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The authors test the effects of pH level on different kinds of dental cement to model the long-term effects of reflux-induced stomach acid exposure.

The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.

The authors looked at genes and pathways that are enriched in glioblastoma multiforme.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

This study used an improved CMS-seq method to profile 5hmC in ormalin-fixed and paraffin-embedded (FFPE) samples from HNC tumors and adjacent normal tissues, identifying three genes (PRKD2, HADHA, and AIPL1) with promising potential as biomarkers for Head and neck cancer (HNC) diagnosis.

Here, recognizing that brain aneurysms pose a severe threat, often misdiagnosed and leading to high mortality, particularly in younger individuals, the authors explored a novel computer-aided engineering approach. They used magnetic resonance angiography images and computational fluid dynamics, to improve aneurysm detection and risk assessment, aiming for more personalized treatment.

Here the authors sought to investigate whether and how cerebral stroke and other health-related variables are influenced together and amongst each other by using statistical analyses. Their analysis suggested relations between nearly all variables considered, with the strongest association between having heart disease and a cerebral stroke.

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.