Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.

Background: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.

Methods: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study.

Association of Bile Acids with Connectivity of Executive Control and Default Mode Networks in Patients with Major Depression.

Objective: Bile acids may contribute to pathophysiologic markers of Alzheimer's disease, including disruptions of the executive control network (ECN) and the default mode network (DMN). Cognitive dysfunction is common in major depressive disorder (MDD), but whether bile acids impact these networks in MDD patients is unknown.

Methods: Resting state functional magnetic resonance imaging (fMRI) scans and blood measures of four bile acids from 74 treatment-naïve adults with MDD were analyzed.

A multiplex method for rapidly identifying viral protease inhibitors.

With current treatments addressing only a fraction of pathogens and new viral threats constantly evolving, there is a critical need to expand our existing therapeutic arsenal. To speed the rate of discovery and better prepare against future threats, we establish a high-throughput platform capable of screening compounds against 40 diverse viral proteases simultaneously. This multiplex approach is enabled by using cellular biosensors of viral protease activity combined with DNA-barcoding technology, as well as several design innovations that increase assay sensitivity and correct for plate-to-plate variation.

Extended reality as a modality to train non-technical skills in healthcare: A scoping review.

The need to train non-technical skills (NTS) has seen a growing emphasis in recent literature, as they have been associated with improved patient outcomes. NTS training often utilizes live simulations where healthcare workers can practice these skills, but simulations like this can be expensive and resource intensive to run. Training technical skills using extended reality tools (e.

Therapy-induced senescent cancer cells contribute to cancer progression by promoting ribophorin 1-dependent PD-L1 upregulation.

Conventional chemotherapy- and radiotherapy-induced cancer senescence, which is characterized by poor proliferation, drug resistance, and senescence-associated secretory phenotype, has gained attention as contributing to cancer relapse and the development of an immunosuppressive tumor microenvironment. However, the association between cancer senescence and anti-tumor immunity is not fully understood. Here, we demonstrate that senescent cancer cells increase the level of PD-L1 by promoting its transcription and glycosylation.