Objective: To explore the relationship between non suicidal self injury (NSSI) behavior and serum lipid levels as well as thyroid function among college students with depression. Methods: A total of 169 college students with depression in the psychiatry departments of tertiary hospitals (grade 3A and 3B) in Ningbo from December 2023 to April 2025 were selected. The Adolescent Self injury Scale (ASIS) was used to assess the presence of NSSI, and participants were accordingly divided into a NSSI group ( n =51) and a non NSSI group ( n =118). General demographic data (including gender, age, and family situation) were collected from both groups. Blood tests were performed to measure lipid profiles [triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C)] and thyroid hormones [triiodothyronine (T3), thyroxine (T4), free triiodothyronine (FT3), free thyroxine (FT4), thyroid stimulating hormone (TSH)]. Multivariate Logistic regression was employed to analyze risk factors for NSSI, and receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of serum lipid and thyroid hormone levels for NSSI occurrence in college students with depression. Results: The levels of TC, LDL-C, and TSH in the NSSI group were (4.02±0.73) mmol/L, (2.32±0.36) mmol/L, and (6.57±1.95) mU/L , which were significantly higher than those in the non NSSI group [(3.41±0.56) mmol/L, (2.00±0.27) mmol/L, and ( 4.48± 1.09) mU/L, respectively] ( t =5.32, 5.60, 7.20, all P <0.05). Logistic regression analysis revealed that college students from single parent/reconstituted families, those who had experienced school bullying, and those with higher levels of TC, LDL-C, and TSH had a significantly increased risk of engaging in NSSI ( OR =5.22, 6.12, 5.90, 83.64, 3.64, all P <0.05). ROC curve analysis demonstrated that the combined detection of TC, LDL-C, and TSH had high diagnostic efficacy for predicting NSSI in college students with depression, with a sensitivity of 86.3% and a specificity of 94.9%. Conclusions NSSI behavior in college students with depression is associated with serum lipid levels and thyroid function. These biomarkers may serve as useful reference indicators for assessing the conditions of these patients.

Background Regulatory interactions between microRNAs (miRNAs) and messenger RNAs (mRNAs) are involved in the progression of pulmonary fibrosis, which can either promote or inhibit the development of this disease. Objective To explore the miRNA-mRNA regulatory network during the progression of silica (SiO₂)-induced pulmonary fibrosis in mice using integrated mRNA-seq and miRNA-seq analysis. Methods A mouse model of pulmonary fibrosis was established by dynamic SiO₂ dust exposure. The experimental design included a blank control group and four SiO₂-exposed groups (7, 14, 28, and 56 d, n=10 per group). Successful model induction was confirmed by histopathological analysis (HE and Masson staining), hydroxyproline (HYP) quantification, and expression of key fibrosis-related cytokines [fibroblast growth factor (FGF), interleukin-6 (IL-6), transforming growth factor-β (TGF-β), and tumor necrosis factor-α (TNF-α)]. Lung tissues from mice in each group were subjected to sequencing, and Mfuzz was used for time-series gene clustering to identify dynamic progression patterns. DESeq2 was utilized to identify differentially expressed genes (DEGs) and differentially expressed miRNAs. Enrichment analysis of DEGs was performed to identify critical signaling pathways and biological processes underlying pulmonary fibrosis progression. Expression of four selected miRNAs was subsequently validated by real-time quantitative polymerase chain reaction (RT-qPCR). The target mRNAs of key miRNAs were comprehensively predicted by integrating miRBase, starBase, and miRTarBase to construct the regulatory networks and investigate potential functions. Results SiO₂ exposure led to time-dependent aggravation of pulmonary fibrosis in mice, evidenced by increased fibrous deposition, elevated HYP levels (P < 0.01), and up-regulation of four kinds of pro-fibrotic cytokines (P < 0.01) compared with the NT group. Mfuzz clustering revealed the stage-specific characteristics. Compared to controls, 231, 662, 448, and 1020 DEGs were identified after SiO₂ exposure at 7, 14, 28, and 56 d, respectively, primarily enriched in immune responses and chemokine signaling. During critical fibrotic phases—7 d (acute inflammation and initiation) and 28 d (chronic inflammation and establishment)—18 differentially expressed miRNAs were identified; notably mmu-miR-135b-5p was significantly dysregulated at both time points. The expression trends of the four key miRNAs (mmu-miR-135b-5p, mmu-miR-708-5p, mmu-miR-21a-3p, and mmu-miR-205-5p) were consistent with the sequencing results. Furthermore, bioinformatics databases were used to predict the target mRNAs of key miRNAs. The constructed network highlighted critical miRNA-mRNA pairs—including mmu-miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, mmu-miR-205-5p and Ereg which were closely associated with inflammatory response, extracellular matrix deposition, and fibroblast activation. Conclusion The progression of pulmonary fibrosis is accompanied by dynamic changes in miRNA-mRNA regulatory networks. The identified miRNA-target axes (e.g., miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, and mmu-miR-205-5p and Ereg—) may play important roles in fibrogenesis and provide potential therapeutic targets for pulmonary fibrosis.

Background Regulatory interactions between microRNAs (miRNAs) and messenger RNAs (mRNAs) are involved in the progression of pulmonary fibrosis, which can either promote or inhibit the development of this disease. Objective To explore the miRNA-mRNA regulatory network during the progression of silica (SiO₂)-induced pulmonary fibrosis in mice using integrated mRNA-seq and miRNA-seq analysis. Methods A mouse model of pulmonary fibrosis was established by dynamic SiO₂ dust exposure. The experimental design included a blank control group and four SiO₂-exposed groups (7, 14, 28, and 56 d, n=10 per group). Successful model induction was confirmed by histopathological analysis (HE and Masson staining), hydroxyproline (HYP) quantification, and expression of key fibrosis-related cytokines [fibroblast growth factor (FGF), interleukin-6 (IL-6), transforming growth factor-β (TGF-β), and tumor necrosis factor-α (TNF-α)]. Lung tissues from mice in each group were subjected to sequencing, and Mfuzz was used for time-series gene clustering to identify dynamic progression patterns. DESeq2 was utilized to identify differentially expressed genes (DEGs) and differentially expressed miRNAs. Enrichment analysis of DEGs was performed to identify critical signaling pathways and biological processes underlying pulmonary fibrosis progression. Expression of four selected miRNAs was subsequently validated by real-time quantitative polymerase chain reaction (RT-qPCR). The target mRNAs of key miRNAs were comprehensively predicted by integrating miRBase, starBase, and miRTarBase to construct the regulatory networks and investigate potential functions. Results SiO₂ exposure led to time-dependent aggravation of pulmonary fibrosis in mice, evidenced by increased fibrous deposition, elevated HYP levels (P < 0.01), and up-regulation of four kinds of pro-fibrotic cytokines (P < 0.01) compared with the NT group. Mfuzz clustering revealed the stage-specific characteristics. Compared to controls, 231, 662, 448, and 1020 DEGs were identified after SiO₂ exposure at 7, 14, 28, and 56 d, respectively, primarily enriched in immune responses and chemokine signaling. During critical fibrotic phases—7 d (acute inflammation and initiation) and 28 d (chronic inflammation and establishment)—18 differentially expressed miRNAs were identified; notably mmu-miR-135b-5p was significantly dysregulated at both time points. The expression trends of the four key miRNAs (mmu-miR-135b-5p, mmu-miR-708-5p, mmu-miR-21a-3p, and mmu-miR-205-5p) were consistent with the sequencing results. Furthermore, bioinformatics databases were used to predict the target mRNAs of key miRNAs. The constructed network highlighted critical miRNA-mRNA pairs—including mmu-miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, mmu-miR-205-5p and Ereg which were closely associated with inflammatory response, extracellular matrix deposition, and fibroblast activation. Conclusion The progression of pulmonary fibrosis is accompanied by dynamic changes in miRNA-mRNA regulatory networks. The identified miRNA-target axes (e.g., miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, and mmu-miR-205-5p and Ereg—) may play important roles in fibrogenesis and provide potential therapeutic targets for pulmonary fibrosis.

Health economics evidence plays an important role in linking clinical value evidence with health resource allocation decisions in the development of clinical practice guidelines. It can not only effectively balance clinical effectiveness and economic feasibility but also avoid forming "idealized" recommendations that are detached from the affordability of the healthcare system or the burden-bearing capacity of patients. To promote guideline developers to use health economics evidence more standardizedly and fully, this paper conducts an in-depth analysis of the current application status, existing challenges, access channels, and application processes of health economics evidence in current guidelines, and on this basis, puts forward considerations and suggestions for strengthening and standardizing the application of health economics evidence in China's clinical practice guidelines.

Tropical diseases pose a significant challenge to global public health. Their epidemiological footprints have expanded due to global climate warming, population migration, and lifestyle transformations, affecting even non-tropical areas. "One Health" emphasizes interdisciplinary, intersectoral, and interregional cooperation and communication, which is of critical importance in the prevention and control of tropical diseases. Tropical disease education constitutes an essential component of preventive medicine education; however, it faces outdated content and methods that fail to keep up with the rapid changes in tropical disease scenarios and emerging prevention needs. In 2019, the Ministry of Education launched the "Double Ten Thousand Plan" to establish national and provincial first-class undergraduate disciplines, aiming to improve the quality of undergraduate education comprehensively. Building a first-class preventive medicine program bears the responsibility of cultivating high-level public health professionals. This paper aims to develop a "Tropical Medicine" course tailored to national conditions while highlighting local characteristics. The teaching process is reformed in four aspects: actively integrating the One Health concept, setting up professional teaching institutions, constructing virtual simulation laboratories, and strengthening academic exchanges and cooperation, specific suggestions are given in this regard.

BACKGROUND: Ferroptosis-related genes play a crucial role in regulating intracellular iron homeostasis and lipid peroxidation, and they are involved in the regulation of tumor growth and drug resistance. The expression of ferroptosis-related genes in tumor tissues can be used to predict patients' future survival times, aiding doctors and patients in anticipating disease progression. Based on the sequencing data of lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database, this study identified genes involved in the regulation of ferroptosis, constructed a prognostic model, and evaluated the predictive performance of the model. METHODS: A total of 1467 ferroptosis-related genes were obtained from the GeneCards database. Gene expression profiles and clinical data from 541 LUAD patients were collected from the TCGA database. The expression data of all ferroptosis-related genes were extracted, and differentially expressed genes were identified using R software. Survival analysis was performed on these genes to screen for those with prognostic value. Subsequently, a prognostic risk scoring model for ferroptosis-related genes was constructed using LASSO regression model. Each LUAD patient sample was scored, and the patients were divided into high-risk and low-risk groups based on the median score. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves were generated to assess model performance, followed by validation in an external dataset. Finally, univariate and multivariate Cox regression analyses were conducted to evaluate the independent prognostic value and clinical relevance of the model. RESULTS: Through survival analysis, 121 ferroptosis-related genes associated with prognosis were initially identified. Based on this, a LUAD prognostic risk scoring model was constructed using 12 ferroptosis-related genes (ALG3, C1QTNF6, CCT6A, GLS2, KRT6A, LDHA, NUPR1, OGFRP1, PCSK9, TRIM6, IGF2BP1 and MIR31HG). The results indicated that patients in the high-risk group had significantly shorter survival time than those in the low-risk group (P<0.001), and the model demonstrated good predictive performance in both the training set (1-yr AUC=0.721) and the external validation set (1-yr AUC=0.768). Risk scores were significantly associated with the prognosis of LUAD patients in both univariate and multivariate Cox regression analyses (P<0.001), suggesting that this score is an important prognostic factor for LUAD patients. CONCLUSIONS This study successfully established a LUAD risk scoring model composed of 12 ferroptosis-related genes. In the future, this model is expected to be used in conjunction with the tumor-node-metastasis (TNM) staging system for prognostic predictions in LUAD patients.
Humans ; Ferroptosis/genetics* ; Prognosis ; Adenocarcinoma of Lung/pathology* ; Lung Neoplasms/pathology* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Middle Aged ; ROC Curve

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*