Application of machine learning to serum miRNA profiles generated through next generation sequencing identifies a biologically relevant miRNA signature which can be deployed as a qPCR test to assist the diagnosis of epithelial ovarian cancer.

A freely available computer program that takes into account specific local conditions enables users to predict the impact of adopting different diagnostic strategies on the spread of tuberculosis in their region.

Mass spectrometry on plasma from patients with typhoid fever and other febrile disease identified and validated 24 metabolites that can distinguish typhoid from other febrile diseases, providing a new approach for typhoid diagnostics.

Children with a parent of cancer have a higher rate of hospital contact for injury, especially immediately after the cancer diagnosis.

A newly developed lateral flow assay can diagnose mutational signatures of major SARS CoV-2 variants in a rapid, inexpensive, and sequence-free manner.

PCR testing for SARS-CoV-2 from samples taken from smartphone screens, Phone Screen Testing, provides a sensitive, cost-effective, simple, and non-invasive new method that could boost COVID-19 mass test screening.

Behavioral and neural data converge in showing that medical experience reduces reactions to others' pain and distrust toward the target affects the processing of his feedback.

Diagnosing the presence and clinical significance of prostate cancer can be achieved using AI-based analysis of peripheral blood natural killer (NK) cell subset profiles.

The introduction of rapid diagnostic testing for Plasmodium falciparum infections can explain an increased emergence of diagnostic resistant pfhrp2-deleted parasites in sub-Saharan Africa.

Applying deep learning technology for the large-scale curation of symptoms from unstructured EHR clinical notes accurately predicts the differential signals of COVID-19 diagnosis over the week preceding typical PCR testing.