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Abstract Background Hybridization differences caused by target sequence differences can be a confounding factor in analyzing gene expression on microarrays, lead to false positives and reduce power to detect real expression differences. We prepared an R Bioconductor compatible package to detect, characterize and remove such probes in Affymetrix 3’IVT and exon-based arrays on the basis of correlation of signal intensities from probes within probe sets. Results Using completely mouse genomes we determined type 1 (false negatives) and type 2 (false positives) errors with high accuracy and we show that our method routinely outperforms previous methods. When detecting 76.2% of known SNP/indels in mouse expression data, we obtain at most 5.5% false positives. At the same level of false positives, best previous method detected 72.6%. We also show that probes with differing binding affinity both hinder differential expression detection and introduce artifacts in cancer-healthy tissue comparison. Conclusions Detection and removal of such probes should be a routine step in Affymetrix data preprocessing. We prepared a user friendly R package, compatible with Bioconductor, that allows the filtering and improving of data from Affymetrix microarrays experiments.

Abstract Background Genome-wide expression, sequence and association studies typically yield large sets of gene candidates, which must then be further analysed and interpreted. Information about these genes is increasingly being captured and organized in ontologies, such as the Gene Ontology. Relationships between the gene sets identified by experimental methods and biological knowledge can be made explicit and used in the interpretation of results. However, it is often difficult to assess the statistical significance of such analyses since many inter-dependent categories are tested simultaneously. Results We developed the program package FUNC that includes and expands on currently available methods to identify significant associations between gene sets and ontological annotations. Implemented are several tests in particular well suited for genome wide sequence comparisons, estimates of the family-wise error rate, the false discovery rate, a sensitive estimator of the global significance of the results and an algorithm to reduce the complexity of the results. Conclusion FUNC is a versatile and useful tool for the analysis of genome-wide data. It is freely available under the GPL license and also accessible via a web service.

School reopenings in 2021 and 2022 coincided with the rapid emergence of new SARS-CoV-2 variants in the United States. In-school mitigation efforts varied, depending on local COVID-19 mandates and resources. Using a stochastic age-stratified agent-based model of SARS-CoV-2 transmission, we estimate the impacts of multiple in-school strategies on both infection rates and absenteeism, relative to a baseline scenario in which only symptomatic cases are tested and positive tests trigger a 10-day isolation of the case and 10-day quarantine of their household and classroom. We find that monthly asymptomatic screening coupled with the 10-day isolation and quarantine period is expected to avert 55.4% of infections while increasing absenteeism by 104.3%. Replacing quarantine with test-to-stay would reduce absenteeism by 66.3% (while hardly impacting infection rates), but would require roughly 10-fold more testing resources. Alternatively, vaccination or mask wearing by 50% of the student body is expected to avert 54.1% or 43.1% of infections while decreasing absenteeism by 34.1% or 27.4%, respectively. Separating students into classrooms based on mask usage is expected to reduce infection risks among those who wear masks (by 23.1%), exacerbate risks among those who do not (by 27.8%), but have little impact on overall risk. A combined strategy of monthly screening, household and classroom quarantine, a 50% vaccination rate, and a 50% masking rate (in mixed classrooms) is expected to avert 81.7% of infections while increasing absenteeism by 90.6%. During future public health emergencies, such analyses can inform the rapid design of resource-constrained strategies that mitigate both public health and educational risks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Aims: Right ventricular to pulmonary artery (RV-PA) coupling has been established as a prognostic marker in patients with severe tricuspid regurgitation (TR) undergoing transcatheter tricuspid valve interventions (TTVI). RV-PA coupling assesses right ventricular systolic function related to pulmonary artery pressure levels, which are ideally measured by right heart catheterization. This study aimed to improve the RV-PA coupling concept by relating tricuspid annular plane systolic excursion (TAPSE) to mean pulmonary artery pressure (mPAP) levels. Moreover, instead of right heart catheterization, this study sought to employ an extreme gradient boosting (XGB) algorithm to predict mPAP levels based on standard echocardiographic parameters., Methods and Results: This multicentre study included 737 patients undergoing TTVI for severe TR; among them, 55 patients from one institution served for external validation. Complete echocardiography and right heart catheterization data were available from all patients. The XGB algorithm trained on 10 echocardiographic parameters could reliably predict mPAP levels as evaluated on right heart catheterization data from external validation (Pearson correlation coefficient R: 0.68; P value: 1.3 × 10-8). Moreover, predicted mPAP (mPAPpredicted) levels were superior to echocardiographic systolic pulmonary artery pressure (sPAPechocardiography) levels in predicting 2-year mortality after TTVI [area under the curve (AUC): 0.607 vs. 0.520; P value: 1.9 × 10-6]. Furthermore, TAPSE/mPAPpredicted was superior to TAPSE/sPAPechocardiography in predicting 2-year mortality after TTVI (AUC: 0.633 vs. 0.586; P value: 0.008). Finally, patients with preserved RV-PA coupling (defined as TAPSE/mPAPpredicted > 0.617 mm/mmHg) showed significantly higher 2-year survival rates after TTVI than patients with reduced RV-PA coupling (81.5% vs. 58.8%, P < 0.001). Moreover, independent association between TAPSE/mPAPpredicted levels and 2-year mortality after TTVI was confirmed by multivariate regression analysis (P value: 6.3 × 10-4)., Conclusion: Artificial intelligence-enabled RV-PA coupling assessment can refine risk stratification prior to TTVI without necessitating invasive right heart catheterization. A comparison with conservatively treated patients is mandatory to quantify the benefit of TTVI in accordance with RV-PA coupling., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The interplay between the right ventricle and the pulmonary artery, known as right ventricular to pulmonary artery (RV-PA) coupling, is crucial for assessing right ventricular systolic function against the afterload from the pulmonary circulation. Pulmonary artery pressure levels are ideally measured by right heart catheterization. Yet, echocardiography represents the most utilized method for evaluating pulmonary artery pressure levels, albeit with limitations in accuracy. This study therefore aims to evaluate the prognostic significance of right ventricular to pulmonary artery (RV-PA) coupling expressed as tricuspid annular plane systolic excursion (TAPSE) related to systolic pulmonary artery pressure (sPAP) levels measured by right heart catheterization (TAPSE/sPAP invasive ) or estimated by transthoracic echocardiography (TAPSE/sPAP echocardiography ) in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement (TAVR)., Methods: Using data from a bicentric registry, this study compares TAPSE/sPAP invasive vs. TAPSE/sPAP echocardiography in predicting 1-year all-cause mortality after TAVR., Results: Among 333 patients with complete echocardiography and right heart catheterization data obtained before TAVR, their mean age was 79.8 ± 6.74 years, 39.6% were female, and general 1-year survival was 89.8%. sPAP invasive and sPAP echocardiography showed only moderate correlation (Pearson correlation coefficient R : 0.53, p  value: <0.0001). TAPSE/sPAP invasive was superior to TAPSE/sPAP echocardiography in predicting 1-year all-cause mortality after TAVR (area under the curve: 0.662 vs. 0.569, p value: 0.025). Patients with reduced TAPSE/sPAP invasive levels (< 0.365 mm/mmHg) evidenced significantly lower 1-year survival rates than patients with preserved TAPSE/sPAP invasive levels (81.8 vs. 93.6%, p value: 0.001; hazard ratio for 1-year mortality: 3.09 [95% confidence interval: 1.55-6.17]). Echocardiographic follow-up data revealed that patients with reduced RV-PA coupling suffer from persistent right ventricular dysfunction (TAPSE: 16.6 ± 4.05 mm vs. 21.6 ± 4.81 mm in patients with preserved RV-PA coupling) and severe tricuspid regurgitation (diagnosed in 19.7 vs. 6.58% in patients with preserved RV-PA coupling)., Conclusions: RV-PA coupling expressed as TAPSE/sPAP invasive can refine stratification of severe aortic stenosis patients into low-risk and high-risk cohorts for mortality after TAVR. Moreover, it can help to anticipate persistent extra-aortic valve cardiac damage, which will demand further treatment., Competing Interests: The authors report no conflict of interest., (© 2024 The Author(s).)

Introduction: Today, modern technology is used to diagnose and treat cardiovascular disease. These medical devices provide exact measures and raw data such as imaging data or biosignals. So far, the Broad Integration of These Health Data into Hospital Information Technology Structures-Especially in Germany-is Lacking, and if data integration takes place, only non-Evaluable Findings are Usually Integrated into the Hospital Information Technology Structures. A Comprehensive Integration of raw Data and Structured Medical Information has not yet Been Established. The aim of this project was to design and implement an interoperable database (cardio-vascular-information-system, CVIS) for the automated integration of al medical device data (parameters and raw data) in cardio-vascular medicine., Methods: The CVIS serves as a data integration and preparation system at the interface between the various devices and the hospital IT infrastructure. In our project, we were able to establish a database with integration of proprietary device interfaces, which could be integrated into the electronic health record (EHR) with various HL7 and web interfaces., Results: In the period between 1.7.2020 and 30.6.2022, the data integrated into this database were evaluated. During this time, 114,858 patients were automatically included in the database and medical data of 50,295 of them were entered. For technical examinations, more than 4.5 million readings (an average of 28.5 per examination) and 684,696 image data and raw signals (28,935 ECG files, 655,761 structured reports, 91,113 x-ray objects, 559,648 ultrasound objects in 54 different examination types, 5,000 endoscopy objects) were integrated into the database. Over 10.2 million bidirectional HL7 messages (approximately 14,000/day) were successfully processed. 98,458 documents were transferred to the central document management system, 55,154 materials (average 7.77 per order) were recorded and stored in the database, 21,196 diagnoses and 50,353 services/OPS were recorded and transferred. On average, 3.3 examinations per patient were recorded; in addition, there are an average of 13 laboratory examinations., Discussion: Fully automated data integration from medical devices including the raw data is feasible and already creates a comprehensive database for multimodal modern analysis approaches in a short time. This is the basis for national and international projects by extracting research data using FHIR., Competing Interests: MB is employed by Fleischhacker GmbH & Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Martens, Haase, Mastella, Henkel, Spinner, Hahn, Zou, Fava Sanches, Allescher, Heid, Strauss, Maier, Lachmann, Schmidt, Westphal, Haufe, Federle, Rueckert, Boeker, Becker, Laugwitz, Steger and Müller.)

Colleges and universities in the US struggled to provide safe in-person education throughout the COVID-19 pandemic. Testing coupled with isolation is a nimble intervention strategy that can be tailored to mitigate the changing health and economic risks associated with SARS-CoV-2. We developed a decision-support tool to aid in the design of university-based screening strategies using a mathematical model of SARS-CoV-2 transmission. Applying this framework to a large public university reopening in the fall of 2021 with a 60% student vaccination rate, we find that the optimal strategy, in terms of health and economic costs, is twice weekly antigen testing of all students. This strategy provides a 95% guarantee that, throughout the fall semester, case counts would not exceed twice the CDC's original high transmission threshold of 100 cases per 100k persons over 7 days. As the virus and our medical armament continue to evolve, testing will remain a flexible tool for managing risks and keeping campuses open. We have implemented this model as an online tool to facilitate the design of testing strategies that adjust for COVID-19 conditions as well as campus-specific populations, resources, and priorities., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections., (© 2023. The Author(s).)

Scientists have grappled with reconciling biological evolution 1,2 with the immutable laws of the Universe defined by physics. These laws underpin life's origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary 3-5 . We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an 'object' on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (A), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists., (© 2023. The Author(s).)

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model. 1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al. 2 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: Men and women differ regarding comorbidities, pathophysiology, and the progression of valvular heart diseases., Objectives: This study sought to assess sex-related differences regarding clinical characteristics and the outcome of patients with severe tricuspid regurgitation (TR) undergoing transcatheter tricuspid valve intervention (TTVI)., Methods: All 702 patients in this multicenter study underwent TTVI for severe TR. The primary outcome was 2-year all-cause mortality., Results: Among 386 women and 316 men in this study, men were more often diagnosed with coronary artery disease (52.9% in men vs 35.5% in women; P = 5.6 × 10 -6 ). Subsequently, the underlying etiology for TR in men was predominantly secondary ventricular (64.6% in men vs 50.0% in women; P = 1.4 × 10 -4 ), whereas women more often presented with secondary atrial etiology (41.7% in women vs 24.4% in men, P = 2.0 × 10 -6 ). Notably, 2-year survival after TTVI was similar in women and men (69.9% in women vs 63.7% in men; P = 0.144). Multivariate regression analysis identified dyspnea expressed as New York Heart Association functional class, tricuspid annulus plane systolic excursion (TAPSE), and mean pulmonary artery pressure (mPAP) as independent predictors for 2-year mortality. The prognostic significance of TAPSE and mPAP differed between sexes. Consequently, we looked at right ventricular-pulmonary arterial coupling expressed as TAPSE/mPAP and identified sex-specific thresholds to best predict survival; women with a TAPSE/mPAP ratio <0.612 mm/mm Hg displayed a 3.43-fold increased HR for 2-year mortality (P < 0.001), whereas men with a TAPSE/mPAP ratio <0.434 mm/mm Hg displayed a 2.05-fold increased HR for 2-year mortality (P = 0.001)., Conclusions: Even though men and women differ in the etiology of TR, both sexes show similar survival rates after TTVI. The TAPSE/mPAP ratio can improve prognostication after TTVI, and sex-specific thresholds should be applied to guide future patient selection., Competing Interests: Funding Support and Author Disclosures Dr Lachmann has received funding from the Technical University of Munich (Clinician Scientist Grant) and from the Else Kröner-Fresenius Foundation (Clinician Scientist Grant). All authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Aims: Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach., Methods and Results: Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 ± 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 ± 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 ± 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 ± 0.360 cm2) and highest systolic pulmonary artery pressures (68.4 ± 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 ± 10.4%) and enlarged LVESD (53.2 ± 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 ± 113 mL; right atrial area: 46.0 ± 8.83 cm2) although LVEF was only slightly reduced (51.5 ± 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed., Conclusion: ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future., Competing Interests: Conflict of interest: None declared ., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting., Methods: In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50)., Results: Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3)., Conclusions: The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.

Cardiogenesis relies on the precise spatiotemporal coordination of multiple progenitor populations. Understanding the specification and differentiation of these distinct progenitor pools during human embryonic development is crucial for advancing our knowledge of congenital cardiac malformations and designing new regenerative therapies. By combining genetic labelling, single-cell transcriptomics, and ex vivo human-mouse embryonic chimeras we uncovered that modulation of retinoic acid signaling instructs human pluripotent stem cells to form heart field-specific progenitors with distinct fate potentials. In addition to the classical first and second heart fields, we observed the appearance of juxta-cardiac field progenitors giving rise to both myocardial and epicardial cells. Applying these findings to stem-cell based disease modelling we identified specific transcriptional dysregulation in first and second heart field progenitors derived from stem cells of patients with hypoplastic left heart syndrome. This highlights the suitability of our in vitro differentiation platform for studying human cardiac development and disease., (© 2023. The Author(s).)

Background Tricuspid regurgitation (TR) frequently develops in patients with long-standing pulmonary hypertension, and both pathologies are associated with increased morbidity and mortality. This study aimed to improve prognostic assessment in patients with severe TR undergoing transcatheter tricuspid valve intervention (TTVI) by relating the extent of TR to pulmonary artery pressures. Methods and Results In this multicenter study, we included 533 patients undergoing TTVI for moderate-to-severe or severe TR. The proportionality framework was based on the ratio of tricuspid valve effective regurgitant orifice area to mean pulmonary artery pressure. An optimal threshold for tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio was derived on 353 patients with regard to 2-year all-cause mortality and externally validated on 180 patients. Patients with a tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio ≤1.25 mm 2 /mm Hg (defining proportionate TR) featured significantly lower 2-year survival rates after TTVI than patients with disproportionate TR (56.6% versus 69.6%; P =0.005). In contrast with patients with disproportionate TR (n=398), patients with proportionate TR (n=135) showed more pronounced mPAP levels (37.9±9.06 mm Hg versus 27.9±8.17 mm Hg; P <2.2×10 -16 ) and more severely impaired right ventricular function (tricuspid annular plane systolic excursion: 16.0±4.11 versus 17.0±4.64 mm; P =0.012). Moreover, tricuspid valve effective regurgitant orifice area was smaller in patients with proportionate TR when compared with disproportionate TR (0.350±0.105 cm 2 versus 0.770±0.432 cm 2 ; P <2.2×10 -16 ). Importantly, proportionate TR remained a significant predictor for 2-year mortality after adjusting for demographic and clinical variables (hazard ratio, 1.7; P =0.006). Conclusions The proposed proportionality framework promises to improve future risk stratification and clinical decision-making by identifying patients who benefit the most from TTVI (disproportionate TR). As a next step, randomized controlled studies with a conservative treatment arm are needed to quantify the net benefit of TTVI in patients with proportionate TR.

In response to COVID-19, schools across the United States closed in early 2020; many did not fully reopen until late 2021. Although regular testing of asymptomatic students, teachers, and staff can reduce transmission risks, few school systems consistently used proactive testing to safeguard return to classrooms. Socioeconomically diverse public school districts might vary testing levels across campuses to ensure fair, effective use of limited resources. We describe a test allocation approach to reduce overall infections and disparities across school districts. Using a model of SARS-CoV-2 transmission in schools fit to data from a large metropolitan school district in Texas, we reduced incidence between the highest and lowest risk schools from a 5.6-fold difference under proportional test allocation to 1.8-fold difference under our optimized test allocation. This approach provides a roadmap to help school districts deploy proactive testing and mitigate risks of future SARS-CoV-2 variants and other pathogen threats.

Although open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation., Competing Interests: All authors declare no competing interests., (© 2022 The Authors.)

Objective: A novel artificial intelligence-based phenotyping approach to stratify patients with severe aortic stenosis (AS) prior to transcatheter aortic valve replacement (TAVR) has been proposed, based on echocardiographic and haemodynamic data. This study aimed to analyse the recovery of extra-aortic valve cardiac damage in accordance with this novel stratification system following TAVR., Methods: The proposed phenotyping approach was previously established employing data from 366 patients with severe AS from a bicentric registry. For this consecutive study, echocardiographic follow-up data, obtained on day 147±75.1 after TAVR, were available from 247 patients (67.5%)., Results: Correction of severe AS by TAVR significantly reduced the proportion of patients suffering from concurrent severe mitral regurgitation (from 9.29% to 3.64%, p value: 0.0015). Moreover, pulmonary artery pressures were ameliorated (estimated systolic pulmonary artery pressure: from 47.2±15.8 to 43.3±15.1 mm Hg, p value: 0.0079). However, right heart dysfunction as well as the proportion of patients with severe tricuspid regurgitation remained unchanged. Clusters with persistent right heart dysfunction ultimately displayed 2-year survival rates of 69.2% (95% CI 56.6% to 84.7%) and 74.6% (95% CI 65.9% to 84.4%), which were significantly lower compared with clusters with little or no persistent cardiopulmonary impairment (88.3% (95% CI 83.3% to 93.5%) and 85.5% (95% CI 77.1% to 94.8%))., Conclusions: This phenotyping approach preprocedurally identifies patients with severe AS, who will not recover from extra-aortic valve cardiac damage following TAVR and whose survival is therefore significantly reduced. Importantly, not the degree of pulmonary hypertension at initial presentation, but the irreversibility of right heart dysfunction determines prognosis., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Failure of the right ventricle plays a critical role in any type of heart failure. However, the mechanism remains unclear, and there is no specific therapy. Here, we show that the right ventricle predominantly expresses alternative complement pathway-related genes, including Cfd and C3aR1. Complement 3 (C3)-knockout attenuates right ventricular dysfunction and fibrosis in a mouse model of right ventricular failure. C3a is produced from C3 by the C3 convertase complex, which includes the essential component complement factor D (Cfd). Cfd-knockout mice also show attenuation of right ventricular failure. Moreover, the plasma concentration of CFD correlates with the severity of right ventricular failure in patients with chronic right ventricular failure. A C3a receptor (C3aR) antagonist dramatically improves right ventricular dysfunction in mice. In summary, we demonstrate the crucial role of the C3-Cfd-C3aR axis in right ventricular failure and highlight potential therapeutic targets for right ventricular failure., (© 2022. The Author(s).)

Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.

Aims: Hypothesizing that aortic outflow velocity profiles contain more valuable information about aortic valve obstruction and left ventricular contractility than can be captured by the human eye, features of the complex geometry of Doppler tracings from patients with severe aortic stenosis (AS) were extracted by a convolutional neural network (CNN)., Methods and Results: After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Among 366 eligible patients [age: 79.8 ± 6.77 years; 146 (39.9%) women] with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR), good quality Doppler tracings from 101 patients were analysed. The convolutional part of the pre-trained VGG-16 model in conjunction with principal component analysis and k-means clustering distinguished two shapes of aortic outflow velocity profiles. Kaplan-Meier analysis revealed that mortality in patients from Cluster 2 ( n = 40, 39.6%) was significantly increased [hazard ratio (HR) for 2-year mortality: 3; 95% confidence interval (CI): 1-8.9]. Apart from reduced cardiac output and mean aortic valve gradient, patients from Cluster 2 were also characterized by signs of pulmonary hypertension, impaired right ventricular function, and right atrial enlargement. After training an extreme gradient boosting algorithm on these 101 patients, validation on the remaining 265 patients confirmed that patients assigned to Cluster 2 show increased mortality (HR for 2-year mortality: 2.6; 95% CI: 1.4-5.1, P -value: 0.004)., Conclusion: Transfer learning enables sophisticated pattern recognition even in clinical data sets of limited size. Importantly, it is the left ventricular compensation capacity in the face of increased afterload, and not so much the actual obstruction of the aortic valve, that determines fate after TAVR., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Background: As SARS-CoV-2 vaccines are administered worldwide, the COVID-19 pandemic continues to exact significant human and economic costs. Mass testing of unvaccinated individuals followed by isolation of positive cases can substantially mitigate risks and be tailored to local epidemiological conditions to ensure cost effectiveness., Methods: Using a multi-scale model that incorporates population-level SARS-CoV-2 transmission and individual-level viral load kinetics, we identify the optimal frequency of proactive SARS-CoV-2 testing, depending on the local transmission rate and proportion immunized., Findings: Assuming a willingness-to-pay of US$100,000 per averted year of life lost (YLL) and a price of $10 per test, the optimal strategy under a rapid transmission scenario ( R e ∼ 2.5) is daily testing until one third of the population is immunized and then weekly testing until half the population is immunized, combined with a 10-day isolation period of positive cases and their households. Under a low transmission scenario ( R e ∼ 1.2), the optimal sequence is weekly testing until the population reaches 10% partial immunity, followed by monthly testing until 20% partial immunity, and no testing thereafter., Interpretation: Mass proactive testing and case isolation is a cost effective strategy for mitigating the COVID-19 pandemic in the initial stages of the global SARS-CoV-2 vaccination campaign and in response to resurgences of vaccine-evasive variants., Funding: US National Institutes of Health, US Centers for Disease Control and Prevention, HK Innovation and Technology Commission, China National Natural Science Foundation, European Research Council, and EPSRC Impact Acceleration Grant., Competing Interests: We declare no competing interests. Dr Chinazzi, Dr Pastore y Piontti and Prof. Alessandro Vespignani report grants from Metabiota Inc, outside the submitted work. Prof. Benjamin J. Cowling reports honoraria from AstraZeneca, GSK, Moderna, Pfizer, Roche and Sanofi Pasteur. The authors report no other potential conflicts of interest., (© 2022 The Author(s).)

Objectives: This study aimed to improve echocardiographic assessment of pulmonary hypertension (PH) in patients presenting with severe tricuspid regurgitation (TR)., Background: Echocardiographic assessment of PH in patients with severe TR carries several pitfalls for underestimation, hence concealing the true severity of PH in very sick patients in particular, and ultimately obscuring the impact of PH on survival after transcatheter tricuspid valve intervention (TTVI)., Methods: All patients in this study underwent TTVI for severe TR between 2016 and 2020. To predict the mean pulmonary artery pressure (mPAP) solely based on echocardiographic parameters, we trained an extreme gradient boosting (XGB) algorithm. The derivation cohort was constituted by 116 out of 162 patients with both echocardiography and right heart catheterization data, preprocedurally obtained, from a bicentric registry. Moreover, 142 patients from an independent institution served for external validation., Results: Systolic pulmonary artery pressure was consistently underestimated by echocardiography in comparison to right heart catheterization (40.3 ± 15.9 mm Hg vs 44.1 ± 12.9 mm Hg; P = 0.0066), and the assessment was most discrepant among patients with severe defects of the tricuspid valve and impaired right ventricular systolic function. Using 9 echocardiographic parameters as input variables, an XGB algorithm could reliably predict mPAP levels (R = 0.96, P < 2.2 × 10 -16 ). Moreover, patients with elevations in predicted mPAP levels ≥29.9 mm Hg showed significantly reduced 2-year survival after TTVI (58.3% [95% CI: 41.7%-81.6%] vs 78.8% [95% CI: 68.7%-90.5%]; P = 0.026). Importantly, the poor prognosis associated with elevation in predicted mPAP levels was externally confirmed (HR for 2-year mortality: 2.9 [95% CI: 1.5-5.7]; P = 0.002)., Conclusions: PH in patients with severe TR can be reliably assessed based on echocardiographic parameters in conjunction with an XGB algorithm, and elevations in predicted mPAP levels translate into increased mortality after TTVI., Competing Interests: Funding Support and Author Disclosures Dr Lachmann has received funding from the Technical University of Munich (clinician scientist grant) and from the Else Kröner-Fresenius Foundation (clinician scientist grant). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities., Competing Interests: The authors declare no competing interest., (Copyright © 2022 the Author(s). Published by PNAS.)

We used the incidence of spike gene target failures identified during PCR testing to provide an early projection of the prevalence of severe acute respiratory syndrome coronavirus 2 variant B.1.1.7 in a university setting in Texas, USA, before sequencing results were available. Findings from a more recent evaluation validated those early projections.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Non-coding RNAs have already been linked to CVD development and progression. While microRNAs (miRs) have been well studied in blood samples, there is little data on tissue-specific miRs in cardiovascular relevant tissues and their relation to cardiovascular risk factors. Tissue-specific miRs derived from Arteria mammaria interna (IMA) from 192 coronary artery disease (CAD) patients undergoing coronary artery bypass grafting (CABG) were analyzed. The aims of the study were 1) to establish a reference atlas which can be utilized for identification of novel diagnostic biomarkers and potential therapeutic targets, and 2) to relate these miRs to cardiovascular risk factors. Overall, 393 individual miRs showed sufficient expression levels and passed quality control for further analysis. We identified 17 miRs-miR-10b-3p, miR-10-5p, miR-17-3p, miR-21-5p, miR-151a-5p, miR-181a-5p, miR-185-5p, miR-194-5p, miR-199a-3p, miR-199b-3p, miR-212-3p, miR-363-3p, miR-548d-5p, miR-744-5p, miR-3117-3p, miR-5683 and miR-5701-significantly correlated with cardiovascular risk factors (correlation coefficient >0.2 in both directions, p -value ( p < 0.006, false discovery rate (FDR) <0.05). Of particular interest, miR-5701 was positively correlated with hypertension, hypercholesterolemia, and diabetes. In addition, we found that miR-629-5p and miR-98-5p were significantly correlated with acute myocardial infarction. We provide a first atlas of miR profiles in IMA samples from CAD patients. In perspective, these miRs might play an important role in improved risk assessment, mechanistic disease understanding and local therapy of CAD.