Objective: To analyze the epidemiological characteristics of pulmonary tuberculosis (PTB) among college students in Yangzhou from 2020 to 2024, so as to provide a scientific basis for developing prevention and control strategies. Methods: An epidemiological investigation was conducted among 162 college students with PTB, and 7 134 of their contacts were screened. Data were obtained from the tuberculosis information management system and on campus screening records. Using descriptive epidemiological methods, trends in incidence, seasonal distribution, and bacteriological characteristics were analyzed. Results: From 2020 to 2024, the annual average incidence of pulmonary tuberculosis among college students in Yangzhou was 29.42 per 100 000, showing an overall fluctuating downward trend ( χ 2=12.36, P <0.01). Cases were mainly concentrated in summer and autumn, with the highest proportion in autumn (41.36%, 67/162), followed by summer (23.46%, 38/162). The proportion of etiologically positive cases increased from 37.21% in 2020 to 71.43% in 2024; among positive cases, the proportion of latent tuberculosis infection (LTBI) decreased from 66.67% (10/15) to 26.67% (4/15). The etiological positive rate was higher in females than in males ( χ 2= 11.76 , P <0.01). Comparison of screening methods showed that among index cases, the LTBI detection rate of the recombinant Mycobacterium tuberculosis fusion protein skin test (C-TST) was higher than that of the tuberculin skin test (TST), but the difference was not statistically significant ( χ 2=0.65, P =0.42); among close contacts, the detection rate of TST was higher than that of C-TST (15.1%,10.1%; χ 2=5.23, P <0.05). Conclusion From 2020 to 2024, the annual average incidence of pulmonary tuberculosis among college students in Yangzhou showed an overall fluctuating downward trend, with differences in TB infection screening methods and gender.

Objective To introduce an innovative technique, the "balance-shaped sternal elevation device" and its application in the subxiphoid uniportal video-assisted thoracoscopic surgery (VATS) for anterior mediastinal masses resection. Methods Patients who underwent single-port thoracoscopic assisted anterior mediastinal tumor resection through the xiphoid process at the Department of Thoracic Surgery, West China Hospital, Sichuan University from May to June 2024 were included, and their clinical data were analyzed. Results A total of 7 patients were included, with 3 males and 4 females, aged 28-72 years. The diameter of the tumor was 1.9-17.0 cm. The operation time was 62-308 min, intraoperative blood loss was 5-100 mL, postoperative chest drainage tube retention time was 0-9 days, pain score on the 7th day after surgery was 0-2 points, and postoperative hospital stay was 3-12 days. All patients underwent successful and complete resection of the masses and thymus, with favorable postoperative recovery. Conclusion The "balance-shaped sternal elevation device" effectively expands the retrosternal space, providing surgeons with satisfactory surgical views and operating space. This technique significantly enhances the efficacy and safety of minimally invasive surgery for anterior mediastinal masses, reduces trauma and postoperative pain, and accelerates patient recovery, demonstrating important clinical significance and application value.

OBJECTIVE To establish the fingerprint of Gerbera delavayi and the methods for the content determination of 11 components in G. delavayi. METHODS High-performance liquid chromatography（HPLC）was adopted to establish the fingerprints of 13 batches of G. delavayi（No. S1-S13）， and the similarities were evaluated according to Similarity Evaluation System of Chromatographic Fingerprint of TCM （2012 edition）， while the common peaks were identified. Hierarchical clustering analysis （HCA）， principal component analysis （PCA） and orthogonal partial least square-discriminant analysis （OPLS-DA） were carried out by using SPSS 25.0 software and SIMCA 14.1 software. The contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， 3，8-dihydroxy-4-methoxy-2-oxo-2H-1-benzopyran-5-carboxylic acid， caffeic acid， 3-hydroxy-4-methoxy-2- oxo-2H-1-benzopyran- 5-carboxylic acid， luteolin-7-O-β-D-glucoside， isochlorogenic acid A， apigenin-7-O-β-D-glucoside， isochlorogenic acid C and xanthotoxin were determined by HPLC. RESULTS The similarities in HPLC fingerprint of 13 batches of G. delavayi were 0.801-0.994； a total of 38 common peaks were identified and 13 common peaks were identified. The results of HCA showed that S1-S5 and S7 were clustered into one group， S6 into one category， S8 into one category， S9 and S11 into one category， S10， S12 and S13 into one category， and the results of PCA were consistent with them. The results of OPLS-DA showed that variable importance values for the projection of peak 7 （chlorogenic acid）， peak 21 （isochlorogenic acid A）， peak 26 （xanthotoxin）， peak 19 （isochlorogenic acid B）， peak 33， peak 13， peak 23 （isochlorogenic acid C）， peak 2 （new chlorogenic acid）， peak 17 （luteolin-7-O-β-D- glucoside） were greater than 1. The above 11 components had good linearity in their respective detection concentration ranges （r was greater than 0.999）. RSDs of precision， repeatability， and stability tests were not more than 2% （n＝6）. The average recovery rates were 92.54%-105.55%， and the RSDs were 0.83%-1.93% （n＝6）. The average contents of 11 components were 0.744， 5.014， 0.646， 0.431， 0.069， 0.582， 0.979， 2.754， 0.157， 1.284 and 2.943 mg/g， respectively. CONCLUSIONS The constructed HPLC fingerprint and content determination methods are simple， accurate and stable， which can provide reference for quality control of G. delavayi. Xanthotoxin， chlorogenic acid， isochlorogenic acid A， luteolin-7-O- β -D-glucoside， isochlorogenic acid C and new chlorogenic acid can be used as markers for G. delavayi.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

OBJECTIVE To provide standardized whole-process guidance on drug traceability codes for medical institutions in Sichuan province， ensuring medication safety and compliance with medical insurance supervision requirements. METHODS Based on evidence-based principles and expert consensus， Expert Consensus on Whole-process Management of Drug Traceability Codes in Medical Institutions of Sichuan Province （hereinafter referred to as the Consensus） was formulated through systematic literature review， field investigations， establishment of a multidisciplinary expert committee and multiple rounds of questionnare consultation via the modified Delphi method， and finalized through consensus meetings. RESULTS & CONCLUSIONS The Consensus clarifies key operating procedures for code verification， code assignment and code return， whole-process operational standards for drug warehouse acceptance and storage， drug warehouse outbound delivery and pharmacy acceptance check， drug distribution and dispensing in pharmacy and intravenous admixture center， medication administration in nursing units and examination departments， as well as drug return process. Key recommendations are proposed such as improving the core functions of the drug traceability system， unifying the hospital-wide traceability code database， strengthening the management of traceability codes for backup medications， establishing a management organization and institutional framework， and optimizing the architectural design and data governance requirements of the drug traceability system. The release of the Consensus will provide scientific， standardized and implementable practical guidelines for medical institutions of Sichuan province， helping to improve closed-loop management of the drug traceability system， strengthen medication safety and fulfil medical insurance fund supervision.

Objective: To explore the association between negative life events and smartphone addiction among middle school students, so as to provide theoretical support and practical guidance for prevention and intervention of smartphone addiction among middle school students. Methods: Using cluster sampling, 8 890 students were selected to survey from 27 junior high schools and 3 senior high schools in a district of Shenzhen in 2022 (baseline) and 2023 (followup). Data were collected through selfresigned questionnaires on basic information, the Smartphone Addiction Scale-Short Version, and the Adolescent Selfrating Life Events Checklist. Mixedeffects models were employed to analyze the association. Results: Compared to 2022, the punishment scores of middle school students in 2023 [1.00 (0.00, 6.00) and 1.00 (0.00, 6.00)] decreased (Z=4.27), while the scores of interpersonal stress, learning stress and adaptation [4.00(0.00, 8.00), 4.00(0.00, 8.00); 4.00(1.00, 8.00), 5.00(2.00, 9.00); 2.00 (0.00, 6.00), 3.00 (0.00, 7.00)] increased (Z=-3.04, -8.36, -6.80) (P<0.01). Mixedeffects models revealed a positive doseresponse relationship between negative life events and smartphone addiction (OR=1.08-1.17, P<0.01). Stepwise regression showed independent positive effects of interpersonal stress (OR=1.05), academic stress (OR=1.03), and adaptation stress (OR=1.11) on smartphone addiction (P<0.01). Subgroup analysis of nonaddicted students in 2022 confirmed persistent associations for academic stress (OR=1.03) and adaptation (OR=1.07) (P<0.01). Conclusion Negative life events exhibit a positive doseresponse relationship with smartphone addiction, particularly interpersonal stress, academic stress, and adaptationrelated events.

Objective: To explore the potential causal association between adolescent compulsive behaviour and smartphone addiction based on longitudinal data, so as to provide reference for the establishment of adolescent smartphone addiction interventions. Methods: A preliminary survey and follow-up were conducted on 8 907 middle and high school students in a district of Shenzhen in 2022 and 2023, respectively. Compulsive behaviours were measured by using the Mental Health Inventory for Middle School Students-60 Items (MMHI-60), smartphone addiction was assessed by using the Smartphone Addiction Scale-Short Version ( SAS- SV), and the associations between compulsive behaviours and smartphone addiction were analysed by using multilevel mixed-effects models and subgroup analyses. Results: Smartphone addiction detection rates among middle school students were significantly associated with genders, father s education level, mother s education level, study load subgroups, and whether or not they were single-parent families, and there were statistical differences ( χ 2=17.21-175.34, P <0.05). Students with compulsive behaviours were 2.98 times more likely to develop smartphone addiction than those without compulsive behaviours ( OR=2.98, 95%CI=2.77-3.22, P <0.05). Subgroup analysis of middle school students without smartphone addiction in the first year found that compulsive behaviours significantly predicted smartphone addiction ( OR= 1.76 , 95%CI=1.54-2.01, P <0.05). Conclusion There is a potential causal association between obsessive-compulsive behaviours and smartphone addiction in middle school students, and obsessive-compulsive behaviours in middle school students could significantly predicted the occurrence of smartphone addiction.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.