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IntroductionDelayed graft function (DGF) following renal transplantation is a manifestation of acute kidney injury (AKI) leading to poor long-term outcome. Current treatments have limited effectiveness in preventing DGF. Interleukin-18 (IL18), a biomarker of AKI, induces interferon-γ expression and immune activation. GSK1070806, an anti-IL18 monoclonal antibody, neutralizes activated (mature) IL18 released from damaged cells following inflammasome activation. This phase IIa, single-arm trial assessed the effect of a single dose of GSK1070806 on DGF occurrence post donation after circulatory death (DCD) kidney transplantation.MethodsThe 3 mg/kg intravenous dose was selected based on prior studies and physiologically based pharmacokinetic (PBPK) modeling, indicating the high likelihood of a rapid and high level of IL18 target engagement when administered prior to kidney allograft reperfusion. Utilization of a Bayesian sequential design with a background standard-of-care DGF rate of 50% based on literature, and confirmed via extensive registry data analyses, enabled a statistical efficacy assessment with a minimal sample size. The primary endpoint was DGF frequency, defined as dialysis requirement ≤7 days post transplantation (except for hyperkalemia). Secondary endpoints included safety, pharmacokinetics and pharmacodynamic biomarkers.ResultsGSK1070806 administration was associated with IL18-GSK1070806 complex detection and increased total serum IL18 levels due to IL18 half-life prolongation induced by GSK1070806 binding. Interferon-γ-induced chemokine levels declined or remained unchanged in most patients. Although the study was concluded prior to the Bayesian-defined stopping point, 4/7 enrolled patients (57%) had DGF, exceeding the 50% standard-of-care rate, and an additional two patients, although not reaching the protocol-defined DGF definition, demonstrated poor graft function. Six of seven patients experienced serious adverse events (SAEs), including two treatment-related SAEs.ConclusionOverall, using a Bayesian design and extensive PBPK dose modeling with only a small sample size, it was deemed unlikely that GSK1070806 would be efficacious in preventing DGF in the enrolled DCD transplant population.Trial registrationNCT02723786.

Individuals with pulmonary arterial hypertension experience debilitating symptoms and psychological distress which may influence their cortisol regulation. We describe associations between diurnal salivary cortisol/cortisone levels and quality of life in adults with pulmonary arterial hypertension. Findings suggest potential clinical utility of cortisol/cortisone assessment as applied to a pulmonary arterial hypertension population.

Abstract Background Preterm birth (PTB) is a serious health problem. PTB complications is the main cause of death in infants under five years of age worldwide. The ability to accurately predict risk for PTB during early pregnancy would allow early monitoring and interventions to provide personalized care, and hence improve outcomes for the mother and infant. Objective This study aims to predict the risks of early preterm (

Abstract 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.

The rumen is the largest of the four chambers of the “stomach” in ruminant animals, which harbors an incredibly dense, diverse, and dynamic microbial community crucial for feedstuff degradation, animal health, and production. The primary objective of this article is to enhance knowledge and comprehension of rumen microbiology by providing an introductory-level overview of the field of rumen microbiology. Ruminants possess a distinctive digestive system optimized for the microbial breakdown of complex plant materials. The ruminant ”stomach” consists of four chambers (e.g., reticulum, rumen, omasum, and abomasum), which is home to a microbial population that degrades feedstuffs consumed by ruminant animals. Dr. Robert Hungate and Dr. Marvin Bryant’s groundbreaking research in the 1960s laid the foundation for understanding the function of the ruminal microbial ecosystem. Recent advancements (e.g., next-generation sequencing) have provided the field with deeper insight into populations, boosting our understanding of how the microbial population of the rumen functions in a variety of conditions. The ruminal microbial ecosystem is comprised of bacteria, along with archaea, protozoa, bacteriophage, and fungi, each contributing to the symbiotic relationship between the microbial ecosystem and the host animal that is essential for optimal animal health and efficient animal production. Traditional anaerobic growth techniques have facilitated the study of individual anaerobic bacteria but have been limited by dependence on growth in laboratory conditions. The development of 16S rRNA sequencing allows the identification of microbial populations that cannot be grown and allows an unbiased view of microbial diversity. Diet shapes the rumen microbial population composition, influencing animal production metrics such as feed efficiency, methane emissions, and immunological functions. Feed additives (e.g., essential oils, eubiotics) hold promise by manipulating and unraveling the microbial biochemical potential for improving animal health, feed efficiency, environmental impacts, and overall production sustainability. Future research impacts include the development of probiotics, prebiotics, and genetic strategies for optimizing the rumen microbiome’s multifaceted impacts.

Classically, ATM is known for its role in sensing double-strand DNA breaks, and subsequently signaling for their repair. Non-canonical roles of ATM include transcriptional silencing, ferroptosis, autophagy and angiogenesis. Angiogenesis mediated by ATM signaling has been shown to be VEGF-independent via p38 signaling. Independently, p38 signaling has been shown to upregulate metalloproteinase expression, including MMP-2 and MMP-9, though it is unclear if this is linked to ATM. Here, we demonstrate ATM regulates aminopeptidase-N (CD13/APN/ANPEP) at the protein level. Positive correlation was seen between ATM activity and CD13 protein expression using both “wildtype” (WT) and knockout (KO) ataxia telangiectasia (AT) cells through western blotting; with the same effect shown when treating neuroblastoma cancer cell line SH-SY5Y, as well as AT-WT cells, with ATM inhibitor (ATMi; KU55933). However, qPCR along with publically available RNAseq data from Hu et al. (J. Clin. Invest., 2021, 131, e139333), demonstrated no change in mRNA levels of CD13, suggesting that ATM regulates CD13 levels via controlling protein degradation. This is further supported by the observation that incubation with proteasome inhibitors led to restoration of CD13 protein levels in cells treated with ATMi. Migration assays showed ATM and CD13 inhibition impairs migration, with no additional effect observed when combined. This suggests an epistatic effect, and that both proteins may be acting in the same signaling pathway that influences cell migration. This work indicates a novel functional interaction between ATM and CD13, suggesting ATM may negatively regulate the degradation of CD13, and subsequently cell migration.

Summary: Existing medical treatments for endometriosis-related pain are often ineffective, underscoring the need for new therapeutic strategies. In this study, we applied a computational drug repurposing pipeline to stratified and unstratified disease signatures based on endometrial gene expression data to identify potential therapeutics from existing drugs, based on expression reversal. Of 3,131 unique genes differentially expressed by at least one of six endometriosis signatures, only 308 (9.8%) were in common; however, 221 out of 299 drugs identified, (73.9%) were shared. We selected fenoprofen, an uncommonly prescribed NSAID that was the top therapeutic candidate for further investigation. When testing fenoprofen in an established rat model of endometriosis, fenoprofen successfully alleviated endometriosis-associated vaginal hyperalgesia, a surrogate marker for endometriosis-related pain. These findings validate fenoprofen as a therapeutic that could be utilized more frequently for endometriosis and suggest the utility of the aforementioned computational drug repurposing approach for endometriosis.

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.

Abstract The vaginal ecosystem is closely tied to human health and reproductive outcomes, yet its dynamics in the wake of childbirth remain poorly characterized. Here, we profile the vaginal microbiota and cytokine milieu of participants sampled longitudinally throughout pregnancy and for at least one year postpartum. We show that delivery, regardless of mode, is associated with a vaginal pro-inflammatory cytokine response and the loss of Lactobacillus dominance. By contrast, neither the progression of gestation nor the approach of labor strongly altered the vaginal ecosystem. At 9.5-months postpartum—the latest timepoint at which cytokines were assessed—elevated inflammation coincided with vaginal bacterial communities that had remained perturbed (highly diverse) from the time of delivery. Time-to-event analysis indicated a one-year postpartum probability of transitioning to Lactobacillus dominance of 49.4%. As diversity and inflammation declined during the postpartum period, dominance by L. crispatus, the quintessential health-associated commensal, failed to return: its prevalence before, immediately after, and one year after delivery was 41%, 4%, and 9%, respectively. Revisiting our pre-delivery data, we found that a prior live birth was associated with a lower odds of L. crispatus dominance in pregnant participants—an outcome modestly tempered by a longer ( > 18-month) interpregnancy interval. Our results suggest that reproductive history and childbirth in particular remodel the vaginal ecosystem and that the timing and degree of recovery from delivery may help determine the subsequent health of the woman and of future pregnancies.

Introduction In 2018, the American College of Obstetricians and Gynecologists recommended low-dose aspirin to prevent the onset of pre-eclampsia among women who were at high risk. Factors influencing women’s acceptance of this recommendation span multiple sectors and levels. Understanding how these factors interact will help stakeholders design effective population-level intervention strategies. Our study aims to identify and map relationships among factors influencing the medication decisions of pregnant women at risk of hypertensive disorders.Methods and analysis Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines will be followed for this review. A research librarian developed a comprehensive search strategy to retrieve published and unpublished English studies after 1 January 1980, involving factors that influence pregnant women’s uptake and adherence to medication for gestational hypertensive disorders. This literature includes perceptions, patterns, acceptance, refusal, tendencies, probability and service utilisation. We will search PubMed, Embase, Web of Science and CINAHL. Reference lists of the selected papers will be searched manually to identify more relevant studies. A two-stage independent screening, consisting of title and abstract screening, followed by full-text screening, will be conducted by two independent reviewers to identify eligible articles. Extracted data will be recorded in a customised variable extraction form and input into a Microsoft Access database. The PRISMA-ScR will be used to guide the presentation of the results, which will be presented in a table and causal map to demonstrate the relationships between extracted variables and medication uptake and adherence. A conceptual simulation model will be formulated to validate the logic of the relationships between variables and identify knowledge gaps. Lastly, experts and stakeholders will be invited to critique and comment on the results.Ethics and dissemination This study does not require ethical approval. The full review results will be presented at a relevant conference and submitted to a peer-reviewed scientific journal for publication.

BACKGROUND: Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures. OBJECTIVE: This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome. STUDY DESIGN: Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins. RESULTS: A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42–0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18–0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34–0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27–0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins. CONCLUSION: Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Nitrofurantoin, a broad-spectrum nitrofuran class antibiotic, is applied as a first-line antibiotic in treating human urinary tract infections (UTIs) due to its great efficacy and high achievable concentration. The interest in using this antibiotic in companion animals has increased due to the growing demand for effective antibiotics to treat UTIs caused by multidrug-resistant bacteria. Currently, the susceptibility interpretations for nitrofurantoin are based on the breakpoints set for humans, while the canine-specific breakpoints are still unavailable. In this study, we assessed the concentration of nitrofurantoin reaching the dog’s urine using the recommended oral dosing regimen. In addition, we examined the efficacy of this breakpoint concentration against the common canine UTI pathogens, Escherichia coli, Staphylococcus pseudintermedius, and Enterococcus faecium. Eight experimental beagle dogs were treated with ~5 mg/kg of nitrofurantoin macrocrystal PO 8qh for 7 days. The urine samples were collected via cystocentesis at 2, 4, and 6 h after administration on day 2 and day 7 and used to quantify nitrofurantoin concentrations by ultra-high performance liquid chromatography. The results showed that 26.13–315.87 μg/mL nitrofurantoin was detected in the dogs’ urine with a mean and median concentration of 104.82 and 92.75 μg/mL, respectively. Additionally, individual dogs presented with urinary nitrofurantoin concentrations greater than 64 μg/mL for at least 50% of the dosing intervals. This concentration efficiently killed E. coli, and S. pseudintermedius, but not E. faecium strains carrying an MIC90 value equal to 16, 16, and 128 μg/mL, respectively. Taken together, these results suggest that the value of 64 μg/mL may be set as a breakpoint against UTI pathogens, and nitrofurantoin could be an effective therapeutic drug against E. coli and S. pseudintermedius for canine UTIs.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Abstract Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.

The prevalence of food allergy continues to rise globally, carrying with it substantial safety, economic, and emotional burdens. Although preventative strategies do exist, the heterogeneity of allergy trajectories and clinical phenotypes has made it difficult to identify patients who would benefit from these strategies. Therefore, further studies investigating the molecular mechanisms that differentiate these trajectories are needed. Large-scale omics studies have identified key insights into the molecular mechanisms for many different diseases, however the application of these technologies to uncover the drivers of food allergy development is in its infancy. Here we review the use of omics approaches in food allergy and highlight key gaps in knowledge for applying these technologies for the characterization of food allergy development.

Maternal obesity is a risk factor for pregnancy complications. Obesity caused by a high-fat diet (HFD) may alter maternal glucose/glycogen metabolism. Here, our objective was to investigate whether the placental vasculature is altered via changes in gene expression and glycogen-rich cells using a preclinical mouse model of diet-induced obesity. We subjected female FVB/N mice to one of three feeding regimens: regular chow (RC) given at preconception and during pregnancy (Control); RC given at preconception and then a HFD during pregnancy (HFD-P); or HFD initiated 4 weeks preconception and during pregnancy (HFD-PreCP). Daily food consumption and weekly maternal weights were recorded. Maternal blood glucose levels were measured at preconception and 4 gestational epochs (E6.5-E9.5, E10.5-E12.5, E13.5-E15.5, E16.5-E19.5). At E8.5-E16.5, total RNA in placentas were isolated for gene expression analyses. Placentas were also collected for HE and periodic acid Schiff's (PAS) staining and glycogen content assays. Dams in the HFD-P and HFD-PreCP groups gained significantly more weight than controls. Pre- and antenatal glucose levels were also significantly higher (15%-30%) in HFD-PreCP dams. Expression of several placental genes were also altered in HFD dams compared with controls. Consumption of the HFD also led to phenotypic and morphologic changes in glycogen trophoblasts (GlyTs) and uterine natural killer (uNK) cells. Alterations in vascularity were also observed in the labyrinth of HFD-PreCP placentas, which correlated with decreased placental efficiency. Overall, we observed that a HFD induces gestational obesity in mice, alters expression of placental genes, affects glucose homeostasis, and alters glycogen-positive GlyTs and uNK cells. All these changes may lead to impaired placental vascular development, and thus heighten the risk for pregnancy complications.

Prevalence rates of End-Stage Kidney Failure (ESKF) have risen in across the world in recent years, making it one of the most common chronic illnesses. The main treatment for ESKF is haemodialysis, where one is ‘connected’ to a dialysis machine to clean and filter the blood via a surgically-created portal, also known as ‘vascular access’. Without functioning vascular access, dialysis is impossible. People with ESKF have different experiences with their access modalities, but universally describe their access point as a ‘lifeline’. Previous research has emphasised the impact it can have on wellbeing (Kalloo et al., 2016; Casey et al., 2014; Quinn et al., 2008), and specifically on short- and long-term outcomes.Capturing Quality of Life (QoL) within ESKF populations has traditionally focused upon assessing wellbeing from an objective, normative, top-down stance, rather than appreciating the nuanced effect vascular access can have as experienced by those living with kidney failure. In this article, we argue current QoL measures used with ESKF groups are insufficient at capturing the impact of vascular access on wellbeing. Using the accounts of twenty-four haemodialysis patients, we share insights into the direct and indirect influences vascular access has upon QoL, using Nussbaum's Capabilities Approach as an analytical lens. By prioritising and privileging the voices of those directly affected, the Vascular Access Specific Quality of Life (VA Specific-QOL) model provides a starting point for a more representative way to assess wellbeing in this group.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Summary: 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.

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.

Heme, a metalloporphyrin, or more specifically, a tetrapyrrole containing ferrous iron, is an ancient molecule [...]

Objectives The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.Study design A retrospective cohort study.Setting Two medical centres from the USA.Participants Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.Outcome measures Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.Results A model to determine gestational age was developed (R2=0.98) and validated (R2=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R2=−0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.Conclusions In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.

Preeclampsia is one of the most severe pregnancy complications and a leading cause of maternal death. However, early diagnosis of preeclampsia remains a clinical challenge. Alterations in the normal immune adaptations necessary for the maintenance of a healthy pregnancy are central features of preeclampsia. However, prior analyses primarily focused on the static assessment of select immune cell subsets have provided limited information for the prediction of preeclampsia. Here, we used a high-dimensional mass cytometry immunoassay to characterize the dynamic changes of over 370 immune cell features (including cell distribution and functional responses) in maternal blood during healthy and preeclamptic pregnancies. We found a set of eight cell-specific immune features that accurately identified patients well before the clinical diagnosis of preeclampsia (median area under the curve (AUC) 0.91, interquartile range [0.82–0.92]). Several features recapitulated previously known immune dysfunctions in preeclampsia, such as elevated pro-inflammatory innate immune responses early in pregnancy and impaired regulatory T (Treg) cell signaling. The analysis revealed additional novel immune responses that were strongly associated with, and preceded the onset of preeclampsia, notably abnormal STAT5ab signaling dynamics in CD4+T cell subsets (AUC 0.92, p = 8.0E-5). These results provide a global readout of the dynamics of the maternal immune system early in pregnancy and lay the groundwork for identifying clinically-relevant immune dysfunctions for the prediction and prevention of preeclampsia.