Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

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.

This paper summarized professor ZHANG Boli's experience in syndrome differentiation and treatment of metabolic syndrome （MetS） based on the theory of "diseases related to damp， turbidity， phlegm， and rheum". It is considered that damp， turbidity， phlegm， and rheum are the key pathological factors in the onset of MetS. The core pathogenesis of this disease includes the failure of the liver to govern the free flow of qi， the depletion of spleen and kidney， and the accumulation and impairment caused by damp， turbidity， phlegm， and rheum. It is important to emphasize early intervention， sequential treatment， and thorough elimination of pathogenic factors. The treatment takes fortifying the spleen and promoting middle jiao （焦）， soothing the liver and regulating qi， and tonifying the kidney to restore transformation as the fundamental principles. Furthermore， targeted treatments are implemented according to the evolution of damp， turbidity， phlegm， and rheum， including treating damp early， examining the tongue coating to eliminate turbidity， differentiating and treating concurrent phlegm， and applying warm dredging moderately. At the same time， specific medicinals are combined based on abnormal indicators such as blood glucose， blood lipids， blood pressure， and uric acid， aiming to address both root causes and symptoms simultaneously.

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

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

To investigate the aging behavior of fluoroelastomer FM-1D in hot air,with compression set as a parameter for seal aging,aging tests were conducted on fluoroelastomer FM-1D under different initial compression rates in a hot air environment ranging from 60℃to 150℃.The aging kinetic equation relating compression set of FM-1D to temperature and time was established,in order to predict its aging life at 25℃.The test results indicated that when the initial compression rate was 30%,the compression set exhibited an Arrhenius relationship with both test temperature and time.However,at a initial compression rate of 50%,the compression set gradually conformed to the Arrhenius relationship as time extended.These findings provide a reference for determining accelerated testing methods and maintenance cycles for FM-1D rubber materials.

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.

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 retrospectively analyze the clinical efficacy of Qingchi San in the treatment of mild-to-moderate ul-cerative colitis.METHODS A retrospective analysis was conducted on 221 ulcerative colitis patients treated at the Gastroenterology Department of First Teaching Hospital of Tianjin University of Traditional Chinese Medicine from December 2014 to February 2024.Participants were divided into an observation group(n=121)and a control group(n=100).The control group received oral me-salazine,while the observation group received Qingchi San enema in addition to the control group's treatment.Both groups received a 4-week course of treatment.The two groups were compared in terms of clinical efficacy,clinical remission rate,TCM syndrome score,time to intestinal bleeding remission,and changes in inflammatory markers(C-reactive protein,erythrocyte sedimentation rate,and white blood cells)and coagulation markers(platelets and D-dimer).RESULTS After treatment,the total effective rate and clinical remission rate in the observation group were significantly higher than those in the control group(P<0.05,P<0.01).The TCM syn-drome scores for each item(diarrhea,abdominal pain,bloody stools with mucus,and tenesmus)in the observation group were signifi-cantly improved compared with those in the control group(P<0.01).The time to intestinal bleeding remission in the observation group was significantly shorter than that in the control group(P<0.01).C-reactive protein,erythrocyte sedimentation rate,and white blood cell count in the observation group were significantly decreased compared with those in the control group(P<0.05,P<0.01).There was no statistically significant difference in coagulation markers(platelets and D-dimer)between the two groups(P>0.05).No signif-icant treatment-related adverse reactions,such as liver and kidney damage,were observed during treatment.CONCLUSION Qing-chi San enema can improve the clinical efficacy of patients with mild-to-moderate ulcerative colitis,improve clinical symptoms,short-en bleeding time,and reduce inflammatory markers.