A research paper by scientists from Shanghai Jiao Tong University developed and clinically evaluated an artificial intelligence (AI)-enabled wearable ultrasound device for noninvasive estimation of elevated central venous pressure (CVP) by imaging the internal jugular vein (IJV) and common carotid artery (CCA) in acute and critical patients.

The new research paper, published on Aug. 05 in the journal Cyborg and Bionic Systems, has developed an integrated system that combines a wearable ultrasound patch with artificial intelligence to estimate elevated CVP noninvasively from neck vascular imaging.

A research paper by scientists at Shenzhen Institutes of Advanced Technology proposed a single source dual drive flexible knee joint assisted exoskeleton device to assist elderly people in daily walking.

The research paper, published on Aug 11, 2026 in the journal Cyborg and Bionic Systems.

Researchers at the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine) have identified a ‘master switch’ responsible for driving tumour growth in an aggressive form of breast cancer. Blocking the switch with a targeted drug was also found to reduce cancer cell growth and reduce its spread.

This World Breast Cancer Research Day, the Alliance for Clinical Trials in Oncology is highlighting the importance of clinical research in advancing better treatments and improving early detection to ultimately help prevent and cure breast cancer. Although both men and women can develop breast cancer, the disease disproportionately affects women. On average, women in the United States have about a 1 in 8 chance of developing breast cancer during their lifetime, according to the National Cancer Institute. In 2026, an estimated 321,910 women will be diagnosed with breast cancer and about 42,140 will die from the disease. An estimated 2,670 men will also be diagnosed with breast cancer this year.   

The study introduces an integrated engineering framework that combines transcriptomic analysis with a transcriptome-constrained genome-scale metabolic model (GEM) to identify metabolic engineering targets for enhanced bioplastic production. Rather than relying on conventional trial-and-error strain engineering, the approach predicts genetic modifications that redirect metabolic flux toward product biosynthesis, providing a new strategy for agricultural waste valorization and sustainable biomanufacturing.