Born and raised in Japan, Dr. Ando received an MD-PhD degree from the Aichi Medical University. After anesthesia training, Dr. Ando came to Stanford to pursue clinical and basic research experience. During his postdoctoral fellowship in Dr. Gaudilliere's laboratory, Dr. Ando worked on publication of “A next-generation single-cell technology (mass cytometry) to study the feto-maternal immune system,” a project designed to evaluate the immune response associated with preterm birth. In addition, Dr. Ando performs research in Obstetric Anesthesia, such as respiratory monitoring after cesarean sections and labor satisfaction, to obtain clinical research experience and to understand the key differences in medicine between the United States and Japan.
After his postdoctoral fellowship, Dr. Ando has maintained his status as a researcher in Dr. Gaudilliere's laboratory, continuing work relating to pregnancy and preterm birth.
Dr. Ando divides his efforts between laboratory research and the clinic.
- OB Anesthesia
Clinical Assistant Professor, Anesthesiology, Perioperative and Pain Medicine
Honors & Awards
Frederick P. Zuspan Award, 53th Annual Society for Obstetric Anesthesia and Perinatology (May 2021)
Best Paper Finalist, 52th Annual Society for Obstetric Anesthesia and Perinatology (Sep 2020)
Best Paper Finalist, 50th Annual Society for Obstetric Anesthesia and Perinatology (May 2018)
2nd Best Paper, 49th Annual Society for Obstetric Anesthesia and Perinatology (May 2017)
Boards, Advisory Committees, Professional Organizations
Member, American Society of Anesthesiologists (2012 - Present)
Member, Society for Obstetric Anesthesia and Perinatology (2016 - Present)
Board Certified Anesthesiologist, Japanese Society of Anesthesiologists (2017 - Present)
Medical Education: Aichi Medical University (2009) Japan
Internship: Tushima City Hospital (2011) Japan
PhD, Aichi Medical University Graduate School of Medicine, Anesthesiology and Pharmacology (2015)
Residency: Aichi Medical University Hospital (2016) Japan
Board Certification: Japanese Society of Anesthesiologists, Anesthesia (2017)
Postdoctoral Fellow, Stanford University School of Medicine, Anesthesiology (2018)
Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.
Cell reports. Medicine
The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.
View details for DOI 10.1016/j.xcrm.2022.100680
View details for PubMedID 35839768
Assessment of Patient-Reported Outcome Measures for Maternal Postpartum Depression Using the Consensus-Based Standards for the Selection of Health Measurement Instruments Guideline: A Systematic Review.
JAMA network open
2022; 5 (6): e2214885
Importance: Maternal depression is frequently reported in the postpartum period, with an estimated prevalence of approximately 15% during the first postpartum year. Despite the high prevalence of postpartum depression, there is no consensus regarding which patient-reported outcome measure (PROM) should be used to screen for this complex, multidimensional construct.Objective: To evaluate psychometric measurement properties of existing PROMs of maternal postpartum depression using the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) guideline and identify the best available patient-reported screening measure.Evidence Review: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. PubMed, CINAHL, Embase, and Web of Science were searched on July 1, 2019, for validated PROMs of postpartum depression, and an additional search including a hand search of references from eligible studies was conducted in June 2021. Included studies evaluated 1 or more psychometric measurement properties of the identified PROMs. A risk-of-bias assessment was performed to evaluate methods of each included study. Psychometric measurement properties of each PROM were rated according to COSMIN criteria. A modified Grading of Recommendations Assessment, Development, and Evaluation approach was used to assess the level of evidence supporting each rating, and a recommendation class (A, recommended for use; B, further research required; or C, not recommended) was given based on the overall quality of each included PROM.Findings: Among 10 264 postpartum recovery studies, 27 PROMs were identified. Ten PROMs (37.0%) met the inclusion criteria and were used in 43 studies (0.4%) involving 22 095 postpartum women. At least 1 psychometric measurement property was assessed for each of the 10 validated PROMs identified. Content validity was sufficient in all PROMs. The Edinburgh Postnatal Depression Scale (EPDS) demonstrated adequate content validity and a moderate level of evidence for sufficient internal consistency (with sufficient structural validity), resulting in a recommendation of class A. The other 9 PROMs evaluated received a recommendation of class B.Conclusions and Relevance: The findings of this systematic review suggest that the EPDS is the best available patient-reported screening measure of maternal postpartum depression. Future studies should focus on evaluating the cross-cultural validity, reliability, and measurement error of the EPDS to improve understanding of its psychometric properties and utility.
View details for DOI 10.1001/jamanetworkopen.2022.14885
View details for PubMedID 35749118
An immune signature of postoperative cognitive dysfunction (POCD)
LIPPINCOTT WILLIAMS & WILKINS. 2022: 577-578
View details for Web of Science ID 000840283000229
Multimodal, coached telehealth prehabilitation has high compliance and improves exercise and cognitive capacity prior to surgery: a pilot study
LIPPINCOTT WILLIAMS & WILKINS. 2022: 415
View details for Web of Science ID 000840283000157
Association of Prehabilitation With Postoperative Opioid Use in Colorectal Surgery: An Observational Cohort Study.
The Journal of surgical research
1800; 273: 226-232
INTRODUCTION: Preoperative optimization programs have demonstrated positive effects on perioperative physical function and surgical outcomes. In nonsurgical populations, physical activity and healthy diet may reduce pain and pain medication requirement, but this has not been studied in surgical patients. Our aim was to determine whether a preoperative diet and exercise intervention affects postoperative pain and pain medication use.METHODS: Patients undergoing abdominal colorectal surgery were invited to participate in a web-based patient engagement program. Those enrolling in the first and third time periods received information on the standard perioperative pathway (enhanced recovery after surgery [ERAS]). Those enrolling in the second time period also received reminders on nutrition and exercise (PREHAB+ERAS). The primary outcome was postoperative inpatient opioid use. The secondary outcomes were inpatient postoperative pain scores and nonopioid pain medication use.RESULTS: The ERAS and PREHAB+ERAS groups were similar in demographic and operative characteristics. Subgroup analysis of patients who activated their accounts demonstrated that the two groups had similar average maximum daily pain scores, but the PREHAB+ERAS group (n=158) used 15.9 fewer oral morphine equivalents per postoperative inpatient day than the ERAS group (n=92), representing a 30% decrease (53mg versus 37.1mg, P=0.04). The two groups used comparable amounts of acetaminophen, gabapentin, and ketorolac. Generalized linear models demonstrated that PREHAB+ERAS, minimally invasive surgery, and older age were associated with lower inpatient opioid use.CONCLUSIONS: Access to a web-based preoperative diet and exercise program may reduce inpatient opioid use after major elective colorectal surgery. Further studies are necessary to determine whether the degree of adherence to nutrition and physical activity recommendations has a dose-dependent effect on opioid use.
View details for DOI 10.1016/j.jss.2021.12.023
View details for PubMedID 35101683
Integrated Single-Cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study.
Annals of surgery
OBJECTIVE: The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery.SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs.METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery.RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82).CONCLUSIONS: The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.
View details for DOI 10.1097/SLA.0000000000005348
View details for PubMedID 34954754
Multi-Omic, Longitudinal Profile of Third-Trimester Pregnancies Identifies a Molecular Switch That Predicts the Onset of Labor.
SPRINGER HEIDELBERG. 2021: 233A-234A
View details for Web of Science ID 000675441000486
Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset.
Science translational medicine
2021; 13 (592)
Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 * 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 * 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.
View details for DOI 10.1126/scitranslmed.abd9898
View details for PubMedID 33952678
Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts - implications for clinical biomarker studies.
The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care.The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes.Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE.The model derived in the Stanford cohort was highly significant (p = 3.9E-15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E-01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E-21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E-02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E-454, R = 0.92) and Detroit cohorts (p = 1.1.E-92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences.Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the "same" clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.
View details for DOI 10.1080/14767058.2021.1888915
View details for PubMedID 33653202
Single-Cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum.
Frontiers in immunology
2021; 12: 714090
Although most causes of death and morbidity in premature infants are related to immune maladaptation, the premature immune system remains poorly understood. We provide a comprehensive single-cell depiction of the neonatal immune system at birth across the spectrum of viable gestational age (GA), ranging from 25 weeks to term. A mass cytometry immunoassay interrogated all major immune cell subsets, including signaling activity and responsiveness to stimulation. An elastic net model described the relationship between GA and immunome (R=0.85, p=8.75e-14), and unsupervised clustering highlighted previously unrecognized GA-dependent immune dynamics, including decreasing basal MAP-kinase/NFκB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-α; and decreasing frequency of regulatory and invariant T cells, including NKT-like cells and CD8+CD161+ T cells. Knowledge gained from the analysis of the neonatal immune landscape across GA provides a mechanistic framework to understand the unique susceptibility of preterm infants to both hyper-inflammatory diseases and infections.
View details for DOI 10.3389/fimmu.2021.714090
View details for PubMedID 34497610
View details for PubMedCentralID PMC8420969
A Peripheral Immune Signature of Labor Induction.
Frontiers in immunology
2021; 12: 725989
Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8+ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.
View details for DOI 10.3389/fimmu.2021.725989
View details for PubMedID 34566984
View details for PubMedCentralID PMC8458888
A systematic review of patient-reported outcome measures used to assess sleep in postpartum women using Consensus Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines.
We performed a systematic review to identify the best patient-reported outcome measure (PROM) of postpartum sleep in women.We searched 4 databases for validated PROMs used to assess postpartum sleep. Studies were considered if they evaluated at least 1 psychometric measurement property of a PROM. An overall rating was assigned for each psychometric measurement property of each PROM based upon COSMIN criteria. A modified GRADE approach was used to assess the level of evidence and recommendations were then made for each PROM.We identified 15 validation studies of 8 PROMs, in 9,070 postpartum women. An adequate number of sleep domains was assessed by 5 PROMs: Bergen Insomnia Scale (BIS), Pittsburgh Sleep Quality Index (PSQI), General Sleep Disturbance Scale (GSDS), Athens Insomnia Scale (AIS) and the Sleep Symptom Checklist (SSC). BIS and GSDS were the only PROMs to demonstrate adequate content validity and at least a low level of evidence of sufficient internal consistency, resulting in Class A recommendations. The BIS was the only PROM, which is easily accessible and free to use for non-commercial research, that achieved a Class A recommendation.The BIS is the best currently available PROM of postpartum sleep. However, this PROM fails to assess several important domains such as sleep duration (and efficiency), chronotype, sleep-disordered breathing and medication usage. Future studies should focus on evaluating the psychometric measurement properties of BIS in the North American setting and in different cultural groups, or to develop a more specific PROM of postpartum sleep.
View details for DOI 10.1093/sleep/zsab128
View details for PubMedID 34013345
Use of Patient-Reported Outcome Measures to Assess Outpatient Postpartum Recovery: A Systematic Review.
JAMA network open
2021; 4 (5): e2111600
Outpatient postpartum recovery is an underexplored area of obstetrics. There is currently no consensus regarding which patient-reported outcome measure (PROM) clinicians and researchers should use to evaluate postpartum recovery.To evaluate PROMs of outpatient postpartum recovery using Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines.An initial literature search performed in July 2019 identified postpartum recovery PROMs and validation studies. A secondary search in July 2020 identified additional validation studies. Both searches were performed using 4 databases (Web of Science, Embase, PubMed, and CINAHL), with no date limiters. Studies with PROMs evaluating more than 3 proposed outpatient postpartum recovery domains were considered. Studies were included if they assessed any psychometric measurement property of the included PROMs in the outpatient postpartum setting. The PROMs were assessed for the following 8 psychometric measurement properties, as defined by COSMIN: content validity, structural validity, internal consistency, cross-cultural validity and measurement invariance, reliability, measurement error, hypothesis testing, and responsiveness. Psychometric measurement properties were evaluated in each included study using the COSMIN criteria by assessing (1) the quality of the methods (very good, adequate, doubtful, inadequate, or not assessed); (2) overall rating of results (sufficient, insufficient, inconsistent, or indeterminate); (3) level of evidence assessed using the Grading of Recommendations, Assessment, Development and Evaluations assessment tool; and (4) level of recommendation, which included class A (recommended for use; showed adequate content validity with at least low-quality evidence for sufficient internal consistency), class B (not class A or class C), or class C (not recommended).In total, 15 PROMs (7 obstetric specific and 8 non-obstetric specific) were identified, evaluating outpatient postpartum recovery in 46 studies involving 19 165 women. The majority of psychometric measurement properties of the included PROMs were graded as having very-low-level or low-level evidence. The best-performing PROMs that received class A recommendations were the Maternal Concerns Questionnaire, the Postpartum Quality of Life tool, and the World Health Organization Quality of Life-BREF. The remainder of the evaluated PROMs had insufficient evidence to make recommendations regarding their use (and received class B recommendations).This review found that the best-performing PROMs currently available to evaluate outpatient postpartum recovery were the Maternal Concerns Questionnaire, the Postpartum Quality of Life tool, and the World Health Organization Quality of Life-BREF; however, these tools all had significant limitations. This study highlights the need to focus future efforts on robustly developing and validating a new PROM that may comprehensively evaluate outpatient postpartum recovery.
View details for DOI 10.1001/jamanetworkopen.2021.11600
View details for PubMedID 34042993
A systematic review of patient-reported outcome measures used to assess postpartum pain using Consensus Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines.
British journal of anaesthesia
We performed a systematic review using Consensus Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines to identify the best available patient-reported outcome measure (PROM) of postpartum pain.This review follows COSMIN guidelines. We searched four databases with no date limiters, for previously identified validated PROMs used to assess postpartum pain. PROMs evaluating more than one author-defined domain of postpartum pain were assessed. We sought studies evaluating psychometric properties. An overall rating was then assigned based upon COSMIN analysis, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to assess the level of evidence for psychometric properties of included PROMs. These assessments were used to make recommendations and identify the best PROM to assess postpartum pain.We identified 19 studies using seven PROMs (involving 3511 women), which evaluated postpartum pain. All included studies evaluated ≥1 psychometric property of the included PROMs. An adequate number of pain domains was assessed by the Brief Pain Inventory (BPI), Short Form-BPI (SF-BPI), and McGill Pain Questionnaire (MPQ). The SF-BPI was the only PROM to demonstrate adequate content validity and at least a low-level of evidence for sufficient internal consistency, resulting in a Class A recommendation (the best performing instrument, recommended for use).SF-BPI is the best currently available PROM to assess postpartum pain. However, it fails to assess several important domains and only just met the criteria for a Class A recommendation. Future studies are warranted to develop, evaluate, and implement a new PROM designed to specifically assess postpartum pain.
View details for DOI 10.1016/j.bja.2021.03.035
View details for PubMedID 34016441
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions
NATURE MACHINE INTELLIGENCE
View details for DOI 10.1038/s42256-020-00232-8
View details for Web of Science ID 000579336000001
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.
Nature machine intelligence
2020; 2 (10): 619-628
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
IMMUNE PROFILING TO PREDICT RECOVERY OUTCOMES AFTER SURGERY
LIPPINCOTT WILLIAMS & WILKINS. 2020: 66–67
View details for Web of Science ID 000587668800152
Multi-Omic, Longitudinal Profile of Third-Trimester Pregnancies Identifies a Molecular Switch That Predicts the Onset of Labor.
SPRINGER HEIDELBERG. 2020: 89A
View details for Web of Science ID 000525432600082
Factors associated with persistent pain after childbirth: a narrative review.
British journal of anaesthesia
A systematic literature search was performed to identify studies that reported risk factors for persistent pain after childbirth. Many studies have sought to identify risk factors for post-delivery pain in different populations, using different methodologies and different outcome variables. Studies of several different but interrelated post-partum pain syndromes have been conducted. Factors strongly and specifically associated with persistent incisional scar pain after Caesarean delivery include a coexisting persistent pain problem in another part of the body and severe acute postoperative pain. For persistent vaginal and perineal pain, operative vaginal delivery and the magnitude of perineal trauma have been consistently linked. History of pregnancy-related and pre-pregnancy back pain and heavier body weight are robust risk factors for persistent back pain after pregnancy. Unfortunately, limitations, particularly small samples and lack of a priori sample size calculation designed to detect specific effect sizes for risk of persistent pain outcomes, preclude definitive conclusions about many other predictors and the strength of outcome associations. In future studies, assessments of specific phenotypes using a rigorous analysis with appropriate predetermined sample sizes and validated instruments are needed to allow elucidation of stronger and reliable associations. Interventional studies targeting the most robustly associated, modifiable risk factors, such as acute post-partum pain, may lead to solutions for the prevention and treatment of these common problems that impact a large population.
View details for DOI 10.1016/j.bja.2019.12.037
View details for PubMedID 31955857
Multiomic immune clockworks of pregnancy.
Seminars in immunopathology
Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.
View details for DOI 10.1007/s00281-019-00772-1
View details for PubMedID 32020337
VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.
2020; 11 (1): 3738
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
Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries.
JAMA network open
2020; 3 (12): e2029655
Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies.To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019.Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites.The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways.This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.
View details for DOI 10.1001/jamanetworkopen.2020.29655
View details for PubMedID 33337494
Society for Obstetric Anesthesia and Perinatology Consensus Statement: Monitoring Recommendations for Prevention and Detection of Respiratory Depression Associated With Administration of Neuraxial Morphine for Cesarean Delivery Analgesia
ANESTHESIA AND ANALGESIA
2019; 129 (2): 458–74
View details for DOI 10.1213/ANE.0000000000004195
View details for Web of Science ID 000475951100036
Determinants of women's dissatisfaction with anaesthesia care in labour and delivery.
Patient-centred care and factors associated with patient satisfaction with anaesthesia have been widely studied. However, the most important considerations in the setting of obstetric anaesthesia are uncertain. Identification of, and addressing, factors that contribute to patient dissatisfaction may improve quality of care. We sought to identify factors associated with<100% satisfaction with obstetric anaesthesia care. At total of 4297 women treated by anaesthetists provided satisfaction data 24h after vaginal and 48h after caesarean delivery. As 78% of women were 100% satisfied, we studied factors associated with the dichotomous variable, 100% satisfied vs. < 100% satisfied. We evaluated patient characteristics and peripartum factors using multivariable sequential logistic regression. The following factors were strongly associated with maternal dissatisfaction after vaginal delivery: pain intensity during the first stage of labour; pain intensity during the second stage of labour; postpartum pain intensity; delay >15min in providing epidural analgesia and postpartum headache (all p<0.0001). Pruritus (p=0.005) also contributed to dissatisfaction after vaginal delivery, whereas non-Hispanic ethnicity was negatively associated with dissatisfaction (p=0.01). After caesarean delivery, the intensity of postpartum pain (p<0.0001), headache (p=0.001) and pruritus (p=0.001) were linked to dissatisfaction. Hispanic ethnicity also had a negative relationship with dissatisfaction after caesarean delivery (p=0.005). Thus, inadequate or delayed analgesia and treatment-related side-effects are associated with maternal dissatisfaction with obstetric anaesthesia care. Development of protocols to facilitate identification of ineffective analgesia and provide an appropriate balance between efficacy and side-effects, are important goals to optimise maternal satisfaction.
View details for DOI 10.1111/anae.14756
View details for PubMedID 31264207
Postpartum chronic pelvic pain and pelvic girdle pain.
View details for DOI 10.23736/S0375-9393.19.13893-X
View details for PubMedID 31238646
Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia
FRONTIERS IN IMMUNOLOGY
View details for DOI 10.3389/fimmu.2019.01305
View details for Web of Science ID 000470999000001
Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.
Bioinformatics (Oxford, England)
2019; 35 (1): 95–103
Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementation: Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary information: Supplementary data are available at Bioinformatics online.
View details for PubMedID 30561547
Systemic Immunologic Consequences of Chronic Periodontitis.
Journal of dental research
Chronic periodontitis (ChP) is a prevalent inflammatory disease affecting 46% of the US population. ChP produces a profound local inflammatory response to dysbiotic oral microbiota that leads to destruction of alveolar bone and tooth loss. ChP is also associated with systemic illnesses, including cardiovascular diseases, malignancies, and adverse pregnancy outcomes. However, the mechanisms underlying these adverse health outcomes are poorly understood. In this prospective cohort study, we used a highly multiplex mass cytometry immunoassay to perform an in-depth analysis of the systemic consequences of ChP in patients before (n = 28) and after (n = 16) periodontal treatment. A high-dimensional analysis of intracellular signaling networks revealed immune system-wide dysfunctions differentiating patients with ChP from healthy controls. Notably, we observed exaggerated proinflammatory responses to Porphyromonas gingivalis-derived lipopolysaccharide in circulating neutrophils and monocytes from patients with ChP. Simultaneously, natural killer cell responses to inflammatory cytokines were attenuated. Importantly, the immune alterations associated with ChP were no longer detectable 3 wk after periodontal treatment. Our findings demarcate systemic and cell-specific immune dysfunctions in patients with ChP, which can be temporarily reversed by the local treatment of ChP. Future studies in larger cohorts are needed to test the boundaries of generalizability of our results.
View details for DOI 10.1177/0022034519857714
View details for PubMedID 31226001
ASSESSMENT OF MATERNAL PERIPHERAL IMMUNE SYSTEM BY MASS CYTOMETRY TO PREDICT THE ONSET OF LABOR
LIPPINCOTT WILLIAMS & WILKINS. 2018: 403
View details for Web of Science ID 000460106500230
Mass Cytometry and Proteomic Based Prediction of the Onset of Labor.
SAGE PUBLICATIONS INC. 2018: 153A
View details for Web of Science ID 000429928200292
Centrally administered isoproterenol induces sympathetic outflow via brain prostaglandin E-2-mediated mechanisms in rats
AUTONOMIC NEUROSCIENCE-BASIC & CLINICAL
2015; 189: 1–7
Brain β-adrenoceptor stimulation can induce elevations of plasma levels of noradrenaline. However, there have been no detailed studies related to signaling pathways downstream of β-adrenoceptors responsible for central sympathetic outflow. In the present study, we pharmacologically examined the possibility that centrally administered isoproterenol can induce elevations of plasma noradrenaline levels in a brain prostaglandin-dependent manner. In addition, we also examined whether or not intracerebroventricular administration of isoproterenol could release endogenously synthesized prostaglandin (PG) E2 in the hypothalamic paraventricular nucleus (PVN) by using the brain microdialysis technique combined with liquid chromatography-ion trap tandem mass spectrometry (LC-ITMS(n)). Under urethane anesthesia, a femoral venous line was inserted for infusion of saline and a femoral arterial line was inserted for collecting blood samples. Next, animals were placed in a stereotaxic apparatus for application of test agents. Catecholamines in the plasma were extracted by alumina absorption and were assayed by high-performance liquid chromatography with electrochemical detection. Quantification of PGE2 in rat PVN microdialysates was performed by the LC-ITMS(n) method. We demonstrated that centrally administered isoproterenol-induced elevations of plasma noradrenaline could be mediated via activation of β-adrenoceptors and the downstream phospholipase A2-cyclooxygenase pathway. Furthermore, PGE2 in the PVN and the PGE2 receptor EP3 subtype appear to play an important role in the process. Our results suggest that central isoproterenol-induced sympathetic outflow is mediated via brain PGE2 in a PGE2 receptor EP3 subtype-dependent manner.
View details for PubMedID 25549851
- Particulate matter in bicarbonate Ringer’s solution Experimental and clinical cardiology. 2014: 139-142
Right ventricular perforation due to a stabilizing bar installed for the Nuss procedure
2013; 79 (7): 820–21
View details for Web of Science ID 000330327500018
View details for PubMedID 23419344
Role of naofen in apoptosis of hepatocytes induced by lipopolysaccharide through mitochondrial signaling in rats
2012; 42 (7): 696–705
Lipopolysaccharide (LPS) causes apoptosis of hepatocytes, which is probably mediated by inflammatory substances released from Kupffer cells (KCs). Recently, we have reported that naofen, a newly found intracellular WD40-repeat protein, has a role in inducing the apoptosis in HEK293 cells. Hence, the present study was undertaken to investigate a role of naofen in the LPS-induced apoptosis of rat hepatocytes. Rats were treated with i.v. injections of LPS, and livers were extirpated to evaluate expression of naofen and apoptosis. In in vitro experiments, hepatocytes and KCs were separately isolated from rat livers. The incubation medium for KCs treated with LPS (KC-CM) was used for hepatocyte culture. Intravenous injections of LPS enhanced the expression of naofen in the livers. Livers showed terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL)-positive staining, and elevated caspase-3 activity. In isolated KCs or hepatocytes, LPS hardly affected naofen expression and caspase-3 activity, whereas incubation of hepatocytes with KC-CM enhanced both naofen expression and caspase-3 activation. Transfection of hepatocyte with naofen siRNA prevented such effects of KC-CM, and clearly eliminated KC-CM-induced reduction of Bcl-2 and Bcl-xL. In contrast, overexpression of naofen in hepatocytes downregulated Bcl-2 and Bcl-xL, released cytochrome c from mitochondria, and activated caspase-3. These results indicate that LPS may induce the hepatic apoptosis in association with enhanced naofen expression, and that naofen may mediate the activation of caspase-3 through downregulating the Bcl-2 and Bcl-xL expression, and releasing cytochrome c from mitochondria to cytoplasm.
View details for PubMedID 22409254