Replication-incompetent VSV-based vaccine elicits protective responses against SARS-CoV-2 and influenza virus.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses lead to severe respiratory illnesses and death in humans, exacerbated in individuals with underlying health conditions, remaining substantial global public health concerns. Here, we developed a bivalent replication-incompetent single-cycle pseudotyped vesicular stomatitis virus vaccine that incorporates both a prefusion-stabilized SARS-CoV-2 spike protein lacking a furin cleavage site and a full-length influenza A virus neuraminidase protein. Vaccination of K18-hACE2 or C57BL/6J mouse models generated durable levels of neutralizing antibodies, T cell responses, and protection from morbidity and mortality upon challenge with either virus.

Neuromodulatory effect of transcranial direct current stimulation on cue reactivity and craving in young adults with internet gaming disorder: an event-related potential study.

Objective: This study assessed the effects of transcranial direct current stimulation (tDCS) on cue reactivity and craving for game-related cues using event-related potentials (ERPs) in internet gaming disorder (IGD) patients.

Methods: At baseline, a series of game-related and neutral pictures were shown to both IGD and healthy controls (HCs) while ERPs were recorded. Late positive potentials (LPP) were used to investigate cue reactivity.

Description of STRIVE-ON Study Protocol: Safety and Tolerability of GTX-104 (Nimodipine Injection for IV Infusion) Compared with Oral Nimodipine in Patients Hospitalized for Aneurysmal Subarachnoid Hemorrhage (aSAH): A Prospective, Randomized, Phase III Trial (STRIVE-ON).

Oral nimodipine is the only drug approved in North America for patients with aneurysmal subarachnoid hemorrhage (aSAH). However, bioavailability is variable and frequently poor, leading to fluctuations in peak plasma concentrations that cause dose-limiting hypotension. Furthermore, administration is problematic in patients who cannot swallow.

A pediatric emergency prediction model using natural language process in the pediatric emergency department.

This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021) using electronic medical records. Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts.

Establishing freshwater sediment sample pretreatment methods for monitoring microplastics and its challenges.

Microplastics (MPs) have been detected in various environmental matrices, drinking water, and food, and their presence is an ecological and human health concern. Most research on MPs has focused solely on their detection and analysis. However, sample pretreatment methods are critical for accurate MP analysis and must be properly established.