A review in the Chinese Journal of Natural Medicines examines how natural products can improve the in vivo fate of mesenchymal stem cells (MSCs), with potential benefits for cell homing, survival, differentiation, and immunoregulatory function in regenerative medicine and disease treatment.

A study in the Chinese Journal of Natural Medicines reports the first high-resolution structure of the major G-quadruplex in the TMPRSS2 promoter and shows how berberine binds and stabilizes it, offering a structural basis for antiviral strategies aimed at suppressing TMPRSS2 expression.

As the electric vehicle (EV) market surges, the biggest anxiety for owners and manufacturers remains the battery. How long will it last? Is it safe? Accurately predicting a battery's State of Health (SOH) is notoriously difficult under real-world driving conditions. Now, a research collaboration including Jilin University and major automaker China FAW Group has developed a novel deep learning model that cracks this code, achieving prediction errors of less than 1% even in dynamic environments.

 Researchers have developed an integrated gray wolf optimization algorithm-based hybrid estimation framework that combines sample entropy, localized voltage area, and fuzzy entropy to accurately estimate the state of health of bipolar lead-acid batteries. Partial charging profiles are utilized to extract and validate battery health feature attributes based on gray relational grades. The proposed hybrid models utilize two pairs of battery health attributes: localized voltage area paired with either fuzzy entropy or sample entropy. The average mean absolute error and average root mean squared error values are below 1.02 percent and 1.5 percent respectively for the localized voltage area and fuzzy entropy health attribute pair.