Objective: To explore the association between meat consumption and anxiety symptoms in first year junior high school students in Yunnan Province, and to provide theoretical support for preventing and relieving anxiety symptoms in junior high school students. Methods: From October to December 2022, a random cluster sampling method was used to select 8 500 first year junior high school students from 11 counties in Yunnan Province as the survey subjects for a questionnaire survey. The study used Food Frequency Questionnaire and the Chinese version of the Depression Anxiety Stress Scale-21 (DASS-21) to assess the meat consumption and anxiety symptoms of junior high school students.The distribution differences in anxiety symptoms among first year junior high school students with different demographic characteristics were analyzed statistically by using the Chi-square test,and the association between meat consumption and anxiety symptoms in students was analyzed by using a generalized linear model. Results: The detection rate of anxiety symptoms was 48.47%. After controlling for demographic variables and confounding factors, the consumption of livestock meat, poultry meat, processed meat, cured meat, barbecued meat and raw skin meat was statistically significant with anxiety symptoms ( β =-0.05, 0.04, 0.04, 0.08, 0.14, 0.17, all P <0.05). Stratified by ethnicity, The consumption of livestock meat, cured meat and barbecue was statistically correlated with anxiety symptoms in Han adolescents ( β =-0.07, 0.14, 0.22 ); the consumption of processed meat and raw skin meat was statistically correlated with anxiety symptoms in ethnic minority adolescents ( β =0.08, 0.18) (all P <0.05). Conclusions There is a statistical association between meat comsumption and the risk of anxiety symptoms in first year junior high school students in Yunnan Province. Guidance on meat consumption should be strengthened to prevent the occurrence of anxiety symptoms.

ObjectiveTo investigate the associations of multiple glycolipid metabolic indicators and their cumulative abnormality burden with incident colorectal cancer risk in a general population-based prospective cohort, and to examine the mediating role of glycolipid metabolic abnormalities in the relationships between smoking, alcohol consumption, physical activity, and colorectal cancer incidence. MethodsA total of 17 897 eligible participants recruited from the Taizhou Cohort between 2011 and 2014 were included. Cox proportional hazards regression models were used to assess the associations of conventional and derived glycolipid indicators, as well as a glycolipid abnormality index constructed from total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, fasting plasma glucose, and insulin, with incident colorectal cancer risk. Restricted cubic spline models were applied to evaluate dose-response relationships for major indicators. Receiver operating characteristic curves were generated to compare predictive performance across indicators. Mediation analyses were conducted to assess the mediating effects of the glycolipid abnormality index on the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence. ResultsDuring a median follow-up of 11.2 years, 102 incident colorectal cancer cases were identified, with an incidence density of 51.2 per 100 000 person-years. After adjustment for potential confounders, Cox proportional hazards regression analyses showed that decreased high-density lipoprotein cholesterol was associated with a 1.74-fold higher risk of colorectal cancer (HR=1.74, 95%CI: 1.03‒2.95), elevated low-density lipoprotein cholesterol was associated with a 1.78-fold higher risk (HR=1.78, 95%CI: 1.05‒3.02), and abnormal fasting plasma glucose was associated with a 1.86-fold higher risk (HR=1.86, 95%CI: 1.15‒3.02). Triglycerides and fasting plasma glucose showed an approximately linear increasing association with colorectal cancer risk in multivariable restricted cubic spline models. The glycolipid abnormality index showed a clear gradient association with colorectal cancer risk; participants with three or more abnormal indicators had a 3.08-fold higher risk than those without abnormalities (HR=3.08, 95%CI: 1.60‒5.92). The area under the curve was 0.795, higher than that of individual glycolipid indicators and other combined indices, and gender-stratified analyses showed generally consistent patterns. Glycolipid metabolic abnormalities partially mediated the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence, with mediation proportions of 8.33%, 9.45%, and 9.04%, respectively. ConclusionGlycolipid metabolic abnormalities are associated with an increased risk of incident colorectal cancer. The glycolipid abnormality index shows an increasing relationship with colorectal cancer risk and demonstrates better discrimination, and it partially mediates the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence in the overall population.

Alzheimer's disease (AD), characterized by β-amyloid (Aβ) aggregation and neuroinflammation, remains a formidable clinical challenge. Herein, we present an innovative nose-to-brain delivery platform utilizing lactoferrin (Lf)-functionalized lipid nanoparticles (LNPs) co-encapsulating α-mangostin (α-M) and β-site APP cleaving enzyme 1 (BACE1) siRNA (siB). This dual-modal therapeutic system synergistically combines the neuroprotective and microglia-reprogramming capabilities of α-M with the transcriptional silencing of BACE1 via siB, thereby simultaneously inhibiting Aβ production and enhancing its clearance. Fabricated via a microfluidic approach, the LNPs exhibited uniform particle size distribution, great encapsulation efficiency, and robust colloidal stability. Upon intranasal administration, Lf-functionalization enabled superior brain-targeting efficacy through receptor-mediated transcytosis. In vitro studies demonstrated that α-M reversed Aβ-induced low-density lipoprotein receptor downregulation, promoting microglial phagocytosis and autophagic degradation of Aβ, while siB effectively suppressed BACE1 expression, abrogating Aβ synthesis. In vivo investigations in APP/PS1 transgenic mice revealed remarkable cognitive recovery, substantial Aβ plaque reduction, and alleviation of neuroinflammation and oxidative stress. This intricately designed LNP system, exploiting a non-invasive and efficient nose-to-brain delivery route, provides a biocompatible, synergistic, and transformative therapeutic strategy for the multifaceted management of AD.

Several types of arthritis share the common feature that the generation of inflammatory mediators leads to joint cartilage degradation. However, the shared mechanism is largely unknown. H2BK120ub1 was reportedly involved in various inflammatory diseases but its role in the shared mechanism in inflammatory joint conditions remains elusive. The present study demonstrated that levels of cartilage degradation, H2BK120ub1, and its regulator WW domain-containing adapter protein with coiled-coil (WAC) were increased in cartilage in human rheumatoid arthritis (RA) and osteoarthritis (OA) patients as well as in experimental RA and OA mice. By regulating H2BK120ub1 and H3K27me3, WAC regulated the secretion of inflammatory and cartilage-degrading factors. WAC influenced the level of H3K27me3 by regulating nuclear entry of the H3K27 demethylase KDM6B, and acted as a key factor of the crosstalk between H2BK120ub1 and H3K27me3. The cartilage-specific knockout of WAC demonstrated the ability to alleviate cartilage degradation in collagen-induced arthritis (CIA) and collagenase-induced osteoarthritis (CIOA) mice. Through molecular docking and dynamic simulation, doxercalciferol was found to inhibit WAC and the development of cartilage degradation in the CIA and CIOA models. Our study demonstrated that WAC is a key factor of cartilage degradation in arthritis, and targeting WAC by doxercalciferol could be a viable therapeutic strategy for treating cartilage destruction in several types of arthritis.

OBJECTIVES: To explore the mechanism by which Tougu Xiaotong Capsule (TXC) promotes chondrogenic differentiation and cartilage repair in mice with osteoarthritis (OA). METHODS: Fifty 8-week-old male C57BL mice were randomly divided into normal control group, cartilage damage (induced by subchondral ring-shaped drilling) model group and TXC treatment groups at low, moderate and high doses (184, 368 and 736 mg/kg, respectively). Saline (in normal control and model groups) and TXC were administered after modeling by daily gavage for 6 consecutive weeks. The changes of cartilage damage in the mice were assessed by measuring thermal withdrawal latency (TWL) and mechanical withdrawal threshold (MWT) and using micro-CT, modified safranine O and fast green staining, HE staining, and qPCR. Primary cultures of mouse synovial mesenchymal stem cells (SMSCs) with lentivirus vector transfection for interfering CXCL12, TXC treatment, or both for 24 h were examined for chondrogenic differentiation using immunofluorescence staining, scratch assay, immunocytochemistry, and Western blotting. RESULTS: In mouse models with cartilage damage, TXC treatment at the moderate dose significantly alleviated joint pain, promoted cartilage repair, and upregulated the mRNA expression levels of CXCL12, GDF5, collagen II, aggrecan, Comp and Sox9 in the cartilage tissue. In primary mouse SMSCs, CXCL12 knockdown resulted in significant reduction of GDF5 protein expression, migration ability and Sox9 protein expression, and these changes were obviously reversed by TXC treatment. CONCLUSIONS TXC promotes chondrogenic differentiation of mouse SMSCs to promote repair of cartilage damage in mice by activating the CXCL12/GDF5 pathway.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Osteoarthritis/metabolism* ; Male ; Growth Differentiation Factor 5/metabolism* ; Mice, Inbred C57BL ; Mice ; Chemokine CXCL12/metabolism* ; Signal Transduction/drug effects* ; Cell Differentiation/drug effects* ; Cartilage, Articular/drug effects* ; Mesenchymal Stem Cells/cytology*

Traditional Chinese medicine (TCM) is an ancient medical system distinctive and effective in treating cancer, depression, coronavirus disease 2019 (COVID-19), and other diseases. However, the relatively abstract diagnostic methods of TCM lack objective measurement, and the complex mechanisms of action are difficult to comprehend, which hinders the application and internationalization of TCM. Recently, while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research, the rise of machine learning (ML) has significantly enhanced their integration with TCM. This article introduces representative methodological cases in quality control, mechanism research, diagnosis, and treatment processes of TCM, revealing the potential applications of ML technology in TCM. Furthermore, the challenges faced by ML in TCM applications are summarized, and future directions are discussed.

OBJECTIVES: This study investigates independent risk factors for postoperative internal fixation device infection in patients with maxillofacial fractures and proposes an early warning model based on the synthetic minority over-sampling technique (SMOTE) algorithm. METHODS: A total of 1 104 patients who underwent surgical treatment for maxillofacial fractures at Oral and Maxillofacial Surgery Department, Affiliated Hospital of Nantong University from January 2021 to December 2024 were retrospectively analyzed. The patients were divided into two groups based on the presence of postoperative internal fixation device infection: the infection group (27 cases) and non-infection group (1 077 cases). Clinical data from both groups were collected and subjected to statistical analysis. Univariate and binary Logistic regression analysis were used to identify risk factors for postoperative internal fixation device infection in maxillofacial fractures. Subsequently, a Logistic regression model was established, and the dataset was improved based on the SMOTE algorithm to construct an early warning model with the improved dataset. The prediction performance of the models was compared and validated. RESULTS: Among the 1 104 patients who underwent surgical treatment for maxillofacial fractures, 27 cases of postoperative internal fixation device infections were identified, corresponding to an infection rate of 2.45% (27/1 104). Age, diabetes history, fracture severity, and oral hygiene status were all identified as risk factors for postoperative internal fixation device infections in maxillofacial fractures (all P<0.05). The prediction model based on the original data (P1). The prediction model based on the SMOTE algorithm (P2). Receiver operating characteristic (ROC) curve analysis shows that the area under curve (AUC) for the P2 model was 0.882, the P1 model was 0.861, indicating the superior predictive performance of the P2 model. The DeLong test results show that the difference in AUC between the two models was statistically significant (P<0.05). CONCLUSIONS Age, diabetes history, postoperative fracture severity, and oral hygiene status are all risk factors for infections associated with internal fixation devices after maxillofacial fracture surgery. The proposed early warning model demonstrated good predictive performance. Medical professionals can utilize this model to effectively intervene and anticipate infections related to internal fixation devices after maxillofacial fracture surgery.
Humans ; Algorithms ; Retrospective Studies ; Male ; Female ; Fracture Fixation, Internal/instrumentation* ; Risk Factors ; Middle Aged ; Adult ; Logistic Models ; Surgical Wound Infection/epidemiology* ; Aged ; Internal Fixators/adverse effects* ; Maxillofacial Injuries/surgery* ; Adolescent

Objective Chronic atrophic gastritis(CAG)is often accompanied by intestinal flora disorder and intestinal symptoms,forming the phenomenon of"stomach disease involving intestine".This study explored the dynamic correlation between intestinal symptoms and qi-stagnation degree in patients with CAG qi-stagnation syndrome and analyzed the characteristics of gut microbiota from the perspective of"spleen-stomach system serving as the pivotal hub of qi movement"in TCM.Methods According to the syndrome element differentiation method,410 patients with CAG were divided into four groups:non-qi-stagnation group,mild qi-stagnation group,moderate qi-stagnation group and severe qi-stagnation group.Correlation analysis and 16S intestinal flora sequencing technology were used to analyze the correlation and differential flora between the degree of CAG qi-stagnation and intestinal symptoms.Results Patients with CAG qi-stagnation syndrome were often accompanied by intestinal symptoms such as frequent flatulence,poor defecation and alternating loose-constipated stools.The frequency of cases was significantly positively correlated with the degree of qi-stagnation"non-mild-moderate-severe"(P<0.05).There was a difference in the abundance of gut microbiota between the four groups of CAG qi-stagnation none,mild,moderate and severe.The relative abundance of Streptococcus,Subdoligranulum,Eubacterium_coprostanoligenes_group and Haemophilus was positively correlated with the degree of qi-stagnation.The relative abundance of Ruminococcus_torques_group and Butyricicoccus showed a negative correlation,and Haemophilus was statistically significant among the four groups(P<0.05).Conclusion This study can provide clinical evidence and micro-mechanism for the connotation of"gastrointestinal co-morbidities"and"different diseases with the same syndrome",which may open up new ideas for clinical diagnosis and treatment.

Traditional Chinese medicine(TCM)is an ancient medical system distinctive and effective in treating cancer,depression,coronavirus disease 2019(COVID-19),and other diseases.However,the relatively abstract diagnostic methods of TCM lack objective measurement,and the complex mechanisms of action are difficult to comprehend,which hinders the application and internationalization of TCM.Recently,while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research,the rise of machine learning(ML)has significantly enhanced their inte-gration with TCM.This article introduces representative methodological cases in quality control,mechanism research,diagnosis,and treatment processes of TCM,revealing the potential applications of ML technology in TCM.Furthermore,the challenges faced by ML in TCM applications are summarized,and future directions are discussed.