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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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Becker, Dai, Chang, Feyaerts, Stelzer, Zhang, Berson, Saarunya, De Francesco, Espinosa, Kim, Marić, Mataraso, Payrovnaziri, Phongpreecha, Ravindra, Shome, Tan, Thuraiappah, Xue, Mayo, Quaintance, Laborde, King, Dhabhar, Gotlib, Wong, Angst, Shaw, Stevenson, Gaudilliere and Aghaeepour.)

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia., Competing Interests: S.R.Q. is a founder, consultant, and shareholder of Mirvie. M.N.M. is a shareholder of Mirvie. D.A.R. is a shareholder of Blue Willow Biologics, Cantata Bio, Evelo Biosciences, Karius, Proderm IQ, and Second Genome. D.A.R. is an advisor to Cantata Bio and Visby Medical. K.G.S. is paid advisor of Mission Biocapital and Infinant Health; equity holder and paid advisor of Avexegen; and equity holder of mProbe. M.P.S. is a co-founder and scientific advisor of Personalis, SensOmics, Qbio, January AI, Fodsel, Filtricine, Protos, RTHM, Iollo, Marble Therapeutics, and Mirvie, and a scientific advisor of Genapsys, Jupiter, Neuvivo, Swaza, and Mitrix., (© 2022 The Authors.)

Background: 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., Objective: 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., Study Design: 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., Results: 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., Conclusions: 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.

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 AUC validation  = 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., Competing Interests: Declaration of interests S.D.B. has consulted for Regeneron, Sanofi, Novartis, and Janssen on topics unrelated to this study and owns stocks in AbCellera Biologics. The other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches., Methods and Analysis: Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function., Ethics and Dissemination: The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories., Trial Registration Number: NCT04285112., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Background: Chronic pain affects one-fifth of US adults. Reductions in opioid prescribing have been associated with increased non-prescription opioid use and, chronologically, increased stimulant (methamphetamine and cocaine) use. While non-prescription opioid use is commonly attributed to pain self-management, the role of stimulants in managing pain is unclear., Methods: We analyzed baseline data from a longitudinal study of patients with chronic non-cancer pain in an urban safety-net healthcare system who had been prescribed an opioid for ≥3 of the last 12 months, and had a history of non-prescription opioid, cocaine, or amphetamine use ( N  = 300). We estimated the prevalence and identified correlates of stimulant use to treat pain among a subgroup of patients who reported past-year stimulant use ( N  = 105). Data sources included computer-assisted questionnaire (demographics, substance use, pain), clinical exam and procedures (pain, pain tolerance), and chart abstraction (opioid prescriptions). We conducted bivariate analyses to assess associations between demographics, pain characteristics, non-opioid therapies, substance use, opioid prescriptions, and self-reported symptoms, with reporting using stimulants to treat pain. Demographic variables and those with significant bivariate associations were included in a multivariable logistic regression model., Results: Fifty-two percent of participants with past-year stimulant use reported using stimulants in the past year to treat pain. Participants who used stimulants for pain reported slightly higher average pain in the past 3 months (median of 8 (IQR: 6-8) vs 7 (7-9) out of 10, p  = 0.049). In the multivariable analysis, female gender (AOR= 3.20, 95% CI: 1.06-9.63, p  = 0.039) and higher score on the Douleur Neuropathique 4 neuropathic pain questionnaire (AOR = 1.34, 95% CI: 1.05-1.70, p  = 0.017) were associated with past-year stimulant use to treat pain., Conclusion: Stimulants may be used for pain self-management, particularly for neuropathic pain and among women. Our findings suggest an underexplored motivation for stimulant use in an era of reduced access to prescribed opioids.

Purpose of Review: Postoperative complications including infections, cognitive impairment, and protracted recovery occur in one-third of the 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on our healthcare system. However, the accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain as major clinical challenges., Recent Findings: Although multifactorial in origin, the dysregulation of immunological mechanisms that occur in response to surgical trauma is a key determinant of postoperative complications. Prior research, primarily focusing on inflammatory plasma markers, has provided important clues regarding their pathogenesis. However, the recent advent of high-content, single-cell transcriptomic, and proteomic technologies has considerably improved our ability to characterize the immune response to surgery, thereby providing new means to understand the immunological basis of postoperative complications and to identify prognostic biological signatures., Summary: The comprehensive and single-cell characterization of the human immune response to surgery has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers, ultimately providing patients and surgeons with actionable information to improve surgical outcomes. Although recent studies have generated a wealth of knowledge, laying the foundation for a single-cell atlas of the human immune response to surgery, larger-scale multiomic studies are required to derive robust, scalable, and sufficiently powerful models to accurately predict the risk of postoperative complications in individual patients., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Among chronic opioid users, the association between decreasing or increasing preoperative opioid utilization and postoperative outcomes is unknown. The authors hypothesized that decreasing utilization would be associated with improved outcomes and increasing utilization with worsened outcomes., Methods: Using commercial insurance claims, the authors identified 57,019 chronic opioid users (10 or more prescriptions or 120 or more days supplied during the preoperative year), age 18 to 89 yr, undergoing one of 10 surgeries between 2004 and 2018. Patients with a 20% or greater decrease or increase in opioid utilization between preoperative days 7 to 90 and 91 to 365 were compared to patients with less than 20% change (stable utilization). The primary outcome was opioid utilization during postoperative days 91 to 365. Secondary outcomes included alternative measures of postoperative opioid utilization (filling a minimum number of prescriptions during this period), postoperative adverse events, and healthcare utilization., Results: The average age was 63 ± 13 yr, with 38,045 (66.7%) female patients. Preoperative opioid utilization was decreasing for 12,347 (21.7%) patients, increasing for 21,330 (37.4%) patients, and stable for 23,342 (40.9%) patients. Patients with decreasing utilization were slightly less likely to fill an opioid prescription during postoperative days 91 to 365 compared to stable patients (89.2% vs. 96.4%; odds ratio, 0.323; 95% CI, 0.296 to 0.352; P < 0.001), though the average daily doses were similar among patients who continued to utilize opioids during this timeframe (46.7 vs. 46.5 morphine milligram equivalents; difference, 0.2; 95% CI, -0.8 to 1.2; P = 0.684). Of patients with increasing utilization, 93.6% filled opioid prescriptions during this period (odds ratio, 0.57; 95% CI, 0.52 to 0.62; P < 0.001), with slightly lower average daily doses (44.3 morphine milligram equivalents; difference, -2.2; 95% CI, -3.1 to -1.3; P < 0.001). Except for alternative measures of persistent postoperative opioid utilization, there were no clinically significant differences for the secondary outcomes., Conclusions: Changes in preoperative opioid utilization were not associated with clinically significant differences for several postoperative outcomes including postoperative opioid utilization., (Copyright © 2021, the American Society of Anesthesiologists. All Rights Reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Ando, Hédou, Feyaerts, Han, Ganio, Tsai, Peterson, Verdonk, Tsai, Marić, Wong, Angst, Aghaeepour, Stevenson, Blumenfeld, Sultan, Carvalho, Stelzer and Gaudillière.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Peterson, Hedou, Ganio, Stelzer, Feyaerts, Harbert, Adusumelli, Ando, Tsai, Tsai, Han, Ringle, Houghteling, Reiss, Lewis, Winn, Angst, Aghaeepour, Stevenson and Gaudilliere.)

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., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Objectives: To identify perioperative practice patterns that predictably impact postoperative pain., Background: Despite significant advances in perioperative medicine, a significant portion of patients still experience severe pain after major surgery. Postoperative pain is associated with serious adverse outcomes that are costly to patients and society., Methods: The presented analysis took advantage of a unique observational data set providing unprecedented detailed pharmacological information. The data were collected by PAIN OUT, a multinational registry project established by the European Commission to improve postoperative pain outcomes. A multivariate approach was used to derive and validate a model predictive of pain on postoperative day 1 (POD1) in 1008 patients undergoing back surgery., Results: The predictive and validated model was highly significant (P = 8.9E-15) and identified modifiable practice patterns. Importantly, the number of nonopioid analgesic drug classes administered during surgery predicted decreased pain on POD1. At least 2 different nonopioid analgesic drug classes (cyclooxygenase inhibitors, acetaminophen, nefopam, or metamizol) were required to provide meaningful pain relief (>30%). However, only a quarter of patients received at least 2 nonanalgesic drug classes during surgery. In addition, the use of very short-acting opioids predicted increased pain on POD1, suggesting room for improvement in the perioperative management of these patients. Although the model was highly significant, it only accounted for a relatively small fraction of the observed variance., Conclusion: The presented analysis offers detailed insight into current practice patterns and reveals modifications that can be implemented in today's clinical practice. Our results also suggest that parameters other than those currently studied are relevant for postoperative pain including biological and psychological variables., Competing Interests: The authors report no conflicts of interest., (Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: Competing interests The authors declare no competing interests.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Glucocorticoids (GC) are a controversial yet commonly used intervention in the clinical management of acute inflammatory conditions, including sepsis or traumatic injury. In the context of major trauma such as surgery, concerns have been raised regarding adverse effects from GC, thereby necessitating a better understanding of how GCs modulate the immune response. Here we report the results of a randomized controlled trial (NCT02542592) in which we employ a high-dimensional mass cytometry approach to characterize innate and adaptive cell signaling dynamics after a major surgery (primary outcome) in patients treated with placebo or methylprednisolone (MP). A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of immune cell signaling trajectories, particularly in the adaptive compartments. By contrast, key innate signaling responses previously associated with pain and functional recovery after surgery, including STAT3 and CREB phosphorylation, are not affected by MP. These results imply cell-specific and pathway-specific effects of GCs, and also prompt future studies to examine GCs' effects on clinical outcomes likely dependent on functional adaptive immune responses.

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.

Based upon our recent insights into the determinants of preterm birth, which is the leading cause of death in children under five years of age worldwide, we describe potential analytic frameworks that provides both a common understanding and, ultimately the basis for effective, ameliorative action. Our research on preterm birth serves as an example that the framing of any human health condition is a result of complex interactions between the genome and the exposome. New discoveries of the basic biology of pregnancy, such as the complex immunological and signaling processes that dictate the health and length of gestation, have revealed a complexity in the interactions (current and ancestral) between genetic and environmental forces. Understanding of these relationships may help reduce disparities in preterm birth and guide productive research endeavors and ultimately, effective clinical and public health interventions.

As part of the American Society of Anesthesiology Brain Health Initiative goal of improving perioperative brain health for older patients, over 30 experts met at the fifth International Perioperative Neurotoxicity Workshop in San Francisco, CA, in May 2016, to discuss best practices for optimizing perioperative brain health in older adults (ie, >65 years of age). The objective of this workshop was to discuss and develop consensus solutions to improve patient management and outcomes and to discuss what older adults should be told (and by whom) about postoperative brain health risks. Thus, the workshop was provider and patient oriented as well as solution focused rather than etiology focused. For those areas in which we determined that there were limited evidence-based recommendations, we identified knowledge gaps and the types of scientific knowledge and investigations needed to direct future best practice. Because concerns about perioperative neurocognitive injury in pediatric patients are already being addressed by the SmartTots initiative, our workshop discussion (and thus this article) focuses specifically on perioperative cognition in older adults. The 2 main perioperative cognitive disorders that have been studied to date are postoperative delirium and cognitive dysfunction. Postoperative delirium is a syndrome of fluctuating changes in attention and level of consciousness that occurs in 20%-40% of patients >60 years of age after major surgery and inpatient hospitalization. Many older surgical patients also develop postoperative cognitive deficits that typically last for weeks to months, thus referred to as postoperative cognitive dysfunction. Because of the heterogeneity of different tools and thresholds used to assess and define these disorders at varying points in time after anesthesia and surgery, a recent article has proposed a new recommended nomenclature for these perioperative neurocognitive disorders. Our discussion about this topic was organized around 4 key issues: preprocedure consent, preoperative cognitive assessment, intraoperative management, and postoperative follow-up. These 4 issues also form the structure of this document. Multiple viewpoints were presented by participants and discussed at this in-person meeting, and the overall group consensus from these discussions was then drafted by a smaller writing group (the 6 primary authors of this article) into this manuscript. Of course, further studies have appeared since the workshop, which the writing group has incorporated where appropriate. All participants from this in-person meeting then had the opportunity to review, edit, and approve this final manuscript; 1 participant did not approve the final manuscript and asked for his/her name to be removed.

Motivation: High-parameter single-cell technologies can reveal novel cell populations of interest, but studying or validating these populations using lower-parameter methods remains challenging., Results: Here, we present GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time. A series of case studies of complex cell types illustrates how simplified enrichment strategies can enable more efficient assays, reveal novel biomarkers and clarify underlying biology., Availability and Implementation: The GateFinder algorithm is implemented as a free and open-source package for BioConductor: https://nalab.stanford.edu/gatefinder., Supplementary Information: Supplementary data are available at Bioinformatics online.

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies., (Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

Unlabelled: The study of complex regional pain syndrome (CRPS) in humans is complicated by inhomogeneities in available study cohorts. We hoped to characterize early CRPS-like features in patients undergoing hand surgery. Forty-three patients were recruited from a hand surgery clinic that had elective surgeries followed by cast immobilization. On the day of cast removal, patients were assessed for vasomotor, sudomotor, and trophic changes, and edema and pain sensitization using quantitative sensory testing. Pain intensity was assessed at the time of cast removal and after 1 additional month, as was the nature of the pain using the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS). Skin biopsies were harvested for the analysis of expression of inflammatory mediators. We identified vascular and trophic changes in the surgical hands of most patients. Increased sensitivity to punctate, pressure, and cold stimuli were observed commonly as well. Moreover, levels of IL-6, TNF-alpha, and the mast cell marker tryptase were elevated in the skin of hands ipsilateral to surgery. Moderate-to-severe pain persisted in the surgical hands for up to 1 month after cast removal. Exploratory analyses suggested interrelationships between the physical, quantitative sensory testing, and gene expression changes and pain-related outcomes., Perspective: This study has identified CPRS-like features in the limbs of patients undergoing surgery followed by immobilization. Further studies using this population may be useful in refining our understanding of CRPS mechanisms and treatments for this condition., (Published by Elsevier Inc.)

Unlabelled: The objectives of this study were to test the effects of low-dose ketorolac and hydromorphone added to continuous local anesthetic wound instillation on surgical-site inflammatory mediators, postoperative pain, and opioid consumption. Sixty healthy women undergoing cesarean delivery were enrolled in this randomized, double-blinded study. Patients were randomized to receive a subcutaneous wound instillation of bupivacaine .5% at 10 mg/hour (active control), bupivacaine .5% with ketorolac .6 mg/hour, or bupivacaine .5% with hydromorphone .04 mg/hour for 48 hours postcesarean. Wound exudate was sampled at 4, 24, and 48 hours postcesarean and assayed for interleukins IL-1β, IL-2, IL-6, IL-8, IL-10, and IL-12, tumor necrosis factor (TNF-α), interferon (INF-γ), and granulocyte-macrophage colony stimulating factor (GM-CSF). The addition of ketorolac to bupivacaine significantly decreased IL-6 (P = .012) and IL-10 (P = .005) compared to plain bupivacaine. Ketorolac, but not hydromorphone, was associated with a decrease in pain (P = .018) and analgesic use (P = .020) following cesarean delivery. Our results are compatible with the view that significant analgesics effects are mediated through local modulation of inflammatory events. Low-dose ketorolac administered into surgical wounds exert significant anti-inflammatory and analgesic effects and may be a valuable analgesic alternative to systemic nonsteroidal anti-inflammatories (NSAIDs) but with potentially fewer side effects., Perspective: This article demonstrates that low-dose ketorolac administered into wounds modulates local inflammatory events, decreases postoperative pain, and reduces opioid consumption. These results suggest that administration of NSAIDs into surgical wounds may be an analgesic alternative to higher systemic dosing of NSAIDs., (Copyright © 2013 American Pain Society. Published by Elsevier Inc. All rights reserved.)

Opioids are the cornerstone medication for the management of moderate to severe pain. Unfortunately, vast inter-individual differences in dose requirements complicate their effective and safe clinical use. Mechanisms underlying such differences are incompletely understood, are likely multifactorial, and include genetic and environmental contributions. While accumulating evidence suggests that variants of several genes account for some of the observed response variance, the relative contribution of these factors remains unknown. This study used a twin paradigm to provide a global estimate of the genetic and environmental contributions to inter-individual differences in pain sensitivity and analgesic opioid effects. Eighty one monozygotic and 31 dizygotic twin pairs successfully underwent a computer-controlled infusion with the μ-opioid agonist alfentanil in a single occasion, randomized, double-blind and placebo-controlled study design. Pain sensitivity and analgesic effects were assessed with experimental heat and cold pressor pain models along with important covariates including demographic factors, depression, anxiety, and sleep quality. Significant heritability was detected for cold pressor pain tolerance and opioid-mediated elevations in heat and cold pressor pain thresholds. Genetic effects accounted for 12-60% of the observed response variance. Significant familial effects accounting for 24-32% of observed variance were detected for heat and cold pressor pain thresholds and opioid-mediated elevation in cold pressor pain tolerance. Significant covariates included age, gender, race, education, and anxiety. Results provide a strong rationale for more detailed molecular genetic studies to elucidate mechanisms underlying inter-individual differences in pain sensitivity and analgesic opioid responses. Such studies will require careful consideration of the studied pain phenotype., (Copyright © 2012 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.)

Unlabelled: Clinically recorded pain scores are abundant in patient health records but are rarely used in research. The use of this information could help improve clinical outcomes. For example, a recent report by the Institute of Medicine stated that ineffective use of clinical information contributes to undertreatment of patient subpopulations--especially women. This study used diagnosis-associated pain scores from a large hospital database to document sex differences in reported pain. We used de-identified electronic medical records from Stanford Hospital and Clinics for more than 72,000 patients. Each record contained at least 1 disease-associated pain score. We found over 160,000 pain scores in more than 250 primary diagnoses, and analyzed differences in disease-specific pain reported by men and women. After filtering for diagnoses with minimum encounter numbers, we found diagnosis-specific sex differences in reported pain. The most significant differences occurred in patients with disorders of the musculoskeletal, circulatory, respiratory and digestive systems, followed by infectious diseases, and injury and poisoning. We also discovered sex-specific differences in pain intensity in previously unreported diseases, including disorders of the cervical region, and acute sinusitis (P = .01, .017, respectively). Pain scores were collected during hospital encounters. No information about the use of pre-encounter over-the-counter medications was available. To our knowledge, this is the largest data-driven study documenting sex differences of disease-associated pain. It highlights the utility of electronic medical record data to corroborate and expand on results of smaller clinical studies. Our findings emphasize the need for future research examining the mechanisms underlying differences in pain., Perspective: This article highlights the potential of electronic medical records to conduct large-scale pain studies. Our results are consistent with previous studies reporting pain differences between sexes and also suggest that clinicians should pay increased attention to this idea., (Copyright © 2012 American Pain Society. All rights reserved.)

Unlabelled: Some recent studies suggested a role of the endogenous opioid system in modulating opioid-induced hyperalgesia (OIH). In order to test this hypothesis, we conducted a prospective randomized, placebo-controlled, 2-way crossover study in healthy human volunteers. We utilized a well-established model of inducing OIH after a brief exposure to the μ-opioid agonist remifentanil using intradermal electrical stimulation. Patients were exposed to a randomized 90-minute infusion of remifentanil or saline placebo during 2 separate occasions. Development of OIH was quantified using changes in the average radius of the area of secondary hyperalgesia generated by electrical pain stimulation. A 23.6% (20.2) increase in area of secondary hyperalgesia over baseline was observed in the postinfusion period of the remifentanil session, demonstrating development of OIH (P = .03). In order to test endogenous opioid system modulation of OIH, patients were given a 1-time bolus of naloxone, which had no effect on the size of the hyperalgesic lesion in either the remifentinal or placebo session. These results suggested that the endogenous opioid system did not appear to modulate OIH., Perspective: Experimental evidence suggested that the endogenous opioid system did not significantly affect opioid-induced hyperalgesia. Consequently, this study suggested that alternative mechanisms such as pronociceptive stimulation and neuroplastic changes might be responsible for expression of OIH., (Copyright © 2011 American Pain Society. Published by Elsevier Inc. All rights reserved.)

Opioids are the cornerstone medication for the treatment of moderate to severe pain. However, analgesic opioid requirements and the propensity to suffer from aversive opioid effects, including fatal respiratory depression and addiction, vary widely among patients. The factors underlying the substantial response variance remain largely unknown and need clarification for using opioids more effectively in appropriately selected patients. This ongoing study takes advantage of the twin paradigm to estimate the genetic and environmental contributions to inter-individual differences in opioid responses. Evidence of significant heritability will justify more detailed and extensive genomic studies. The enrollment target is 80 monozygotic and 45 dizygotic twin pairs who undergo a target-controlled infusion of the opioid alfentanil and saline placebo in sequential but randomized order. In a laboratory-type setting, well-defined pharmacodynamic endpoints are measured to quantify pain sensitivity, analgesic opioid effects, and aversive opioid effects including respiratory depression, sedation and reinforcing affective responses. First results obtained in 159 participants provide evidence for the feasibility and utility of this interventional study paradigm to estimate familial aggregation and heritability components of relevant drug effects. Areas highlighted in this report include recruitment strategies, required infrastructure and personnel, selection of relevant outcome measures, drug infusion algorithm minimizing pharmacokinetic variability, and considerations for optimizing data quality and quantity without hampering feasibility. Applying the twin paradigm to complex and potentially harmful studies comprehensively characterizing pharmacological response profiles is without much precedent. Methods and first results including heritability estimates for heat and cold pain sensitivity should be of interest to investigators considering similar studies.

Unlabelled: This document reports the consensus of an interdisciplinary panel of research and clinical experts charged with reviewing the use of opioids for chronic noncancer pain (CNCP) and formulating guidelines for future research. Prescribing opioids for chronic noncancer pain has recently escalated in the United States. Contrasting with increasing opioid use are: 1) The lack of evidence supporting long-term effectiveness; 2) Escalating misuse of prescription opioids including abuse and diversion; and 3) Uncertainty about the incidence and clinical salience of multiple, poorly characterized adverse drug events (ADEs) including endocrine dysfunction, immunosuppression and infectious disease, opioid-induced hyperalgesia and xerostomia, overdose, falls and fractures, and psychosocial complications. Chief among the limitations of current evidence are: 1) Sparse evidence on long-term opioid effectiveness in chronic pain patients due to the short-term time frame of clinical trials; 2) Insufficiently comprehensive outcome assessment; and 3) Incomplete identification and quantification of ADEs. The panel called for a strategic interdisciplinary approach to the problem domain in which basic scientists and clinicians cooperate to resolve urgent issues and generate a comprehensive evidence base. It offered 4 recommendations in 3 areas: 1) A research strategy for studying the effectiveness of long-term opioid pharmacotherapy; 2) Improvements in evidence-generation methodology; and 3) Potential research topics for generating new evidence., Perspective: Prescribing opioids for CNCP has outpaced the growth of scientific evidence bearing on the benefits and harms of these interventions. The need for a strong evidence base is urgent. This guideline offers a strategic approach to creating a comprehensive evidence base to guide safe and effective management of CNCP., (Copyright 2010 American Pain Society. All rights reserved.)

It is widely accepted that chronic opioid therapy is associated with the development of pharmacological tolerance. More controversial is the question as to whether acute opioid administration can result in "acute tolerance." The aim of this double-blind, placebo-controlled study in thirty-six healthy human volunteers was to examine whether a 3-h intravenous infusion delivering two different but clinically relevant doses of the mu-opioid receptor agonist remifentanil would result in tolerance to analgesic, respiratory depressant and/or sedative opioid effects. The blood remifentanil concentration versus opioid effect relationship was determined before and after the 3-h infusion. Tolerance was inferred if the potency of remifentanil was significantly lower after the 3-h infusion. Opioid analgesia was assessed with the aid of the cold pressor test and models of electrical and heat pain. Respiratory depression was assessed by measuring arterial pCO2 and minute ventilation. Subjective sedation scores were assessed on a visual analogue scale. Mixed effects modeling was used to relate the steady-state blood remifentanil concentration to each pharmacodynamic assessment. Neither dose of remifentanil produced detectable tolerance to any of the measured opioid effects following a 3-h infusion. The study was adequately powered to detect a decrease in potency of 5-24% for analgesia, 20-48% for respiratory depression, and 32% for sedative effects. These results suggest that short-term administration of clinically useful doses of remifentanil is not associated with the development of significant tolerance to analgesic, respiratory depressant, or sedative opioid effects.