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.

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.

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.

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.

Objective:To conduct a comparative analysis of the radiation damage to zebrafish embryos and the associated biological mechanism after ultra-high dose rate (FLASH) and conventional dose rate irradiation.Methods:Zebrafish embryos at 4 h post-fertilization were exposed to conventional and FLASH irradiation (9 MeV electron beam). The mortality and hatchability of zebrafish after radiation exposure were recorded. Larvae at 96 h post-irradiation underwent morphological scoring, testing of reactive oxygen species (ROS) levels, and analysis of changes in oxidative stress indicators.Results:Electron beam irradiation at doses of 2-12 Gy exerted subtle effects on the mortality and hatchability of zebrafish embryos. However, single high-dose irradiation (≥ 6 Gy) could lead to developmental malformation of larvae, with conventional irradiation showing the most significant effects ( t = 0.87-9.75, P < 0.05). In contrast, after FLASH irradiation (≥ 6 Gy), the ROS levels in zebrafish and its oxidative stress indicators including superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA) were significantly reduced ( t = 0.42-15.19, P < 0.05). There was no statistically significant difference in ROS levels in incubating solutions after conventional and FLASH irradiation ( P > 0.05). Conclusions:Compared to conventional irradiation, FLASH irradiation can reduce radiation damage to zebrafish embryos, and this is in a dose-dependent manner. The two irradiation modes lead to different oxidative stress levels in zebrafish, which might be a significant factor in the reduction of radiation damage with FLASH irradiation.

Objective:To construct a predictive model of radiomics based on laparoscopic grayscale ultrasound features and investigate its value in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) during laparoscopic liver resection.Methods:A total of 74 patients (74 lesions)with HCC confirmed by postoperative pathology, who underwent a laparoscopic ultrasonography during laparoscopic hepatectomy were prospectively enrolled in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from March 2022 to August 2023. The general clinical information of the patients was recorded, and the features were extracted and screened from tumor regions in gray-scale ultrasound images, and eventually the radiomics prediction models were constructed, respectively. Pathological results were used as gold standard to compare the effectiveness of different models in predicting MVI.Results:In the 74 HCC lesions, 12 lesions were MVI positive.The MVI imaging prediction model of HCC lesions was constructed from the screened clinical features, laparoscopic gray scale ultrasound image features, as well as combined screened clinical features, respectively. The obtained data sets were randomly divided into 5 parts (4 parts with 15 lesions, 1 part with 14 lesions), and the effectiveness of the model was trained and tested by the method of 5 folds interaction validation. The performance of support vector machine(SVM) radiomics model based on the characteristics of laparoscopic gray scale ultrasound in predicting the MVI of HCC was the best. Compared with clinical model and combined Adaboost model, the SVM, radiomics model had higher area under ROC curve (0.836 vs 0.696, 0.804), accuracy (0.852 vs 0.687, 0.838), sensitivity (0.900 vs 0.900, 0.833) and specificity (0.837 vs 0.644, 0.838). Conclusions:The radiomics model based on the characteristics of laparoscopic gray-scale ultrasound is an innovative potential approach to predict the MVI status of HCC lesions during laparoscopic hepatectomy.

Objective:To investigate the accuracy of magnetic resonance imaging (MRI) cine sequences in determining the range of tumor motion in radiotherapy, providing a basis for the precise delineation of the target volume in motion for radiation therapy.Methods:A modified chest motion phantom was placed in a MRI scanner, and a water-filled sphere was used to simulate a tumor. True fast imaging with steady precession (TrueFISP) MRI cine sequences from Siemens were used to capture the two-dimensional motion images of the simulated tumor. The phantom experiments were divided into three modes: head-foot motion mode, rotation motion mode, and actual respiratory waveform mode. In the head-foot motion mode, respiratory motion period (3, 4, 5, 6, 7 and 8 s), amplitude (5, 10 and 15 mm), and respiratory waveform of the simulated tumor (sin and cos4) were set, resulting in a total of 36 motion combinations. In the rotation motion mode, a cos4 waveform was used for respiration, with respiratory periods of 3, 4, 5, 6, 7 and 8 s, head-foot motion set amplitudes of 5, 10 and 15 mm, and anterior-posterior (AP) and left-right (LR) motion set amplitudes in three combinations ([2.5, 2.5] mm, [2.5, 5.0] mm, [5.0, 5.0] mm), resulting in a total of 54 motion combinations. In the actual respiratory waveform mode, respiratory waveforms of 5 randomly selected patients from Affiliated Cancer Hospital of Zhengzhou University were obtained. Under each motion combination, TrueFISP cine images (30 frames, with an acquisition time of 11 s per frame) were obtained. The code was used to automatically identify the two-dimensional coordinates of the center of the simulated tumor in each image, and sin and cos4 functions were separately employed to fit the tumor position in the motion direction, thereby obtaining the fitted motion period and amplitude. The difference between the maximum and minimum values of the tumor's center coordinates in the head-to-foot direction is taken as the range of movement, referred to as the calculated amplitude. For the actual respiratory waveform, the distance between the measured maximum and minimum positions is used to calculate the amplitude.Results:In the head-foot motion mode, the fitted amplitudes of both sin and cos4 waveforms deviated from the set amplitudes by 0-0.51 mm, with relative deviations of 0%-4.2%. The deviation range between the calculated amplitudes and the set amplitudes of the two waveforms were 0.08-0.94 mm, with relative deviations of 1.1%-6.3%. In the rotation motion mode, the fitted amplitudes deviated from the set amplitudes by 0-0.61 mm, with relative deviations of 0%-6.2%. And the deviation range between the calculated amplitudes and the set amplitudes were 0.16-0.94 mm, with relative deviations of 0%-6.3%. In the actual respiratory waveform motion mode, the deviation range between the calculated amplitudes and the set amplitudes were 0.10-0.48 mm, with relative deviations of 2.2%-8.6%.Conclusion:TrueFISP cine sequences show minimal deviations in determining the range of tumor head-foot motion and effectively captures the tumor's movement state, thereby providing important support for the precise definition of the tumor movement target area during radiotherapy .