Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

Objective:To observe the tumor control and the degree of radiation-induced lung injury (RILI) between FLASH irradiation and conventional dose rate (CONV) irradiation, and compare the changes in plasma proteomic profiles of mice following whole-lung FLASH and CONV irradiation using proteomics method.Methods:A mouse model with metastatic lung cancer was established. After whole-lung irradiation, changes in normal lung capacity were monitored using CT scans. Then, a RILI model was constructed to examine pathological alterations in lung tissues following whole-lung CONV and FLASH irradiation. Plasma samples were collected from mice receiving whole-lung CONV irradiation ( n = 5) and whole-lung FLASH irradiation ( n = 5), followed by comparison with samples from the control group of healthy mice (also referred to as the healthy control group). These plasma samples were analyzed using isobaric tags for relative and absolute quantification (iTRAQ)-based proteomics, followed by the screening and identification of differentially expressed proteins using high-throughput bioinformatics. Moreover, protein-protein interaction (PPI) network analysis was conducted to identify hub genes using the STRING database and Cytoscape software. Results:Whole-lung FLASH and CONV irradiation produced consistent tumor control, with the former significantly reducing RILI compared to the latter. A total of 609 proteins were identified through proteomic analysis. Among them, 89 differentially expressed proteins were detected in the whole-lung FLASH group. Gene Ontology (GO) enrichment analysis indicated that up-regulated genes were primarily associated with stress and inflammatory responses, whereas down-regulated genes were related to ATP metabolism and angiogenesis regulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that up-regulated genes were predominantly enriched in unfolded protein response pathways, while down-regulated genes were mainly involved in metabolic pathways and oxidative phosphorylation. Integrated PPI analysis and subsequent validation via reverse transcription-polymerase chain reaction (RT-PCR) revealed four key genes.Conclusions:Compared to the whole-lung CONV irradiation, whole-lung FLASH irradiation reduces the RILI of normal lung tissues while maintaining equivalent tumor control in metastatic lung cancer. Proteomic analysis of differentially expressed proteins in plasma after whole-lung FLASH and CONV irradiation provides valuable insights into the molecular mechanisms underlying the FLASH effect.

Objective To investigate mutations in OTU domain-containing protein 3(OTUD3)and their functional mechanisms in the initiation and progression of colorectal cancer(CRC).Methods Gene expression profiling interactive analysis(GEPIA2)and the human protein atlas database(THPA)were used to analyze gene transcription and protein expressions.Samples from 32 patients with CRC were collected to identify OTUD3 mutants.Based on the information about mutation sitesof OTUD3 in an existing database,a plasmid vector containing the OTUD3 gene mutant was constructed.Plasmid vectors containing the phosphatase and tensin homolog deleted on chromosome 10(PTEN)and the OTUD3 gene mutant were co-transfected into HCT116 cells.Western blotting,half-life,immunoprecipitation,ubiquitination,and hybrid algorithm molecular docking(H-DOCK)assays were employed to find out whether and why the OTUD3 mutants affected PTEN protein levels.Functional alterations in CRC cells after OTUD3 mutation were verified by CCK-8 cell proliferation,transwell cell invasion,scratch,and clonal formation assays.Results OTUD3 mutations were highly frequent in CRC.OTUD3 mutants R178W and N321S resulted in the loss of function of the stable PTEN protein,leading to enhanced proliferation,invasion,migration,and survival of CRC.Conclusion In CRC,OTUD3 mutation reduces the ability to stabilize PTEN and promotes the occurrence and development of CRC.

Objective To investigate how deubiquitinase OTU domain-containing protein 3(OTUD3)suppresses the progression of hepatocellular carcinoma via gut-liver axis metabolic remodeling and microbiome dynamics.Methods A total of 24 male 2-week-old littermate C57BL/6J mice(12 wild-type and 12 Otud3-/-)were divided into two differential genotype groups before 6 mice from each group were randomly chosen to receive intraperitoneal injections of N-nitrosodieth-ylamine(DEN)for hepatocellular carcinoma(HCC)induction.The mice were divided into four groups(n=6/group):Otud3+/+control(WT CON),Otud3-/-control(KO CON),Otud3+/+DEN-induced HCC(WT DEN),and Otud3-/-DEN-induced HCC(KO DEN).At 40 weeks of age,liver tissues were collected for metabolomic profiling,and fecal samples were obtained for 16S rRNA sequencing.Results Multivariate analyses,including principal component analysis(PCA),partial least squares-discriminant analysis(PLS-DA),sparse partial least squares-discriminant analysis(sPLS-DA),and orthogonal partial least squares-discriminant analysis(OrthoPLS-DA),demonstrated complete intergroup separability.Fifty-four differential metabolites were identified between the WT DEN and KO DEN groups through metabolomic profiling,with gut-liver axis-associated pathways such as cholesterol metabolism and fatty acid biosynthesis revealed by KEGG pathway analysis.Microbiome analysis indicated an upregulation of Bacteroides at the genus level in the KO DEN group compared to WT DEN.Pearson correlation analysis highlighted amino acids and derivatives as predominant metabolite classes and revealed Bacteroidetes and Firmicutesas the dominant gut microbial phyla.Conclusion OTUD3 suppresses HCC progression by modulating gut-liver axis metabolism,potentially mediated by elevated betaine and increased abundance of Odoribacter,Alistipes,and Lachnoclostridium.

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

Paclitaxel (PTX), a valuable natural product derived from Taxus species, exhibits remarkable anti-cancer properties. It penetrates nanopores in microtubule walls, interacting with tubulin on the lumen surface and disrupting microtubule dynamics, thereby inducing cytotoxic effects in cancer cells. PTX and its derivatives have gained approval for treating various diseases due to their low toxicity, high efficiency, and broad-spectrum application. The widespread success and expanding applications of PTX have led to increased demand, raising concerns about accessibility. Consequently, researchers globally have focused on developing alternative production methods and applying nanocarriers in PTX delivery systems to enhance bioavailability. This review examines the challenges and advancements in PTX sourcing, production, physicochemical properties, anti-cancer mechanisms, clinical applications, trials, and chemo-immunotherapy. It aims to provide a comprehensive reference for the rational development and effective utilization of PTX.
Humans ; Paclitaxel/pharmacology* ; Antineoplastic Agents, Phytogenic/pharmacology* ; Neoplasms/drug therapy* ; Animals ; Taxus/chemistry*

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Objective:To observe the tumor control and the degree of radiation-induced lung injury (RILI) between FLASH irradiation and conventional dose rate (CONV) irradiation, and compare the changes in plasma proteomic profiles of mice following whole-lung FLASH and CONV irradiation using proteomics method.Methods:A mouse model with metastatic lung cancer was established. After whole-lung irradiation, changes in normal lung capacity were monitored using CT scans. Then, a RILI model was constructed to examine pathological alterations in lung tissues following whole-lung CONV and FLASH irradiation. Plasma samples were collected from mice receiving whole-lung CONV irradiation ( n = 5) and whole-lung FLASH irradiation ( n = 5), followed by comparison with samples from the control group of healthy mice (also referred to as the healthy control group). These plasma samples were analyzed using isobaric tags for relative and absolute quantification (iTRAQ)-based proteomics, followed by the screening and identification of differentially expressed proteins using high-throughput bioinformatics. Moreover, protein-protein interaction (PPI) network analysis was conducted to identify hub genes using the STRING database and Cytoscape software. Results:Whole-lung FLASH and CONV irradiation produced consistent tumor control, with the former significantly reducing RILI compared to the latter. A total of 609 proteins were identified through proteomic analysis. Among them, 89 differentially expressed proteins were detected in the whole-lung FLASH group. Gene Ontology (GO) enrichment analysis indicated that up-regulated genes were primarily associated with stress and inflammatory responses, whereas down-regulated genes were related to ATP metabolism and angiogenesis regulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that up-regulated genes were predominantly enriched in unfolded protein response pathways, while down-regulated genes were mainly involved in metabolic pathways and oxidative phosphorylation. Integrated PPI analysis and subsequent validation via reverse transcription-polymerase chain reaction (RT-PCR) revealed four key genes.Conclusions:Compared to the whole-lung CONV irradiation, whole-lung FLASH irradiation reduces the RILI of normal lung tissues while maintaining equivalent tumor control in metastatic lung cancer. Proteomic analysis of differentially expressed proteins in plasma after whole-lung FLASH and CONV irradiation provides valuable insights into the molecular mechanisms underlying the FLASH effect.

Objective To explore the effect of hydrogen molecules on the psychological state of submariners after long voyage.Methods A total of 197 submariners who had participated in a long-voyage mission were selected as the research objects by cluster sampling.They were randomly assigned to observation group or control group.The observation group was given hydrogen inhalation and hydrogen-enriched drinking water for 30 days,while the control group was given air inhalation and purified water.Before and after the intervention,mental health status was assessed by the Symptom Checklist 90(SCL-90),Self-rating Anxiety Scale(SAS),and Self-rating Depression Scale(SDS).The cognitive ability,such as digit conversion,visual memory,tracking goal,and number span were tested.Results After the intervention,the score of each dimension and positive items of SCL-90,as well as the total scores of SAS and SDS were decreased in both groups(P<0.05 or P<0.01),and the scores of depression and anxiety and positive items of SCL-90,as well as the total scores of SAS and SDS in the observation group were lower than those in the control group(P<0.01).The single correct response time for digit conversion in the observation group was shorter than that in the control group(P<0.01).Conclusion Hydrogen molecule can improve mental health and cognitive performance of submariners after long voyage.

Objective To observe the effect of drinking hydrogen-rich water on preventing heat stroke(HS)of workers in special positions.Methods A total of 384 workers in special positions were selected with cluster sampling in February 2022.They were assigned to control group or observation group by a random number table,with 192 cases in each group.Double-blind experiment method was adopted.On the basis of the prevention and health education of HS,routine protection and daily drinking water in the study subjects,the observation group drank 1.5 L of hydrogen-rich water every day(200-300 ml each time,5-7 times a day,the hydrogen concentration of hydrogen-rich water was 1 800 μg/L),and the control group drank 1.5 L of pure water every day(200-300 ml each time,5-7 times a day)for 6 months.On the day after the intervention,all the workers underwent 10 km weight-bearing cross-country training.During the intervention period,the incidences of HS and its precursor,the detection rate of heat tolerance difference before and after the intervention,core temperature,serum markers of heat intolerance,and inflammatory factors were observed.Results During the intervention period,the precursor of HS appeared in 2 patients(1.04%)in the observation group and 11 patients(5.73%)in the control group,and 1 patient(0.52%)was diagnosed with HS in the control group.The incidences of HS and its precursor in the observation group were lower than those in the control group(P<0.01).After the weight-bearing training,the core body temperature was increased in both groups(P<0.01),but the core body temperature in the observation group was lower than that in the control group(P<0.01).The detection rate of heat tolerance difference,serum heat intolerance markers(alanine aminotransferase,creatine kinase,and creatinine)and inflammatory factors(interleukin-1,interleukin-6,and tumor necrosis factor-α)in the observation group were lower than those in control group after intervention(P<0.05),and those before intervention(P<0.05).Conclusion Drinking hydrogen-rich water can improve the thermal tolerance of workers in special positions,and inhibit or reduce the inflammatory response caused by high heat.It can be used to prevent HS in special positions.