Objective: To explore the relationship between social jetlag and depressive symptoms in junior high school students, as well as the potential gender differences, so as to provide a reference for developing effective interventions for depressive symptoms and promoting adolescents mental health. Methods: In October 2024, a total of 3 516 students from grades 7 to 9 were recruited from 4 junior high schools in Chongqing Municipality using a combination of cluster sampling and convenience sampling. A questionnaire survey was conducted using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Munich Chronotype Questionnaire (MCTQ). Statistical analyses included the χ 2 test, binary Logistic regression analysis, and stratified Logistic regression analysis. Results: The detection rate of depressive symptoms among the junior high school students was 34.3%. The number of students with social jetlag >2 h was 714 (20.3%), >1-2 h was 1 455(41.4%), and ≤1 h was 1 347(38.3%). Results from the binary Logistic regression analysis showed that compared to the group with social jetlag ≤1 h, the risk of depressive symptoms in the group with social jetlag >2 h was higher ( OR=1.59, 95%CI=1.28-1.98, P <0.01). Gender stratified analysis revealed that among females, the risk of depressive symptoms was higher in the groups with social jetlag of >1-2 h and >2 h compared to the ≤1 h group ( OR = 1.34 and 2.05, 95% CI =1.03-1.75 and 1.48-2.83, both P <0.05). However, among males, the associations were not statistically significant ( OR =1.11 and 1.29, 95% CI =0.86-1.43 and 0.95-1.77, both P >0.05). Conclusions Social jetlag is positively associated with depressive symptoms in junior high school students, demonstrating a threshold effect and gender differences. The findings suggest that reducing social jetlag may decrease the risk of depressive symptoms in adolescents, and targeted intervention measures should be developed considering different gender characteristics.

The European Society of Cardiology (ESC) released the "2024 ESC guidelines for the management of elevated blood pressure and hypertension" on August 30, 2024. This guideline updates the 2018 "Guidelines for the management of arterial hypertension." One notable update is the introduction of the concept of "elevated blood pressure" (120-139/70-89 mm Hg). Additionally, a new systolic blood pressure target range of 120-129 mm Hg has been proposed for most patients receiving antihypertensive treatment. The guideline also includes numerous additions or revisions in areas such as non-pharmacological interventions and device-based treatments for hypertension. This article interprets the guideline's recommendations on definition and classification of elevated blood pressure and hypertension, and cardiovascular disease risk assessment, diagnosing hypertension and investigating underlying causes, preventing and treating elevated blood pressure and hypertension. We provide a comparison interpretation with the 2018 "Guidelines for the management of arterial hypertension" and the "2017 ACC/AHA guideline on the prevention, detection, evaluation, and management of high blood pressure in adults."

Triple-negative breast cancer (TNBC) represents a distinctive subtype, characterized by the absence of estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2 (HER2). Due to its high inter-tumor and intra-tumor heterogeneity, TNBC poses significant chanllenges for personalized diagnosis and treatment. The advant of clustered regular interspaced short palindromic repeats (CRISPR) technology has profoundly enhanced our understanding of the structure and function of the TNBC genome, providing a powerful tool for investigating the occurrence and development of diseases. This review focuses on the application of CRISPR/Cas technology in the personalized diagnosis and treatment of TNBC. We begin by discussing the unique attributes of TNBC and the limitations of current diagnostic and treatment approaches: conventional diagnostic methods provide limited insights into TNBC, while traditional chemotherapy drugs are often associated with low efficacy and severe side effects. The CRISPR/Cas system, which activates Cas enzymes through complementary guide RNAs (gRNAs) to selectively degrade specific nucleic acids, has emerged as a robust tool for TNBC research. This technology enables precise gene editing, allowing for a deeper understanding of TNBC heterogeneity by marking and tracking diverse cell clones. Additionally, CRISPR facilitates high-throughput screening to promptly identify genes involved in TNBC growth, metastasis, and drug resistance, thus revealing new therapeutic targets and strategies. In TNBC diagnostics, CRISPR/Cas was applied to develop molecular diagnostic systems based on Cas9, Cas12, and Cas13, each employing distinct detection principles. These systems can sensitively and specifically detect a variety of TNBC biomarkers, including cell-specific DNA/RNA and circulating tumor DNA (ctDNA). In the realm of precision therapy, CRISPR/Cas has been utilized to identify key genes implicated in TNBC progression and treatment resistance. CRISPR-based screening has uncovered potential therapeutic targets, while its gene-editing capabilities have facilitated the development of combination therapies with traditional chemotherapy drugs, enhancing their efficacy. Despite its promise, the clinical translation of CRISPR/Cas technology remains in its early stages. Several clinical trials are underway to assess its safety and efficacy in the treatment of various genetic diseases and cancers. Challenges such as off-target effects, editing efficiency, and delivery methods remain to be addressed. The integration of CRISPR/Cas with other technologies, such as 3D cell culture systems, human induced pluripotent stem cells (hiPSCs), and artificial intelligence (AI), is expected to further advance precision medicine for TNBC. These technological convergences can offer deeper insights into disease mechanisms and facilitate the development of personalized treatment strategies. In conclusion, the CRISPR/Cas system holds immense potential in the precise diagnosis and treatment of TNBC. As the technology progresses and becomes more costs-effective, its clinical relevance will grow, and the translation of CRISPR/Cas system data into clinical applications will pave the way for optimal diagnosis and treatment strategies for TNBC patients. However, technical hurdles and ethical considerations require ongoing research and regulation to ensure safety and efficacy.

Objective:To investigate the effects of body habitus and gender on radiation dose assessment methodologies in low-dose chest CT, with particular emphasis on clarifying discrepancies among various dose quantification approaches and their associations with patient characteristics.Methods:Imaging data from 19 371 patients who underwent low-dose chest CT at the First Affiliated Hospital of Chongqing Medical University between January 2021 and January 2024 were retrospectively analyzed. Patients were categorized into eight groups based on water-equivalent diameter (WED) and gender: Group A (150 mm≤WED<210 mm; 71 males, 1 032 females), Group B (210 mm≤WED<260 mm; 4 525 males, 8 005 females), Group C (260 mm≤WED<300 mm; 4 234 males, 1 105 females), and Group D (WED≥300 mm; 357 males, 42 females). WED, size-specific dose estimate (SSDE), and organ dose-based effective dose(ED Radimetrics)were calculated using Radimetrics software. Scanner-reported dose metrics, including volume CT dose index (CTDIvol), dose-length product (DLP), and DLP-derived effective dose(ED DLP), were recorded. The ratios of SSDE/CTDIvol and ED Radimetrics/ED DLP were used to quantify discrepancies between dose evaluation methods. The Kruskal-Wallis test was employed to analyze dose metric differences across WED groups within the same gender, while the Wilcoxon rank-sum test compared gender-based differences within each WED group. Results:All dose metrics significantly increased with WED for both genders (all P<0.05). Within the same WED group, ED Radimetrics was significantly higher in females ( P<0.05), whereas ED DLP was higher in males ( P<0.05). The SSDE/CTDIvol ratio decreased with increasing WED, declining from 1.74 in Group A to 1.16 in Group D for females and from 1.68 to 1.12 for males. The ED Radimetrics/ED DLP ratio exhibited a decreasing trend with WED in females (1.82 to 1.30) but showed an initial increase in males (1.29 in Group A to 1.31 in Group B) before decreasing to 0.94 in Group D (all intergroup P<0.05). SSDE/CTDIvol and ED Radimetrics/ED DLP ratios of females were consistently higher than that of males within each WED group (all P<0.05). Conclusions:Patient body habitus and gender significantly influence radiation dose distribution in low-dose chest CT. Larger body habitus is associated with higher radiation doses, while females receive greater ED Radimetrics than males within comparable body habitus. Traditional dose metrics (CTDIvol and ED DLP) were underestimated for patients with small body sizes and female individuals.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the factors influencing left atrial fibrosis in patients with atrial fibrillation(AF)and the association of Periostin protein,serum transforming growth factor-β2(TGF-β2)with left atrial fibrosis.Methods:We enrolled 100 AF patients admitted to Gansu Provincial People's Hospital between March 2021 and March 2023.They were divided into control group(＜10％,n=53)and fibrosis group(≥10％,n=47)according to their left atrial low voltage region.Univariate and multivariate Logistic regression were used to analyze the influ-encing factors of left atrial fibrosis in AF patients and construct a nomogram model.The diagnostic value of related factors and their combined detection for left atrial fibrosis in AF patients were analyzed by receiver operating char-acteristic curve(ROC).Spearman correlation analysis was used to analyze the association of Periostin protein,TGF-β2 with left atrial fibrosis in AF patients.Results:Compared to patients in the control group,those in the fibrosis group had significant higher left atrial diameter(LAD)[(37.08±3.19)mm vs.(33.45±2.45)mm],levels of ser-um uric acid(SUA)[(313.75±49.06)μmol/L vs.(279.88±38.15)μmol/L],Periostin protein[(83.27±3.98)ng/L vs.(75.21±3.04)ng/L],TGF-β2[(4346.84±321.34)ng/L vs.(4186.02±306.91)ng/L],and signifi-cant lower left atrial ejection fraction(LVEF)[(62.28±5.00)％vs.(67.24±3.07)％](P＜0.05 or＜0.01).Multivariate Logistic regression analysis showed that LAD(OR=1.663,95％CI 1.238～3.887,P=0.001),SUA(OR=1.586,95％CI 1.164～2.892,P＜0.001),Periostin protein(OR=1.997,95％CI 1.513～4.585,P=0.001),TGF-β2(OR=2.013,95％CI 1.543～5.864,P＜0.001)were independent risk factors for left atrial fi-brosis in AF patients,while LVEF was an independent protective factor(OR=0.524,95％CI 0.141～0.920,P=0.002).The nomogram model for left atrial fibrosis in AF patients:logit(P)=4.631+0.445 × LVEF+0.546 × LAD+0.575 × SUA+0.530 × Periostin protein+0.347 × TGF-β2.ROC curve showed that the area under the curve(AUC)of combined detection(0.893,95％CI 0.842～0.932)was significantly higher than SUA(AUC=0.637,95％CI 0.566～0.704),LVEF(AUC=0.701,95％CI 0.632～0.763),LAD(AUC=0.649,95％CI 0.579～0.715),Periostin protein(AUC=0.676,95％CI 0.606～0.740),TGF-β2(AUC=0.641,95％CI 0.570～0.707)alone(Z=5.265,6.399,6.379,6.040,6.483,P＜0.001 all).Spearman correlation analysis showed that Perios-tin protein and TGF-β2 were significantly positive correlated with left atrial fibrosis in AF patients(r=0.536,0.578,P＜0.001 all).Conclusion:Periostin protein and TGF-β2 were independent risk factors for left atrial fi-brosis in AF patients and were significantly positive correlated with it,a combination of above-mentioned indexes,cardiac function indexes and uric acid had good diagnostic value for left atrial fibrosis.

AIM:To investigate the effects of the Qingfei-Jiedu-Huatan formula(QJHF)on damage to the lung vascular endothelial barrier induced by Klebsiella pneumoniae in mice with severe pneumonia,as well as to elucidate its underlying mechanisms.METHODS:Fifty-one C57BL/6J mice were randomly divided into control group(n=6),model group(n=15),QJHF group(n=15),and ceftriaxone sodium(CRO)group(n=15).Severe pneumonia was in-duced in the mice by a single tracheal intubation with 50 μL of 1×1011 CFU/mL Klebsiella pneumoniae on day 0.Six hours after modeling,the mice in QJHF and CRO groups received their respective treatments,while those in control and model groups were administered an equal volume of saline.All mice were sacrificed on day 3 after the end of gavage.Lung histo-pathological changes were assessed using hematoxylin-eosin(HE)staining.Levels of tumor necrosis factor-α(TNF-α),interleukin-1β(IL-1β),and IL-6 in lung tissues were measured by enzyme-linked immunosorbent assay(ELISA).Flow cytometry was used to detect CD11b+Ly6g+cells in bronchoalveolar lavage fluid(BALF).Proteomics and network pharma-cology analyses were conducted to elucidate the mechanisms of drug action.Western blot was conducted to assess the ex-pression levels of vascular endothelial cadherin(VE-cadherin),zonula occludens-1(ZO-1),occludin,vascular endothe-lial growth factor(VEGF),P38 mitogen-activated protein kinase(P38),and phosphorylated P38(p-P38)in lung tis-sues.RESULTS:Treatment with QJHF significantly attenuated the symptoms such as mental status and respiratory dis-tress,reduced mortality,mitigated lung tissue lesions,and decreased levels of IL-6,TNF-α,IL-1β,as well as BALF to-tal protein concentration,total cell count and neutrophil content in a mouse model of severe pneumonia(P＜0.05 or P＜0.01).Additionally,QJHF increased the expression of VE-cadherin,ZO-1 and occludin proteins in lung tissues.Pro-teomic analysis demonstrated that QJHF modulated the expression of 129 proteins in the lung tissues of mice suffering from severe pneumonia.Network pharmacology identified 328 potential targets associated with 14 major bioactive components of QJHF and 1 665 genes related to severe pneumonia,with 125 overlapping genes between the two datasets.The construc-tion of a protein-protein interaction(PPI)network,along with Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses of the regulated proteins and overlapping genes,indicated that QJHF primarily in-fluenced the PI3K-Akt,MAPK and Rap1 signaling pathways,as well as VEGFR.Western blot analysis showed that QJHF significantly inhibited the expression of VEGF and P38 in lung tissues(P＜0.05 or P＜0.01).CONCLUSION:Treatment with QJHF attenuates severe pneumonia in mice,potentially by inhibiting VEGF/P38 signaling to protect the vascular endothelial barrier.

Gastric cancer remains one of the most prevalent and lethal malignancies worldwide,characterized by an insidious onset,challenges in early detection,and a poor prognosis in advanced stages.Conventional diagnostic approaches are often constrained by subjective interpretation and inherent limitations in accuracy and efficiency,rendering them insufficient to meet the demands of precision medicine.In recent years,the rapid advancement of artificial intelligence(AI),particularly deep learning(DL)-based techniques,has opened new avenues for the precise diagnosis and management of gastric cancer.Emerging evidence suggests that AI-assisted endoscopic systems significantly enhance lesion detection rates and diagnostic efficiency,while AI-driven radiomics models offer precise predictions of tumor invasion depth,lymph node involvement,and peritoneal metastasis.Additionally,AI-powered pathology analysis has markedly improved both diagnostic accuracy and efficiency.Moreover,integrative AI models leveraging multi-omics data have demonstrated great potential in predicting responses to chemotherapy and targeted therapies,as well as facilitating personalized prognostic assessments.However,despite these promising advancements,the clinical implementation of AI in gastric cancer remains hindered by challenges such as the lack of standardized datasets,limited model generalizability,and insufficient algorithm interpretability.This review systematically synthesized the latest advancements in AI applications for gastric cancer diagnosis,treatment response evaluation,and prognostic prediction.Furthermore,it critically examined key technical challenges in current AI methodologies and explored future directions in AI-driven precision medicine for gastric cancer.By addressing these challenges,we aimed to foster the widespread adoption and clinical translation of AI technologies,ultimately advancing precision oncology and improving patient outcomes.

Objective To explore the changes of durability properties of reusable surgical gowns when used in dry and wet conditions.Methods Reusable surgical gowns made of single-layer polyester fiber or 3-layer composite material were selected as test samples,and a Martindale abrasion and pilling tester was used as the basic test platform and modified to form fixtures suitable for the wet state environment.The reusable surgical gowns underwent abrasion experiments in wet and dry conditions to observe the changes in their fiber structure,and were subjected to water penetration resistance and swelling strength tests.Results Visually the reusable surgical gowns had few changes of the microscopic textile fiber structure in dry and wet conditions,and the gowns made of single-layer polyster fiber gained advantages over the outer layers of those of 3-layer composite material in abrasion resistance with the same friction cycles.In dry and wet conditions,the hydrostatic pressure values of the gowns of single-layer polyster fiber gradually decreased with the increase of the degree of abrasion,which were always lower than those of the gowns of 3-layer composite material;the swelling strength of the gowns of single-layer polyster fiber was always greater than that of the gowns of 3-layer composite material,which decreased with the deterioration of the wear more significantly than that of the gowns of 3-layer composite material.Conclusion The reusable surgical gowns made of single-layer polyester fiber or 3-layer composite material have few differences in durability and protective properties at the early stages of ablation in dry and wet conditions.The durability of the gowns decreases as the degree of wear increases,while the trend of the decrease is slowing down until the fabric breaks down and completely loses its barrier effect.[Chinese Medical Equipment Journal,2025,46(6):28-33]