BRC News

A BRC-supported programme to help students from disadvantaged backgrounds get into biomedical research has entered its second year.

It is the first time anyone has shown these findings several years before diagnosis.

Using MRI as a screening test alongside PSA density allowed detection of cancers that would have been missed by the blood test alone, according to research from UCL, UCLH and King’s College London.

The British Heart Foundation has appointed Professor Williams as its first Chief Scientific and Medical Officer.

The software can identify malaria parasites in blood samples almost as accurately as experts staffing microscopes used in standard diagnostic procedures.

Initial results from an early phase study investigating a new ‘gene silencing’ therapy for Alzheimer’s disease (AD) suggest the treatment has the potential to alter the course of the disease.

The latest early phase cancer trials newsletter from the NIHR UCLH Clinical Research Facility (CRF) is out now. 

Professor Jonathan Schott has received the 2023 Bill Thies Award for Distinguished Service to The Alzheimer’s Association International Society to Advance Alzheimer's Research and Treatment.

The research published by BRC staff should benefit any NHS organisation wanting to develop new digital and data-based approaches to care.

A rapid brain imaging protocol for the diagnosis and monitoring of the disease would make it possible to acquire scans in just five minutes instead of 20 to 30 minutes.

New technology could make health research more efficient and faster, according to results from a live pilot study.

NIne out of ten research participants would do it again, according to the latest patient research experience survey.

Researchers can apply for one of 8 stalls at a community researach engagement event and for up to £500 to develop an interactive activity.

Researchers and people living with Parkinson’s have produced new information booklets for clinicians and patients to support easier conversations about Parkinson’s dementia.

New machine learning technology can use real-time A&E data to predict how many patients will need an emergency bed later that day and in which part of the hospital.