, B., Orsi, F., Pereira, P. L., Pua, U., Rhim, H., Ricke, J., Rilling, W., Salem, R., Scheffer, H. J., Sofocleous, C. T., Solbiati, L. A., Solomon, S. B., Soulen, M. C., Sze, D., Uberoi, R., Vogl, T. J., Wang, D. S., Wood, B. J., Goldberg, S. N., Meijerink, M. R.
2021: 203715
Abstract
There is currently no consensus regarding preferred clinical outcome measures following image-guided tumor ablation or clear definitions of oncologic end points. This consensus document proposes standardized definitions for a broad range of oncologic outcome measures with recommendations on how to uniformly document, analyze, and report outcomes. The initiative was coordinated by the Society of Interventional Oncology in collaboration with the Definition for the Assessment of Time-to-Event End Points in Cancer Trials, or DATECAN, group. According to predefined criteria, based on experience with clinical trials, an international panel of 62 experts convened. Recommendations were developed using the validated three-step modified Delphi consensus method. Consensus was reached on when to assess outcomes per patient, per session, or per tumor; on starting and ending time and survival time definitions; and on time-to-event end points. Although no consensus was reached on the preferred classification system to report complications, quality of life, and health economics issues, the panel did agree on using the most recent version of a validated patient-reported outcome questionnaire. This article provides a framework of key opinion leader recommendations with the intent to facilitate a clear interpretation of results and standardize worldwide communication. Widespread adoption will improve reproducibility, allow for accurate comparisons, and avoid misinterpretations in the field of interventional oncology research. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Liddell in this issue.
View details for DOI 10.1148/radiol.2021203715
View details for PubMedID 34581627
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.
NPJ digital medicine
Lee, E. H., Zheng, J. n., Colak, E. n., Mohammadzadeh, M. n., Houshmand, G. n., Bevins, N. n., Kitamura, F. n., Altinmakas, E. n., Reis, E. P., Kim, J. K., Klochko, C. n., Han, M. n., Moradian, S. n., Mohammadzadeh, A. n., Sharifian, H. n., Hashemi, H. n., Firouznia, K. n., Ghanaati, H. n., Gity, M. n., Doğan, H. n., Salehinejad, H. n., Alves, H. n., Seekins, J. n., Abdala, N. n., Atasoy, Ç. n., Pouraliakbar, H. n., Maleki, M. n., Wong, S. S., Yeom, K. W.
2021; 4 (1): 11
Abstract
The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID-) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.
View details for DOI 10.1038/s41746-020-00369-1
View details for PubMedID 33514852
Better characterization of operation for ulcerative colitis through the National surgical quality improvement program: A 2-year audit of NSQIP-IBD.
American journal of surgery
Luo, W. Y., Holubar, S. D., Bordeianou, L., Cosman, B. C., Hyke, R., Lee, E. C., Messaris, E., Saraidaridis, J., Scow, J. S., Shaffer, V. O., Smith, R., Steinhagen, R. M., Vaida, F., Eisenstein, S., National Surgical Quality Improvement Program-Inflammatory Bowel Disease (NSQIP-IBD) Collaborative: Collaborating Institutions and Investigators, Eisenstein, S., Ramamoorthy, S., Hilbert, N., Steinhagen, R., Sylla, P., Divino, C., Miller, R., Deutsch, M., Scow, J., Huggins, P., Shogan, B., Hyman, N., Prachand, V., Sullivan, S., Hull, T., Holubar, S., Jia, X., Anzlovar, N., Bohne, S., Lee, E., Valerian, B., Keenan, M., Goyette, A., Spain, D., Hyke, R., De Leon, E., Saraidaridis, J., Lewis, W. D., Golden, T., Crawford, L., Mutch, M., Smith, R., Hall, B., Hirbe, M., Batten, J., Riccardi, R., Bordeianou, L., Kunitake, H., Antonelli, D., Swierzewski, K., Devaney, L., Messaris, E., Whyte, R., Ward, M., Cotter, M. B., Shaffer, V., Sharma, J., Lewis, J., Sitafalwalla, S., Kapadia, M., Kresowik, T., Belding-Schmitt, M., Fichera, A., Aguilar, D., Mueller, M.
2020
Abstract
INTRODUCTION: There is little consensus of quality measurements for restorative proctocolectomy with ileal pouch-anal anastomosis(RPC-IPAA) performed for ulcerative colitis(UC). The National Surgical Quality Improvement Program(NSQIP) cannot accurately classify RPC-IPAA staged approaches. We formed an IBD-surgery registry that added IBD-specific variables to NSQIP to study these staged approaches in greater detail.METHODS: We queried our validated database of IBD surgeries across 11 sites in the US from March 2017 to March 2019, containing general NSQIP and IBD-specific perioperative variables. We classified cases into delayed versus immediate pouch construction and looked for independent predictors of pouch delay and postoperative Clavien-Dindo complication severity.RESULTS: 430 patients received index surgery or completed pouches. Among completed pouches, 46(28%) and 118(72%) were immediate and delayed pouches, respectively. Significant predictors for delayed pouch surgery included higher UC surgery volume(p = 0.01) and absence of colonic dysplasia(p = 0.04). Delayed pouch formation did not significantly predict complication severity.CONCLUSIONS: Our data allows improved classification of complex operations. Curating disease-specific variables allows for better analysis of predictors of delayed versus immediate pouch construction and postoperative complication severity.SHORT SUMMARY: We applied our previously validated novel NSIP-IBD database for classifying complex, multi-stage surgical approaches for UC to a degree that was not possible prior to our collaborative effort. From this, we describe predictive factors for delayed pouch formation in UC RPC-IPAA with the largest multicenter effort to date.
View details for DOI 10.1016/j.amjsurg.2020.05.035
View details for PubMedID 32928540
A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data.
Nature machine intelligence
Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., Gillies, R., Wong, S., Thieme, A., Leung, A., Gevaert, O.
2020; 2 (5): 274-282
Abstract
Lung cancer is the most common fatal malignancy in adults worldwide, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography (CT) is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of NSCLC patients. We trained and evaluated LungNet on four independent cohorts of NSCLC patients from four medical centers: Stanford Hospital (n = 129), H. Lee Moffitt Cancer Center and Research Institute (n = 185), MAASTRO Clinic (n = 311) and Charité - Universitätsmedizin (n=84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3, and 4, respectively. Further, the survival model can be used, via transfer learning, for classifying benign vs malignant nodules on the Lung Image Database Consortium (n = 1010), with improved performance (AUC=0.85) versus training from scratch (AUC=0.82). LungNet can be used as a noninvasive predictor for prognosis in NSCLC patients and can facilitate interpretation of CT images for lung cancer stratification and prognostication.
View details for DOI 10.1038/s42256-020-0173-6
View details for PubMedID 33791593
View details for PubMedCentralID PMC8008967
Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation.
Nature medicine
Johnson, E. C., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., Thambisetty, M., Montine, T. J., Lee, E. B., Trojanowski, J. Q., Beach, T. G., Reiman, E. M., Haroutunian, V., Wang, M., Schadt, E., Zhang, B., Dickson, D. W., Ertekin-Taner, N., Golde, T. E., Petyuk, V. A., De Jager, P. L., Bennett, D. A., Wingo, T. S., Rangaraju, S., Hajjar, I., Shulman, J. M., Lah, J. J., Levey, A. I., Seyfried, N. T.
2020
Abstract
Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.
View details for DOI 10.1038/s41591-020-0815-6
View details for PubMedID 32284590
4D Flow MR Imaging to Improve Microwave Ablation Prediction Models: A Feasibility Study in an InVivo Porcine Liver.
Journal of vascular and interventional radiology : JVIR
Chiang, J., Loecher, M., Moulin, K., Meloni, M. F., Raman, S. S., McWilliams, J. P., Ennis, D. B., Lee, E. W.
2020
Abstract
PURPOSE: To characterize the effect of hepatic vessel flow using 4-dimensional (4D) flow magnetic resonance (MR) imaging and correlate their effect on microwave ablation volumes in an invivo non-cirrhotic porcine liver model.MATERIALS AND METHODS: Microwave ablation antennas were placed under ultrasound guidance in each liver lobe of swine (n= 3 in each animal) for a total of 9 ablations. Pre- and post-ablation 4D flow MR imaging was acquired to quantify flow changes in the hepatic vasculature. Flow measurements, along with encompassed vessel size and vessel-antenna spacing, were then correlated with final ablation volume from segmented MR images.RESULTS: The linear regression model demonstrated that the preablation measurement of encompassed hepatic vein size (beta= -0.80 ±0.25, 95% confidence interval [CI] -1.15 to -0.22; P= .02) was significantly correlated to final ablation zone volume. The addition of hepatic vein flow rate found via 4D flow MRI (beta= -0.83 ± 0.65, 95% CI -2.50 to 0.84; P= .26), and distance from antenna to hepatic vein (beta= 0.26 ±0.26, 95% CI -0.40 to 0.92; P= .36) improved the model accuracy but not significantly so (multivariate adjusted R2= 0.70 vs univariate (vessel size) adjusted R2= 0.63, P= .24).CONCLUSIONS: Hepatic vein size in an encompassed ablation zone was found to be significantly correlated with final ablation zone volume. Although the univariate 4D flow MR imaging-acquired measurements alone were not found to be statistically significant, its addition to hepatic vein size improved the accuracy of the ablation volume regression model. Pre-ablation 4D flow MR imaging of the liver may assist in prospectively optimizing thermal ablation treatment.
View details for DOI 10.1016/j.jvir.2019.11.034
View details for PubMedID 32178944
Assessment of Panfacial Fractures in the Pediatric Population.
Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
Dalena, M. M., Liu, F. C., Halsey, J. N., Lee, E. S., Granick, M. S.
2020
Abstract
PURPOSE: Management of panfacial fractures is critical and often difficult in adults; however, there is little to no literature regarding these fractures in the pediatric population. In this study, we present our experience to provide insight and further investigation regarding prevention and management strategies within the pediatric population.PATIENTS AND METHODS: We performed a retrospective chart review of all panfacial fractures in the pediatric population between 2002 and 2014 treated at an urban, level 1 trauma center (University Hospital, Newark, NJ). Data including patient demographic characteristics, mechanisms of injury, locations of fractures, concomitant injuries, and surgical management strategies were collected.RESULTS: We identified 82 patients aged 18years or younger who had sustained a panfacial fracture. The mean age at the time of injury was 12.9years, with a male predominance of 64.9%. A total of 335 fractures were identified on radiologic imaging. The most common etiologies were motor vehicle accidents and pedestrians being struck. Orbital, frontal sinus, nasal, and zygoma fractures were the most common fractures. The mean score on the Glasgow Coma Scale on arrival was 12.0. A total of 29 patients were intubated on arrival-or before arrival-at the trauma bay. A surgical airway was required in 9 patients. The most common concomitant injuries were traumatic brain injury, intracranial hemorrhage, and skull fracture. Surgical repair was required in 38 patients. The cephalic-to-caudal approach was used most, followed by caudal to cephalic, medial to lateral, and lateral to medial. Within a year of the initial surgical procedure, 4 patients underwent reoperations for complications. Four patients died.CONCLUSIONS: Pediatric panfacial fractures are rare occurrences; however, the impact of these injuries can be devastating, with concomitant life-threatening injuries and complications. Given the lack of literature, as well as the preventable nature of these injuries, we hope this study can address primary prevention strategies and provide insight toward the management and characteristics of these fractures.
View details for DOI 10.1016/j.joms.2020.03.001
View details for PubMedID 32247625
Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.
European journal of nuclear medicine and molecular imaging
Wang, H. n., Wang, L. n., Lee, E. H., Zheng, J. n., Zhang, W. n., Halabi, S. n., Liu, C. n., Deng, K. n., Song, J. n., Yeom, K. W.
2020
Abstract
High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework.A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia.120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model.We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance.
View details for DOI 10.1007/s00259-020-05075-4
View details for PubMedID 33094432
Simultaneous time of flight-MRA and T2* imaging for cerebrovascular MRI.
Neuroradiology
Lanzman, B. A., Huang, Y. n., Lee, E. H., Iv, M. n., Moseley, M. E., Holdsworth, S. J., Yeom, K. W.
2020
Abstract
3D multi-echo gradient-recalled echo (ME-GRE) can simultaneously generate time-of-flight magnetic resonance angiography (pTOF) in addition to T2*-based susceptibility-weighted images (SWI). We assessed the clinical performance of pTOF generated from a 3D ME-GRE acquisition compared with conventional TOF-MRA (cTOF).Eighty consecutive children were retrospectively identified who obtained 3D ME-GRE alongside cTOF. Two blinded readers independently assessed pTOF derived from 3D ME-GRE and compared them with cTOF. A 5-point Likert scale was used to rank lesion conspicuity and to assess for diagnostic confidence.Across 80 pediatric neurovascular pathologies, a similar number of lesions were reported on pTOF and cTOF (43-40%, respectively, p > 0.05). Rating of lesion conspicuity was higher with cTOF (4.5 ± 1.0) as compared with pTOF (4.0 ± 0.7), but this was not significantly different (p = 0.06). Diagnostic confidence was rated higher with cTOF (4.8 ± 0.5) than that of pTOF (3.7 ± 0.6; p < 0.001). Overall, the inter-rater agreement between two readers for lesion count on pTOF was classified as almost perfect (κ = 0.98, 96% CI 0.8-1.0).In this study, TOF-MRA simultaneously generated in addition to SWI from 3D MR-GRE can serve as a diagnostic adjunct, particularly for proximal vessel disease and when conventional TOF-MRA images are absent.
View details for DOI 10.1007/s00234-020-02499-5
View details for PubMedID 32945913
Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis.
EMBO molecular medicine
Placek, K. n., Benatar, M. n., Wuu, J. n., Rampersaud, E. n., Hennessy, L. n., Van Deerlin, V. M., Grossman, M. n., Irwin, D. J., Elman, L. n., McCluskey, L. n., Quinn, C. n., Granit, V. n., Statland, J. M., Burns, T. M., Ravits, J. n., Swenson, A. n., Katz, J. n., Pioro, E. P., Jackson, C. n., Caress, J. n., So, Y. n., Maiser, S. n., Walk, D. n., Lee, E. B., Trojanowski, J. Q., Cook, P. n., Gee, J. n., Sha, J. n., Naj, A. C., Rademakers, R. n., Chen, W. n., Wu, G. n., Paul Taylor, J. n., McMillan, C. T.
2020: e12595
Abstract
Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post-mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.
View details for DOI 10.15252/emmm.202012595
View details for PubMedID 33270986
Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.
Journal of neurosurgery. Pediatrics
Quon, J. L., Han, M. n., Kim, L. H., Koran, M. E., Chen, L. C., Lee, E. H., Wright, J. n., Ramaswamy, V. n., Lober, R. M., Taylor, M. D., Grant, G. A., Cheshier, S. H., Kestle, J. R., Edwards, M. S., Yeom, K. W.
2020: 1–8
Abstract
Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.
View details for DOI 10.3171/2020.6.PEDS20251
View details for PubMedID 33260138
A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets
Nature Machine Intelligence
Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., Gillies, R., Wong, S., Thieme, A., Leung, A., Gevaert, O.
2020; 2 (5): 274–282
Elevated Risk for Sessile Serrated Polyps in African Americans with Endometrial Polyps.
Digestive diseases and sciences
Ashktorab, H., Sherif, Z., Tarjoman, T., Azam, S., Lee, E., Shokrani, B., Okereke, I., Soleimani, A., Carethers, J. M., Laiyemo, A. O., Aduli, F., Nouraie, M., Habtezion, A., Brim, H.
2019
Abstract
BACKGROUND: Colorectal and endometrial lesions increase with age. It is not known if these two precursor lesions in sporadic cases associate with each other.AIM: To determine the association between colorectal polyps and endometrial polyps (EP) in African Americans.METHODS: We reviewed records of patients referred to gynecology clinics and had colonoscopy at Howard University Hospital from January 2004 to December 2015. We defined cases as all patients who had EP and underwent colonoscopy. For controls, we used EP-free patients who underwent colonoscopy. Logistic regression analysis was used to assess the association between colon polyps and EP.RESULTS: The median age was 60years in 118 Cases and 57years in 664 Controls. The overall colorectal polyps prevalence in the two groups was not statistically different (54% in controls vs. 52% in cases, P=0.60). Sessile serrated adenoma/polyps (SSPs) were more frequent in cases (8% vs. 2% in controls, P=0.003). Sigmoid and rectal locations were more prevalent in controls than cases. In multivariate analysis and after adjusting for age, diabetes mellitus (DM), and BMI, SSPs were associated with EP occurrence with an odds ratio of 4.6 (CI 1.2-16.7, P=0.022).CONCLUSION: Colorectal polyp prevalence was similar in EP patients compared to EP-free controls. However, we observed a significant association between higher-risk SSPs in patients with EP. The prevalence of smoking and DM was higher in these patients. Females with EP might benefit from a screening for colonic lesions in an age-independent manner.
View details for DOI 10.1007/s10620-019-05991-y
View details for PubMedID 31832971
Deep-Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.
Journal of vascular and interventional radiology : JVIR
Ni, J. C., Shpanskaya, K., Han, M., Lee, E. H., Do, B. H., Kuo, W. T., Yeom, K. W., Wang, D. S.
2019
Abstract
PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs.MATERIALS AND METHODS: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set.RESULTS: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction.CONCLUSIONS: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.
View details for DOI 10.1016/j.jvir.2019.05.026
View details for PubMedID 31542278
Reduced field of view echo-planar imaging diffusion tensor MRI for pediatric spinal tumors.
Journal of neurosurgery. Spine
Kim, L. H., Lee, E. H., Galvez, M., Aksoy, M., Skare, S., O'Halloran, R., Edwards, M. S., Holdsworth, S. J., Yeom, K. W.
2019: 1–9
Abstract
OBJECTIVE: Spine MRI is a diagnostic modality for evaluating pediatric CNS tumors. Applying diffusion-weighted MRI (DWI) or diffusion tensor imaging (DTI) to the spine poses challenges due to intrinsic spinal anatomy that exacerbates various image-related artifacts, such as signal dropouts or pileups, geometrical distortions, and incomplete fat suppression. The zonal oblique multislice (ZOOM)-echo-planar imaging (EPI) technique reduces geometric distortion and image blurring by reducing the field of view (FOV) without signal aliasing into the FOV. The authors hypothesized that the ZOOM-EPI method for spine DTI in concert with conventional spinal MRI is an efficient method for augmenting the evaluation of pediatric spinal tumors.METHODS: Thirty-eight consecutive patients (mean age 8 years) who underwent ZOOM-EPI spine DTI for CNS tumor workup were retrospectively identified. Patients underwent conventional spine MRI and ZOOM-EPI DTI spine MRI. Two blinded radiologists independently reviewed two sets of randomized images: conventional spine MRI without ZOOM-EPI DTI, and conventional spine MRI with ZOOM-EPI DTI. For both image sets, the reviewers scored the findings based on lesion conspicuity and diagnostic confidence using a 5-point Likert scale. The reviewers also recorded presence of tumors. Quantitative apparent diffusion coefficient (ADC) measurements of various spinal tumors were extracted. Tractography was performed in a subset of patients undergoing presurgical evaluation.RESULTS: Sixteen patients demonstrated spinal tumor lesions. The readers were in moderate agreement (kappa = 0.61, 95% CI 0.30-0.91). The mean scores for conventional MRI and combined conventional MRI and DTI were as follows, respectively: 3.0 and 4.0 for lesion conspicuity (p = 0.0039), and 2.8 and 3.9 for diagnostic confidence (p < 0.001). ZOOM-EPI DTI identified new lesions in 3 patients. In 3 patients, tractography used for neurosurgical planning showed characteristic fiber tract projections. The mean weighted ADCs of low- and high-grade tumors were 1201 * 10-6 and 865 * 10-6 mm2/sec (p = 0.002), respectively; the mean minimum weighted ADCs were 823 * 10-6 and 474 * 10-6 mm2/sec (p = 0.0003), respectively.CONCLUSIONS: Diffusion MRI with ZOOM-EPI can improve the detection of spinal lesions while providing quantitative diffusion information that helps distinguish low- from high-grade tumors. By adding a 2-minute DTI scan, quantitative diffusion information and tract profiles can reliably be obtained and serve as a useful adjunct to presurgical planning for pediatric spinal tumors.
View details for DOI 10.3171/2019.4.SPINE19178
View details for PubMedID 31277060
Primum non nocere: a call for balance when reporting on CTE.
The Lancet. Neurology
Stewart, W., Allinson, K., Al-Sarraj, S., Bachmeier, C., Barlow, K., Belli, A., Burns, M. P., Carson, A., Crawford, F., Dams-O'Connor, K., Diaz-Arrastia, R., Dixon, C. E., Edlow, B. L., Ferguson, S., Fischl, B., Folkerth, R. D., Gentleman, S., Giza, C. C., Grady, M. S., Helmy, A., Herceg, M., Holton, J. L., Howell, D., Hutchinson, P. J., Iacono, D., Iglesias, J. E., Ikonomovic, M. D., Johnson, V. E., Keene, C. D., Kofler, J. K., Koliatsos, V. E., Lee, E. B., Levin, H., Lifshitz, J., Ling, H., Loane, D. J., Love, S., Maas, A. I., Marklund, N., Master, C. L., McElvenny, D. M., Meaney, D. F., Menon, D. K., Montine, T. J., Mouzon, B., Mufson, E. J., Ojo, J. O., Prins, M., Revesz, T., Ritchie, C. W., Smith, C., Sylvester, R., Tang, C. Y., Trojanowski, J. Q., Urankar, K., Vink, R., Wellington, C., Wilde, E. A., Wilson, L., Yeates, K., Smith, D. H.
2019; 18 (3): 231–33
Cardiovascular, respiratory, and related disorders: key messages from Disease Control Priorities, 3rd edition
LANCET
Prabhakaran, D., Anand, S., Watkins, D., Gaziano, T., Wu, Y., Mbanya, J., Nugent, R., Dis Control Priorities Cardiovasc
2018; 391 (10126): 1224–36
Abstract
Cardiovascular, respiratory, and related disorders (CVRDs) are the leading causes of adult death worldwide, and substantial inequalities in care of patients with CVRDs exist between countries of high income and countries of low and middle income. Based on current trends, the UN Sustainable Development Goal to reduce premature mortality due to CVRDs by a third by 2030 will be challenging for many countries of low and middle income. We did systematic literature reviews of effectiveness and cost-effectiveness to identify priority interventions. We summarise the key findings and present a costed essential package of interventions to reduce risk of and manage CVRDs. On a population level, we recommend tobacco taxation, bans on trans fats, and compulsory reduction of salt in manufactured food products. We suggest primary health services be strengthened through the establishment of locally endorsed guidelines and ensured availability of essential medications. The policy interventions and health service delivery package we suggest could serve as the cornerstone for the management of CVRDs, and afford substantial financial risk protection for vulnerable households. We estimate that full implementation of the essential package would cost an additional US$21 per person in the average low-income country and $24 in the average lower-middle-income country. The essential package we describe could be a starting place for low-income and middle-income countries developing universal health coverage packages. Interventions could be rolled out as disease burden demands and budgets allow. Our outlined interventions provide a pathway for countries attempting to convert the UN Sustainable Development Goal commitments into tangible action.
View details for PubMedID 29108723
Genome-wide Analyses Identify KIF5A as a Novel ALS Gene.
Neuron
Nicolas, A. n., Kenna, K. P., Renton, A. E., Ticozzi, N. n., Faghri, F. n., Chia, R. n., Dominov, J. A., Kenna, B. J., Nalls, M. A., Keagle, P. n., Rivera, A. M., van Rheenen, W. n., Murphy, N. A., van Vugt, J. J., Geiger, J. T., Van der Spek, R. A., Pliner, H. A., Shankaracharya, n. n., Smith, B. N., Marangi, G. n., Topp, S. D., Abramzon, Y. n., Gkazi, A. S., Eicher, J. D., Kenna, A. n., Mora, G. n., Calvo, A. n., Mazzini, L. n., Riva, N. n., Mandrioli, J. n., Caponnetto, C. n., Battistini, S. n., Volanti, P. n., La Bella, V. n., Conforti, F. L., Borghero, G. n., Messina, S. n., Simone, I. L., Trojsi, F. n., Salvi, F. n., Logullo, F. O., D'Alfonso, S. n., Corrado, L. n., Capasso, M. n., Ferrucci, L. n., Moreno, C. d., Kamalakaran, S. n., Goldstein, D. B., Gitler, A. D., Harris, T. n., Myers, R. M., Phatnani, H. n., Musunuri, R. L., Evani, U. S., Abhyankar, A. n., Zody, M. C., Kaye, J. n., Finkbeiner, S. n., Wyman, S. K., LeNail, A. n., Lima, L. n., Fraenkel, E. n., Svendsen, C. N., Thompson, L. M., Van Eyk, J. E., Berry, J. D., Miller, T. M., Kolb, S. J., Cudkowicz, M. n., Baxi, E. n., Benatar, M. n., Taylor, J. P., Rampersaud, E. n., Wu, G. n., Wuu, J. n., Lauria, G. n., Verde, F. n., Fogh, I. n., Tiloca, C. n., Comi, G. P., Sorarù, G. n., Cereda, C. n., Corcia, P. n., Laaksovirta, H. n., Myllykangas, L. n., Jansson, L. n., Valori, M. n., Ealing, J. n., Hamdalla, H. n., Rollinson, S. n., Pickering-Brown, S. n., Orrell, R. W., Sidle, K. C., Malaspina, A. n., Hardy, J. n., Singleton, A. B., Johnson, J. O., Arepalli, S. n., Sapp, P. C., McKenna-Yasek, D. n., Polak, M. n., Asress, S. n., Al-Sarraj, S. n., King, A. n., Troakes, C. n., Vance, C. n., de Belleroche, J. n., Baas, F. n., Ten Asbroek, A. L., Muñoz-Blanco, J. L., Hernandez, D. G., Ding, J. n., Gibbs, J. R., Scholz, S. W., Floeter, M. K., Campbell, R. H., Landi, F. n., Bowser, R. n., Pulst, S. M., Ravits, J. M., MacGowan, D. J., Kirby, J. n., Pioro, E. P., Pamphlett, R. n., Broach, J. n., Gerhard, G. n., Dunckley, T. L., Brady, C. B., Kowall, N. W., Troncoso, J. C., Le Ber, I. n., Mouzat, K. n., Lumbroso, S. n., Heiman-Patterson, T. D., Kamel, F. n., Van Den Bosch, L. n., Baloh, R. H., Strom, T. M., Meitinger, T. n., Shatunov, A. n., Van Eijk, K. R., de Carvalho, M. n., Kooyman, M. n., Middelkoop, B. n., Moisse, M. n., McLaughlin, R. L., Van Es, M. A., Weber, M. n., Boylan, K. B., Van Blitterswijk, M. n., Rademakers, R. n., Morrison, K. E., Basak, A. N., Mora, J. S., Drory, V. E., Shaw, P. J., Turner, M. R., Talbot, K. n., Hardiman, O. n., Williams, K. L., Fifita, J. A., Nicholson, G. A., Blair, I. P., Rouleau, G. A., Esteban-Pérez, J. n., García-Redondo, A. n., Al-Chalabi, A. n., Rogaeva, E. n., Zinman, L. n., Ostrow, L. W., Maragakis, N. J., Rothstein, J. D., Simmons, Z. n., Cooper-Knock, J. n., Brice, A. n., Goutman, S. A., Feldman, E. L., Gibson, S. B., Taroni, F. n., Ratti, A. n., Gellera, C. n., Van Damme, P. n., Robberecht, W. n., Fratta, P. n., Sabatelli, M. n., Lunetta, C. n., Ludolph, A. C., Andersen, P. M., Weishaupt, J. H., Camu, W. n., Trojanowski, J. Q., Van Deerlin, V. M., Brown, R. H., van den Berg, L. H., Veldink, J. H., Harms, M. B., Glass, J. D., Stone, D. J., Tienari, P. n., Silani, V. n., Chiò, A. n., Shaw, C. E., Traynor, B. J., Landers, J. E.
2018; 97 (6): 1268–83.e6
Abstract
To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.
View details for PubMedID 29566793
Cyanotic Congenital Heart Disease : Essential Primer for the Practicing Radiologist
RADIOLOGIC CLINICS OF NORTH AMERICA
Zucker, E. J., Koning, J. L., Lee, E. Y.
2017; 55 (4): 693-+
Abstract
The cyanotic congenital heart diseases are a rare and heterogeneous group of disorders, often requiring urgent neonatal management. Although echocardiography is the mainstay for imaging, continued technological advances have expanded the role for computed tomography and magnetic resonance imaging, helping to limit invasive cardiac catheterization. In this article, the authors review the broad spectrum of cyanotic congenital heart disease, focusing on the utility of advanced noninvasive imaging modalities while highlighting key clinical features and management considerations.
View details for PubMedID 28601176
Analysis and Design of a Passive Switched-Capacitor Matrix Multiplier for Approximate Computing
IEEE JOURNAL OF SOLID-STATE CIRCUITS
Lee, E. H., Wong, S. S.
2017; 52 (1): 261-271
Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)
AUTOPHAGY
Klionsky, D. J., Abdelmohsen, K., Abe, A., Abedin, M. J., Abeliovich, H., Arozena, A. A., Adachi, H., Adams, C. M., Adams, P. D., Adeli, K., Adhihetty, P. J., Adler, S. G., Agam, G., Agarwal, R., Aghi, M. K., Agnello, M., Agostinis, P., Aguilar, P. V., Aguirre-Ghiso, J., Airoldi, E. M., Ait-Si-Ali, S., Akematsu, T., Akporiaye, E. T., Al-Rubeai, M., Albaiceta, G. M., Albanese, C., Albani, D., Albert, M. L., Aldudo, J., Alguel, H., Alirezaei, M., Alloza, I., Almasan, A., Almonte-Beceril, M., Alnemri, E. S., Alonso, C., Altan-Bonnet, N., Altieri, D. C., Alvarez, S., Alvarez-Erviti, L., Alves, S., Amadoro, G., Amano, A., Amantini, C., Ambrosio, S., Amelio, I., Amer, A. O., Amessou, M., Amon, A., An, Z., Anania, F. A., Andersen, S. U., Andley, U. P., Andreadi, C. K., Andrieu-Abadie, N., Anel, A., Ann, D. K., Anoopkumar-Dukie, S., Antonioli, M., Aoki, H., Apostolova, N., Aquila, S., Aquilano, K., Araki, K., Arama, E., Aranda, A., Araya, J., Arcaro, A., Arias, E., Arimoto, H., Ariosa, A. R., Armstrong, J. L., Arnould, T., Arsov, I., Asanuma, K., Askanas, V., Asselin, E., Atarashi, R., Atherton, S. S., Atkin, J. D., Attardi, L. D., Auberger, P., Auburger, G., Aurelian, L., Autelli, R., Avagliano, L., Avantaggiati, M. L., Avrahami, L., Awale, S., Azad, N., Bachetti, T., Backer, J. M., Bae, D., Bae, J., Bae, O., Bae, S. H., Baehrecke, E. H., Baek, S., Baghdiguian, S., Bagniewska-Zadworna, A., Bai, H., Bai, J., Bai, X., Bailly, Y., Balaji, K. N., Balduini, W., Ballabio, A., Balzan, R., Banerjee, R., Banhegyi, G., Bao, H., Barbeau, B., Barrachina, M. D., Barreiro, E., Bartel, B., Bartolome, A., Bassham, D. C., Bassi, M. T., Bast, R. C., Basu, A., Batista, M. T., Batoko, H., Battino, M., Bauckman, K., Baumgarner, B. L., Bayer, K. U., Beale, R., Beaulieu, J., Beck, G. R., Becker, C., Beckham, J. D., Bedard, P., Bednarski, P. J., Begley, T. J., Behl, C., Behrends, C., Behrens, G. M., Behrns, K. E., Bejarano, E., Belaid, A., Belleudi, F., Benard, G., Berchem, G., Bergamaschi, D., Bergami, M., Berkhout, B., Berliocchi, L., Bernard, A., Bernard, M., Bernassola, F., Bertolotti, A., Bess, A. S., Besteiro, S., Bettuzzi, S., Bhalla, S., Bhattacharyya, S., Bhutia, S. K., Biagosch, C., Bianchi, M. W., Biard-Piechaczyk, M., Billes, V., Bincoletto, C., Bingol, B., Bird, S. W., Bitoun, M., Bjedov, I., Blackstone, C., Blanc, L., Blanco, G. A., Blomhoff, H. K., Boada-Romero, E., Boeckler, S., Boes, M., Boesze-Battaglia, K., Boise, L. H., Bolino, A., Boman, A., Bonaldo, P., Bordi, M., Bosch, J., Botana, L. M., Botti, J., Bou, G., Bouche, M., Bouchecareilh, M., Boucher, M., Boulton, M. E., Bouret, S. G., Boya, P., Boyer-Guittaut, M., Bozhkov, P. V., Brady, N., Braga, V. M., Brancolini, C., Braus, G. H., Bravo-San Pedro, J. M., Brennan, L. A., Bresnick, E. H., Brest, P., Bridges, D., Bringer, M., Brini, M., Brito, G. C., Brodin, B., Brookes, P. S., Brown, E. J., Brown, K., Broxmeyer, H. E., Bruhat, A., Brum, P. C., Brumell, J. H., Brunetti-Pierri, N., Bryson-Richardson, R. J., Buch, S., Buchan, A. M., Budak, H., Bulavin, D. V., Bultman, S. J., Bultynck, G., Bumbasirevic, V., Burelle, Y., Burke, R. E., Burmeister, M., Buetikofer, P., Caberlotto, L., Cadwell, K., Cahova, M., Cai, D., Cai, J., Cai, Q., Calatayud, S., Camougrand, N., Campanella, M., Campbell, G. R., Campbell, M., Campello, S., Candau, R., Caniggia, I., Cantoni, L., Cao, L., Caplan, A. B., Caraglia, M., Cardinali, C., Cardoso, S. M., Carew, J. S., Carleton, L. A., Carlin, C. R., Carloni, S., Carlsson, S. R., Carmona-Gutierrez, D., Carneiro, L. A., Carnevali, O., Carra, S., Carrier, A., Carroll, B., Casas, C., Casas, J., Cassinelli, G., Castets, P., Castro-Obregon, S., Cavallini, G., Ceccherini, I., Cecconi, F., Cederbaum, A. I., Cena, V., Cenci, S., Cerella, C., Cervia, D., Cetrullo, S., Chaachouay, H., Chae, H., Chagin, A. S., Chai, C., Chakrabarti, G., Chamilos, G., Chan, E. Y., Chan, M. T., Chandra, D., Chandra, P., Chang, C., Chang, R. C., Chang, T. Y., Chatham, J. C., Chatterjee, S., Chauhan, S., Che, Y., Cheetham, M. E., Cheluvappa, R., Chen, C., Chen, G., Chen, G., Chen, G., Chen, H., Chen, J. W., Chen, J., Chen, M., Chen, M., Chen, P., Chen, Q., Chen, Q., Chen, S., Chen, S., Chen, S. S., Chen, W., Chen, W., Chen, W. Q., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Y., Chen, Y., Chen, Y., Chen, Y., Chen, Y., Chen, Z., Chen, Z., Cheng, A., Cheng, C. H., Cheng, H., Cheong, H., Cherry, S., Chesney, J., Cheung, C. H., Chevet, E., Chi, H. C., Chi, S., Chiacchiera, F., Chiang, H., Chiarelli, R., Chiariello, M., Chieppa, M., Chin, L., Chiong, M., Chiu, G. N., Cho, D., Cho, S., Cho, W. C., Cho, Y., Cho, Y., Choi, A. M., Choi, E., Choi, E., Choi, J., Choi, M. E., Choi, S., Chou, T., Chouaib, S., Choubey, D., Choubey, V., Chow, K., Chowdhury, K., Chu, C. T., Chuang, T., Chun, T., Chung, H., Chung, T., Chung, Y., Chwae, Y., Cianfanelli, V., Ciarcia, R., Ciechomska, I. A., Ciriolo, M. R., Cirone, M., Claerhout, S., Clague, M. J., Claria, J., Clarke, P. G., Clarke, R., Clementi, E., Cleyrat, C., Cnop, M., Coccia, E. M., Cocco, T., Codogno, P., Coers, J., Cohen, E. E., Colecchia, D., Coletto, L., Coll, N. S., Colucci-Guyon, E., Comincini, S., Condello, M., Cook, K. L., Coombs, G. H., Cooper, C. D., Cooper, J. M., Coppens, I., Corasaniti, M. T., Corazzari, M., Corbalan, R., Corcelle-Termeau, E., Cordero, M. D., Corral-Ramos, C., Corti, O., Cossarizza, A., Costelli, P., Costes, S., Costes, S., Coto-Montes, A., Cottet, S., Couve, E., Covey, L. R., Cowart, L. A., Cox, J. S., Coxon, F. P., Coyne, C. B., Cragg, M. S., Craven, R. J., Crepaldi, T., Crespo, J. L., Criollo, A., Crippa, V., Cruz, M. T., Cuervo, A. M., Cuezva, J. M., Cui, T., Cutillas, P. R., Czaja, M. J., Czyzyk-Krzeska, M. F., Dagda, R. K., Dahmen, U., Dai, C., Dai, W., Dai, Y., Dalby, K. N., Valle, L. D., Dalmasso, G., D'Amelio, M., Damme, M., Darfeuille-Michaud, A., Dargemont, C., Darley-Usmar, V. M., Dasarathy, S., Dasgupta, B., Dash, S., Dass, C. R., Davey, H. M., Davids, L. M., Davila, D., Davis, R. J., Dawson, T. M., Dawson, V. L., Daza, P., de Belleroche, J., de Figueiredo, P., Bressan Queiroz De Figueiredo, R. C., de la Fuente, J., De Martino, L., De Matteis, A., De Meyer, G. R., De Milito, A., De Santi, M., de Souza, W., De Tata, V., De Zio, D., Debnath, J., Dechant, R., Decuypere, J., Deegan, S., Dehay, B., Del Bello, B., Del Re, D. P., Delage-Mourroux, R., Delbridge, L. M., Deldicque, L., Delorme-Axford, E., Deng, Y., Dengjel, J., Denizot, M., Dent, P., Der, C. J., Deretic, V., Derrien, B., Deutsch, E., Devarenne, T. P., Devenish, R. J., Di Bartolomeo, S., Di Daniele, N., Di Domenico, F., Di Nardo, A., Di Paola, S., Di Pietro, A., Di Renzo, L., Diantonio, A., Diaz-Araya, G., Diaz-Laviada, I., Diaz-Meco, M. T., Diaz-Nido, J., Dickey, C. A., Dickson, R. C., Diederich, M., Digard, P., Dikic, I., Dinesh-Kumar, S. P., Ding, C., Ding, W., Ding, Z., Dini, L., Distler, J. H., Diwan, A., Djavaheri-Mergny, M., Dmytruk, K., Dobson, R. C., Doetsch, V., Dokladny, K., Dokudovskaya, S., Donadelli, M., Dong, X. C., Dong, X., Dong, Z., Donohue, T. M., Doran, K. S., D'Orazi, G., Dorn, G. W., Dosenko, V., Dridi, S., Drucker, L., Du, J., Du, L., Du, L., Du Toit, A., Dua, P., Duan, L., Duann, P., Dubey, V. K., Duchen, M. R., Duchosal, M. A., Duez, H., Dugail, I., Dumit, V. I., Duncan, M. C., Dunlop, E. A., Dunn, W. A., Dupont, N., Dupuis, L., Duran, R. V., Durcan, T. M., Duvezin-Caubet, S., Duvvuri, U., Eapen, V., Ebrahimi-Fakhari, D., Echard, A., Eckhart, L., Edelstein, C. L., Edinger, A. L., Eichinger, L., Eisenberg, T., Eisenberg-Lerner, A., Eissa, N. T., El-Deiry, W. S., El-Khoury, V., Elazar, Z., Eldar-Finkelman, H., Elliott, C. J., Emanuele, E., Emmenegger, U., Engedal, N., Engelbrecht, A., Engelender, S., Enserink, J. M., Erdmann, R., Erenpreisa, J., Eri, R., Eriksen, J. L., Erman, A., Escalante, R., Eskelinen, E., Espert, L., Esteban-Martinez, L., Evans, T. J., Fabri, M., Fabrias, G., Fabrizi, C., Facchiano, A., Faergeman, N. J., Faggioni, A., Fairlie, W. D., Fan, C., Fan, D., Fan, J., Fang, S., Fanto, M., Fanzani, A., Farkas, T., Faure, M., Favier, F. B., Fearnhead, H., Federici, M., Fei, E., Felizardo, T. C., Feng, H., Feng, Y., Feng, Y., Ferguson, T. A., Fernandez, A. F., Fernandez-Barrena, M. G., Fernandez-Checa, J. C., Fernandez-Lopez, A., Fernandez-Zapico, M. E., Feron, O., Ferraro, E., Ferreira-Halder, C. V., Fesus, L., Feuer, R., Fiesel, F. C., Filippi-Chiela, E. C., Filomeni, G., Fimia, G. M., Fingert, J. H., Finkbeiner, S., Finkel, T., Fiorito, F., Fisher, P. B., Flajolet, M., Flamigni, F., Florey, O., Florio, S., Floto, R. A., Folini, M., Follo, C., Fon, E. A., Fornai, F., Fortunato, F., Fraldi, A., Franco, R., Francois, A., Francois, A., Frankel, L. B., Fraser, I. D., Frey, N., Freyssenet, D. G., Frezza, C., Friedman, S. L., Frigo, D. E., Fu, D., Fuentes, J. M., Fueyo, J., Fujitani, Y., Fujiwara, Y., Fujiya, M., Fukuda, M., Fulda, S., Fusco, C., Gabryel, B., Gaestel, M., Gailly, P., Gajewska, M., Galadari, S., Galili, G., Galindo, I., Galindo, M. F., Galliciotti, G., Galluzzi, L., Galluzzi, L., Galy, V., Gammoh, N., Gandy, S., Ganesan, A. K., Ganesan, S., Ganley, I. G., Gannage, M., Gao, F., Gao, F., Gao, J., Garcia Nannig, L., Vescovi, E. G., Garcia-Macia, M., Garcia-Ruiz, C., Garg, A. D., Garg, P. K., Gargini, R., Gassen, N. C., Gatica, D., Gatti, E., Gavard, J., Gavathiotis, E., Ge, L., Ge, P., Ge, S., Gean, P., Gelmetti, V., Genazzani, A. A., Geng, J., Genschik, P., Gerner, L., Gestwicki, J. E., Gewirtz, D. A., Ghavami, S., Ghigo, E., Ghosh, D., Giammarioli, A. M., Giampieri, F., Giampietri, C., Giatromanolaki, A., Gibbings, D. J., Gibellini, L., Gibson, S. B., Ginet, V., Giordano, A., Giorgini, F., Giovannetti, E., Girardin, S. E., Gispert, S., Giuliano, S., Gladson, C. L., Glavic, A., Gleave, M., Godefroy, N., Gogal, R. M., Gokulan, K., Goldman, G. H., Goletti, D., Goligorsky, M. S., Gomes, A. V., Gomes, L. C., Gomez, H., Gomez-Manzano, C., Gomez-Sanchez, R., Goncalves, D. A., Goncu, E., Gong, Q., Gongora, C., Gonzalez, C. B., Gonzalez-Alegre, P., Gonzalez-Cabo, P., Ana Gonzalez-Polo, R., Goping, I. S., Gorbea, C., Gorbunov, N. V., Goring, D. R., Gorman, A. M., Gorski, S. M., Goruppi, S., Goto-Yamada, S., Gotor, C., Gottlieb, R. A., Gozes, I., Gozuacik, D., Graba, Y., Graef, M., Granato, G. E., Grant, G. D., Grant, S., Gravina, G. L., Green, D. R., Greenhough, A., Greenwood, M. T., Grimaldi, B., Gros, F., Grose, C., Groulx, J., Gruber, F., Grumati, P., Grune, T., Guan, J., Guan, K., Guerra, B., Guillen, C., Gulshan, K., Gunst, J., Guo, C., Guo, L., Guo, M., Guo, W., Guo, X., Gust, A. A., Gustafsson, A. B., Gutierrez, E., Gutierrez, M. G., Gwak, H., Haas, A., Haber, J. E., Hadano, S., Hagedorn, M., Hahn, D. R., Halayko, A. J., Hamacher-Brady, A., Hamada, K., Hamai, A., Hamann, A., Hamasaki, M., Hamer, I., Hamid, Q., Hammond, E. M., Han, F., Han, W., Handa, J. T., Hanover, J. A., Hansen, M., Harada, M., Harhaji-Trajkovic, L., Harper, J. W., Harrath, A. H., Harris, A. L., Harris, J., Hasler, U., Hasselblatt, P., Hasui, K., Hawley, R. G., Hawley, T. S., He, C., He, C. Y., He, F., He, G., He, R., He, X., He, Y., He, Y., Heath, J. K., Hebert, M., Heinzen, R. A., Helgason, G. V., Hensel, M., Henske, E. P., Her, C., Herman, P. K., Hernandez, A., Hernandez, C., Hernandez-Tiedra, S., Hetz, C., Hiesinger, P. R., Higaki, K., Hilfiker, S., Hill, B. G., Hill, J. A., Hill, W. D., Hino, K., Hofius, D., Hofman, P., Hoeglinger, G. U., Hoehfeld, J., Holz, M. K., Hong, Y., Hood, D. A., Hoozemans, J. J., Hoppe, T., Hsu, C., Hsu, C., Hsu, L., Hu, D., Hu, G., Hu, H., Hu, H., Hu, M. C., Hu, Y., Hu, Z., Hua, F., Hua, Y., Huang, C., Huang, H., Huang, K., Huang, K., Huang, S., Huang, S., Huang, W., Huang, Y., Huang, Y., Huang, Y., Huber, T. B., Huebbe, P., Huh, W., Hulmi, J. J., Hur, G. M., Hurley, J. H., Husak, Z., Hussain, S. N., Hussain, S., Hwang, J. j., Hwang, S., Hwang, T. I., Ichihara, A., Imai, Y., Imbriano, C., Inomata, M., Into, T., Iovane, V., Iovanna, J. L., Iozzo, R. V., Ip, N. Y., Irazoqui, J. E., Iribarren, P., Isaka, Y., Isakovic, A. J., Ischiropoulos, H., Isenberg, J. S., Ishaq, M., Ishida, H., Ishii, I., Ishmael, J. E., Isidoro, C., Isobe, K., Isono, E., Issazadeh-Navikas, S., Itahana, K., Itakura, E., Ivanov, A. I., Iyer, A. K., Izquierdo, J. M., Izumi, Y., Izzo, V., Jaeaettelae, M., Jaber, N., Jackson, D. J., Jackson, W. T., Jacob, T. G., Jacques, T. S., Jagannath, C., Jain, A., Jana, N. R., Jang, B. K., Jani, A., Janji, B., Jannig, P. R., Jansson, P. J., Jean, S., Jendrach, M., Jeon, J., Jessen, N., Jeung, E., Jia, K., Jia, L., Jiang, H., Jiang, H., Jiang, L., Jiang, T., Jiang, X., Jiang, X., Jiang, X., Jiang, Y., Jiang, Y., Jimenez, A., Jin, C., Jin, H., Jin, L., Jin, M., Jin, S., Jinwal, U. K., Jo, E., Johansen, T., Johnson, D. E., Johnson, G. V., Johnson, J. D., Jonasch, E., Jones, C., Joosten, L. A., Jordan, J., Joseph, A., Joseph, B., Joubert, A. M., Ju, D., Ju, J., Juan, H., Juenemann, K., Juhasz, G., Jung, H. S., Jung, J. U., Jung, Y., Jungbluth, H., Justice, M. J., Jutten, B., Kaakoush, N. O., Kaarniranta, K., Kaasik, A., Kabuta, T., Kaeffer, B., Kagedal, K., Kahana, A., Kajimura, S., Kakhlon, O., Kalia, M., Kalvakolanu, D. V., Kamada, Y., Kambas, K., Kaminskyy, V. O., Kampinga, H. H., Kandouz, M., Kang, C., Kang, R., Kang, T., Kanki, T., Kanneganti, T., Kanno, H., Kanthasamy, A. G., Kantorow, M., Kaparakis-Liaskos, M., Kapuy, O., Karantza, V., Karim, M. R., Karmakar, P., Kaser, A., Kaushik, S., Kawula, T., Kaynar, A. M., Ke, P., Ke, Z., Kehrl, J. H., Keller, K. E., Kemper, J. K., Kenworthy, A. K., Kepp, O., Kern, A., Kesari, S., Kessel, D., Ketteler, R., Kettelhut, I. D., Khambu, B., Khan, M. M., Khandelwal, V. K., Khare, S., Kiang, J. G., Kiger, A. A., Kihara, A., Kim, A. L., Kim, C. H., Kim, D. R., Kim, D., Kim, E. K., Kim, H. Y., Kim, H., Kim, J., Kim, J. H., Kim, J. C., Kim, J. H., Kim, K. W., Kim, M. D., Kim, M., Kim, P. K., Kim, S. W., Kim, S., Kim, Y., Kim, Y., Kimchi, A., Kimmelman, A. C., Kimura, T., King, J. S., Kirkegaard, K., Kirkin, V., Kirshenbaum, L. A., Kishi, S., Kitajima, Y., Kitamoto, K., Kitaoka, Y., Kitazato, K., Kley, R. A., Klimecki, W. T., Klinkenberg, M., Klucken, J., Knaevelsrud, H., Knecht, E., Knuppertz, L., Ko, J., Kobayashi, S., Koch, J. C., Koechlin-Ramonatxo, C., Koenig, U., Ko, Y. H., Koehler, K., Kohlwein, S. D., Koike, M., Komatsu, M., Kominami, E., Kong, D., Kong, H. J., Konstantakou, E. G., Kopp, B. T., Korcsmaros, T., Korhonen, L., Korolchuk, V. I., Koshkina, N. V., Kou, Y., Koukourakis, M. I., Koumenis, C., Kovacs, A. L., Kovacs, T., Kovacs, W. J., Koya, D., Kraft, C., Krainc, D., Kramer, H., Kravic-Stevovic, T., Krek, W., Kretz-Remy, C., Krick, R., Krishnamurthy, M., Kriston-Vizi, J., Kroemer, G., Kruer, M. C., Kruger, R., Ktistakis, N. T., Kuchitsu, K., Kuhn, C., Kumar, A. P., Kumar, A., Kumar, A., Kumar, D., Kumar, D., Kumar, R., Kumar, S., Kundu, M., Kung, H., Kuno, A., Kuo, S., Kuret, J., Kurz, T., Kwok, T., Kwon, T. K., Kwon, Y. T., Kyrmizi, I., La Spada, A. R., Lafont, F., Lahm, T., Lakkaraju, A., Lam, T., Lamark, T., Lancel, S., Landowski, T. H., Lane, D. J., Lane, J. D., Lanzi, C., Lapaquette, P., Lapierre, L. R., Laporte, J., Laukkarinen, J., Laurie, G. W., Lavandero, S., Lavie, L., LaVoie, M. J., Law, B. Y., Law, H. K., Law, K. B., Layfield, R., Lazo, P. A., Le Cam, L., Le Roch, K. G., Le Stunff, H., Leardkamolkarn, V., Lecuit, M., Lee, B., Lee, C., Lee, E. F., Lee, G. M., Lee, H., Lee, H., Lee, J. K., Lee, J., Lee, J., Lee, J. H., Lee, M., Lee, M., Lee, P. J., Lee, S. W., Lee, S., Lee, S., Lee, S. Y., Lee, S. H., Lee, S. S., Lee, S., Lee, S., Lee, Y., Lee, Y. J., Lee, Y. H., Leeuwenburgh, C., Lefort, S., Legouis, R., Lei, J., Lei, Q., Leib, D. A., Leibowitz, G., Lekli, I., Lemaire, S. D., Lemasters, J. J., Lemberg, M. K., Lemoine, A., Leng, S., Lenz, G., Lenzi, P., Lerman, L. O., Barbato, D. L., Leu, J. I., Leung, H. Y., Levine, B., Lewis, P. A., Lezoualc'h, F., Li, C., Li, F., Li, F., Li, J., Li, K., Li, L., Li, M., Li, M., Li, Q., Li, R., Li, S., Li, W., Li, W., Li, X., Li, Y., Lian, J., Liang, C., Liang, Q., Liao, Y., Liberal, J., Liberski, P. P., Lie, P., Lieberman, A. P., Lim, H. J., Lim, K., Lim, K., Lima, R. T., Lin, C., Lin, C., Lin, F., Lin, F., Lin, F., Lin, K., Lin, K., Lin, P., Lin, T., Lin, W., Lin, Y., Lin, Y., Linden, R., Lindholm, D., Lindqvist, L. M., Lingor, P., Linkermann, A., Liotta, L. A., Lipinski, M. M., Lira, V. A., Lisanti, M. P., Liton, P. B., Liu, B., Liu, C., Liu, C., Liu, F., Liu, H., Liu, J., Liu, J., Liu, J., Liu, K., Liu, L., Liu, L., Liu, Q., Liu, R., Liu, S., Liu, S., Liu, W., Liu, X., Liu, X., Liu, X., Liu, X., Liu, X., Liu, X., Liu, Y., Liu, Y., Liu, Z., Liu, Z., Liuzzi, J. P., Lizard, G., Ljujic, M., Lodhi, I. J., Logue, S. E., Lokeshwar, B. L., Long, Y. C., Lonial, S., Loos, B., Lopez-Otin, C., Lopez-Vicario, C., Lorente, M., Lorenzi, P. L., Lorincz, P., Los, M., Lotze, M. T., Lovat, P. E., Lu, B., Lu, B., Lu, J., Lu, Q., Lu, S., Lu, S., Lu, Y., Luciano, F., Luckhart, S., Lucocq, J. M., Ludovico, P., Lugea, A., Lukacs, N. W., Lum, J. J., Lund, A. H., Luo, H., Luo, J., Luo, S., Luparello, C., Lyons, T., Ma, J., Ma, Y., Ma, Y., Ma, Z., Machado, J., Machado-Santelli, G. M., Macian, F., MacIntosh, G. C., MacKeigan, J. P., Macleod, K. F., MacMicking, J. D., MacMillan-Crow, L. 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2016; 12 (1): 1-222
The incidental pulmonary nodule in a child Part 1: recommendations from the SPR Thoracic Imaging Committee regarding characterization, significance and follow-up
PEDIATRIC RADIOLOGY
Westra, S. J., Brody, A. S., Mahani, M. G., Guillerman, R. P., Hegde, S. V., Iyer, R. S., Lee, E. Y., Newman, B., Podberesky, D. J., Thacker, P. G.
2015; 45 (5): 628-633
Abstract
No guidelines are in place for the follow-up and management of pulmonary nodules that are incidentally detected on CT in the pediatric population. The Fleischner guidelines, which were developed for the older adult population, do not apply to children. This review summarizes the evidence collected by the Society for Pediatric Radiology (SPR) Thoracic Imaging Committee in its attempt to develop pediatric-specific guidelines.Small pulmonary opacities can be characterized as linear or as ground-glass or solid nodules. Linear opacities and ground-glass nodules are extremely unlikely to represent an early primary or metastatic malignancy in a child. In our review, we found a virtual absence of reported cases of a primary pulmonary malignancy presenting as an incidentally detected small lung nodule on CT in a healthy immune-competent child.Because of the lack of definitive information on the clinical significance of small lung nodules that are incidentally detected on CT in children, the management of those that do not have the typical characteristics of an intrapulmonary lymph node should be dictated by the clinical history as to possible exposure to infectious agents, the presence of an occult immunodeficiency, the much higher likelihood that the nodule represents a metastasis than a primary lung tumor, and ultimately the individual preference of the child's caregiver. Nodules appearing in children with a history of immune deficiency, malignancy or congenital pulmonary airway malformation should not be considered incidental, and their workup should be dictated by the natural history of these underlying conditions.
View details for DOI 10.1007/s00247-014-3267-7
View details for Web of Science ID 000353234800002
View details for PubMedID 25655369
The Major Genetic Determinants of HIV-1 Control Affect HLA Class I Peptide Presentation
SCIENCE
Pereyra, F., Jia, X., McLaren, P. J., Telenti, A., de Bakker, P. I., Walker, B. D., Ripke, S., Brumme, C. J., Pulit, S. L., Carrington, M., Kadie, C. M., Carlson, J. M., Heckerman, D., Graham, R. R., Plenge, R. M., Deeks, S. G., Gianniny, L., Crawford, G., Sullivan, J., Gonzalez, E., Davies, L., Camargo, A., Moore, J. M., Beattie, N., Gupta, S., Crenshaw, A., Burtt, N. P., Guiducci, C., Gupta, N., Carrington, M., Gao, X., Qi, Y., Yuki, Y., Piechocka-Trocha, A., Cutrell, E., Rosenberg, R., Moss, K. L., Lemay, P., O'Leary, J., Schaefer, T., Verma, P., Toth, I., Block, B., Baker, B., Rothchild, A., Lian, J., Proudfoot, J., Alvino, D. M., Vine, S., Addo, M. M., Allen, T. M., Altfeld, M., Henn, M. R., Le Gall, S., Streeck, H., Haas, D. W., Kuritzkes, D. R., Robbins, G. K., Shafer, R. W., Gulick, R. M., Shikuma, C. M., Haubrich, R., Riddler, S., Sax, P. E., Daar, E. S., Ribaudo, H. J., Agan, B., Agarwal, S., Ahern, R. L., Allen, B. L., Altidor, S., Altschuler, E. L., Ambardar, S., Anastos, K., Anderson, B., Anderson, V., Andrady, U., Antoniskis, D., Bangsberg, D., Barbaro, D., Barrie, W., Bartczak, J., Barton, S., Basden, P., Basgoz, N., Bazner, S., Bellos, N. C., Benson, A. M., Berger, J., Bernard, N. F., Bernard, A. M., Birch, C., Bodner, S. J., Bolan, R. K., Boudreaux, E. T., Bradley, M., Braun, J. F., Brndjar, J. E., Brown, S. J., Brown, K., Brown, S. T., Burack, J., Bush, L. M., Cafaro, V., Campbell, O., Campbell, J., Carlson, R. H., Carmichael, J. K., Casey, K. K., Cavacuiti, C., Celestin, G., Chambers, S. T., Chez, N., Chirch, L. M., Cimoch, P. J., Cohen, D., Cohn, L. E., Conway, B., Cooper, D. A., Cornelson, B., Cox, D. T., Cristofano, M. V., Cuchural, G., Czartoski, J. L., Dahman, J. M., Daly, J. S., Davis, B. T., Davis, K., Davod, S. M., Deeks, S. G., deJesus, E., Dietz, C. A., Dunham, E., Dunn, M. E., Ellerin, T. B., Eron, J. J., Fangman, J. J., Farel, C. E., Ferlazzo, H., Fidler, S., Fleenor-Ford, A., Frankel, R., Freedberg, K. A., French, N. K., Fuchs, J. D., Fuller, J. D., Gaberman, J., Gallant, J. E., Gandhi, R. T., Garcia, E., Garmon, D., Gathe, J. C., Gaultier, C. R., Gebre, W., Gilman, F. D., Gilson, I., Goepfert, P. A., Gottlieb, M. S., Goulston, C., Groger, R. K., Gurley, T. D., Haber, S., Hardwicke, R., Hardy, W. D., Harrigan, P. R., Hawkins, T. N., Heath, S., Hecht, F. M., Henry, W. K., Hladek, M., Hoffman, R. P., Horton, J. M., Hsu, R. K., Huhn, G. D., Hunt, P., Hupert, M. J., Illeman, M. L., Jaeger, H., Jellinger, R. M., John, M., Johnson, J. A., Johnson, K. L., Johnson, H., Johnson, K., Joly, J., Jordan, W. C., Kauffman, C. A., Khanlou, H., Killian, R. K., Kim, A. Y., Kim, D. D., Kinder, C. A., Kirchner, J. T., Kogelman, L., Kojic, E. M., Korthuis, T., Kurisu, W., Kwon, D. S., Lamar, M., Lampiris, H., Lanzafame, M., Lederman, M. M., Lee, D. M., Lee, J. M., Lee, M. J., Lee, E. T., Lemoine, J., Levy, J. A., Llibre, J. M., Liguori, M. A., Little, S. J., Liu, A. Y., Lopez, A. J., Loutfy, M. R., Loy, D., Mohammed, D. Y., Man, A., Mansour, M. K., Marconi, V. C., Markowitz, M., Marques, R., Martin, J. N., Martin, H. L., Mayer, K. H., McElrath, M. J., McGhee, T. A., McGovern, B. H., McGowan, K., McIntyre, D., Mcleod, G. X., Menezes, P., Mesa, G., Metroka, C. E., Meyer-Olson, D., Miller, A. O., Montgomery, K., Mounzer, K. C., Nagami, E. H., Nagin, I., Nahass, R. G., Nelson, M. O., Nielsen, C., Norene, D. L., O'Connor, D. H., Ojikutu, B. O., Okulicz, J., Oladehin, O. O., Oldfield, E. C., Olender, S. A., Ostrowski, M., Owen, W. F., Pae, E., Parsonnet, J., Pavlatos, A. M., Perlmutter, A. M., Pierce, M. N., Pincus, J. M., Pisani, L., Price, L. J., Proia, L., Prokesch, R. C., Pujet, H. C., Ramgopal, M., Rathod, A., Rausch, M., Ravishankar, J., Rhame, F. S., Richards, C. S., Richman, D. D., Robbins, G. K., Rodes, B., Rodriguez, M., Rose, R. C., Rosenberg, E. S., Rosenthal, D., Ross, P. E., Rubin, D. S., Rumbaugh, E., Saenz, L., Salvaggio, M. R., Sanchez, W. C., Sanjana, V. M., Santiago, S., Schmidt, W., Schuitemaker, H., Sestak, P. M., Shalit, P., Shay, W., Shirvani, V. N., Silebi, V. I., Sizemore, J. M., Skolnik, P. R., Sokol-Anderson, M., Sosman, J. M., Stabile, P., Stapleton, J. T., Starrett, S., Stein, F., Stellbrink, H., Sterman, F. L., Stone, V. E., Stone, D. R., Tambussi, G., Taplitz, R. A., Tedaldi, E. M., Telenti, A., Theisen, W., Torres, R., Tosiello, L., Tremblay, C., Tribble, M. A., Trinh, P. D., Tsao, A., Ueda, P., Vaccaro, A., Valadas, E., Vanig, T. J., Vecino, I., Vega, V. M., Veikley, W., Wade, B. H., Walworth, C., Wanidworanun, C., Ward, D. J., Warner, D. A., Weber, R. D., Webster, D., Weis, S., Wheeler, D. A., White, D. J., Wilkins, E., Winston, A., Wlodaver, C. G., van't Wout, A., Wright, D. P., Yang, O. O., Yurdin, D. L., Zabukovic, B. W., Zachary, K. C., Zeeman, B., Zhao, M.
2010; 330 (6010): 1551-1557
Abstract
Infectious and inflammatory diseases have repeatedly shown strong genetic associations within the major histocompatibility complex (MHC); however, the basis for these associations remains elusive. To define host genetic effects on the outcome of a chronic viral infection, we performed genome-wide association analysis in a multiethnic cohort of HIV-1 controllers and progressors, and we analyzed the effects of individual amino acids within the classical human leukocyte antigen (HLA) proteins. We identified >300 genome-wide significant single-nucleotide polymorphisms (SNPs) within the MHC and none elsewhere. Specific amino acids in the HLA-B peptide binding groove, as well as an independent HLA-C effect, explain the SNP associations and reconcile both protective and risk HLA alleles. These results implicate the nature of the HLA-viral peptide interaction as the major factor modulating durable control of HIV infection.
View details for DOI 10.1126/science.1195271
View details for Web of Science ID 000285153500069
View details for PubMedID 21051598
Automated electric charge measurements of fluid microdrops using the Millikan method
METROLOGIA
Lee, E. R., Halyo, V., Lee, I. T., PERL, M. L.
2004; 41 (5): S147-S158
Large bulk matter search for fractional charge particles
PHYSICAL REVIEW D
Lee, I. T., Fan, S., Halyo, V., Lee, E. R., Kim, P. C., PERL, M. L., Rogers, H., Loomba, D., Lackner, K. S., Shaw, G.
2002; 66 (1)
The effect of HF vapor pre-treatment on the cobalt salicide process
THIN SOLID FILMS
Lee, S. Y., Cao, W. Q., Lee, E., Lo, P., Lee, S. K.
2002; 405 (1-2): 73-76
A new method for searching for free fractional charge particles in bulk matter
REVIEW OF SCIENTIFIC INSTRUMENTS
Loomba, D., Halyo, V., Lee, E. R., Lee, I. T., Kim, P. C., Perl, M. L.
2000; 71 (9): 3409-3414
Search for free fractional electric charge elementary particles using an automated Millikan oil drop technique
PHYSICAL REVIEW LETTERS
Halyo, V., Kim, P., Lee, E. R., Lee, I. T., Loomba, D., PERL, M. L.
2000; 84 (12): 2576-2579
DIRECT TEMPERATURE-MEASUREMENT
CANCER RESEARCH
Fessenden, P., Lee, E. R., Samulski, T. V.
1984; 44 (10): 4799-4804
SOME COMMENTS ON ELASTIC-PLASTIC ANALYSIS
INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES
Lee, E. H.
1981; 17 (9): 859-872
VARIATIONAL STUDY OF NONLINEAR SPLINE CURVES
SIAM REVIEW
Lee, E. H., FORSYTHE, G. E.
1973; 15 (1): 120-133
