Objective To investigate the association between physical activity levels and blood lipids among college freshmen, and to provide scientific evidence for the health management of college freshmen. Methods An electronic questionnaire survey on physical activity was conducted on freshmen of a university, and fasting blood biochemical indicators were detected. The International Physical Activity Questionnaire (IPAQ) short form was used to evaluate the physical activity levels of the participants. Dyslipidemia was defined as an abnormality in any one of the following serum lipid parameters: total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), or non-high-density lipoprotein cholesterol. Binary logistic regression and stratified analyses were employed to explore the relationship between physical activity and blood lipids. Results A total of 3 401 participants were included, with an average age of 18.45 ± 0.92 years, and 60.5% were female. The prevalence of dyslipidemia was 17.7%, with a higher rate among males (22.1%) than females (14.8%). After adjusting for confounding factors related to blood lipids, high-intensity physical activity was negatively associated with the risk of elevated LDL-C among males (OR = 0.36, 95% CI: 0.13–0.99, P = 0.049). Conclusion Among freshmen at a medical college in Hubei Province, high-intensity physical activity is negatively associated with the risk of elevated LDL-C in males, but this association needs to be further confirmed by larger prospective cohort studies.

High myopia(HM)is one of the leading causes of irreversible visual impairment, characterized by pathological changes such as axial elongation and multidimensional abnormalities in fundus biometric parameters. This review systematically summarizes the dynamic characteristics of fundus biometric parameters(including those of the retina, optic disc, macula, and choroid)in HM patients, as visualized via multimodal imaging techniques [e.g., optical coherence tomography(OCT)and optical coherence tomography angiography(OCTA)], and their association with HM progression and related complications. The article further highlights the strategies and advantages of multimodal imaging integration and discusses recent advances and challenges in combining artificial intelligence(AI)with these imaging modalities to automate fundus parameter analysis, lesion detection, risk stratification, and clinical decision-making for HM. This review aims to provide an evidence-based foundation for the early warning, precise intervention, and personalized management of HM, thereby facilitating a clinical paradigm shift from “reactive treatment” to “active health management”.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

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.

Depression,as a severe psychological and psychiatric disorder,significantly impairs the long-term physical and mental health of patients.Current depression detection methods are plagued by strong subjectivity,limited techniques,and inadequate intelligence.Previous studies have mostly relied on single-modal signal analysis,making it difficult to comprehensively reflect the multidimensional characteristics of depression.Based on the independently developed intelligent depression detection system,wearable devices are used to collect prefrontal dual-lead EEG signals,PPG signals,and single-lead ECG signals.Data from 30 patients with depression and 40 healthy controls are collected and analyzed.A multimodal depression recognition model named RBLF-Net is proposed,which integrates spatiotemporal features,weighted attention,and random forests to utilize the multi-signal features for depression recognition.The model exhibits superior performance in the five-fold cross-validation,achieving a classification accuracy of 81.43%,a precision of 81.02%,and a recall rate of 81.25%,outperforming other comparative models,and thus providing an intelligent analysis approach for depression recognition from the perspective of multi-modal fusion.

Objective To observe the value of synthetic MRI(SyMRI)MAGnetic resonance image Compilation(MAGiC)sequence parameters for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma.Methods Sixty-six patients with pathologically confirmed cervical cancer were retrospectively enrolled and divided into cervical squamous cell carcinoma group(n=56)and cervical adenocarcinoma group(n=10).Quantitative MAGiC parameters were collected and compared between groups,and those being significantly different were combined to construct a logistic regression model.The performance of each parameter alone and their combination for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma was evaluated with receiver operating characteristic(ROC)curve and the area under the curve(AUC).Results In cervical adenocarcinoma group,lesions's T1 and T2 were higher,while R1 and R2 were lower than those in cervical squamous cell carcinoma group(all P＜0.05).No statistically significant difference of proton density was found between groups(P＞0.05).The AUC of T1,T2,R1,R2 alone and their combination for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma was 0.959,0.945,0.961,0.942 and 0.996,respectively,and no significant difference was found between each two ones(Z=0.267 to 1.396,all P＞0.05).Conclusion SyMRI had high value for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma.

Objective To observe the value of synthetic MRI(SyMRI)MAGnetic resonance image Compilation(MAGiC)sequence parameters for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma.Methods Sixty-six patients with pathologically confirmed cervical cancer were retrospectively enrolled and divided into cervical squamous cell carcinoma group(n=56)and cervical adenocarcinoma group(n=10).Quantitative MAGiC parameters were collected and compared between groups,and those being significantly different were combined to construct a logistic regression model.The performance of each parameter alone and their combination for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma was evaluated with receiver operating characteristic(ROC)curve and the area under the curve(AUC).Results In cervical adenocarcinoma group,lesions's T1 and T2 were higher,while R1 and R2 were lower than those in cervical squamous cell carcinoma group(all P＜0.05).No statistically significant difference of proton density was found between groups(P＞0.05).The AUC of T1,T2,R1,R2 alone and their combination for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma was 0.959,0.945,0.961,0.942 and 0.996,respectively,and no significant difference was found between each two ones(Z=0.267 to 1.396,all P＞0.05).Conclusion SyMRI had high value for differentiating cervical squamous cell carcinoma and cervical adenocarcinoma.

Objective To investigate the biological function of the proprotein convertase subtilisin/kexin type 9(PCSK9)in prostate cancer(PCa)and its effect on ferroptosis sensitivity Methods Bioinformatics was used to analyze the relationship between the expression of PCSK9 and the prognosis of prostate cancer.The expression of PCSK9 in PCa cell lines were detected using RT-qPCR.PCa cells with PCSK9 knockdown were constructed using siRNA,and the The effect of PCSK9 on cell proliferation,migration,and invasion were detected using CCK-8 assays and Transwell assays.The Cancer Therapeutics Response Portal(CTRP)was employed to investigate the correlation between PCSK9 and ferroptosis drug sensitivity,and PCa cells with PCSK9 knockdown were treated with the ferroptosis inducer(RSL3)to detect the sensitivity to ferroptosis.Results Bioinformatics showed low expression of PCSK9 had longer disease specific survival(P<0.05).The results of the in vitro experiments showed that PCSK9 knockdown significantly inhibited the proliferation,migration,and invasion of PCa cells(P<0.001).Furthermore,CTRP analysis showed that cellular sensitivity to ferroptosis inducers correlated with the expression level of PCSK9.PCSK9 knockdown cells exhibited higher sensitivity to the ferroptosis inducer RSL3.Conclusion Knockdown of PCSK9 inhibits the proliferation,migration,and invasion of PCa cells,and increases the sensitivity of cells to ferroptosis inducers.PCSK9 may provide new insights for the treatment of PCa.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

Objective To explore the impact of suture configuration and fixation type on the biome-chanical strength of rotator cuff repair,using a factorial design study.Methods Sixteen fresh-frozen porcine shoulder samples were randomized into an anchorless double-row suture bridge transosseous su-tures(DS)group,an anchored double-row suture bridge transosseous-equivalent(DE)group,an an-chorless X-BOX construct transosseous sutures(XS)group,and an anchored X-BOX construct transos-seous-equivalent(XE)group,each of four,according to suture configuration(double-row suture bridge,traditional X-BOX construct)and fixation type(suture anchors,transosseous sutures).Then,their fatigue resistance(first-cycle excursion,gap length difference ratio,and the percentage of ex-posed footprints)and the failure strength(the maximum failure load and the re-tear type)were mea-sured using a biomechanical material testing machine.Results Different suture configurations affected failure strength(F=39.559,P<0.001),with the double-row suture bridge groups(693.07±58.35 N,746.76±138.57 N)showing significantly higher failure strength,compared to the traditional X-BOX groups(462.90±18.91 N,421.43±90.76 N).However,the fixation type did not significantly im-pact failure strength(F=1.161,P=0.302).Moreover,the suture configuration influenced the gap differ-ence ratio(F=7.781,P=0.016),but had no significant correlation with other fatigue resistance indica-tors(P>0.05).Meanwhile,failure strength and fatigue resistance were not correlated with fixation type,and the interaction between suture and fixation type(P>0.05).The incidence of failure types for the four suture configurations was as follows:Type I tendon tear:XS>XE>DS=DE;type II tendon tear:DS>XE>XS=DE;fixing material-related failure:DE>DS=XE=XS.Conclusion The failure strength and gap formation ratio in rotator cuff repair under fatigue loading are influenced by suture configuration,whereas no significant association has been observed with respect to fixation method,whether using transosseous sutures or suture anchors.