In what represents just the company’s third PCT filing, Houston-based Starling Medical Inc.’s co-founders, Hannah McKenney and William Hendricks, seek to gain further protection for their at-home urine diagnostic patient-monitoring platform that eliminates the traditional use of catching containers and dipsticks.

Cellecta Inc. made remote diagnostics and decentralized clinical trials significantly easier with the launch of its Drivermap Exp assay. The microsampling kit provides a “molecular snapshot” of 19,000 human protein-encoding genes from just 30 microliters of dried whole blood.

Deepull Diagnostics SL raised €50 million (US$56.93 million) in an oversubscribed series C financing round to complete clinical validation studies of its UllCORE diagnostic system for direct-from-blood rapid pathogen detection.

Seoul, South Korea-based Voinosis Co. Ltd. filed for potential worldwide protection of its AI-based system that allows for the early detection cognitive impairment, dementia, Alzheimer’s disease and hearing loss through voice analysis.

Health care technology company Wearoptimo Ltd. developed a next-generation microwearable sensor that accurately detects hydration levels, outperforming the needle-based gold standard.

The U.S. FDA granted breakthrough device designation to Mursla Bio Ltd.'s Evoliver. The biopsy blood test uses extracellular vesicles to survey hepatocellular carcinoma in high-risk cirrhotic patients.

The use of Renalytix plc’s Kidneyintelx biomarker-based prognostic test led to increased and targeted use of sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients at risk of diabetic kidney disease, compared to untested patients, according to data from a study.

Neuranics Ltd. recently raised $8 million in seed funding which is “extremely important” for the company as it looks to scale its magnetic sensing technology, which detects muscle activity without touching the skin, Noel McKenna, CEO told BioWorld. “The funds will help accelerate our go to market strategy and advance our R&D roadmap,” he said.

AI could significantly improve the value of patient recalls following mammography, but so far radiologists seem reluctant to rely on computer-aided readings. Radiologists tend to trust their own judgment – and that of their colleagues – in mammogram readings far more than AI-based diagnostics, even when the AI is much more accurate, a prospective trial analysis published in Radiology by Karolinska Institutet researchers found.