Personalized Medicine – News and Features

Researchers have created an intricate molecular map of the human body and its complex physiological processes based on the analysis of thousands of molecules measured in blood, urine and saliva samples from 391 volunteers.

Researchers have developed an AI model that can perform an array of diagnostic tasks for multiple forms of cancer, improving upon existing AI systems.

Researchers have developed a new mechanism for predicting the aggressiveness of triple-negative breast cancer (TNBC). This method uses patient-derived xenograft models to more accurately forecast cancer recurrence.

Researchers have developed a noninvasive technique that could dramatically improve the way doctors monitor intracranial hypertension, a condition where increased pressure in the brain can lead to severe outcomes like strokes and hemorrhages.

A identified 813 potential cancer driver genes via splicing, expanding targets beyond traditional mutation-centric models. Using the "spotter" algorithm, researchers found significant non-overlap with existing mutation databases.

Researchers developed Spacia, a computer model that improves detection of cell-to-cell communication (CCC) using spatially resolved transcriptomics data. Spacia, which uses multi-instance learning, provides deeper insights into diseases.

Researchers have pioneered a light-based method for analyzing dried blood crystals, enhancing cancer detection. The technique shows 90% accuracy in diagnosing and classifying cancer with less pain and risk compared to traditional methods.

A machine-learning tool can help distinguish subtypes of rheumatoid arthritis, which may help scientists find ways to improve care by detecting unexpected tissue changes that humans might miss.

Research reveals that overgrowth of methane-producing archaea in the gut is linked to severe constipation. The study suggests that identifying and treating intestinal methanogen overgrowth (IMO) could lead to more effective, personalized treatments.

A newly developed system reveals brain structure patterns that predict variations in certain regions of the individual’s genetic code, which are linked to autism.