Obesity and its related metabolic diseases have become a major global public health threat, and its rising incidence significantly increases the risk of cardiovascular and cerebrovascular diseases, diabetes and other complications. Pancreatic lipase is a key enzyme that converts food-borne lipids into triglycerides and fatty acids, and the effective inhibition of its activity has become an important strategy for the treatment of obesity. This paper discusses the screening methods of pancreatic lipase inhibitors, and summarizes and reviews the basic principles, advantages and disadvantages and application status of traditional screening methods, modern new screening methods and virtual screening methods. In view of the problems faced by the screening methods of pancreatic lipase inhibitors, future research urgently needs to move towards a collaborative innovation path of multi-technology integration, intelligent screening and complex systematization of traditional Chinese medicine, so as to open up new research paradigms.

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 summarize the laboratory findings of a case of hemolytic disease of the fetus and newborn (HDFN) caused by Rh system anti-c antibodies and to review the literature, so as to explore the characteristics of anti-c HDFN. Methods: The ABO blood type, Rh blood type, direct antiglobulin test (DAT) results, and the presence of unexpected antibodies and their titers were determined by serological methods. The cases of anti-c HDFN in our laboratory in China and abroad were statistically analyzed, and the incidence of severe HDFN caused by anti-c, anti-D and anti-E was compared. Results: The blood type of the child was B (Rh CcDee) with a positive DAT. Anti-c antibody was detected in both serum and eluate, with a serum antibody titer of 4. The mother’s blood type was AB (Rh CCDee) with a negative DAT, and anti-c antibody was detected in the serum with a titer of 128. Among 20 cases of anti-c HDFN, 17 were DAT positive, and 9 (45%, 9/20) underwent blood transfusion or exchange transfusion. The incidence of severe HDFN was 47.60% (10/21) for anti-c, 47.60% (10/21) for anti-D and 31.30% (5/16) for anti-E. Conclusion: Maternal pregnancy and/or blood transfusion are the main reasons for the production of Rh alloantibodies such as anti-c. The prevention and management of anti-c should be similar to that of anti-D. Rh antigen-matched (five antigens of Rh blood group) transfusion is necessary for women of childbearing age to avoid antibody production, and Rh typing and antibody screening during prenatal examination is recommended to ensure early detection, intervention and treatment.

Abstract Modern western medicine typically focuses on treating specific symptoms or diseases, and traditional Chinese medicine (TCM) emphasizes the interconnections of the body’s various systems under external environment and takes a holistic approach to preventing and treating diseases. Phenomics was initially introduced to the field of TCM in 2008 as a new discipline that studies the laws of integrated and dynamic changes of human clinical phenomes under the scope of the theories and practices of TCM based on phenomics. While TCM Phenomics 1.0 has initially established a clinical phenomic system centered on Zhenghou (a TCM definition of clinical phenome), bottlenecks remain in data standardization, mechanistic interpretation, and precision intervention. Here, we systematically elaborates on the theoretical foundations, technical pathways, and future challenges of integrating digital medicine with TCM phenomics under the framework of “TCM phenomics 2.0”, which is supported by digital medicine technologies such as artificial intelligence, wearable devices, medical digital twins, and multi-omics integration. This framework aims to construct a closed-loop system of “Zhenghou–Phenome–Mechanism–Intervention” and to enable the digitization, standardization, and precision of disease diagnosis and treatment. The integration of digital medicine and TCM phenomics not only promotes the modernization and scientific transformation of TCM theory and practice but also offers new paradigms for precision medicine. In practice, digital tools facilitate multi-source clinical data acquisition and standardization, while AI and big data algorithms help reveal the correlations between clinical Zhenghou phenomes and molecular mechanisms, thereby improving scientific rigor in diagnosis, efficacy evaluation, and personalized intervention. Nevertheless, challenges persist, including data quality and standardization issues, shortage of interdisciplinary talents, and insufficiency of ethical and legal regulations. Future development requires establishing national data-sharing platforms, strengthening international collaboration, fostering interdisciplinary professionals, and improving ethical and legal frameworks. Ultimately, this approach seeks to build a new disease identification and classification system centered on phenomes and to achieve the inheritance, innovation, and modernization of TCM diagnostic and therapeutic patterns.

Objective To investigate the correlation between levels of intercellular adhesion molecule-1 (ICAM-1), platelet surface P-selectin (CD62P), and inflammatory factors and cerebral artery stenosis in patients with acute cerebral infarction (ACI). Methods A total of 305 patients with ACI complicated with cerebral artery stenosis admitted to Zhongwu Hospital of Suqian City and Xinyi People's Hospital from January 2021 to December 2023 were selected as the research subjects. According to the degree of cerebral artery stenosis, they were divided into grade I group (stenosis degree<50%, n=85), grade II group (stenosis degree of 50%-75%, n=128), and grade III group (stenosis degree>75%, n=92). Sixty-eight ACI patients without cerebral artery stenosis during the same period were included in the reference group. The levels of serum inflammatory factors [C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-8 (IL-8)], ICAM-1 and CD62P were compared among the four groups. Spearman analysis was used to analyze the correlation between each factor and degree of cerebral artery stenosis. Results The levels of CRP, TNF-α, IL-6, IL-8, ICAM-1 and CD62P in the grade I, II and III groups were higher than those in the reference group. The levels of these factors were higher in the grade II and III groups than those in the grade I group, while the levels of various factors were higher in the grade III group than those in the grade II group (P<0.05). Spearman analysis showed that CRP, TNF-α, IL-6, IL-8, ICAM-1, and CD62P were positively correlated with the degree of cerebral artery stenosis in patients with ACI complicated with cerebral artery stenosis (P<0.05). Conclusion The levels of serum inflammatory factors, ICAM-1 and CD62P are significantly correlated with cerebral artery stenosis degree in patients with ACI.

Objective: To investigate the inhibitory effect of Elesclomol (ES) + Cu2 + on the proliferation of human hepatoma cell lines PLC/PRF/5 and Huh-7 and its potential to induce Cuproptosis. Methods: Human hepatoma cell lines PLC/PRF/5 and Huh_7 cells were Cultured in vitro. ES solution , Cu2 + solution and copper chelating agent ammonium tetrathiomolybdate VI (ATTM) solution was treated separately or in combination. The effect of ES + Cu2 + on the survival rate of human hepatoma cell lines PLC/PRF/5 and Huh_7 cells and the effect of ES + Cu2 + on the survival rate after pretreatment with copper chelating agent ATTM were evaluated using CCK_8 kit. The cell death induced by ES + Cu2 + was detected by flow cytometry and the changes of ES + Cu2 + after pretreatment with copper chelating agent ATTM. The expression of Cuproptosis related proteins ATPase copper transporting beta (ATP7B) ,ferredoxin 1 (FDX1) , dihydrolipoamide s_acetyltransferase(DLAT) and superoxide dismutase 1 (SOD1) were detected by Western blot. The effect of ES + Cu2 + on cell proliferation and the reverse effect after ATTM pretreatment was detected by cell scratch assay. Results: The toxicity of ES + Cu2 + to human hepatocellular carcinoma cell lines PLC/PRF/5 and Huh_7 was significantly dose_dependent (P < 0. 05) . Compared with the control group , the combined application of ES and Cu2 + had a more significant inhibitory effect on hepatocellular carcinoma cells than ES or Cu2 + alone (P < 0. 05) , and copper chelating agent ATTM could reverse the inhibitory effect of ES + Cu2 + on hepatocellular carcinoma cells (P < 0. 05) . Flow cytometry results showed that compared with the control group , the proportion of cell death in PLC/PRF/5 and Huh_7 cells treated with ES + Cu2 + increased , while the proportion of cell death decreased after ATTM intervention (P < 0. 05) . The results of cell scratch test showed that the migration ability of PLC/PRF/5 and Huh_7 cells was decreased after ES + Cu2 + treatment , however, the addition of ATTM reversed the inhibitory effect of ES + Cu2 + on cell migration (P < 0. 05) . Compared with the control group , the expression levels of copper death related proteins ATP7B , FDX1 , DLAT and SOD1 decreased after ES + Cu2 + treatment , but the addition of ATTM reversed the expression trend of these proteins (P < 0. 05) . Conclusion The combination of ES and Cu2 + can effectively inhibit the proliferation and migration of PLC/PRF/5 and Huh_7 of hepatocellular carcinoma cells , and induce Cuproptosis , which provides a new strategy for the treatment of hepatocellular carcinoma.

Monogenic hereditary cerebral small vessel disease （CSVD） is a group of genetic disorders characterized by cerebrovascular lesions， including cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy， cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy， COL4A1 and COL4A2-related CSVD， and other rare types such as retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations and leukoencephalopathy with calcifications and cysts. These diseases are often caused by specific gene mutations and exhibit highly heterogeneous pathological mechanisms and clinical manifestations， including inflammatory response， abnormal gene expression， and microangiopathy. The advances in imaging findings and biomarkers have provided new methods for early diagnosis， while treatment strategies include stem cell therapy， immunotherapy，gene editing， and molecular targeted therapy. However， further studies are needed to develop individualized treatment regimens for different subtypes. This article reviews the important advances in the pathogenesis， clinical features， diagnostic methods， and treatment modalities of monogenic hereditary CSVD in recent years， in order to provide guidance for future research and clinical practice.

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 analyze the diagnostic process and clinical characteristics of autoimmune gastritis(AIG)in order to improve the awareness and diagnostic proficiency of this disease.Methods A retrospective cohort study was conducted on 114 patients diagnosed with AIG in Army Medical Center of PLA between January 2021 and June 2024.Comprehensive statistical analysis was performed on clinical data,including demographic characteristics(age,sex),clinical symptoms,comorbidities,diagnostic process,Helicobacter pylori(H.pylori)infection and treatment history,laboratory indicators[results of routine blood test,anemia-related indices,thyroid function,anti-parietal cell antibody(APCA),intrinsic factor antibody(IFA)],and gastrointestinal endoscopic findings(frequency and endoscopic features).Results Among the 114 patients,males accounted for 28.1%(32/114)and females for 71.9%(82/114),and they were at a mean age of 56.3±8.4 years.Predominant symptoms included epigastric/upper abdominal pain(47.4%,54/114)and postprandial fullness(43.0%,49/114),while 24.6%(28/114)reported acid reflux or heartburn.Diagnostic delay occurred in 76.4%(87/114)of patients,with a median delay duration of 11.5 months.Primary diagnostic clues were endoscopic reverse gradient atrophy(significantly more severe mucosal atrophy in the gastric corpus/fundus versus antrum;53.5%,61/114)and repeated H.pylori eradication failure(≥2 attempts;22.8%,26/114).Positivity rate of thyroid peroxidase antibody(TPOAb)and thyroglobulin antibody(TgAb)was 56.9%(33/58)and 36.2%(21/58),respectively.APCA positive rate was 98.8%(81/82),IFA positive rate was 34.1%(28/82),and dual-antibody rate was 32.9%(27/82).Anemia was present in 25.7%(26/101)of the patients.Gastric neuroendocrine tumors(NET)were found in 12.2%(14/114),intraepithelial neoplasia in 5.3%(6/114),and gastric adenocarcinoma in 0.9%(1/114).Among colonoscopy-examined patients,tubular adenomas occurred in 25.0%(13/52)and colorectal malignancies in 3.4%(2/58).There were 18.4%(21/114)patients having gallbladder-related diseases,7.9%(9/114)having diabetes mellitus,and 1.8%(2/114)of subacute combined degeneration of the spinal cord.Conclusion AIG is frequently associated with diagnostic delay.The reverse pattern of atrophy on endoscopy serves as a critical diagnostic clue,necessitating enhanced recognition in endoscopists.Patients with recurrent H.pylori eradication failure(≥2 attempts)should be evaluated for AIG.