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.

Exploring the mechanisms underlying mitochondrial dysfunction in lung diseases and the potential of mitochondrial transplantation in treating lung diseases. Mitochondria, the primary sites of ROS production within cells, play an important role in energy metabolism, cell cycle, and apoptosis; therefore, mitochondrial dysfunction may affect the physiological processes and influence pathological outcomes in several diseases, including cardiovascular diseases, neurodegenerative diseases, respiratory diseases, and cancer.

The incidence of intrauterine adhesions (IUA) following surgical abortion is reportedly approximately 19%. It is associated with adverse reproductive outcomes. Therefore, there has been a search for preventive modalities that can facilitate endometrial regeneration and decrease the disease burden. This study aimed to estimate the incidence of IUAs after surgical abortion following intrauterine infusion of autologous platelet-rich plasma using the American Fertility Society (AFS) scoring system through hysteroscopy.

Scientists have unveiled a sunlight-powered catalyst that transforms methane—the main component of natural gas—directly into ethanol, a high-value liquid fuel and chemical feedstock. The new material, described today in Frontiers in Energy, achieves an apparent quantum efficiency of 9.4 %, a record for photocatalytic methane-to-ethanol conversion, while operating under near-ambient conditions.

To achieve effective intraday dispatch of photovoltaic (PV) power generation systems, a reliable ultra-short-term power generation forecasting model is required. Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble prediction model, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation. Unlike other machine learning models, the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation. In addition, based on the structure of GBDD, this paper proposes a strategy that can improve the prediction performance of other types of prediction models. The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models. The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.