Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic mutation detection in cancer diagnostics and RNA-based genomic studies.

The School of Computing and Data Science (CDS) of the University of Hong Kong (HKU) has embarked on a new chapter, launching its first-ever dual-site semester across Hong Kong and Shanghai. As the 2025–2026 academic year enters its second semester, CDS students in the exclusive Tech Immersion Fellowship programme can now split their studies between HKU’s main campus and the newly opened HKU-CDS Teaching and Research Site in Shanghai—a strategic move that redefines cross-border tech education.

The University of Hong Kong (HKU) and the South China University of Technology (SCUT) formalised a partnership through a Strategic Collaboration Agreement to establish the Hong Kong Base of the State Key Laboratory of Subtropical Building and Urban Science (SKL-SBUS). A launch ceremony was held today (21 January) at the Convocation Room, HKU, marking a strengthened partnership between the two universities in the fields of architecture and urban science.

A recent study led by the Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine of the University of Hong Kong (HKUMed), in collaboration with the University of Hong Kong–Shenzhen Hospital, has demonstrated that combining an innovative coronary imaging technology known as the caFFR system, with diabetes drug SGLT2 inhibitors can significantly reduce the risks of major adverse cardiovascular events (MACE), heart failure and death among patients with type 2 diabetes mellitus (T2DM) and coronary artery disease. This dual‑pronged strategy provides a precise measurement of coronary blood flow while lowering glucose levels, offering an effective approach to treating exceptionally high-risk patients. The findings were published in the Diabetes and Metabolism Journal.

Talking to oneself is a trait which feels inherently human - but it’s not just humans who can reap the benefits of such self-talk. Published in Neural Computation, scientists from the Okinawa Institute of Science and Technology (OIST) have demonstrated the potential of inner speech to improve AI learning, showing how AI models can generalize across different tasks more easily when supported by both inner speech and short-term memory.

POSTECH, the University of Wisconsin–Madison, and the University of Tokyo control oxide electronic structures via local atomic arrangements at moiré interfaces.

Seeing chemistry unfold inside living cells is one of the biggest challenges of modern bioimaging. Raman microscopy offers a powerful way to meet this challenge by reading the unique vibrational “signatures” of molecules. However, cells are extraordinarily complex environments filled with thousands of biomolecules. To make specific molecules stand out, researchers often attach small chemical “probes,” such as alkyne tags, that produce signals in a so-called cell-silent spectral window where native cellular components do not scatter light. This allows Raman microscopes to selectively detect the tagged molecules against an otherwise crowded molecular background. Despite this advantage, the widespread adoption of Raman microscopy in biology has been limited by one fundamental problem: Raman signals are extremely weak. In a new study published in Angewandte Chemie International Edition, TIFR researchers report a molecular design strategy that dramatically amplifies Raman signals, enabling the development of Raman sensors that can detect biomolecules using standard spontaneous Raman microscopes. These new Raman sensors enable sensitive, ratiometric imaging of biological molecules and ions, such as hydrogen peroxide molecules, copper ions, and pH, inside living cells. The work is supported by the Department of Atomic Energy, Government of India, and the Japan Science and Technology Agency, with the research team comprising Prof. Ankona Datta and Sujit Das from the Tata Institute of Fundamental Research, Mumbai, and Heqi Xi, Itsuki Yamamoto, and Prof. Katsumasa Fujita from the University of Osaka, Japan.