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Abstract Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a ‘clock’ of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal ‘clock’ of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman’s), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, “Cells & Proteomics” had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000–2022. However, when focusing on the period from 2015 to 2022, topics “Cells & Proteomics” and “Pulmonology” exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in “Aneurysms”, “Covid 19 Pandemic”, and “Artificial intelligence & Machine Learning”. Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

Background: A critical size bone defect is a clinical scenario in which bone is lost or excised due to trauma, infection, tumor, or other causes, and cannot completely heal spontaneously. The most common treatment for this condition is autologous bone grafting to the defect site. However, autologous bone graft is often insufficient in quantity or quality for transplantation to these large defects. Recently, tissue engineering methods using mesenchymal stem cells (MSCs) have been proposed as an alternative treatment. However, the underlying biological principles and optimal techniques for tissue regeneration of bone using stem cell therapy have not been completely elucidated. Methods: In this study, we compare the early cellular dynamics of healing between bone graft transplantation and MSC therapy in a murine chronic femoral critical-size bone defect. We employ high-dimensional mass cytometry to provide a comprehensive view of the differences in cell composition, stem cell functionality, and immunomodulatory activity between these two treatment methods one week after transplantation. Results: We reveal distinct cell compositions among tissues from bone defect sites compared with original bone graft, show active recruitment of MSCs to the bone defect sites, and demonstrate the phenotypic diversity of macrophages and T cells in each group that may affect the clinical outcome. Conclusion: Our results provide critical data and future directions on the use of MSCs for treating critical size defects to regenerate bone.Translational Potential of this article: This study showed systematic comparisons of the cellular and immunomodulatory profiles among different interventions to improve the healing of the critical-size bone defect. The results provided potential strategies for designing robust therapeutic interventions for the unmet clinical need of treating critical-size bone defects.

IntroductionFew studies have evaluated the presence of Post COVID-19 conditions (PCC) in people from Latin America, a region that has been heavily afflicted by the COVID-19 pandemic. In this study, we describe the frequency, co-occurrence, predictors, and duration of 23 symptoms in a cohort of Mexican patients with PCC.MethodsWe prospectively enrolled and followed adult patients hospitalized for severe COVID-19 at a tertiary care centre in Mexico City. The incidence of PCC symptoms was determined using questionnaires. Unsupervised clustering of PCC symptom co-occurrence and Kaplan–Meier analyses of symptom persistence were performed. The effect of baseline clinical characteristics was evaluated using Cox regression models and reported with hazard ratios (HR).ResultsWe found that amongst 192 patients with PCC, respiratory problems were the most prevalent and commonly co-occurred with functional activity impairment. 56% had ≥5 persistent symptoms. Symptom persistence probability at 360 days 0.78. Prior SARS-CoV-2 vaccination and infection during the Delta variant wave were associated with a shorter duration of PCC. Male sex was associated with a shorter duration of functional activity impairment and respiratory symptoms. Hypertension and diabetes were associated with a longer duration of functional impairment. Previous vaccination accelerated PCC recovery.DiscussionIn our cohort, PCC symptoms were frequent (particularly respiratory and neurocognitive ones) and persistent. Importantly, prior SARS-CoV-2 vaccination resulted in a shorter duration of PCC.

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.

The name of the author, Brice Gaudillière, was missing. The correct list of authors is shown here: Dorien Feyaerts, Christopher Urbschat, Brice Gaudillière, Ina A. Stelzer. The original article can be found online at https://doi.org/10.1007/s00281-022-00931-x B Correction to: Seminars in Immunopathology b https://doi.org/10.1007/s00281-022-00931-x The article contained an incomplete author list. [Extracted from the article]

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400–35,000 features down to 4–34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl. Stabl selects sparse and reliable biomarker candidates from predictive models. [ABSTRACT FROM AUTHOR]

This article is a contribution to the special issue on: Single-cell and spatial multi-omics in clinical outcomes studies - Guest Editor: Brice Gaudillière Recent emphasis on precision medicine acknowledges the drawbacks of a one-size-fits-all approach to patient care. In each clinical use-case, the potential of high-dimensional single-cell analysis of the tumor microenvironment combined with other omics modalities, such as genomics and radiomics, for improving cancer screening and surveillance of individualized therapeutic treatment is emphasized. [Extracted from the article]

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs. [ABSTRACT FROM AUTHOR]

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes. [ABSTRACT FROM AUTHOR]

Oral mucosal pathologies comprise an array of diseases with worldwide prevalence and medical relevance. Affecting a confined space with crucial physiological and social functions, oral pathologies can be mutilating and drastically reduce quality of life. Despite their relevance, treatment for these diseases is often far from curative and remains vastly understudied. While multiple factors are involved in the pathogenesis of oral mucosal pathologies, the host's immune system plays a major role in the development, maintenance, and resolution of these diseases. Consequently, a precise understanding of immunological mechanisms implicated in oral mucosal pathologies is critical (1) to identify accurate, mechanistic biomarkers of clinical outcomes; (2) to develop targeted immunotherapeutic strategies; and (3) to individualize prevention and treatment approaches. Here, we review key elements of the immune system's role in oral mucosal pathologies that hold promise to overcome limitations in current diagnostic and therapeutic approaches. We emphasize recent and ongoing multiomic and single-cell approaches that enable an integrative view of these pathophysiological processes and thereby provide unifying and clinically relevant biological signatures. [ABSTRACT FROM AUTHOR]

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems. [ABSTRACT FROM AUTHOR]

First-line immunotherapy in non-small-cell lung cancer largely improved patients' survival. PD-L1 testing is required before immune checkpoint inhibitor initiation. However, this biomarker fails to accurately predict patients' response. On the other hand, immunotherapy exposes patients to immune-related toxicity, the mechanisms of which are still unclear. Hence, there is an unmet need to develop clinically approved predictive biomarkers to better select patients who will benefit the most from immune checkpoint inhibitors and improve risk management. Single-cell technologies provide unprecedented insight into the tumor and its microenvironment, leading to the discovery of immune cells involved in immune checkpoint inhibitor response or toxicity. In this review, we will underscore the potential of the single-cell approach to identify candidate biomarkers improving non-small-cell lung cancer patients' care. [ABSTRACT FROM AUTHOR]

Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell– and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT. [ABSTRACT FROM AUTHOR]

The immune system establishes during the prenatal period from distinct waves of stem and progenitor cells and continuously adapts to the needs and challenges of early postnatal and adult life. Fetal immune development not only lays the foundation for postnatal immunity but establishes functional populations of tissue-resident immune cells that are instrumental for fetal immune responses amidst organ growth and maturation. This review aims to discuss current knowledge about the development and function of tissue-resident immune populations during fetal life, focusing on the brain, lung, and gastrointestinal tract as sites with distinct developmental trajectories. While recent progress using system-level approaches has shed light on the fetal immune landscape, further work is required to describe precise roles of prenatal immune populations and their migration and adaptation to respective organ environments. Defining points of prenatal susceptibility to environmental challenges will support the search for potential therapeutic targets to positively impact postnatal health. [ABSTRACT FROM AUTHOR]

The field of infectious diseases currently takes a reactive approach and treats infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (eg, immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in "omics" technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (ie, prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (ie, transcriptomics, epigenomics, metabolomics, proteomics) hold the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Single-cell technologies have immense potential to shed light on molecular and biological processes that drive human diseases. Mass cytometry (or Cytometry by Time Of Flight mass spectrometry, CyTOF) has already been employed in clinical studies to comprehensively survey patients' circulating immune system. As interest in the 'bedside' application of mass cytometry is growing, the delineation of relevant methodological issues is called for. This report uses a newly generated dataset to discuss important methodological considerations when mass cytometry is implemented in a clinical study. Specifically, the use of whole blood samples versus peripheral blood mononuclear cells (PBMCs), design of mass-tagged antibody panels, technical and analytical implications of sample barcoding, and application of traditional and unsupervised approaches to analyze high-dimensional mass cytometry datasets are discussed. A mass cytometry assay was implemented in a cross-sectional study of 19 women with a history of term or preterm birth to determine whether immune traits in peripheral blood differentiate the two groups in the absence of pregnancy. Twenty-seven phenotypic and 11 intracellular markers were simultaneously analyzed in whole blood samples stimulated with lipopolysaccharide (LPS at 0, 0.1, 1, 10, and 100 ng mL−1) to examine dose-dependent signaling responses within the toll-like receptor 4 (TLR4) pathway. Complementary analyses, grounded in traditional or unsupervised gating strategies of immune cell subsets, indicated that the prpS6 and pMAPKAPK2 responses in classical monocytes are accentuated in women with a history of preterm birth (FDR<1%). The results suggest that women predisposed to preterm birth may be prone to mount an exacerbated TLR4 response during the course of pregnancy. This important hypothesis-generating finding points to the power of single-cell mass cytometry to detect biologically important differences in a relatively small patient cohort. © 2015 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]