Predicted Cardiovascular Risk by the PREVENT Equations in US Adults With Stage 1 Hypertension.

BACKGROUND: The 2017 American College of Cardiology/American Heart Association blood pressure guideline recommends initiation of antihypertensive medication for adults with stage 1 hypertension (systolic blood pressure, 130-139 mm Hg, or diastolic blood pressure, 80-89 mm Hg) and 10-year atherosclerotic cardiovascular disease (ASCVD) risk ≥10% estimated by the pooled cohort equations (PCEs). In 2023, the American Heart Association published the predicting risk of cardiovascular disease events (PREVENT) equations to estimate ASCVD and total cardiovascular disease risk. METHODS: We analyzed US National Health and Nutrition Examination Survey data from 2013 to 2020 for 1703 adults aged 30 to 79 years without self-reported cardiovascular disease with stage 1 hypertension. We estimated 10-year ASCVD risk by the PCEs and 10-year ASCVD and total cardiovascular disease risk by the base PREVENT equations. Analyses were weighted to represent noninstitutionalized US adults with stage 1 hypertension. RESULTS: Mean 10-year ASCVD risk was 5.4% (95% CI, 5.0%-5.9%) and 2.9% (95% CI, 2.7%-3.1%) using the PCEs and PREVENT equations, respectively. The proportion with 10-year ASCVD risk of 10% to <15% and ≥15% was 8.1% and 7.8% estimated by the PCEs, respectively, and 3.0% and 0.3% estimated by the PREVENT equations, respectively. No participants had a 10-year ASCVD risk ≥10% on the PREVENT equations and <10% on the PCEs, while 12.5% had a 10-year ASCVD risk ≥10% on the PCEs and <10% on the PREVENT equations. The mean 10-year total cardiovascular disease risk estimated by the PREVENT equations was lower than the mean 10-year ASCVD risk on the PCEs. CONCLUSIONS: Among US adults with stage 1 hypertension, the 10-year predicted ASCVD risk estimated by the PREVENT equations was approximately half the risk estimated by the PCEs.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.

Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts. Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs. Methods: An FCHD single-lead ("lead I" from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen's kappa. Results: The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78. Conclusion: Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Five-Year Cumulative Cardiovascular Health and Clinical Events in Patients With Chronic Kidney Disease: The CRIC Study.

BACKGROUND: Higher cardiovascular health (CVH) score is associated with lower risks of cardiovascular disease (CVD) and mortality in the general population. However, it is unclear whether cumulative CVH is associated with CVD, end-stage kidney disease (ESKD), and death in patients with chronic kidney disease. METHODS AND RESULTS: Among individuals from the prospective CRIC (Chronic Renal Insufficiency Cohort) Study, we used the percentage of the maximum possible CVH score attained from baseline to the year 5 visit to calculate cumulative CVH score. Multivariable-adjusted Cox proportional hazards regression was used to investigate the associations of cumulative CVH with risks of adjudicated CVD (myocardial infarction, stroke, and heart failure), ESKD, and all-cause mortality. A total of 3939 participants (mean age, 57.7 years; 54.9% men) were included. The mean (SD) cumulative CVH score attained during 5 years was 55.5% (12.3%). Over a subsequent median 10.2-year follow-up, 597 participants developed CVD, 656 had ESKD, and 1324 died. A higher cumulative CVH score was significantly associated with lower risks of CVD, ESKD, and mortality, independent of the CVH score at year 5. Multivariable-adjusted hazard ratios and 95% CIs per 10% higher cumulative CVH score during 5 years were 0.81 (0.69-0.95) for CVD, 0.82 (0.70-0.97) for ESKD, and 0.80 (0.72-0.89) for mortality. CONCLUSIONS: Among patients with chronic kidney disease stages 2 to 4, a better CVH status maintained throughout 5 years is associated with lower risks of CVD, ESKD, and all-cause mortality. The findings support the need for interventions to maintain ideal CVH status for prevention of adverse outcomes in the population with chronic kidney disease.

Social Determinants of Health and Incident Apparent Treatment-Resistant Hypertension Among White and Black US Adults: The REGARDS Study.

BACKGROUND: We examined the association of multilevel social determinants of health with incident apparent treatment-resistant hypertension (aTRH). METHODS AND RESULTS: We analyzed data from 2774 White and 2257 Black US adults from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study taking antihypertensive medication without aTRH at baseline to estimate the association of social determinants of health with incident aTRH. Selection of social determinants of health was guided by the Healthy People 2030 domains of education, economic stability, social context, neighborhood environment, and health care access. Blood pressure (BP) was measured during study visits, and antihypertensive medication classes were identified through a pill bottle review. Incident aTRH was defined as (1) systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg, or systolic BP ≥130 mm Hg or diastolic BP ≥80 mm Hg for those with diabetes or chronic kidney disease while taking ≥3 classes of antihypertensive medication or (2) taking ≥4 classes of antihypertensive medication regardless of BP level, at the follow-up visit. Over a median 9.5 years of follow-up, 15.9% of White and 24.0% of Black adults developed aTRH. A percent of the excess aTRH risk among Black versus White adults was mediated by low education (14.2%), low income (16.0%), not seeing a friend or relative in the past month (8.1%), not having someone to care for them if ill or disabled (7.6%), lack of health insurance (10.6%), living in a disadvantaged neighborhood (18.0%), and living in states with poor public health infrastructure (6.0%). CONCLUSIONS: Part of the association between race and incident aTRH risk was mediated by social determinants of health.

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Question answering systems for health professionals at the point of care-a systematic review.

OBJECTIVES: Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. MATERIALS AND METHODS: We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems. RESULTS: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians' questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. DISCUSSION: While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.

Association between lead and circulating markers of inflammation among traffic enforcers in Metro Manila, Philippines: the MMDA traffic enforcer's health study.

PURPOSE: Several epidemiological studies have linked lead (Pb) exposure to induced oxidative stress and the promotion of inflammatory response. We performed a within-subjects study (repeated measures study) to evaluate the relationship between the concentration of blood lead (B-Pb) and toenail lead (T-Pb) and circulating markers of inflammation. METHODS: We evaluated the associations between B-Pb concentrations and T-Pb concentrations and circulating markers of inflammation, soluble intracellular adhesion molecule-1 (s-ICAM-1), soluble vascular adhesion molecule-1 (s-VCAM-1), and high-sensitivity C-reactive protein (hs-CRP) on 158 traffic enforcers from the Metropolitan Manila Development Authority (MMDA) traffic enforcer's health study. Linear mixed-effects models with random subject-specific intercepts were fitted to estimate the association between B-Pb and T-Pb exposure and circulating markers of inflammation, adjusting for confounding factors. RESULTS: Traffic enforcers were middle-aged men (89.4%) with a mean age (± SD) of 37.1 years ± 8.9 years and had a total of 293 valid markers of inflammation measurements. B-Pb concentration was related to increased hs-CRP levels. A 10% increase in B-Pb was associated with a 5.7% increase in hs-CRP level [95% confidence interval (95% CI): 1.3-10.1]. However, B-Pb was not associated with s-ICAM-1 and s-VCAM-1. Furthermore, no associations were observed between T-Pb and all the circulating markers of inflammation. CONCLUSIONS: Low-level B-Pb may increase hs-CRP among traffic enforcers. Moreover, the study suggests that Pb via the oxidative and inflammation pathways may have an essential role in the development of cardiovascular disease. Furthermore, MMDA and the Department of Labor and Employment can use our study's findings as evidence to conduct routine screening of blood heavy metals, especially Pb, among MMDA and other traffic enforcers as part of their yearly medical examination.

Use of resuscitative endovascular balloon occlusion of the aorta (REBOA) and ultrasound-guided left stellate ganglion block to rescue out of hospital cardiac arrest due to refractory ventricular fibrillation: A case report.

Out of hospital cardiac arrest from shockable rhythms that is refractory to standard treatment is a unique challenge. Such patients can achieve neurological recovery even with long low-flow times if perfusion can somehow be restored to the heart and brain. Extracorporeal cardiopulmonary resuscitation is an effective treatment for refractory cardiac arrest if applied early and accurately, but often cannot be directly implemented by frontline providers and has strict inclusion/exclusion criteria. We present the case of a novel treatment strategy for out of hospital cardiac arrest due to refractory ventricular fibrillation utilizing Resuscitative Endovascular Balloon Occlusion of the Aorta-assisted cardiopulmonary resuscitation and intra-arrest left stellate ganglion blockade to achieve return of spontaneous circulation and eventual good neurological outcome after 101 minutes of downtime.

Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies.

OBJECTIVE: The goal of this work was to develop robust techniques for the processing and identification of SUA using artificial intelligence (AI) image classification models. METHODS: Ultrasound images obtained retrospectively were analyzed for blinding, text removal, AI training, and image prediction. After developing and testing text removal methods, a small n-size study (40 images) using fastai/PyTorch to classify umbilical cord images. This data set was expanded to 286 lateral-CFI images that were used to compare: different neural network performance, diagnostic value, and model predictions. RESULTS: AI-Optical Character Recognition method was superior in its ability to remove text from images. The small n-size mixed single umbilical artery determination data set was tested with a pretrained ResNet34 neural network and obtained and error rate average of 0.083 (n = 3). The expanded data set was then tested with several AI models. The majority of the tested networks were able to obtain an average error rate of <0.15 with minimal modifications. The ResNet34-default performed the best with: an image-classification error rate of 0.0175, sensitivity of 1.00, specificity of 0.97, and ability to correctly infer classification. CONCLUSION: This work provides a robust framework for ultrasound image AI classifications. AI could successfully classify umbilical cord types of ultrasound image study with excellent diagnostic value. Together this study provides a reproducible framework to develop AI-specific ultrasound classification of umbilical cord or other diagnoses to be used in conjunction with physicians for optimal patient care.

Prevalence of hepatitis C among pregnant women in an Appalachian population.

The opioid crisis has adversely affected West Virginia's pregnant and infant populations. With high rates of opioid use disorder and neonatal abstinence syndrome, West Virginia has the highest rates of Hepatitis C (HCV) acute infection among pregnant women. To better understand how HCV impacts an already high-risk population, the study purpose was to (1) describe its prevalence among women receiving prenatal care at a single tertiary care clinic in Appalachia and compare with state and national rates, and (2) determine whether it is associated with preterm birth (gestation <37 weeks). Data were collected on a retrospective cohort of pregnant patients universally screened for HCV between 2017 and 2021. The study cohort had an HCV infection rate of 119/988 = 11.94% or 119.4 per 1000. This is five times the rate of 22.6 per 1000 live births in West Virginia in 2014 and 35 times the national rate of 3.4 per 1000 live births (MMWR Morb Mortal Wkly Rep 66, 2017 and 470). Viral loads were detected in 63 (6.38%) of patients. The study cohort with birth outcome data had high rates of tobacco use (326/720; 45.3%) and substance abuse (209/720; 29.0%). The preterm birth rate was 17.8% (128/720), almost double the national average (10.09%) (Natl Vital Stat Rep 70, 2021 and 1). There was no statistically significant difference in preterm birth between HCV-positive (15/92; 16.3%) and HCV-negative (113/628; 18.0%) patients. HCV infection in our population presents a significant public health issue and missed opportunity for treatment in a population with continuity of care challenges. These findings could be used to justify a pilot program for early postpartum referral for treatment.

The Microbial Ecology of Estuarine Ecosystems.

Human civilization relies on estuaries, and many estuarine ecosystem services are provided by microbial communities. These services include high rates of primary production that nourish harvests of commercially valuable species through fisheries and aquaculture, the transformation of terrestrial and anthropogenic materials to help ensure the water quality necessary to support recreation and tourism, and mutualisms that maintain blue carbon accumulation and storage. Research on the ecology that underlies microbial ecosystem services in estuaries has expanded greatly across a range of estuarine environments, including water, sediment, biofilms, biological reefs, and stands of seagrasses, marshes, and mangroves. Moreover, the application of new molecular tools has improved our understanding of the diversity and genomic functions of estuarine microbes. This review synthesizes recent research on microbial habitats in estuaries and the contributions of microbes to estuarine food webs, elemental cycling, and interactions with plants and animals, and highlights novel insights provided by recent advances in genomics.

Race differences in ambulatory blood pressure monitoring parameters.

BACKGROUND: Mean systolic and diastolic blood pressure (SBP and DBP) on ambulatory blood pressure (BP) monitoring (ABPM) are higher among Black compared with White adults. With 48 to 72 BP measurements obtained over 24 h, ABPM can generate parameters other than mean BP that are associated with increased risk for cardiovascular events. There are few data on race differences in ABPM parameters other than mean BP. METHODS: To estimate differences between White and Black participants in ABPM parameters, we used pooled data from five US-based studies in which participants completed ABPM (n = 2580). We calculated measures of SBP and DBP level, including mean, load, peak, and measures of SBP and DBP variability, including average real variability (ARV) and peak increase. RESULTS: There were 1513 (58.6%) Black and 1067 (41.4%) White participants with mean ages of 56.1 and 49.0 years, respectively. After multivariable adjustment, asleep SBP and DBP load were 5.7% (95% CI: 3.5-7.9%) and 2.7% (95% CI: 1.1-4.3%) higher, respectively, among Black compared with White participants. Black compared with White participants also had higher awake DBP ARV (0.3 [95%CI: 0.0-0.6] mmHg) and peak increase in DBP (0.4 [95% CI: 0.0-0.8] mmHg). There was no evidence of Black:White differences in awake measures of SBP level, asleep peak SBP or DBP, awake and asleep measures of SBP variability or asleep measures of DBP variability after multivariable adjustment. CONCLUSION: Asleep SBP load, awake DBP ARV and peak increase in awake DBP were higher in Black compared to White participants, independent of mean BP on ABPM.

Genomic evidence that microbial carbon degradation is dominated by iron redox metabolism in thawing permafrost.

Microorganisms drive many aspects of organic carbon cycling in thawing permafrost soils, but the compositional trajectory of the post-thaw microbiome and its metabolic activity remain uncertain, which limits our ability to predict permafrost-climate feedbacks in a warming world. Using quantitative metabarcoding and metagenomic sequencing, we determined relative and absolute changes in microbiome composition and functional gene abundance during thaw incubations of wet sedge tundra collected from northern Alaska, USA. Organic soils from the tundra active-layer (0-50 cm), transition-zone (50-70 cm), and permafrost (70+ cm) depths were incubated under reducing conditions at 4 °C for 30 days to mimic an extended thaw duration. Following extended thaw, we found that iron (Fe)-cycling Gammaproteobacteria, specifically the heterotrophic Fe(III)-reducing Rhodoferax sp. and chemoautotrophic Fe(II)-oxidizing Gallionella sp., increased by 3-5 orders of magnitude in absolute abundance within the transition-zone and permafrost microbiomes, accounting for 65% of community abundance. We also found that the abundance of genes for Fe(III) reduction (e.g., MtrE) and Fe(II) oxidation (e.g., Cyc1) increased concurrently with genes for benzoate degradation and pyruvate metabolism, in which pyruvate is used to generate acetate that can be oxidized, along with benzoate, to CO2 when coupled with Fe(III) reduction. Gene abundance for CH4 metabolism decreased following extended thaw, suggesting dissimilatory Fe(III) reduction suppresses acetoclastic methanogenesis under reducing conditions. Our genomic evidence indicates that microbial carbon degradation is dominated by iron redox metabolism via an increase in gene abundance associated with Fe(III) reduction and Fe(II) oxidation during initial permafrost thaw, likely increasing microbial respiration while suppressing methanogenesis in wet sedge tundra.

Revisiting Relation Extraction in the era of Large Language Models.

Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a sequence-to-sequence task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision. We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the few-shot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results. We release this model as a new baseline for RE tasks.

Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges.

We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work (Marshall et al., 2020), the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality. The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART (Lewis et al., 2019), and a multi-headed architecture intended to provide greater transparency to end-users. Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present. The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs. The demonstration video is available at: https://vimeo.com/735605060 The prototype, source code, and model weights are available at: https://sanjanaramprasad.github.io/trials-summarizer/.

A functional microbiome catalog crowdsourced from North American rivers.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept 1 , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures 2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

Prevalence and Spatial Distribution of Gastroschisis in an Industrial Watershed.

The relationship between the congenital defect of gastroschisis and environmental toxins is poorly understood. We examined gastroschisis incidence, risk factors, and spatial association in a geographic region with known environmental pollution and hazardous waste sites. An observational study of fetal and neonatal gastroschisis diagnosed from 1/1/2006 to 12/31/2020 was conducted in a southern West Virginia (WV) tertiary care hospital. Emerging hot spot analysis and Ripley's K-Function examined the spatial relationship between gastroschisis cases and Environmental Protection Agency (EPA) Federal Registry Sites (FRS). A total of 63 gastroschisis cases provided a prevalence rate of 14.6 per 10000 live births. Gastroschisis was associated with younger maternal age, decreased pre-pregnancy BMI, and increased maternal tobacco use. Relative to FRS sites, spatial clusters were identified with emerging hot spot analysis. Observed Ripley K was higher at all measured bands. Results suggest a potential geographic association between gastroschisis cases and EPA-designated hazardous waste sites.