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Purpose: To assess preoperative optical coherence tomography (OCT) findings of foveal-splitting retinal detachment (RD) and determine postoperative outcomes. Methods: Consecutive patients who underwent RD surgery over a 1-year period were included. Patients diagnosed with a detachment extending to the edge of the fovea on fundus examination (i.e., macula-On/Off) underwent macular OCT scanning. Visual acuity (VA) after 1 year of macula-On/Off, macula-On, and macula-Off eyes was compared. Results: A total of 85 eyes were included, 8 of which had a macula-On/Off RD. On preoperative OCT, all macula-On/Off RD eyes had foveal detachment extending beyond the foveal center over a median distance of 632 µm. Mean VA of the macula-On/Off eyes had improved from 20/160 to 20/40 at 1 year postoperatively (p = 0.035). The preoperative VA of macula-On/Off eyes was significantly better than macula-Off eyes (p = 0.032) and lower than macula-On eyes (p = 0.004). At 1 year, the VA of macula-On/Off eyes was no different from that of the macula-On eyes (p = 0.320), and tended to be better than that of the macula-Off eyes (p = 0.062). Conclusion: Preoperative OCT revealed a shallow RD extending beyond the foveal center in eyes with clinical foveal-splitting RD. These eyes, termed macula-On/Off RD eyes, had a preoperative VA between macula-On and macula-Off eyes, while their final VA was close to those with macula-On RD.

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.

Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear.This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention.A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy.Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor.Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.

Summary We developed the variant-Set Test for Association using Annotation infoRmation (STAAR) workflow description language (WDL) workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the WDL allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline. Availability and implementation The workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow. Supplementary information Supplementary data are available at Bioinformatics online.

The

Background: Cardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (eg, InlineVF), but their accuracy and availability may be limited. Objective: To develop an open-source deep learning model to estimate CMR-derived LV mass. Methods: Within participants of the UK Biobank prospective cohort undergoing CMR, we trained 2 convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5065 individuals), while the second (ML4Hseg) performed LV segmentation informed by InlineVF (version D13A) contours. We compared ML4Hreg, ML4Hseg, and InlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex. Results: We generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r = 0.864, 95% confidence interval [CI] 0.847–0.880; MAE 10.41 g, 95% CI 9.82–10.99) than ML4Hreg (r = 0.843, 95% CI 0.823–0.861; MAE 10.51, 95% CI 9.86–11.15, P = .01) and InlineVF (r = 0.795, 95% CI 0.770–0.818; MAE 14.30, 95% CI 13.46–11.01, P < .01). LVH defined using ML4Hseg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51–3.04), atrial fibrillation (1.75, 95% CI 1.37–2.20), and heart failure (4.67, 95% CI 3.28–6.49). Conclusions: ML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.

To assess the sequence of anatomical resolution of myopic foveoschisis (MFS) after vitrectomy.Monocentric retrospective observational case series.The files of consecutive patients with MFS who underwent vitreoretinal surgery and were followed postoperatively for at least 6 months were reviewed.Patients underwent pars plana vitrectomy for MFS. The central foveal thickness (CFT) was measured. The presence of a foveal involvement, and/or outer retinoschisis (ORS), with or without inner retinoschisis (IRS), and foveal detachment (FD) were analyzed. Anatomical success was defined as the resolution of foveal ORS and FD.The main outcome was the time to resolution of the different morphologic features of MFS after surgery.Thirty-nine eyes of 36 patients were included in the analysis. The mean follow-up was 14.8 ± 12.9 months (range, 6-84 months). Anatomical success was achieved in 82% of cases at the end of the follow-up and in80% of cases during the first year. The CFT was significantly decreased in 79% of cases at 3 months. Inner retinoschisis, present in 18 eyes (46%), resolved in all cases after a median time of 1 month. Foveal ORS, present in all cases, resolved in 82% of cases after a median time of 3 months. The FD, present in 23 eyes (59%), resolved in 91% of cases after a median time of 6 months. Extrafoveal ORS resolved in 59% of cases after a median time of 12 months. The mean best-corrected visual acuity significantly improved from 0.80 ± 0.64 logarithm of the minimum angle of resolution (logMAR) (Snellen Eq 20/148) to 0.48 ± 0.52 logMAR (Snellen Eq 20/70).Most MFS (80%) are completely resolved during the first year. The decrease in CFT and early resolution of IRS could be used as early biomarkers of surgical success.

Artificial intelligence (AI) models applied to 12-lead electrocardiogram (ECG) waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. We hypothesized that there may be a genetic basis for ECG-AI based risk estimates. We applied an ECG-AI model for predicting incident AF to ECGs from 39,986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk. We identified three signals (P−8) at established AF susceptibility loci marked by the sarcomeric gene TTN, and sodium channel genes SCN5A and SCN10A. We also identified two novel loci near the genes VGLL2 and EXT1. In contrast, a GWAS of risk estimates from a clinical variable model indicated a different genetic profile. Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel, and height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.

BackgroundInter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice because quantification requires medical imaging.ObjectivesWe hypothesized that a deep learning model trained on an individual’s body shape outline – or “silhouette” – would enable accurate estimation of specific fat depots, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. We additionally set out to study whether silhouette-estimated VAT/ASAT ratio may stratify risk of cardiometabolic diseases independent of body mass index (BMI) and waist circumference.MethodsTwo-dimensional coronal and sagittal silhouettes were constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used to train a convolutional neural network to predict VAT, ASAT, and GFAT volumes, and VAT/ASAT ratio. Logistic and Cox regressions were used to determine the independent association of silhouette-predicted VAT/ASAT ratio with type 2 diabetes and coronary artery disease.ResultsMean age of the study participants was 65 years and 51% were female. A deep learning model trained on silhouettes enabled accurate estimation of VAT, ASAT, and GFAT volumes (R2: 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (ΔR2 = 0.05-0.13). Next, we studied VAT/ASAT ratio, a nearly BMI- and waist circumference-independent marker of unhealthy fat distribution. While the comparator model poorly predicted VAT/ASAT ratio (R2: 0.17-0.26), a silhouette-based model enabled significant improvement (R2: 0.50-0.55). Silhouette-predicted VAT/ASAT ratio was associated with increased prevalence of type 2 diabetes and coronary artery disease.ConclusionsBody silhouette images can estimate important measures of fat distribution, laying the scientific foundation for population-based assessment.

Background To compare different clinical and Spectral-Domain Optical Coherence Tomography (SD-OCT) features of high myopic eyes with Stickler syndrome (STL) with matched controls. Methods Patients with genetically confirmed STL with axial length ≥ 26 mm and controls matched for axial length were included. The following data were obtained from SD-OCT scans and fundus photography: choroidal and retinal thickness (respectively, CT and RT), peripapillary atrophy area (PAA), presence of posterior staphyloma (PS). Results Twenty-six eyes of 17 patients with STL and 25 eyes of 19 controls were evaluated. Compared with controls, patients with STL showed a greater CT subfoveally, at 1000 μm from the fovea at both nasal and temporal location, and at 2000 and 3000 μm from the fovea in nasal location (respectively, 188.7±72.8 vs 126.0±88.7 μm, 172.5±77.7 vs 119.3±80.6 μm, 190.1±71.9 vs 134.9±79.7 μm, 141.3±56.0 vs 98.1±68.5 μm, and 110.9±51.0 vs 67.6±50.7 μm, always PP2, P=0.03), compared with controls. Conclusions This study shows that high myopic patients with STL show a greater CT, a lower PAA and a lower prevalence of PS, compared with controls matched for axial length. These findings could be relevant for the development and progression of myopic maculopathy in patients with STL.

Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for direct imaging assessments. We developed an abdominal MRI-based machine-learning algorithm to accurately estimate liver fat (correlation coefficients, 0.97-0.99) from a truth dataset of 4,511 middle-aged UK Biobank participants, enabling quantification in 32,192 additional individuals. 17% of participants had predicted liver fat levels indicative of steatosis, and liver fat could not have been reliably estimated based on clinical factors such as BMI. A genome-wide association study of common genetic variants and liver fat replicated three known associations and identified five newly associated variants in or near the MTARC1, ADH1B, TRIB1, GPAM, and MAST3 genes (p < 3 × 10-8). A polygenic score integrating these eight genetic variants was strongly associated with future risk of chronic liver disease (hazard ratio > 1.32 per SD score, p < 9 × 10-17). Rare inactivating variants in the APOB or MTTP genes were identified in 0.8% of individuals with steatosis and conferred more than 6-fold risk (p < 2 × 10-5), highlighting a molecular subtype of hepatic steatosis characterized by defective secretion of apolipoprotein B-containing lipoproteins. We demonstrate that our imaging-based machine-learning model accurately estimates liver fat and may be useful in epidemiological and genetic studies of hepatic steatosis.

Introduction: Deep learning-derived representations of 12-lead electrocardiograms (ECGs) may allow for atrial fibrillation (AF) risk prediction. However, it remains unclear whether ECG-based artificial intelligence improves prediction beyond established clinical risk factors for AF and whether predictions are generalizable. Methods: Within a dataset comprising over 500,000 individuals receiving regular primary care at a multi-institutional network, we trained a convolutional neural network to predict incident AF using 12-lead ECGs (“ECG-AI”). ECG-AI was trained in individuals with ≥1 ECG performed at Massachusetts General Hospital (MGH) within 3 years prior to start of follow-up. We then fit a Cox proportional hazards model with incident AF as the outcome and a) logit-transformed ECG-AI AF probability, and b) the Cohorts for Aging and Genomic Epidemiology AF (CHARGE-AF) score, as covariates (“CH-AI”). We compared the discrimination and calibration of CHARGE-AF versus CH-AI in three independent samples: MGH (n=4,166), Brigham and Women’s Hospital (BWH, n=37,963) and the UK Biobank (n=41,034). Based on available follow-up, AF was evaluated at 5 years in MGH and BWH, and 2 years in the UK Biobank. Results: ECG-AI was trained in 36,081 individuals with an ECG performed at MGH (mean age 55±17, 53% female). CH-AI had substantially better discrimination (area under the receiver operating characteristic curve [AUROC]: MGH 0.838, BWH 0.777, UK Biobank 0.746; average precision [AP] 0.30, 0.21, 0.06) versus CHARGE-AF (AUROC: 0.802, 0.752, 0.732; AP 0.21, 0.17, 0.02, Figure ). CH-AI was well-calibrated in MGH (calibration error 0.012) and BWH (0.019), but overestimated AF risk in the UK Biobank (0.068). Calibration in the UK Biobank was excellent after recalibration to the sample-level 2-year AF hazard (error 7.1x10 -5 ). Conclusions: A model combining clinical AF risk factors with deep learning-derived ECG-based AF risk is favorable for predicting 5-year risk of AF.

Introduction: Bradyarrhythmias, diseases characterized by slow heart rates, are associated with substantial morbidity. Contemporary estimates of the frequency of bradyarrhythmias and associated outcomes in the community are needed. Methods: We examined the prevalence and incidence of bradyarrhythmias in a multi-institutional electronic health record cohort comprised of individuals receiving primary care. We used an automated tool to ascertain diagnoses from 12-lead electrocardiogram (ECG) diagnostic statements. We defined bradyarrhythmia groups based on location and severity of disease. We tested associations between clinical risk factors and incident bradyarrhythmia groups using multivariable proportional hazards regression. To characterize potential associations between bradyarrhythmias and major adverse cardiovascular events (MACE, a composite of myocardial infarction, heart failure, stroke, and death), we matched incident bradyarrhythmia and non-bradyarrhythmia individuals 1:1 on age, sex, and duration of preceding follow-up, and included bradyarrhythmia exposure in multivariable regression with MACE as the outcome. Results: Of a total of 520,868 individuals (mean age 48, 62% female, 3.5 million ECGs), the prevalence of any bradyarrhythmia was 10%. After excluding individuals with prevalent bradyarrhythmia, a total of 49,513 events occurred in 440,370 individuals as shown in Figure 1. Bradyarrhythmias were associated with common modifiable cardiovascular risk such as diabetes (HR 1.26, 95% CI [1.22-1.29]) and hypertension (HR 1.24, 95% CI [1.22-1.27]). Bradyarrhythmias were significantly associated with MACE in the atrioventricular delay (HR 1.07, 95% CI [1.01-1.14]) and conduction delay groups (HR 1.19, 95% CI [1.13-1.25]). Conclusions: Bradyarrhythmias are common and increase with older age and male sex. Incident bradyarrhythmias are associated with cardiovascular risk factors and may be associated with MACE.

Myocardial interstitial fibrosis is a common thread in multiple cardiovascular diseases including heart failure, atrial fibrillation, conduction disease and sudden cardiac death. To investigate the biologic pathways that underlie interstitial fibrosis in the human heart, we developed a machine learning model to measure myocardial T1 time, a marker of myocardial interstitial fibrosis, in 42,654 UK Biobank participants. Greater T1 time was associated with impaired glucose metabolism, systemic inflammation, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation and conduction disease. In genome-wide association analysis, we identified 12 independent loci associated with native myocardial T1 time with evidence of high genetic correlation between the interventricular septum and left ventricle free wall (r2g = 0.82). The identified loci implicated genes involved in glucose homeostasis (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Transcriptome-wide association studies highlighted the role of expression of ADAMTSL1 and SLC2A12 in human cardiac tissue in modulating myocardial tissue characteristics and interstitial fibrosis. Harnessing machine learning to perform large-scale phenotyping of interstitial fibrosis in the human heart, our results yield novel insights into biologically relevant pathways for myocardial fibrosis and prioritize investigation of pathways for the development of anti-fibrotic therapies.

BackgroundThe left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined genetics of thoracic aortic diameter in a single plane. We sought to elucidate the genetic basis for the diameter of the LVOT, the aortic root, and the ascending aorta.MethodsWe used deep learning to analyze 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at six locations in the ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these polygenic scores and disease incidence.Results79 loci were significantly associated with at least one diameter. Of these, 35 were novel, and a majority were associated with one or two diameters. A polygenic score of aortic diameter approximately 13mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm in UK Biobank participants (n=427,016; HR=1.42 per standard deviation; CI=1.34-1.50, P=6.67×10−21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n=426,502; HR=1.08 per standard deviation; CI=1.03-1.12, P=5×10−6).ConclusionsWe detected distinct common genetic loci underpinning the diameters of the LVOT, the aortic root, and at several segments in the ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding the genetic contributions to the diameter of the proximal aorta may enable identification of individuals at risk for life-threatening aortic disease and facilitate prioritization of therapeutic targets.

Rare disease investigators constantly face challenges in identifying additional cases to build evidence for gene‐disease causality. The Matchmaker Exchange (MME) addresses this limitation by providing a mechanism for matching patients across genomic centers via a federated network. The MME has revolutionized searching for additional cases by making it possible to query across institutional boundaries, so that what was once a laborious and manual process of contacting researchers is now automated and computable. However, while the MME network is beginning to scale, the growth of additional nodes is limited by the lack of easy‐to‐use solutions that can be implemented by any rare disease database owner, even one without significant software engineering resources. Here we describe matchbox, which is an open‐source, platform‐independent, portable bridge between any given rare disease genomic center and the MME network, which has already led to novel gene discoveries. We also describe how matchbox greatly reduces the barrier to participation by overcoming challenges for new databases to join the MME. Refereed/Peer-reviewed

Multiple summer events, including large indoor gatherings, in Provincetown, Massachusetts (MA), in July 2021 contributed to an outbreak of over one thousand COVID-19 cases among residents and visitors. Most cases were fully vaccinated, many of whom were also symptomatic, prompting a comprehensive public health response, motivating changes to national masking recommendations, and raising questions about infection and transmission among vaccinated individuals. To characterize the outbreak and the viral population underlying it, we combined genomic and epidemiological data from 467 individuals, including 40% of known outbreak-associated cases. The Delta variant accounted for 99% of sequenced outbreak-associated cases. Phylogenetic analysis suggests over 40 sources of Delta in the dataset, with one responsible for a single cluster containing 83% of outbreak-associated genomes. This cluster was likely not the result of extensive spread at a single site, but rather transmission from a common source across multiple settings over a short time. Genomic and epidemiological data combined provide strong support for 25 transmission events from, including many between, fully vaccinated individuals; genomic data alone provides evidence for an additional 64. Together, genomic epidemiology provides a high-resolution picture of the Provincetown outbreak, revealing multiple cases of transmission of Delta from fully vaccinated individuals. However, despite its magnitude, the outbreak was restricted in its onward impact in MA and the US, likely due to high vaccination rates and a robust public health response.

As the largest conduit vessel, the aorta is responsible for the conversion of phasic systolic inflow from ventricular ejection into more continuous blood delivery to peripheral arteries. Distension during systole and recoil during diastole conserves ventricular energy and is enabled by the specialized composition of the aortic extracellular matrix. Aortic distensibility decreases with age and prematurely in vascular disease. To discover genetic determinants of aortic distensibility we trained a deep learning model to quantify aortic size throughout the cardiac cycle and calculate aortic distensibility and aortic strain in 42,342 participants in the UK Biobank with available cardiac magnetic resonance imaging. In up to 40,028 participants with genetic data, common variant analysis identified 12 and 26 loci for ascending and 11 and 21 loci for descending aortic distensibility and strain, respectively. Of the newly identified loci, 22 were specific to strain or distensibility and were not identified in a thoracic aortic diameter GWAS within the same samples. Loci associated with both aortic diameter and aortic strain or distensibility demonstrated a consistent, inverse directionality. Transcriptome-wide analyses, rare-variant burden tests, and analyses of gene expression in single nucleus RNA sequencing of human aorta were performed to prioritize genes at individual loci. Loci highlighted multiple genes involved in elastogenesis, matrix degradation, and extracellular polysaccharide generation. Characterization of the genetic determinants of aortic function may provide novel targets for medical intervention in aortic disease.

Prediction models are commonly used to estimate risk for cardiovascular diseases; however, performance may vary substantially across relevant subgroups of the population. Here we investigated the variability of performance and fairness across a variety of subgroups for risk prediction of two common diseases, atherosclerotic cardiovascular disease (ASCVD) and atrial fibrillation (AF). We calculated the Cohorts for Heart and Aging in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) for AF and the Pooled Cohort Equations (PCE) score for ASCVD in three large data sets: Explorys Life Sciences Dataset (Explorys, n = 21,809,334), Mass General Brigham (MGB, n = 520,868), and the UK Biobank (UKBB, n = 502,521). Our results demonstrate important performance heterogeneity of established cardiovascular risk scores across subpopulations defined by age, sex, and presence of preexisting disease. For example, in CHARGE-AF, discrimination declined with increasing age, with concordance index of 0.72 [ 95% CI, 0.72–0.73 ] for the youngest (45–54y) subgroup to 0.57 [ 0.56–0.58 ], for the oldest (85–90y) subgroup in Explorys. The statistical parity difference (i.e., likelihood of being classified as high risk) was considerable between males and females within the 65–74y subgroup with a value of -0.33 [ 95% CI, -0.33–-0.33 ]. We observed also that large segments of the population suffered from both decreased discrimination (i.e.

Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements - an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses.

SummaryWe developed the STAAR WDL workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the workflow description language (WDL) allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate, and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline.Availability and implementationThe workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow.

Το ARDS χαρακτηρίζεται από αναπνευστική ανεπάρκεια μη καρδιογενούς αιτιολογίας και έχει υψηλή θνητότητα. Τα χαρακτηριστικά μοριακά συμβάματα είναι η ενεργοποίηση των κυψελιδικών μακροφάγων, με επακόλουθη παραγωγή των προφλεγμονωδών μεσολαβητών IL-1 και TNF-α, η συσσώρευση πολυμορφοπυρήνων στους πνεύμονες, η καταστροφή του ενδοθηλίου με αύξηση της έκφρασης των μορίων συγκόλλησης ICAM και VCAM στο ενδοθήλιο των μικρών πνευμονικών αγγείων, η ενεργοποίηση του συμπληρώματος και η βλάβη του κυψελιδικού επιθηλίου. Στο πειραματικό μοντέλο της ενδοτραχειακής χορήγησης LPS, η τελευταία προκαλεί πνευμονικές αλλοιώσεις που προσομοιάζουν με αυτές του ARDS, διαμέσου της ενεργοποίησης του παράγοντα NF-κΒ, με αποτέλεσμα την επαγωγή της έκφρασης των μεσολαβητών της φλεγμονής IL-1 β, IL-6, IL-8 και TNF-a, όπως και αύξηση της έκφρασης των μορίων προσκόλλησης. Για να επιτευχθεί ο σκοπός της παρούσας διατριβής, χρησιμοποιήθηκαν 4 ομάδες επίμυων, στις οποίες χορηγήθηκε ενδοτραχειακά ενδοτοξίνη. Στην πρώτη και δεύτερη ομάδα πειραματοζώων (ομάδες ελέγχου) προκλήθηκε ευθανασία δύο και 24 ώρες αντίστοιχα μετά τη χορήγηση LPS. Στα πειραματόζωα της τρίτης ομάδας χορηγήθηκε αδρεναλίνη υποδόρια μισή ώρα μετά την χορήγηση της ενδοτοξίνης και στα πειραματόζωα της τέταρτης ομάδας χορηγήθηκε 17β-οιστραδιόλη μισή ώρα μετά την ενδοτραχειακή χορήγηση LPS. Τα πειραματόζωα των δύο αυτών τελευταίων ομάδων υποβλήθηκαν σε ευθανασία δύο και 24 ώρες αντίστοιχα μετά τη χορήγηση LPS. Σε όλες τις ομάδες αμέσως πριν την πρόκληση της ευθανασίας ελήφθη δείγμα αίματος το οποίο χρησιμοποιήθηκε για τον προσδιορισμό των επιπέδων της κυτοκίνης IL-6. Οι πνεύμονες των πειραματοζώων στάλθηκαν για ιστολογική εξέταση και ανοσοϊστοχημεία. Η παθολογοανατομική μελέτη έδειξε ότι η αδρεναλίνη προκαλεί μείωση της φλεγμονής των πνευμόνων (p0.05). Προφανώς η μείωση της φλεγμονής που προκαλείται από την οιστραδιόλη δεν σχετίζεται με την επίδρασή της στην έκφραση των μορίων συγκολλήσεως, παρά το γεγονός ότι τα οιστρογόνα φαίνεται ότι δρουν ανασταλτικά στην έκφραση κάποιων γονιδίων που εξαρτώνται από τον παράγοντα NF-kB.

For any given level of overall adiposity – as commonly quantified by body mass index (BMI) within clinical practice – individuals vary considerably in fat distribution. We and others have noted that increased visceral fat (VAT) is associated with increased cardiometabolic risk, while gluteofemoral fat (GFAT) may be protective. Familial partial lipodystrophy (FPLD) – often caused by rare variants in the LMNA gene – represents an extreme example of this paradigm, leading to a severe shift to visceral fat with subsequent insulin resistance and adverse metabolic profile. By contrast, the inherited basis of body fat distribution in the broader population is not fully understood. Here, we studied up to 38,965 UK Biobank participants with VAT, abdominal subcutaneous (ASAT), and GFAT volumes precisely quantified using abdominal MRI. Because genetic associations with these raw depot volumes were largely driven by variants known to affect BMI, we next studied six phenotypes of local adiposity: VAT adjusted for BMI (VATadjBMI), ASATadjBMI, GFATadjBMI, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 178 unique loci associated with at least one adiposity trait, including 29 newly-identified loci. Rare variant association studies extend prior evidence of association for PDE3B as an important modulator of fat distribution. Sex-specific analyses of local adiposity traits noted overall higher estimated heritability in females, increased effect sizes for identified loci, and 25 female-specific associations. Individuals in the extreme tails of fat distribution phenotypes were highly enriched for predisposing common variants, as quantified using polygenic scores. Taking GFATadjBMI as an example, individuals with extreme values were 3.8-fold (95%CI 2.8 to 5.2) more likely to have a polygenic score within the top 5% of the distribution. These results – using more precise and BMI-independent measures of local adiposity – confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

International audience; Purpose: Post-transplant lymphoproliferative disorder (PTLD) represents a spectrum of disorders associated with Epstein Barr Virus infection in up to 80% of cases in the setting of pharmacologic immunosuppression following hematopoietic stem cell or solid organ transplantation. Ocular involvement is a rare finding in PTLD.Observation: We report the case of a 38-year-old man who presented with unilateral retinal infiltrates as first manifestation of PTLD relapse. Diagnosis relied on the presence of EBV DNA in anterior chamber fluids and vitrectomy that showed the presence of a B cell clone. Systemic relapse of PTLD was detected 12 weeks after retinal findings. Treatment of ocular disease included systemic injections of rituximab and intravitreal injections of methotrexate, halting the extension of retinal infiltrates.Conclusion: Ocular involvement in PTLD is rare and needs to be acknowledged because it can precede a systemic relapse of the hematological condition.