Visualizing the WHO "My Five Moments for Hand Hygiene," Framework: A Virtual Reality Training Program for Improving Hand Hygiene Adherence Among Nurses.

Background: Healthcare-associated infections (HAIs) contribute to patient morbidity and mortality. Hand hygiene is essential for preventing HAIs, but training can fail to transfer to clinical practice. Experiential learning through virtual reality (VR) may improve adherence by offering realistic practice opportunities and feedback.

Light-dependent variations in fatty acid profiles and gene expression in Antarctic microalgal cultures.

Photosynthetic eukaryotic microalgae are key primary producers in the Antarctic sea ice environment. Anticipated changes in sea ice thickness and snow load due to climate change may cause substantial shifts in available light to these ice-associated organisms. This study used a laboratory-based experiment to investigate how light levels, simulating different sea ice and snow thicknesses, affect fatty acid (FA) composition in two ice associated microalgae species, the pennate diatom Nitzschia cf.

A spatiotemporal and machine-learning platform facilitates the manufacturing of hPSC-derived esophageal mucosa.

Human pluripotent stem cell-derived tissue engineering offers great promise for designer cell-based personalized therapeutics, but harnessing such potential requires a deeper understanding of tissue-level interactions. We previously developed a cell replacement manufacturing method for ectoderm-derived skin epithelium. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium despite possessing a similar stratified epithelial structure.

Transformer-based modeling of Clonal Selection and Expression Dynamics reveals resistance mechanisms in breast cancer.

Understanding transcriptional heterogeneity in cancer cells and its implication for treatment response is critical to identify how resistance occurs and may be targeted. Such heterogeneity can be captured by in vitro studies through clonal barcoding methods. We present TraCSED (Transformer-based modeling of Clonal Selection and Expression Dynamics), a dynamic deep learning approach for modeling clonal selection.