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Background: Despite patients with successful revascularization as evidenced by angiographic findings, inadequate clinical management of coronary microcirculatory dysfunction (CMD) may result in preventable adverse outcomes. Therefore, it is imperative to use a multimodal data‑driven predictive model for the occurrence of CMD in patients with acute myocardial infarction (AMI) following emergency percutaneous coronary intervention (PCI). Methods: A prospective case−control analysis was conducted on a cohort of 77 patients with AMI who underwent PCI. The most informative predictors were selected for the predictive model through the application of LASSO analysis and multi‐factor logistic regression. The diagnosis of CMD is based on findings from cardiac magnetic resonance (CMR). Results: Based on the findings from LASSO analysis and multi‐factor logistic regression, variables including sex, neutrophil‐to‐lymphocyte ratio (NLR), Gensini score, and diabetes mellitus were identified as independent predictors for the development of CMD in AMI patients who underwent emergency PCI. The predictive model was evaluated using bootstrap self‐sampling 500 times. The resulting predictive model demonstrated an AUC value of 0.897 (95% CI: 0.827−0.958). The calibration curves exhibited good concordance between the predictions generated by the model and the CMR analysis. Furthermore, decision curve analysis revealed that the predictive model provided valuable clinical benefit in predicting CMD. Conclusions: The multivariate predictive model, constructed using readily available clinical variables in patients with AMI who underwent PCI, demonstrates satisfactory predictability for identifying comorbid CMD, thereby facilitating the identification of high‐risk patients. [ABSTRACT FROM AUTHOR]

Since the 18th National Congress of the Communist Party of China (CPC), the quality of China' s economic development has been continuously improved. However, the year 2022 witnessed a subdued economic growth rate. The triple pressures continue to evolve, with contraction in demand as the primary contradiction facing China' s macro economy in the short term. In contrast to major economies in the world, China grapples with a low economic growth-inflation index combination, a pace of economic expansion trailing behind its potential, and a mounting unemployment rate. The impact of supply on price increases weakens, while the global industrial and supply chains are amidst continuous adjustments. Both the consumer confidence index (CCI) and the purchasing managers' index (PMI) remain low. The expectation for the depreciation of the RMB against US dollar is weakening. In 2023, China will continue to improve the financing environment and carry out a proactive fiscal policy. Simultaneously, there is a moderation trend in the trajectory of the interest rate of the Federal Reserve System hikes. Taken together, these factors (triple pressures) are expected to mitigate the contraction in demand, thereby steering the economy toward robust growth. However, it is crucial to recognize that low economic growth in the long term may compromise the quality of economic development. The year 2023 is the first year of accelerating Chinese modernization after the 20th National Congress of the CPC. China should integrate the strategy to expand internal demand with its efforts to deepen supply-side structural reform. This can foster the coordinated growth of both economic quality and speed, making a good start for Chinese modernization. [ABSTRACT FROM AUTHOR]

An efficient method for the synthesis of M2B10H14 (M = Na and K) has been developed. The two possible formation mechanisms of the B10H142− anion are proposed, in which the NH2BH3− anion acts as a proton abstractor and a hydride donor. Furthermore, the B10H13− and B10H15− intermediates were detected. [ABSTRACT FROM AUTHOR]

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions. [ABSTRACT FROM AUTHOR]