Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background Kurtosis reflecting noise's temporal structure is an effective metric for evaluating noise-induced hearing loss (NIHL), and its threshold is still unclear. Objective To explore the energy range of kurtosis and the threshold of NIHL induced by kurtosis in this energy rangeMethods Using cross-sectional design, 4631 noise-exposed workers in manufacturing industry were selected. The hearing loss and noise exposure data of each worker were collected. Logistic regression model was used to establish the dose-response relationship between noise exposure and hearing loss and estimate the range of energy effects. Restricted cubic spline model was utilized to analyze the dose-response relationship curves of kurtosis to noise-induced hearing loss (NIHI) and high-frequency noise-induced hearing loss (HFNIHL) under different 8 h equivalent sound levels (L_{EX,8 h}), as well as to estimate the kurtosis effect threshold. Results The logistic regression model analysis showed that the prevalence of NIHI and HFNIHL increased significantly with increase of L_{EX,8 h} for steady noise; when L_{EX,8 h} of non-steady noise was 80-100 dB(A), the risk of hearing loss increased with an increase in kurtosis (P<0.05). The restricted cubic spline model showed that when L_{EX,8 h} of non-steady noise was 0-80 dB(A) or >100 dB(A), there was no significant relationship between kurtosis and NIHI or HFNIHL. When L_{EX,8 h} was 80-100 dB(A), there was a significant nonlinear dose-response relationship between kurtosis and NIHL or HFNIHL (P <0.001), and kurtosis > 28.08 exerted effect in elevating NIHI or HFNIHL. Conclusion The effect threshold of kurtosis on NIHL is 28.08 when non-steady noise levels are in the range of 80-100 dB(A). The effect threshold of kurtosis needs further verification.

Background Temporal kurtosis (without frequency weighting, i.e., Z-weighted kurtosis) can evaluate noise-induced hearing loss (NIHL). However, few studies have considered the function of frequency weighting (A- or C-weighted) kurtosis on NIHL. Objective To study the significance of A- and C-weighted kurtosis adjustment for equivalent sound level (L'_{EX,8 h}) in evaluating occupational hearing loss. Methods A cross-sectional survey was used to select 973 noise-exposed workers in seven industries as the subjects. The noise exposure of all workers was assessed by distributions of A-, C-, and Z-weighted kurtosis (e.g., K_A, K_C, and K_Z) and respective adjusted equivalent sound level (e.g., L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z). The significance of A- and C-weighted kurtosis in evaluating NIHL was evaluated by correlations between three types of L'_{EX,8 h} and NIHL, and improvement of noise-induced permanent threshold shift (NIPTS) underestimation predicted by the ISO prediction model (Acoustics—Estimation of noise-induced hearing loss, ISO 1999-2013). Results The median K_A, K_C, and K_Z were 68.33, 28.22, and 19.82, respectively. The binary logistic regression showed that L_{EX, 8 h}-K_A, L_{EX, 8 h}-K_C, and L'_{EX, 8 h}-K_Z were risk factors for NIHL (OR>1, P<0.001). The receiver operating characteristic (ROC) curve showed that when the outcome variable was noise-induced hearing impairment (NIHI), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.625, 0.628, and 0.625, respectively. When the outcome variable was high-frequency noise-induced hearing loss (HFNIHL), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX, 8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.624, 0.623, and 0.622, respectively (P<0.05). The order of underestimation improvement values predicted by L'_{EX,8 h} for NIPTS₁₂₃₄ was: L'_{EX,8 h}-K_A (4.68 dB HL)>L'_{EX,8 h}-K_C (4.38 dB HL)>L'_{EX,8 h}-K_Z (4.28 dB HL) (P<0.001). The order of underestimation improvement values predicted by L'_{EX,8 h}-K for NIPTS₃₄₆ was: L'_{EX,8 h}-K_A (7.20 dB HL)>L'_{EX,8 h}-K_C (6.83 dB HL)>L'_{EX,8 h}-K_Z (6.71 dB HL) (P<0.001). Conclusion The adjustment of A- and C-weighted kurtosis to equivalent sound level L_{EX,8 h} can effectively improve the accuracy of the ISO 1999 prediction model in NIPTS prediction, and compared with the C-weighted, the A-weighted kurtosis can improve the result of the ISO 1999 prediction model in terms of underestimating NIPTS.

background Current assessment of noise-induced hearing loss relies on the hearing threshold statistical distribution table of ISO 7029-2017 standard (ISO 7029), which is based on foreign population data and lacks a hearing threshold distribution table derived from pure-tone audiometry data of the Chinese population, hindering accurate evaluation of hearing loss in this group. Objective To establish a statistical distribution table of hearing threshold level (HTL) for otologically normal Chinese adults and to provide a scientific basis for revising the diagnostic criteria of occupational noise-induced deafness in China. Methods A total of 1271 otologically normal Chinese adults aged 18-60 years were divided into four groups with 10-year age intervals. Based on the pure tone audiometry data and power function operation formula, the relevant parameters of the median and percentile calculation formula of ISO 7029 were adjusted. Based on the adjusted parameters, a statistical distribution table of HTL of normal Chinese adults in otology was preliminarily constructed according to the calculation formula of ISO 7029. A nonparametric rank sum test was used to compare the difference between the adjusted median deviation value and the ISO 7029 calculation. Results Except for a few frequencies (2000, 3000, and 8000 Hz), the value of parameter α adjusted for the auditory threshold deviation of the Chinese population (α_CHN-md) increased with the increase of frequency, while the value of parameter β adjusted for the auditory threshold deviation of the Chinese population (β_CHN-md) decreased with the increase of frequency. The preliminary distribution table of otologically normal Chinese adults' median hearing threshold deviation (ΔH_CHN-md) showed that the hearing threshold deviation increased with age and frequency. At 8000 Hz, the increase in ΔH_CHN-md with age was the greatest for both men and women, from 0 dB to 23 dB and 21 dB respectively. The minimal threshold elevation was noted at 500, 1000, and 2000 Hz, which increased from 0 dB to 2 dB. In otologically normal Chinese adults, the maximum increase of ΔH_CHN-md with frequency was observed at age of 50 years, which increased from 2 dB at 500 Hz to 23 dB at 8000 Hz in men and from 2 dB at 500 Hz to 21 dB at 8000 Hz in women. The comparison results with the ISO 7029 calculation showed that the trend was consistent. The median hearing threshold deviation value of the adjusted Chinese population was lower than that of the ISO 7029 calculation (ΔH_md). However, they had no significant statistical difference (P>0.05). For otologically normal normal Chinese men and women, the maximum difference of ΔH_md between the 50-year-old group and the ISO calculation at 8000 Hz was 7 dB and 9 dB, respectively. Conclusion This preliminary analysis of hearing threshold deviations in otologically normal Chinese adults suggests that current standards may potentially underestimate hearing loss at frequencies below 8000 Hz while possibly overestimating it at 8000 Hz. These findings require further validation through expanded sample sizes to more accurately assess the characteristics of hearing thresholds in the Chinese population and their discrepancies with existing standards.

T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications. The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP’s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.