Thanaphong Phongpreecha
Instructor, Pathology
All Publications
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Prediction of neuropathologic lesions from clinical data.
Alzheimer's & dementia : the journal of the Alzheimer's Association
2023
Abstract
Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life.This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities.Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased.Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.
View details for DOI 10.1002/alz.12921
View details for PubMedID 36681388
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In-Silico Generation of High-Dimensional Immune Response Data in Patients using a Deep Neural Network.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
2022
Abstract
Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/cyto.a.24709
View details for PubMedID 36507780
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A data-driven health index for neonatal morbidities.
iScience
2022; 25 (4): 104143
Abstract
Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.
View details for DOI 10.1016/j.isci.2022.104143
View details for PubMedID 35402862
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Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.
Frontiers in pediatrics
2022; 10: 933266
Abstract
Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.
View details for DOI 10.3389/fped.2022.933266
View details for PubMedID 36582513
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Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE.
Science advances
1800; 7 (51): eabk0473
Abstract
[Figure: see text].
View details for DOI 10.1126/sciadv.abk0473
View details for PubMedID 34910503
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Data-Driven Modeling of Pregnancy-Related Complications.
Trends in molecular medicine
2021
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
View details for DOI 10.1016/j.molmed.2021.01.007
View details for PubMedID 33573911
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Objective Activity Parameters Track Patient-Specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States.
Annals of surgery
2021
Abstract
The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established.Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. While phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity.Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state.The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, six distinct domains of physical function and sleep are identified to represent the objective temporal patterns: "activity capacity" and "moderate and overall activity" (declined immediately after surgery); "sleep disruption and sedentary activity" (increased after surgery); "overall sleep", "sleep onset", and "light activity" (no clear changes were observed after surgery). These patterns can be linked to individual patients' preoperative immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in M-MDSCs predicted a slower recovery.Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.
View details for DOI 10.1097/SLA.0000000000005250
View details for PubMedID 35129529
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Single-cell peripheral immunoprofiling of Alzheimer's and Parkinson's diseases.
Science advances
2020; 6 (48)
Abstract
Peripheral blood mononuclear cells (PBMCs) may provide insight into the pathogenesis of Alzheimer's disease (AD) or Parkinson's disease (PD). We investigated PBMC samples from 132 well-characterized research participants using seven canonical immune stimulants, mass cytometric identification of 35 PBMC subsets, and single-cell quantification of 15 intracellular signaling markers, followed by machine learning model development to increase predictive power. From these, three main intracellular signaling pathways were identified specifically in PBMC subsets from people with AD versus controls: reduced activation of PLCgamma2 across many cell types and stimulations and selectively variable activation of STAT1 and STAT5, depending on stimulant and cell type. Our findings functionally buttress the now multiply-validated observation that a rare coding variant in PLCG2 is associated with a decreased risk of AD. Together, these data suggest enhanced PLCgamma2 activity as a potential new therapeutic target for AD with a readily accessible pharmacodynamic biomarker.
View details for DOI 10.1126/sciadv.abd5575
View details for PubMedID 33239300
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Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions
NATURE MACHINE INTELLIGENCE
2020
View details for DOI 10.1038/s42256-020-00232-8
View details for Web of Science ID 000579336000001
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Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.
Nature machine intelligence
2020; 2 (10): 619-628
Abstract
The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.
View details for DOI 10.1038/s42256-020-00232-8
View details for PubMedID 33294774
View details for PubMedCentralID PMC7720904
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Effect of catalyst and reaction conditions on aromatic monomer yields, product distribution, and sugar yields during lignin hydrogenolysis of silver birch wood.
Bioresource technology
2020; 316: 123907
Abstract
The impact of catalyst choice and reaction conditions during catalytic hydrogenolysis of silver birch biomass are assessed for their effect on aromatic monomer yields and selectivities, lignin removal, and sugar yields from enzymatic hydrolysis. At a reaction temperature of 220°C with no supplemental H2, it was demonstrated that both Co/C and Ni/C exhibited aromatic monomer yields of >50%, which were close to the theoretical maximum expected for the lignin based on total beta-O-4 content and exhibited high selectivities for 4-propylguaiacol and 4-propylsyringol. Pd/C exhibited a significantly different set of products, and using a model lignin dimer, showed a product profile that shifted upon inclusion of supplemental H2, suggesting that the generation of surface hydrogen is critical for this catalyst system. Lignin removal during hydrogenolysis could be correlated to glucose yields and inclusion of lignin depolymerizing catalysts significantly improves lignin removal and subsequent enzymatic hydrolysis yields.
View details for DOI 10.1016/j.biortech.2020.123907
View details for PubMedID 32739581
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Impact of dilute acid pretreatment conditions on p-coumarate removal in diverse maize lines.
Bioresource technology
2020; 314: 123750
Abstract
Prior work has identified that lignins recovered from dilute acid-pretreated corn stover exhibit superior performance in phenol-formaldehyde resins used in wood adhesive applications when compared to diverse process-modified lignins derived from other sources. This improved performance is hypothesized to be due to the higher content of unsubstituted phenolic groups specifically p-coumarate lignin esters. In this work, a diverse set of corn stover samples are employed that exhibit diversity in p-coumarate content and total lignin content to explore the relationship between dilute acid pretreatment conditions, p-coumarate ester hydrolysis, xylan solubilization, and the resulting glucose enzymatic hydrolysis yields. The goal of this study is to identify pretreatment conditions that preserve a significant fraction of the p-coumarate esters while simultaneously achieving high enzymatic hydrolysis yields. Kinetic parameters for p-coumarate ester hydrolysis were quantified and pretreatment-biomass combinations were identified that result in glucose hydrolysis yields of more than 90% while retaining nearly 50mg p-coumarate/g lignin.
View details for DOI 10.1016/j.biortech.2020.123750
View details for PubMedID 32622284
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VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.
Nature communications
2020; 11 (1): 3738
Abstract
High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.
View details for DOI 10.1038/s41467-020-17569-8
View details for PubMedID 32719375
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Multivariate prediction of dementia in Parkinson's disease.
NPJ Parkinson's disease
2020; 6: 20
Abstract
Cognitive impairment in Parkinson's disease (PD) is pervasive with potentially devastating effects. Identification of those at risk for cognitive decline is vital to identify and implement appropriate interventions. Robust multivariate approaches, including fixed-effect, mixed-effect, and multitask learning models, were used to study associations between biological, clinical, and cognitive factors and for predicting cognitive status longitudinally in a well-characterized prevalent PD cohort (n = 827). Age, disease duration, sex, and GBA status were the primary biological factors associated with cognitive status and progression to dementia. Specific cognitive tests were better predictors of subsequent cognitive status for cognitively unimpaired and dementia groups. However, these models could not accurately predict future mild cognitive impairment (PD-MCI). Data collected from a large PD cohort thus revealed the primary biological and cognitive factors associated with dementia, and provide clinicians with data to aid in the identification of risk for dementia. Sex differences and their potential relationship to genetic status are also discussed.
View details for DOI 10.1038/s41531-020-00121-2
View details for PubMedID 32885039
View details for PubMedCentralID PMC7447766
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Systematic Immunophenotyping Reveals Sex-Specific Responses After Painful Injury in Mice.
Frontiers in immunology
2020; 11: 1652
Abstract
Many diseases display unequal prevalence between sexes. The sex-specific immune response to both injury and persistent pain remains underexplored and would inform treatment paradigms. We utilized high-dimensional mass cytometry to perform a comprehensive analysis of phenotypic and functional immune system differences between male and female mice after orthopedic injury. Multivariate modeling of innate and adaptive immune cell responses after injury using an elastic net algorithm, a regularized regression method, revealed sex-specific divergence at 12 h and 7 days after injury with a stronger immune response to injury in females. At 12 h, females upregulated STAT3 signaling in neutrophils but downregulated STAT1 and STAT6 signals in T regulatory cells, suggesting a lack of engagement of immune suppression pathways by females. Furthermore, at 7 days females upregulated MAPK pathways (p38, ERK, NFkB) in CD4T memory cells, setting up a possible heightened immune memory of painful injury. Taken together, our findings provide the first comprehensive and functional analysis of sex-differences in the immune response to painful injury.
View details for DOI 10.3389/fimmu.2020.01652
View details for PubMedID 32849569
View details for PubMedCentralID PMC7403191
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Multivariate prediction of dementia in Parkinson's disease.
NPJ Parkinson's disease
2020; 6 (1): 20
Abstract
Cognitive impairment in Parkinson's disease (PD) is pervasive with potentially devastating effects. Identification of those at risk for cognitive decline is vital to identify and implement appropriate interventions. Robust multivariate approaches, including fixed-effect, mixed-effect, and multitask learning models, were used to study associations between biological, clinical, and cognitive factors and for predicting cognitive status longitudinally in a well-characterized prevalent PD cohort (n = 827). Age, disease duration, sex, and GBA status were the primary biological factors associated with cognitive status and progression to dementia. Specific cognitive tests were better predictors of subsequent cognitive status for cognitively unimpaired and dementia groups. However, these models could not accurately predict future mild cognitive impairment (PD-MCI). Data collected from a large PD cohort thus revealed the primary biological and cognitive factors associated with dementia, and provide clinicians with data to aid in the identification of risk for dementia. Sex differences and their potential relationship to genetic status are also discussed.
View details for DOI 10.1038/s41531-020-00121-2
View details for PubMedID 34429432