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The rapid growth of organic solid waste has recently exacerbated environmental pollution problems, and its improper treatment has led to the loss of a large number of biomass resources. Here, we expound the advantages of microbial agents composting compared with conventional organic solid waste treatment technology, and review the important role of microbial agents composting in organic solid waste composting from the aspects of screening and identification, optimization of conditions, mechanism of action, combination with other technologies and ultra‐high‐temperature and ultra‐low‐temperature microbial composting. We discuss the value of microorganisms with different growth conditions in organic solid waste composting, and put forward a seasonal multi‐temperature composite microbial composting technology. Provide new ideas for the all‐round treatment of microbial agents in organic solid waste in the future. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

To explore the risk factors of atrial fibrillation (AF) in patients with coronary heart disease (CHD), and to construct a risk prediction model. Methods: The participants in this case-control study were from the cardiovascular Department of Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine from June 2016 to June 2023, and they were divided into AF group and non-AF group according to whether AF occurred during hospitalization. The clinical data of the two groups were compared by retrospective analysis. Multivariate Logistic regression analysis was used to investigate the risk factors of AF occurrence in CHD patients. The nomogram model was constructed with R 4.2.6 language "rms" package, and the model's differentiation, calibration and effectiveness were evaluated by drawing ROC curve, calibration curve and decision curve. Results: A total of 1258 patients with CHD were included, and they were divided into AF group (n=92) and non-AF group (n=1166) according to whether AF was complicated. Logistic regression analysis showed that age, coronary multiple branch lesion, history of heart failure, history of drinking, pulmonary hypertension, left atrial diameter, left ventricular end-diastolic diameter and diabetes mellitus were independent risk factors for the occurrence of AF in CHD patients (P < 0.05). The ROC curve showed that the AUC of this model was 0.956 (95% CI (0.916, 0.995)) and the consistency index was 0.966. The calibration curve of the model is close to the ideal curve. The analysis of decision curve shows that the prediction value of the model is better when the probability threshold of the model is 0.042~0.963. Conclusion: The nomogram model established in this study for predicting the risk of AF in patients with CHD has better predictive performance and has certain reference value for clinical identification of high-risk groups prone to AF in patients with CHD. [ABSTRACT FROM AUTHOR]

Detecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies. [ABSTRACT FROM AUTHOR]