Scientists at the University of Pittsburgh School of Medicine discovered a surprising new way the body can fight insulin resistance and diabetes – by boosting a special type of “good” immune cell in fat tissue. Announced today in Nature Communications, the preclinical findings pave the path to develop a medication to treat and prevent type 2 diabetes, potentially replacing or supplementing GLP-1 weight maintenance drugs that lose effectiveness over time.

A recent study published in Science China Life Sciences demonstrates that disulfiram, a drug used to treat alcoholism, can suppress liver cancer by regulating lipid metabolism and angiogenesis. Researchers found that disulfiram upregulates the gene c-FOS by reducing its RNA methylation, which in turn inhibits key molecules involved in lipid accumulation and blood vessel formation in tumors. This discovery highlights a promising repurposing potential for disulfiram in hepatocellular carcinoma therapy.

A new study published in Life Metabolism reports that a single post-meal blood biomarker, 1-hour postprandial SPARC (SPARC-1H), can predict who will benefit most from adopting a Mediterranean diet. The discovery provides one of the clearest examples to date of how precision nutrition can identify individualized dietary responses using a simple blood test rather than complex multi-omics models.

A research team led by Professors Joo-Yong Hahn, Young Bin Song, and Ki Hong Choi of the Division of Cardiology at Samsung Medical Center, Sungkyunkwan University School of Medicine, together with Professor Yong Hwan Park of Samsung Changwon Hospital, has demonstrated that clopidogrel is more effective than aspirin as a long-term antiplatelet therapy in patients at high risk of recurrent cardiovascular events after coronary stent implantation.

Continuous cardiovascular monitoring is vital for preventing and managing heart disease, yet most wearable devices struggle with motion artifacts and require precise alignment over arteries. 

New research following US adolescents ages 11–12 shows that problematic use of mobile phones, social media, and video games was associated with higher risks of mental health problems, sleep disturbance, and suicidal behaviors one year later. The study in the American Journal of Preventive Medicine, published by Elsevier, reveals that the links between problematic screen use and mental health are stronger than those previously reported for overall screen time and highlights the risks of addictive use.

With the rapid development of electric vehicles and energy storage systems (ESSs), accurate state-of-health (SOH) estimation for lithium-ion batteries has become crucial for ensuring safety and optimizing performance. However, SOH estimation under dynamic operating conditions remains challenging, as non-monotonic voltage profiles and irregular current patterns reduce the effectiveness of conventional measurement methods. This paper proposes a comprehensive approach that combines health feature extraction with a parallel deep learning architecture for robust SOH estimation. First, the method extracts four highly correlated health features (K, b, σΔQ, and σδΔQ) from dynamic measurement data collected by sensors, with correlation coefficients between these features and the actual SOH exceeding 0.95. These extracted features are then processed through a novel parallel Temporal Convolutional Networks (TCN)-Transformer hybrid architecture: the TCN captures multi-scale local temporal patterns, while the Transformer models global dependencies. An attention-gated fusion module dynamically integrates complementary feature representations from the two branches and adaptively weights different paths based on degradation features. Experimental validation on three standardized battery datasets (MIT, CALCE, Oxford) shows that the method achieves an estimation accuracy with a root mean square error (RMSE) below 1% under all operating conditions, representing an 8%–70% improvement over conventional methods. Attention weight analysis reveals correlations with aging mechanisms, providing interpretability for model decisions. The proposed method enables practical real-time battery health assessment in dynamic environments.