Language：English   Publishing type：Research paper (scientific journal)  

Long noncoding RNAs (lncRNAs) are critical regulators of immune responses and cellular metabolism. Here, we report a previously unrecognized interaction between MALAT1 and HADHB, which reveals additional regulatory roles for MALAT1 in human macrophages. Our findings demonstrate that MALAT1-HADHB interaction significantly enhances HADHB thiolase activity during the late phase of inflammation via HuR-MTCH2-mediated mitochondrial targeting of MALAT1. MALAT1 also negatively regulates the pro-inflammatory macrophage activation via HADHB. Knockdown of MALAT1 induces metabolic reprogramming, characterized by enhanced glycolysis, increased fatty acid synthesis, and reduced fatty acid oxidation, suggesting that MALAT1 suppresses inflammatory metabolic pathways. This study uncovers the MALAT1-HADHB interaction and demonstrates that MALAT1 regulates macrophage metabolic reprogramming, offering new insights into the metabolic control of inflammation and highlighting MALAT1 as a potential therapeutic target for inflammatory diseases.

DOI： 10.1016/j.isci.2026.115107

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Language：English   Publishing type：Research paper (scientific journal)  

BACKGROUND: Repeated hospitalization due to heart failure (HF) is a significant predictor of mortality. However, there are limited early detection systems for HF progression that can be utilized by patients at home without a cardiac implantable electrical device (CIED). This study aimed to develop an artificial intelligence (AI)-based system utilizing convolutional neural network (CNN) algorithms for the early detection of HF progression using single‑lead electrocardiograms (ECGs), including those obtained from wearable devices such as the Apple Watch®. METHODS: ECG data from 9518 participants, encompassing both HF patients and healthy controls, were used to train the CNN model to diagnose HF status. New York Heart Association (NYHA) classifications were determined by multiple cardiologists at the time of ECG recording. The CNN model was designed to calculate a novel HF-index, derived from the NYHA grades predicted by the AI, as a quantitative measure of real-time HF severity. RESULTS: The CNN model achieved a 91.6 % accuracy in classifying HF severity into NYHA I-II (asymptomatic to mild HF) and NYHA III-IV grades (moderate to severe HF) categories. Furthermore, the model generated a novel HF-index as a real-time indicator of HF severity, which showed a positive correlation (R = 0.74) with plasma B-type natriuretic peptide (BNP) levels, thereby validating its effectiveness in reflecting HF severity. CONCLUSIONS: We successfully constructed a novel at-home HF monitoring system utilizing a portable single‑lead ECG device. This system has been validated for its effectiveness in at-home HF monitoring, representing a significant advancement in remote healthcare for HF management.

DOI： 10.1016/j.ijcard.2025.133203

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