Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study.

Background: Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups. Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process.

Objective: This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability.

The Impact of Cold Ambient Temperature in the Pattern of Influenza Virus Infection.

Background: Prior literature suggests that cold temperature strongly influences the immune function of animals and human behaviors, which may allow for the transmission of respiratory viral infections. However, information on the impact of cold stimuli, especially the impact of temporal change in the ambient temperature on influenza virus transmission, is limited.

Methods: A susceptible-infected-recovered-susceptible model was applied to evaluate the effect of temperature change on influenza virus transmission.

The hematopoietic regulator, ELF-1, enhances the transcriptional response to Interferon-β of the OAS1 anti-viral gene.

Interferon (IFN) therapy is effective in treating cancers, haematological and virus induced diseases. The classical Jak/Stat pathway of IFN signal transduction leading to changes in transcriptional activity is well established but alone does not explain the whole spectrum of cellular responses to IFN. Gene promoters contain cis-acting sequences that allow precise and contextual binding of transcription factors, which control gene expression.

Ultrasound imaging and semi-automatic analysis of active muscle features in electrical stimulation by optical flow.

Ultrasound imaging is an effective way to measure the muscle activity in electrical stimulation studies. However, it is a time consuming task to manually measure pennation angle and muscle thickness, which are the benchmark features to analyze muscle activity from the ultrasound imaging. In previous studies, the muscle features were measured by calculating optical flow of the pennation angle by using only fibers of a muscle from the ultrasound, without carefully considering moving muscle edges during active and passive contraction.