ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Background Fatigue among distribution network live-line workers in complex operational environments has become increasingly severe and requires widespread attention. Objective To investigate the positive rates of fatigue and associated influencing factors of live-line power distribution workers, and to make a reasonable strategy to reduce the fatigue of front-line workers. Methods Power supply companies in Guangdong, Guangxi, and Yunnan provinces were selected by cluster sampling in 2023, and all front-line live-line workers in the selected companies were recruited. The questionnaire used in this study consisted of two parts: one was the Fatigue Scale-14 (FS-14) for investigating fatigue status and the other was for associated influencing factors. A FS-14 score greater than 3 points was defined as fatigue. \begin{document}$ {\chi }^{2} $\end{document} test and logistic regression were employed to analyze the factors affecting fatigue. Results A total of 1341 questionnaires were distributed, and 1217 valid questionnaires were recovered, with a valid response rate of 90.75%. The average fatigue score of the participants was (6.84±3.79) points, and a total of 1054 participants scored over 3, resulting a fatigue positive rate of 86.6%. The logistic regression model showed that working under extreme weather (OR=2.03, 95%CI: 1.38, 2.98), overtime 2-3 times a week (OR=5.24, 95%CI: 1.28, 21.40), overtime everyday (OR=6.95, 95%CI: 1.71, 28.27), frequent wrist rotation (OR=1.95, 95%CI: 1.08, 3.51), and leaning back slightly (OR=2.94, 95%CI: 1.19, 7.28) associated with higher positive rates of fatigue. Conclusion The high positive rate of fatigue among live-line power distribution workers suggests severe fatigue issues. Extreme weather conditions, working overtime frequency, and maintaining wrist rotation and backward bending postures during work are main factors affecting their fatigue.

Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

AIM:To evaluate visual outcomes and visual function in nonamblyopic adults with myopic anisometropia treated with small incision lenticule extraction(SMILE).METHODS:Prospective comparative cohort study. The consecutive patients who underwent SMILE for the treatment of myopia or myopic astigmatism at Zhongshan Ophthalmic Center(Guangzhou, China)between October 2015 and January 2016 were included. They were divided into two groups based on the bilateral difference of a spherical equivalent(SE)refraction ≥1.50 D: the anisometropic myopia group(interocular SE difference ≥1.50 D)and non-anisometropic myopia group(interocular SE difference<1.50 D). Refractive status, uncorrected and corrected distance visual acuity(UDVA and CDVA), and visual function parameters including fusional vergence amplitude, stereoacuity and horizontal phoria were measured preoperatively and at 1 wk,1,3 and 6 mo after surgery.RESULTS:A total of 49 cases(98 eyes)were included in the study, and 19 cases(38 eyes)in the anisometropic group, including 11 males and 8 females, with a mean age of 25.4±6.2 y, and 30 cases(60 eyes)in the non-anisometropic myopia group, including 19 males and 11 females, with a mean age of 26.8±4.6 y. The CDVA of the non-anisometropia group was significantly better than that of the anisometropia group 6 mo postoperatively(P=0.036). However, the safety and efficacy indexes of the two groups did not show significant differences. The fusional vergence(break point and recovery point)of the anisometropia group decreased(P=0.005 and P=0.03)and was significantly lower than that in the non-anisometropia group at 6 mo post operatively(P=0.029 and P=0.046). Both groups showed a significant improvement in distance and near stereopsis at 1, 3 and 6 mo in comparison with the preoperative baseline and 1 wk postoperatively(all P<0.05). No clinically significant change in the amount of ocular alignment in terms of distance and near deviation postoperatively in either groups.CONCLUSION: SMILE is a predictable, effective, and safe method for correcting myopic anisometropia in adults without amblyopia. Although the fusional vergence amplitudes changed, stereopsis can be improved after surgery.

Radiomics enables the extraction of high-throughput quantitative features from ophthalmic images, allowing the identification of subvisual information that is imperceptible to the human eye and offering a novel strategy for the precise diagnosis and treatment of retinal diseases. By quantitatively characterizing subtle differences in retinal structure, texture, and hemodynamic characteristics, and integrating these features with clinical data, radiomics has demonstrated substantial potential in early screening, disease stratification, prediction of treatment responses, and individualized risk assessment of retinal diseases, particularly in common conditions such as diabetic retinopathy and age-related macular degeneration. Despite these promising advances, the clinical translation of radiomics remains challenging. Current limitations include suboptimal model performance and generalizability,as well as insufficient clinical interpretability of radiomic feature and predictive models, which hampers their integration into existing imaging systems and routine clinical workflows. Based on a systematic analysis of relevant articles published over the past five years, this paper summarizes recent progress in the application of radiomics combined with machine learning for the diagnosis and prognostic assessment of retinal diseases, and to discuss the key challenges and future directions for its clinical implementation.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Objective To explore the adverse event signals of children using macrolide drugs （azithromycin, clarithromycin, and erythromycin）, and provide reference for rational medicine use in clinical practice. Methods Data from children under 12 years old were extracted from the US FAERS database spanning from the first quarter of 2004 to the second quarter of 2023. The adverse drug reaction （ADR） signal mining for three macrolide antibiotics was conducted using the Reporting Odds Ratio （ROR） and Bayesian Confidence Propagation Neural Network （BCPNN） methods. Special emphasis was placed on analyzing and contrasting the differences in adverse events among the three drugs. Results A total of 1 615 reports for children under 12 years old were retrieved from the FAERS database, including 1 024 reports of azithromycin, 460 reports of clarithromycin, and 131 reports of erythromycin. Among azithromycin and erythromycin, there were more reports from boys than girls, while for clarithromycin, there were more reports from girls than boys. Oral administration was the most common route of administration for all three drugs. Regarding the outcome of adverse events reported, azithromycin and clarithromycin were primarily associated with other serious adverse events, whereas erythromycin was mainly associated with hospitalization and other serious adverse events. The number of adverse events reported decreased with increasing age, with a higher number of reports in the 0-3 age group. Using the ROR and BCPNN methods for signal detection, 86 signals were identified for azithromycin, 91 for clarithromycin, and 34 for erythromycin. These signals involved 22 System Organ Classes （SOCs）, with azithromycin mainly concentrated in skin and subcutaneous tissue disorders （n=21）, clarithromycin in gastrointestinal disorders （n=15）, and erythromycin in gastrointestinal disorders （n=8）. Twenty-four signals of moderate to high risk were detected, with 13 for azithromycin, 9 for clarithromycin, and 2 for erythromycin. Conclusion The adverse events induced by the three drugs with different risks in different systems. When clinically treating Mycoplasma pneumoniae pneumonia in children, the risk profiles of drugs in different systems should be considered, and personalized dosing should be implemented.

Infective endocarditis (IE) is a life-threatening infectious disease. Although histopathological examination remains the gold standard for definitive diagnosis, it is performed in only a minority of patients; thus, the diagnosis of IE continues to rely predominantly on clinical manifestations. However, IE presents with highly variable systemic symptoms that often lack cardiac-specific features, posing significant diagnostic challenges. To address this complexity, multiple diagnostic criteria have been developed, integrating clinical presentation, imaging findings, and microbiological evidence. Over the past three decades, substantial shifts in the microbiological and epidemiological profiles of IE, coupled with advances in imaging modalities and laboratory diagnostics, have profoundly influenced diagnostic approaches, driving continuous refinement of diagnostic criteria. This review provides a comprehensive overview of the major diagnostic criteria for IE, traces their historical evolution, evaluates recent updates and diagnostic performance, and offers theoretical insights and practical implications to guide future research.

Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

Recent research from the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) indicates that breast cancer is the most prevalent malignant tumor among women, posing a significant threat to women's health. Surgery remains the primary therapeutic modality for breast cancer. Recently, endoscopic and robotic breast surgical techniques have gained acceptance among both surgeons and patients. However, considerable variation exists in surgical approaches and outcomes. To standardize these techniques, facilitate their broader clinical adoption, and ultimately improve patient care, the Endoscopic-robotic Breast Surgery Clinical Trials Consortium (ErBSCTC) of China has developed this guideline. This document encompasses the technologies and instrumentation utilized in endoscopic and robotic breast surgery, surgical techniques, perioperative management, complication handling, long-term follow-up, and oncologic outcomes, aiming to provide evidence-based guidance for healthcare professionals involved in the prevention, diagnosis, and treatment of breast diseases.