The prevalence rate of metabolic associated fatty liver disease （MAFLD） is steadily increasing worldwide， and MAFLD is now considered a significant risk factor for a wide range of metabolic comorbidities. However， current clinical management strategies often address MAFLD from a single-disease perspective， lacking a comprehensive understanding of the central role and systemic impact of MAFLD in the prevention and control of metabolic comorbidities. This article reviews the current evidence supporting MASLD as both a trigger and a key node in systemic metabolic dysfunction and elaborates on how hepatic insulin resistance， lipotoxic injury， inflammatory responses， and dysregulation of hepatokines mediate organ-specific metabolic disorders including cardiovascular disease， chronic kidney disease， and diabetes. With reference to the latest national and international guidelines， this article proposes an integrated multidisciplinary management strategy， including liver-glucose joint intervention and a cross-organ “cardio-renal-hepatic” strategy， and it also advocates for a paradigm shift from conventional liver-focused management toward liver-centered systemic metabolic control， in order to effectively delay the progression of MAFLD and its related multisystem complications.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

BACKGROUND: The optimal antidepressant dosages remain controversial. This study aimed to analyze the efficacy of antidepressants and characterize their dose-response relationships in the treatments of major depressive disorders (MDD). METHODS: We searched multiple databases, including the Embase, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science, for the studies that were conducted between January 8, 2016, and April 30, 2023. The studies are double-blinded, randomized controlled trials (RCTs) involving the adults (≥18 years) with MDD. The primary outcomes were efficacy of antidepressant and the dose-response relationships. A frequentist network meta-analysis was conducted, treating participants with various dosages of the same antidepressant as a single therapy. We also implemented the model-based meta-analysis (MBMA) using a Bayesian method to explore the dose-response relationships. RESULTS: The network meta-analysis comprised 135,180 participants from 602 studies. All the antidepressants were more effective than the placebo; toludesvenlafaxine had the highest odds ratio (OR) of 4.52 (95% confidence interval [CI]: 2.65-7.72), and reboxetine had the lowest OR of 1.34 (95%CI: 1.14-1.57). Moreover, amitriptyline, clomipramine, and reboxetine showed a linear increase in effect size from low to high doses. The effect size of toludesvenlafaxine increased significantly up to 80 mg/day and subsequently maintained the maximal dose up to 160 mg/day while the predictive curves of nefazodone were fairly flat in different dosages. CONCLUSIONS: Although most antidepressants were more efficacious than placebo in treating MDD, no consistent dose-response relationship between any antidepressants was observed. For most antidepressants, the maximum efficacy was achieved at lower or middle prescribed doses, rather than at the upper limit. REGISTRATION No. CRD42023427480; https://www.crd.york.ac.uk/prospero/display_record.php?
Humans ; Antidepressive Agents/therapeutic use* ; Depressive Disorder, Major/drug therapy* ; Dose-Response Relationship, Drug ; Randomized Controlled Trials as Topic

OBJECTIVES: To explore the underlying pharmacological mechanisms and its potential effects of Chinese medicine herbal formula Sini Powder (SNP) on hepatocellular carcinoma (HCC). METHODS: The active components of SNP and their in vivo distribution were identified using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Construction of component-target-disease networks, protein-protein interaction network, Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and molecular docking were employed to analyze the active components and anti-HCC mechanisms of SNP. Cell viability assay and wound healing assay were utilized to confirm the effect of SNP-containing serum (2.5%, 5.0%, 10%, 20%, and 40%), isoprenaline or propranolol (both 10, 100, and 1,000 µ mol/L) on proliferation and migration of HepG 2 or Huh7 cells. Meanwhile, the effect of isoprenaline or propranolol on the β 2 adrenergic receptor (ADRB2) mRNA expression on HepG2 cells were measured by real-time quantitative reverse transcription (RT-qPCR). Mice with subcutaneous tumors were either subjected to chronic restraint stress (CRS) followed by SNP administration (364 mg/mL) or directly treated with SNP (364 mg/mL). These two parallel experiments were performed to validate the effects of SNP on stress responses. Stress-related proteins and hormones were quantified using RT-qPCR, enzyme-linked immunosorbent assay, and immunohistochemistry. Metagenomic sequencing was performed to confirm the influence of SNP on the gut microbiota in the tumor-bearing CRS mice. RESULTS: The distribution of the 12 active components of SNP was confirmed in various tissues and feces. Network pharmacology analysis confirmed the anti-HCC effects of the 5 active components. The potential anti-HCC mechanisms of SNP may involve the epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase Src (SRC) and signal transducer and activator of transcription 3 (STAT3) pathways. SNP-containing serum inhibited the proliferation of HepG2 and Huh7 cells at concentrations of 2.5% and 5.0%, respectively, after 24 h of treatment. Furthermore, SNP suppressed tumor progression in tumor-bearing mice exposed to CRS. SNP treatment also downregulated the expressions of stress-related proteins and pro-inflammatory cytokines, primarily by modulating the gut microbiota. Specifically, the abundance of Alistipes and Prevotella, which belong to the phylum Bacteroidetes, increased in the SNP-treated group, whereas Lachnospira, in the phylum Firmicutes, decreased. CONCLUSION SNP can combat HCC by alleviating stress responses through the regulation of gut microbiota.
Animals ; Gastrointestinal Microbiome/drug effects* ; Liver Neoplasms/microbiology* ; Carcinoma, Hepatocellular/microbiology* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Powders ; Cell Proliferation/drug effects* ; Mice ; Molecular Docking Simulation ; Cell Line, Tumor ; Hep G2 Cells ; Receptors, Adrenergic, beta-2/genetics* ; Stress, Physiological/drug effects* ; Cell Movement/drug effects* ; Male ; Protein Interaction Maps/drug effects* ; Cell Survival/drug effects* ; Proto-Oncogene Mas

Colorectal tumorigenesis generally progresses from adenoma to adenocarcinoma, accompanied by dynamic changes in the tumor microenvironment (TME). A randomized controlled trial has confirmed the efficacy and safety of Shen-Bai-Jie-Du decoction (SBJDD) in preventing colorectal tumorigenesis. However, the mechanism remains unclear. In this study, we employed single-cell RNA sequencing (scRNA-seq) to investigate the dynamic evolution of the TME and validated cell infiltration with multiplex immunohistochemistry and flow cytometry. Bulk RNA sequencing was utilized to assess the underlying mechanisms. Our results constructed the mutually verifiable single-cell transcriptomic atlases in Apc ^Min/+ mice and clinical patients. There was a marked accumulation of CCL22⁺ dendritic cells (DCs) and an enhanced immunosuppressive action, which SBJDD and berberine reversed. Combined treatment with cholesterol and lipopolysaccharide induced characteristic gene expression of CCL22⁺ DCs, which may represent "exhausted DCs". Intraperitoneal injection of these DCs after SBJDD treatment eliminated its therapeutic effects. TMEM131 derived CCL22⁺ DCs generation by TNF signaling pathway and may be a potential target of berberine in retarding colorectal tumorigenesis. These findings emphasize the role of exhausted DCs and the regulatory mechanisms of SBJDD and berberine in colorectal cancer (CRC), suggesting that the multi-component properties of SBJDD may help restore TME homeostasis and offer novel cancer therapy.

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.

Non-functioning pituitary adenomas(NFPAs)are relatively common.Apart from hyperprolactinemia caused by pituitary compression,they typically lack overt hormonal hypersecretion and usually present with clinical symptoms due to mass effects.Previously considered a uniform entity,NFPAs are actually a highly heterogeneous group of tumors,including aggressive subtypes like silent corticotroph adenomas(SCA)and null cell adenomas.The 2022 WHO new classification of pituitary tumors employs transcription factors[e.g.,pituitary-specific transcription factor 1(PIT-1),T-box transcription factor 19(TBX19,also known as TPIT),steroidogenic factor 1(SF-1)]for detailed categorization,allowing precise subclassification of NFPAs into multiple subtypes derived from distinct cell lineages,including silent gonadotroph adenomas,SCA,and plurihormonal PIT-1-positive adenomas.This helps identify highly invasive subtypes with high recurrence risk,guiding clinical diagnosis and treatment,prognostic assessment,and individualized management.The new classification also provides a theoretical basis for targeted therapies of NFPAs(e.g.,somatostatin analogs and temozolomide).This review comprehensively discusses the latest pathological classification of NFPAs and its clinical implications,aiming to enhance understanding of this disease and offer valuable insights for precise diagnosis,treatment,and prognostic assessment.

Objective To investigate the infection with key intestinal parasites in the Jinuo-inhabited areas of Yunnan Province in 2023 and provide a scientific basis for preventing and controlling local parasitic diseases.Methods A cluster random sampling method was used to conduct a survey on human intestinal parasitic diseases in Jinuoshan Township,Jinghong City,Xishuangbanna Dai Autonomous Prefecture,Yunnan Province,from October to November 2023.Fecal samples and information on epidemiological factors were collected from permanent residents of the Jinuo ethnic group.Intestinal helminth eggs were detected and counted using the modified Kato-Katz technique(two slides per fecal sample).Intestinal protozoa were identified via iodine-stained smears.Each fecal sample was subjected to hookworm larval culture using the test tube filter paper method for species identification.Additionally,transparent adhesive tape anal swabs were used to detect Enterobius vermicularis eggs in children aged 3-9 years.Results A total of 1 367 individuals was examined,with 11 species of parasites detected,including 5 helminth species and 6 protozoan species.The overall intestinal parasite infection rate was 25.97%,with helminth and protozoan infection rates at 17.63%and 9.07%,respectively.The infection rate of E.vermicularis in children(via anal swabs)was 16.35%.The infection rates of specific parasites were as follows:Ascaris lumbricoides 0.07%,hookworm 13.97%,Trichuris sp.0.29%,Taenia solium 0.15%,Entamoeba nana 1.68%,Giardia lamblia 1.61%,Entamoeba histolytica 0.07%,Entamoeba coli 0.07%,Iodamolbe butschlii 1.61%,and Blastocystis hominis 4.10%.The degree of Acaris lumbricoides,hookworm,and Trichuris sp.infection was mainly mild.Hookworm species were identified as Necator americanus and Ancylostoma duodenale.No statistically significant differences in infection rates were observed across different village committees,genders,age groups,or occupations(P＞0.05).The infection rates of preschool children and illiterate people were higher at 82.61%and 67.02%,respectively(χ2=344.798,P＜0.01).The infection rate of the Jinuo people was 32.49%,which is higher than that of other ethnic groups(χ2=109.493,P＜0.01).Conclusions The prevalence of intestinal parasitic infections among the Jinuo ethnic group in Yunnan Province remains relatively high.Strengthened prevention and control measures targeting relevant populations are urgently needed,with a particular focus on hookworm infections and E.vermicularis infections in children.

OBJECTIVE To evaluate the clinical efficacy and safety of aspirin versus other anticoagulants in the prevention of thromboembolism after orthopedic surgery. METHODS Retrieved from PubMed， Embase， the Cochrane Library， CNKI， Wanfang data and VIP， randomized controlled trials （RCTs） and cohort studies about aspirin （trial group） versus other anticoagulants （control group） were collected during the inception and June 1st， 2023. After literature screening， data extraction and quality evaluation， the meta-analysis was conducted by using RevMan 5.4 software. RESULTS A total of 22 studies were included， involving 9 RCTs and 13 cohort studies. RCT results showed that the incidences of deep vein thrombosis （DVT） [RR＝1.81， 95%CI（1.36， 2.40）， P＜0.000 1] and postoperative pulmonary embolism （PE） [RR＝1.55， 95%CI（1.01， 2.40）， P＝0.05] in trial group were significantly higher than control group. There was no statistically significant difference in the incidences of postoperative massive bleeding， postoperative surgical site infection， all-cause death， or any bleeding after surgery between 2 groups. In the cohort study， the incidence of any bleeding in trial group was significantly lower than control group [RR＝0.71，95%CI （0.64， 0.79）， P＜0.000 1]， while the differences in other indicators were not statistically significant （P＞0.05）. The results of subgroup analysis based on different anticoagulants showed that in RCT， the incidences of DVT and PE after surgery in patients using low-molecular-weight heparin （LMWH） were significantly lower than using aspirin （P＜0.05）； in the cohort study， the incidences of DVT and PE after surgery were significantly lower in patients using direct oral anticoagulants （DOAC） than using aspirin （P＜0.05）. There was no statistically significant difference in the incidence of major bleeding between patients using aspirin and using DOAC and LWMH （P＞0.05） in both RCT and cohort study. CONCLUSIONS Aspirin is equally safe as other anticoagulants for the prevention of thromboembolism after orthopedic surgery， but its efficacy may not be as good as other anticoagulants. After orthopedic surgery， other anticoagulants should be preferred to prevent venous thromboembolism， and aspirin should be carefully considered.