Dear Experts and Colleagues,

    We are pleased to invite you to present and participate in our first annual Experimental Therapeutics at Ruijin meeting, to be held on June 1st to 4th,  2026, at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Annual meeting will bring together leading investigators to share experiences in translating scientific discoveries into clinical success. We hope the event will provide every participant—faculty, students, and guests alike—with valuable insights and a stimulating exchange of ideas. The organizers are:

    H. Michael Shepard, PhD – Experimental Therapeutics Group, Ruijin Hospital, Shanghai

    Baiyong Shen, MD – Professor and Chief Physician, Ruijin Hospital, Shanghai

    Mone Zaidi, PhD – Icahn School of Medicine at Mount Sinai, New York

    We anticipate a high level of interest in this topic and will provide ample opportunity for meaningful interaction and discussion among participants. Some presentations will highlight programs with regulatory approval, while most will focus on those with preclinical or early clinical proof of concept.

    We sincerely hope you can join us for this exciting event in Shanghai.

    Time: June 1st to 4th, 2026

    Location: 1F Lecture Hall, Building 35, Translational Medicine Building, No. 197 Ruijin 2nd Road, Ruijin Hospital, Shanghai, China

    Contact: ying.zhang@jueqianmed.com

Kyoto, Japan -- Despite their crucial function, public hospitals often face limited resources and financial distress, and an aging population can further exacerbate any imbalances in medical resource distribution. Furthermore, the proportion of aging individuals is not uniform across the country; in Japan, this has lead to regional disparities in healthcare for the elderly.

Previous studies suggest that restructuring public hospitals, though challenging, can alleviate the mismatch in healthcare resources. At least compared to private hospitals, it is more feasible to align public hospitals with regional needs. However, such studies often adopt qualitative approaches while lacking quantitative evidence to measure the effects of reorganization. This inspired a team of researchers at Kyoto University to examine the impact of public hospital restructuring on elderly hospital admissions in Japan.

"Many countries have reformed public hospitals to reduce resource imbalances, but there is limited information on the impact this has on communities and the regional healthcare system," says first author Kenji Kishimoto.

A widely used method for measuring how well streams absorb excess nutrients has a hidden flaw: it systematically overestimates uptake length under high-nutrient conditions. Researchers at Duke Kunshan University have derived a corrected zero-order analytical approach that better captures stream nutrient processing when nutrients are abundant, improving the accuracy of tools used to assess river health and guide restoration decisions.

The MedTech Hackathon GBA 2026, co-organised by The University of Hong Kong’s (HKU) Techno-Entrepreneur Core (TEC) and Techno-Entrepreneur Academy (TEA) in collaboration with Bay Charm, presented the pioneering medical solutions of its top ten finalist teams at the Asia Summit on Global Health this week. The teams showcased their innovations to a distinguished panel of life sciences experts, demonstrating a sophisticated synergy between cutting-edge laboratory research and real-world clinical demand.

Professor Chi Ming Che, Zhou Guangzhao Professor in Natural Sciences and Chair Professor of Chemistry, Department of Chemistry, Faculty of Science, The University of Hong Kong (HKU), and the Laboratory for Synthetic Chemistry and Chemical Biology Limited (LSCCB), and Dr Wei Liu, Department of Chemistry, Faculty of Science, HKU, and LSCCB, have developed an innovative portable AI-enabled optical sensing device that enables rapid, non-invasive cancer risk detection using just a saliva sample. This innovative technology integrates advanced chemical sensing with artificial intelligence and was awarded a Gold Medal with Congratulations of the Jury at the 51st International Exhibition of Inventions of Geneva (2026), recognising its scientific excellence and strong potential for practical application.   

A new study published in Research describes a deep-learning system designed to assist lumbar spine MRI at the point of image acquisition rather than only after scanning. The system, named Lumbar VNet Pro (LVP), was embedded directly into the MRI workflow and evaluated in a multicenter clinical setting. According to the study, the platform supports automated anatomical localization, scan-plane optimization, and real-time structural analysis during MRI scanning, offering a possible route toward more standardized and intelligent imaging workflows.