Hox genes controlling motor neuron subtype identity are expressed in rostro-caudal patterns that are spatially and temporally collinear with their chromosomal organization. Here we demonstrate that Hox chromatin is subdivided into discrete domains, controlled by rostro-caudal patterning signals that trigger rapid, domain-wide clearance of repressive H3K27me3 Polycomb modifications. Treatment of differentiating mouse neural progenitors with retinoic acid (RA) leads to activation and binding of RA receptors (RARs) to Hox1-5 chromatin domains, followed by a rapid domain-wide removal of H3K27me3 and acquisition of cervical spinal identity. Wnt and FGF signals induce expression of Cdx2 transcription factor that binds and clears H3K27me3 from Hox1-9 chromatin domains, leading to specification of brachial/thoracic spinal identity. We propose that rapid clearance of repressive modifications in response to transient patterning signals encodes global rostro-caudal neural identity and that maintenance of these chromatin domains ensures transmission of the positional identity to postmitotic motor neurons later in development.

Background: Artificial intelligence (AI)-enabled analysis of 12-lead electrocardiograms (ECGs) may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. Methods: We trained a convolutional neural network ("ECG-AI") to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit three Cox proportional hazards models, each composed of: a) ECG-AI 5-year AF probability, b) the Cohorts for Heart and Aging in Genomic Epidemiology AF (CHARGE-AF) clinical risk score, and c) terms for both ECG-AI and CHARGE-AF ("CH-AI"). We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve, AUROC) and calibration in an internal test set and two external test sets (Brigham and Women's Hospital and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. Results: The training set comprised 45,770 individuals (age 55±17 years, 53% women, 2,171 AF events), and the test sets comprised 83,162 individuals (age 59±13 years, 56% women, 2,424 AF events). AUROC was comparable using CHARGE-AF (MGH 0.802, 95% CI 0.767-0.836; BWH 0.752, 95% CI 0.741-0.763; UK Biobank 0.732, 95% CI 0.704-0.759) and ECG-AI (MGH 0.823, 95% CI 0.790-0.856; BWH 0.747, 95% CI 0.736-0.759; UK Biobank 0.705, 95% CI 0.673-0.737). AUROC was highest using CH-AI: MGH 0.838, 95% CI 0.807-0.869; BWH 0.777, 95% CI 0.766-0.788; UK Biobank 0.746, 95% CI 0.716-0.776). Calibration error was low using ECG-AI (MGH 0.0212; BWH 0.0129; UK Biobank 0.0035) and CH-AI (MGH 0.012; BWH 0.0108; UK Biobank 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r MGH 0.61, BWH 0.66, UK Biobank 0.41). Conclusions: AI-based analysis of 12-lead ECGs has similar predictive utility to a clinical risk factor model for incident AF and both approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95–0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012–0.030 in C3PO vs. 0.028–0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.

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Objectives This study compared methods of mite retrieval from community cats in the Ohio River Valley region of the USA and determined incidence of parasitic mites in this region. Methods In total, 493 community cats were humanely trapped and anesthetized for a trap-neuter-return program. Cats received a dermatologic examination, ear swabs, superficial skin scraping, flea combing, acetate tape preparation and feces collection. All samples were examined microscopically. Large volumes of hair and scale from flea combing were dissolved in 10% potassium hydroxide and centrifuged with Sheather's solution. Fecal samples were mixed with Sheather's solution, filtered and centrifuged. Results Ear swabs were significantly ( P <0.05) better than other methods for finding chigger mites and Otodectes cynotis, and skin scraping was significantly better than ear swabs for finding Cheyletiella species. Only cats with O cynotis had clinical lesions. Mites remained identifiable for 6 months at room temperature. Mite incidence rates were as follows: Notoedres cati (1/493 cats), 0.002 (95% confidence interval [CI] 0-0.006); Lynxacarus radovskyi (2/493 cats), 0.004 (95% CI 0-0.01); Demodex gatoi (5/493 cats), 0.01 (95% CI 0.001-0.019); chigger mites (10/493 cats), 0.02 (95% CI 0.008-0.033); Cheyletiella species (12/493 cats), 0.024 (95% CI 0.011-0.038); and O cynotis (124/493 cats), 0.252 (95% CI 0.213-0.29). Conclusions and relevance Ear swabs are recommended when O cynotis or chigger mites are suspected. Skin scraping is more likely to yield positive results than ear swabs, but was not significantly better than acetate tape preparations, flea combing or fecal flotation for finding Cheyletiella species. Mites can remain identifiable for prolonged periods at room temperature. With the exception of O cynotis, the incidence of feline parasitic mites in the Ohio River Valley region of the USA is low; however, D gatoi and L radovskyi were present in the area and should be considered in cats with dermatologic disease attributable to them. In this population of community cats, asymptomatic carriage of mites was common.

Background: Electronic health records (EHRs) promise to enable broad-ranging discovery with power exceeding that of conventional research cohort studies. However, research using EHR datasets may be subject to selection bias, which can be compounded by missing data, limiting the generalizability of derived insights. Methods: Mass General Brigham (MGB) is a large New England-based healthcare network comprising seven tertiary care and community hospitals with associated outpatient practices. Within an MGB-based EHR warehouse of >3.5 million individuals with at least one ambulatory care visit, we approximated a community-based cohort study by selectively sampling individuals longitudinally attending primary care practices between 2001-2018 (n=520,868), which we named the Community Care Cohort Project (C3PO). We also utilized pre-trained deep natural language processing (NLP) models to recover vital signs (i.e., height, weight, and blood pressure) from unstructured notes in the EHR. We assessed the validity of C3PO by deploying established risk models including the Pooled Cohort Equations (PCE) and the Cohorts for Aging and Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score, and compared model performance in C3PO to that observed within typical EHR Convenience Samples which included all individuals from the same parent EHR with sufficient data to calculate each score but without a requirement for longitudinal primary care. All analyses were facilitated by the JEDI Extractive Data Infrastructure pipeline which we designed to efficiently aggregate EHR data within a unified framework conducive to regular updates. Results: C3PO includes 520,868 individuals (mean age 48 years, 61% women, median follow-up 7.2 years, median primary care visits per individual 13). Estimated using reports, C3PO contains over 2.9 million electrocardiograms, 450,000 echocardiograms, 12,000 cardiac magnetic resonance images, and 75 million narrative notes. Using tabular data alone, 286,009 individuals (54.9%) had all vital signs available at baseline, which increased to 358,411 (68.8%) after NLP recovery (31% reduction in missingness). Among individuals with both NLP and tabular data available, NLP-extracted and tabular vital signs obtained on the same day were highly correlated (e.g., Pearson r range 0.95-0.99, p<0.01 for all). Both the PCE models (c-index range 0.724-0.770) and CHARGE-AF (c-index 0.782, 95% 0.777-0.787) demonstrated good discrimination. As compared to the Convenience Samples, AF and MI/stroke incidence rates in C3PO were lower and calibration error was smaller for both PCE (integrated calibration index range 0.012-0.030 vs. 0.028-0.046) and CHARGE-AF (0.028 vs. 0.036). Conclusions: Intentional sampling of individuals receiving regular ambulatory care and use of NLP to recover missing data have the potential to reduce bias in EHR research and maximize generalizability of insights.

The intersection of medicine and machine learning (ML) has the potential to transform healthcare. We describe how physiology, a foundational discipline of medical training and practice with a rich quantitative history, could serve as a starting point for the development of a common language between clinicians and ML experts, thereby accelerating real-world impact.

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