Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

Sp3 is essential for normal lung morphogenesis and cell cycle progression during mouse embryonic development.

Members of the Sp family of transcription factors regulate gene expression via binding GC boxes within promoter regions. Unlike Sp1, which stimulates transcription, the closely related Sp3 can either repress or activate gene expression and is required for perinatal survival in mice. Here, we use RNA-seq and cellular phenotyping to show how Sp3 regulates murine fetal cell differentiation and proliferation. Homozygous Sp3-/- mice were smaller than wild-type and Sp+/- littermates, died soon after birth and had abnormal lung morphogenesis. RNA-seq of Sp3-/- fetal lung mesenchymal cells identified alterations in extracellular matrix production, developmental signaling pathways and myofibroblast/lipofibroblast differentiation. The lungs of Sp3-/- mice contained multiple structural defects, with abnormal endothelial cell morphology, lack of elastic fiber formation, and accumulation of lipid droplets within mesenchymal lipofibroblasts. Sp3-/- cells and mice also displayed cell cycle arrest, with accumulation in G0/G1 and reduced expression of numerous cell cycle regulators including Ccne1. These data detail the global impact of Sp3 on in vivo mouse gene expression and development.

Factor Eight Inhibitor Bypass Activity use in cardiac surgery: A propensity matched analysis of safety outcomes.

BACKGROUND: Bleeding during cardiac surgery may be refractory to standard interventions. Off-label use of Factor Eight Inhibitor Bypass Activity (FEIBA) has been described to treat such bleeding. However, reports of safety, particularly thromboembolic outcomes, show mixed results and reported cohorts have been small. METHODS: Adult patients undergoing cardiac surgery on cardiopulmonary bypass between July 1, 2018 and June 30, 2023 at Stanford Hospital were reviewed (n=3335). Patients who received FEIBA to treat post-cardiopulmonary bypass bleeding were matched with those who did not by propensity scores in a 1:1 ratio using nearest neighbor matching (n= 352 per group). The primary outcome was a composite outcome of thromboembolic complications including any one of deep vein thrombosis (DVT), pulmonary embolism (PE), unplanned coronary artery intervention, ischemic stroke, and acute limb ischemia, in the postoperative period. Secondary outcomes included renal failure, reoperation, postoperative transfusion, ICU length of stay (LOS), and 30-day mortality. RESULTS: 704 encounters were included in our propensity matched analysis. The mean dose of FEIBA administered was 7.3 ±5.5 units/kg. In propensity matched multivariate logistic regression models there was no statistically significant difference in odds ratios for thromboembolic outcomes, ICU LOS, or mortality. Patients who received >750 units of FEIBA had an increased odds ratio for acute renal failure (OR 4.14; 95% CI 1.61 to 10.36, p <0.001). In multivariate linear regression, patients receiving FEIBA were transfused more plasma and cryoprecipitate postoperatively. However, only the dose range of 501-750 units was associated with an increase in transfusion of RBCs (ß 2.73; 95% CI 0.68 to 4.78; p=0.009), and platelets (ß 1.74; 95% CI 0.85 to 2.63; p <0.001). CONCLUSIONS: Low dose FEIBA administration during cardiac surgery does not increase risk of thromboembolic events, ICU LOS, or mortality in a propensity matched cohort. Higher doses were associated with increased acute renal failure and postoperative transfusion. Further studies are required to establish the efficacy of activated factor concentrates to treat refractory bleeding during cardiac surgery.

Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.

BACKGROUND: Predicting mortality and specific morbidities before they occur may allow for interventions that may improve health trajectories. HYPOTHESIS: Integrating key maternal and postnatal infant variables in the first 2 weeks of age into machine learning (ML) algorithms will reliably predict survival and specific morbidities in VLBW preterm infants. METHODS: ML algorithms were developed to integrate 47 features for predicting mortality, bronchopulmonary dysplasia (BPD), neonatal sepsis, necrotizing enterocolitis (NEC), intraventricular hemorrhage (IVH), cystic periventricular leukomalacia (PVL), and retinopathy of prematurity (ROP). A retrospective cohort (n = 3341) was used to train and validate the models with a repeated 10-fold cross-validation strategy. These models were then tested on a separate cohort (n = 447) to evaluate the final model performance. RESULTS: Among the seven ML algorithms employed, tree-based ensemble models, specifically Random Forest (RF) and XGBoost, had the best performance metrics. The area under the receiver operating characteristic curve (AUROC) of sepsis with or without meningitis (0.73), NEC (0.73), BPD (0.71), and mortality (0.74) exceeded 0.7, while the area under Precision-Recall curve (AUPRC) for all outcomes was greater than the prevalence, demonstrating effective risk stratification in VLBW preterm infants. CONCLUSIONS: Our study demonstrates the potential of predictive analytics leveraging ML techniques in advancing precision medicine. IMPACT: Reliable prediction of adverse outcomes before they occur has the potential to institute interventions and possibly improve health trajectories in VLBW preterm infants. We used machine learning to develop and test predictive models for mortality and five major morbidities in VLBW preterm infants. Individualized prediction of outcomes and individualized interventions will advance Precision Medicine in Neonatology.

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

BACKGROUND: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning. METHODS: Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis. RESULTS: Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster. CONCLUSION: Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases. IMPACT: Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.

Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.

BACKGROUND: Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties. METHODS: Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist). RESULTS: We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes. CONCLUSIONS: Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.

Association of pregnancy complications and postpartum maternal leukocyte telomeres in two diverse cohorts: a nested case-control study.

BACKGROUND: Biologic strain such as oxidative stress has been associated with short leukocyte telomere length (LTL), as well as with preeclampsia and spontaneous preterm birth, yet little is known about their relationships with each other. We investigated associations of postpartum maternal LTL with preeclampsia and spontaneous preterm birth. METHODS: This pilot nested case control study included independent cohorts of pregnant people with singleton gestations from two academic institutions: Cohort 1 (hereafter referred to as Suburban) were enrolled prior to 20 weeks' gestation between 2012 and 2018; and Cohort 2 (hereafter referred to as Urban) were enrolled at delivery between 2000 and 2012. Spontaneous preterm birth or preeclampsia were the selected pregnancy complications and served as cases. Cases were compared with controls from each study cohort of uncomplicated term births. Blood was collected between postpartum day 1 and up to 6 months postpartum and samples were frozen, then simultaneously thawed for analysis. Postpartum LTL was the primary outcome, measured using quantitative polymerase chain reaction (PCR) and compared using linear multivariable regression models adjusting for maternal age. Secondary analyses were done stratified by mode of delivery and self-reported level of stress during pregnancy. RESULTS: 156 people were included; 66 from the Suburban Cohort and 90 from the Urban Cohort. The Suburban Cohort was predominantly White, Hispanic, higher income and the Urban Cohort was predominantly Black, Haitian, and lower income. We found a trend towards shorter LTLs among people with preeclampsia in the Urban Cohort (6517 versus 6913 bp, p = 0.07), but not in the Suburban Cohort. There were no significant differences in LTLs among people with spontaneous preterm birth compared to term controls in the Suburban Cohort (6044 versus 6144 bp, p = 0.64) or in the Urban Cohort (6717 versus 6913, p = 0.37). No differences were noted by mode of delivery. When stratifying by stress levels in the Urban Cohort, preeclampsia was associated with shorter postpartum LTLs in people with moderate stress levels (p = 0.02). CONCLUSION: Our exploratory results compare postpartum maternal LTLs between cases with preeclampsia or spontaneous preterm birth and controls in two distinct cohorts. These pilot data contribute to emerging literature on LTLs in pregnancy.

Associations between anxiety, sleep, and blood pressure parameters in pregnancy: a prospective pilot cohort study.

BACKGROUND: The potential effect modification of sleep on the relationship between anxiety and elevated blood pressure (BP) in pregnancy is understudied. We evaluated the relationship between anxiety, insomnia, and short sleep duration, as well as any interaction effects between these variables, on BP during pregnancy. METHODS: This was a prospective pilot cohort of pregnant people between 23 to 36 weeks' gestation at a single institution between 2021 and 2022. Standardized questionnaires were used to measure clinical insomnia and anxiety. Objective sleep duration was measured using a wrist-worn actigraphy device. Primary outcomes were systolic (SBP), diastolic (DBP), and mean (MAP) non-invasive BP measurements. Separate sequential multivariable linear regression models fit with generalized estimating equations (GEE) were used to separately assess associations between anxiety (independent variable) and each BP parameter (dependent variables), after adjusting for potential confounders (Model 1). Additional analyses were conducted adding insomnia and the interaction between anxiety and insomnia as independent variables (Model 2), and adding short sleep duration and the interaction between anxiety and short sleep duration as independent variables (Model 3), to evaluate any moderating effects on BP parameters. RESULTS: Among the 60 participants who completed the study, 15 (25%) screened positive for anxiety, 11 (18%) had subjective insomnia, and 34 (59%) had objective short sleep duration. In Model 1, increased anxiety was not associated with increases in any BP parameters. When subjective insomnia was included in Model 2, increased DBP and MAP was significantly associated with anxiety (DBP: ß 6.1, p = 0.01, MAP: ß 6.2 p < 0.01). When short sleep was included in Model 3, all BP parameters were significantly associated with anxiety (SBP: ß 9.6, p = 0.01, DBP: ß 8.1, p < 0.001, and MAP: ß 8.8, p < 0.001). No moderating effects were detected between insomnia and anxiety (p interactions: SBP 0.80, DBP 0.60, MAP 0.32) or between short sleep duration and anxiety (p interactions: SBP 0.12, DBP 0.24, MAP 0.13) on BP. CONCLUSIONS: When including either subjective insomnia or objective short sleep duration, pregnant people with anxiety had 5.1-9.6 mmHg higher SBP, 6.1-8.1 mmHg higher DBP, and 6.2-8.8 mmHg higher MAP than people without anxiety.

Objective Activity Parameters Track Patient-specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States.

OBJECTIVE: The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established. BACKGROUND: Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity. METHODS: Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state. RESULTS: The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery. CONCLUSIONS: Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.

Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses.

Post-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was assessed with the Stroke Impact Scale mood questionnaire (SIS3). Machine learning multivariable regression models were constructed to estimate SIS3 using proteomics data, age, and time since stroke. We also dichotomized participants into better mood (SIS3 > 63) or worse mood (SIS3 ≤ 63) and analyzed candidate proteins. Machine learning models verified that there is indeed a relationship between plasma proteomic data and mood in chronic stroke, with the most accurate prediction of mood occurring when we add age and time since stroke. At the individual protein level, no single protein or set of proteins predicts mood. But by using univariate analyses of the proteins most highly associated with mood we produced a model of chronic post-stroke depression. We utilized the fact that this list contained many proteins that are also implicated in major depression. Also, over 80% of immune proteins that correlate with mood were higher with worse mood, implicating a broadly overactive immune system in chronic post-stroke depression. Finally, we used a comprehensive literature review of major depression and acute post-stroke depression. We propose that in chronic post-stroke depression there is over-activation of the immune response that then triggers changes in serotonin activity and neuronal plasticity leading to depressed mood.

Multiomics, artificial intelligence, and precision medicine in perinatology.

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Omics approaches: interactions at the maternal-fetal interface and origins of child health and disease.

Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician-scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal-fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. IMPACT: Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).

Deleterious and Protective Psychosocial and Stress-Related Factors Predict Risk of Spontaneous Preterm Birth.

OBJECTIVES: The aim of the study was to: (1) Identify (early in pregnancy) psychosocial and stress-related factors that predict risk of spontaneous preterm birth (PTB, gestational age <37 weeks); (2) Investigate whether "protective" factors (e.g., happiness/social support) decrease risk; (3) Use the Dhabhar Quick-Assessment Questionnaire for Stress and Psychosocial Factors (DQAQ-SPF) to rapidly quantify harmful or protective factors that predict increased or decreased risk respectively, of PTB. STUDY DESIGN: This is a prospective cohort study. Relative risk (RR) analyses investigated association between individual factors and PTB. Machine learning-based interdependency analysis (IDPA) identified factor clusters, strength, and direction of association with PTB. A nonlinear model based on support vector machines was built for predicting PTB and identifying factors that most strongly predicted PTB. RESULTS: Higher levels of deleterious factors were associated with increased RR for PTB: General anxiety (RR = 8.9; 95% confidence interval [CI] = 2.0,39.6), pain (RR = 5.7; CI = 1.7,17.0); tiredness/fatigue (RR = 3.7; CI = 1.09,13.5); perceived risk of birth complications (RR = 4; CI = 1.6,10.01); self-rated health current (RR = 2.6; CI = 1.0,6.7) and previous 3 years (RR = 2.9; CI = 1.1,7.7); and divorce (RR = 2.9; CI = 1.1,7.8). Lower levels of protective factors were also associated with increased RR for PTB: low happiness (RR = 9.1; CI = 1.25,71.5); low support from parents/siblings (RR = 3.5; CI = 0.9,12.9), and father-of-baby (RR = 3; CI = 1.1,9.9). These factors were also components of the clusters identified by the IDPA: perceived risk of birth complications (p < 0.05 after FDR correction), and general anxiety, happiness, tiredness/fatigue, self-rated health, social support, pain, and sleep (p < 0.05 without FDR correction). Supervised analysis of all factors, subject to cross-validation, produced a model highly predictive of PTB (AUROC or area under the receiver operating characteristic = 0.73). Model reduction through forward selection revealed that even a small set of factors (including those identified by RR and IDPA) predicted PTB. CONCLUSION: These findings represent an important step toward identifying key factors, which can be assessed rapidly before/after conception, to predict risk of PTB, and perhaps other adverse pregnancy outcomes. Quantifying these factors, before, or early in pregnancy, could identify women at risk of delivering preterm, pinpoint mechanisms/targets for intervention, and facilitate the development of interventions to prevent PTB. KEY POINTS: · Newly designed questionnaire used for rapid quantification of stress and psychosocial factors early during pregnancy.. · Deleterious factors predict increased preterm birth (PTB) risk.. · Protective factors predict decreased PTB risk..

Feature-weighted elastic net: using "features of features" for better prediction.

In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we call the feature-weighted elastic net ("fwelnet"), uses these "features of features" to adapt the relative penalties on the feature coefficients in the elastic net penalty. In our simulations, fwelnet outperforms the lasso in terms of test mean squared error and usually gives an improvement in true positive rate or false positive rate for feature selection. We also apply this method to early prediction of preeclampsia, where fwelnet outperforms the lasso in terms of 10-fold cross-validated area under the curve (0.86 vs. 0.80). We also provide a connection between fwelnet and the group lasso and suggest how fwelnet might be used for multi-task learning.

Prediction of neuropathologic lesions from clinical data.

INTRODUCTION: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life. METHODS: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities. RESULTS: Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased. DISCUSSION: Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.

Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study.

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.

Risk assessment analysis for maternal autoantibody-related autism (MAR-ASD): a subtype of autism.

The incidence of autism spectrum disorder (ASD) has been rising, however ASD-risk biomarkers remain lacking. We previously identified the presence of maternal autoantibodies to fetal brain proteins specific to ASD, now termed maternal autoantibody-related (MAR) ASD. The current study aimed to create and validate a serological assay to identify ASD-specific maternal autoantibody patterns of reactivity against eight previously identified proteins (CRMP1, CRMP2, GDA, NSE, LDHA, LDHB, STIP1, and YBOX) that are highly expressed in developing brain, and determine the relationship of these reactivity patterns with ASD outcome severity. We used plasma from mothers of children diagnosed with ASD (n = 450) and from typically developing children (TD, n = 342) to develop an ELISA test for each of the protein antigens. We then determined patterns of reactivity a highly significant association with ASD, and discovered several patterns that were ASD-specific (18% in the training set and 10% in the validation set vs. 0% TD). The three main patterns associated with MAR ASD are CRMP1 + GDA (ASD% = 4.2 vs. TD% = 0, OR 31.04, p = <0.0001), CRMP1 + CRMP2 (ASD% = 3.6 vs. TD% = 0, OR 26.08, p = 0.0005) and NSE + STIP1 (ASD% = 3.1 vs. TD% = 0, OR 22.82, p = 0.0001). Additionally, we found that maternal autoantibody reactivity to CRMP1 significantly increases the odds of a child having a higher Autism Diagnostic Observation Schedule (ADOS) severity score (OR 2.3; 95% CI: 1.358-3.987, p = 0.0021). This is the first report that uses machine learning subgroup discovery to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of risk for a subset of up to 18% of ASD cases in this study population.

Newborn screen metabolic panels reflect the impact of common disorders of pregnancy.

BACKGROUND: Hypertensive disorders of pregnancy and maternal diabetes profoundly affect fetal and newborn growth, yet disturbances in intermediate metabolism and relevant mediators of fetal growth alterations remain poorly defined. We sought to determine whether there are distinct newborn screen metabolic patterns among newborns affected by maternal hypertensive disorders or diabetes in utero. METHODS: A retrospective observational study investigating distinct newborn screen metabolites in conjunction with data linked to birth and hospitalization records in the state of California between 2005 and 2010. RESULTS: A total of 41,333 maternal-infant dyads were included. Infants of diabetic mothers demonstrated associations with short-chain acylcarnitines and free carnitine. Infants born to mothers with preeclampsia with severe features and chronic hypertension with superimposed preeclampsia had alterations in acetylcarnitine, free carnitine, and ornithine levels. These results were further accentuated by size for gestational age designations. CONCLUSIONS: Infants of diabetic mothers demonstrate metabolic signs of incomplete beta oxidation and altered lipid metabolism. Infants of mothers with hypertensive disorders of pregnancy carry analyte signals that may reflect oxidative stress via altered nitric oxide signaling. The newborn screen analyte composition is influenced by the presence of these maternal conditions and is further associated with the newborn size designation at birth. IMPACT: Substantial differences in newborn screen analyte profiles were present based on the presence or absence of maternal diabetes or hypertensive disorder of pregnancy and this finding was further influenced by the newborn size designation at birth. The metabolic health of the newborn can be examined using the newborn screen and is heavily impacted by the condition of the mother during pregnancy. Utilizing the newborn screen to identify newborns affected by common conditions of pregnancy may help relate an infant's underlying biological disposition with their clinical phenotype allowing for greater risk stratification and intervention.