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.

The compound (E)-1-(4-(3-(5-chloro-6-oxo-3,6-dihydropyridin-1(2H)-yl)-3-oxo-propyl-1-ene-1-yl) phenyl)-3-(4-fluorophenyl) urea (C12), a novel derivative of piperlongumine previously synthesized by our research group, was investigated in this study to examine its effects on human non-small cell lung cancer cell line H1299 in vitro and elucidate its potential mechanism of action. The impact of C12 on the proliferation, migration, and invasion abilities of H1299 cells were assessed using methyl thiazolyl tetrazolium (MTT) assay, wound healing assay, cloning formation assay, and Transwell assay. Flow cytometry was employed to evaluate the influence of C12 on cell cycle progression, reactive oxygen species (ROS) production, mitochondrial membrane potential (MMP), and apoptosis induction in H1299 cells. Western blot analysis was conducted to investigate the expression levels of p21, Cyclin B1, CDK1, Bax, Bcl-2, JNK, p-JNK, Erk1/2, p-Erk1/2, p38 and p-p38 proteins for exploring the anti-tumor mechanism underlying C12's actions. The results demonstrated that C12 exerted inhibitory effects on the proliferation, migration, and invasion capacities of H1299 cells in a time-dependent and concentration-dependent manner. Moreover, C12 induced G2/M phase arrest in the cell cycle, reduced MMP levels, elevated ROS production, and triggered apoptotic processes. Flow cytometry analysis revealed that C12 downregulated Cyclin B1 and CDK1 protein expressions, resulting in G2/M phase arrest. C12 also upregulated Bax/Bcl-2 ratio, promoting apoptosis. Furthermore, C12 activated MAPK signaling pathway by enhancing phosphorylation levels of JNK, Erk1/2, and p38 proteins. In conclusion, C12 significantly suppressed proliferation, migration, and invasion capabilities while inducing cell cycle arrest and apoptosis in H1299 cells. These effects may be attributed to activation of the MAPK signaling pathway.

Eleven compounds were isolated from Eucommia ulmoides by silica gel, Sephadex LH-20, HW-40C column chromatography and semi-preparative HPLC. Their structures were identified by modern spectroscopic methods as neoeucommiate A (1), (7S,8R)-dihydrodehydrodiconiferyl alcohol (2), urolignoside (3), ficusal (4), tiruneesiin (5), glochidioboside (6), forsythialansides B (7), ecdysanol B (8), (+)-syringaresinol-4-O-β-D-glucopyranoside (9), (+)-pinoresinol 4-O-[6ʹʹ-O-vanilloyl]-β-D-glucopyranoside (10), samsesquinoside (11). Compound 1 is a new compound, compounds 3-7, 10 and 11 were isolated from Eucommia ulmoides for the first time.

Objective To investigate the molecular mechanism of Shema Zhichuan Liquid in the treatment of neutrophilic asthma(NA)based on network pharmacology and in vivo experiments.Methods(1)The TCMSP,literature search and Swiss ADME and Swiss Target Prediction databases were used to search and screen the active components and their targets of Shema Zhichuan Liquid.OMIM,GeneCards,DisGeNET and DrugBank databases were used to search and screen NA disease-related targets.The intersection of the active components and NA disease-related targets of Shema Zhichuan Liquid was obtained through the microbiology platform to obtain the potential targets of Shema Zhichuan Liquid for the treatment of NA(common targets).Cytoscape 3.8 software was used to construct the network of"Chinese medicinals-active components-potential targets".The PPI network of potential targets was established by STRING database,and the core targets were obtained by analysing the built-in Mcode plug-in.The Metascape platform was used to enrich the gene ontology(GO),Kyoto Encyclopaedia of Genes and Genomes(KEGG)pathways for the potential targets.(2)BALB/C mice were acclimatised and fed for 1 week and randomly divided into a blank group,NA model group,low-dose group(2.5 g·kg-1)and high-dose group of Shema Zhichuan Liquid(10 g·kg-1),and control group of Dexamethasone(1 mg·kg-1);the NA mouse model was replicated by intraperitoneal injection of sensitizer(OVA+CFA)and nebulized inhalation excitation.OVA/CFA(20 μg OVA+75 μg CFA,0.3 mL)was injected intraperitoneally to sensitize on days 0,7 and 14 respectively,and 5%OVA suspension was nebulized on days 21-30(8 mL each time,40 minutes each time,once a day);1 hour before nebulisation,each group was administered by gastric gavage,and the Dexamethasone control group was administered by intraperitoneal injection once a day.The pathological changes of mouse lung tissue were observed by HE staining;IL-8 content in alveolar lavage fluid was detected by ELISA;mRNA expression levels of NLRP3 and CXCR2 were detected by RT-qPCR;and p-mTOR protein expression levels was detected by immunohistochemistry.Results(1)A total of 826 active component targets and 154 NA disease-related targets were obtained,and 51 potential targets(common targets)for the treatment of NA were obtained from the intersection of the active component and the NA disease-related targets of Shema Zhichuan Liquid.Through the network analysis of"Chinese medicinals-active components-potential targets",quercetin,lignocerotoxin,kaempferol,stigmasterol,naringenin and other key active components were obtained.The PPI network analysis of potential targets yielded 29 core targets,including AKT1,IL6,TNF,EGFR,NLRP3,RELA,MIF,CXCR2,VEGFA,etc..The GO functional enrichment analysis yielded 882 biological process entries,33 cellular component entries,and 61 molecular function entries;KEGG analysis yielded 142 signaling pathways,mainly involving TNF signaling pathway,influenza A signaling pathway,Toll-like receptor pathway,MAPK signaling pathway,mTOR signaling pathway and so on.(2)Results of animal experiments:compared with the blank group,mice in the NA model group showed obvious damage to the airway mucosa,structural disorders,a large number of inflammatory cells infiltration,mucosal congestion,oedema,obvious thickening of the alveolar wall,and narrowing of the alveolar lumen;the level of the inflammatory factor IL-8 in the alveolar lavage fluid was significantly elevated(P＜0.05);the mRNA expressions of NLRP3 and CXCR2 in the lung tissues of the mice were significantly up-regulated(P＜0.01),and the protein expression of p-mTOR was significantly increased.Compared with the NA model group,the structural arrangement of bronchial epithelial cells in the mice in the low-and high-dose groups of Shema Zhichuan Liquid was slightly disordered,with a small amount of inflammatory cell infiltration around the airways and blood vessels,and the congestion and edema of the bronchial mucosa were significantly reduced;the mRNA expression of CXCR2 in the lung tissues of the mice was significantly down-regulated(P＜0.01),and the level of expression of p-mTOR protein was significantly reduced.The IL-8 level in the vesicular lavage fluid of mice in the high-dose group was significantly reduced(P＜0.05);the mRNA expression of NLRP3 in the lung tissue of mice in the low-dose group was significantly down-regulated(P＜0.05).Conclusion The therapeutic effect of Shema Zhichuan Liquid on NA may be achieved through the key active components,such as quercetin,lignocerol and kaempferol,acting on the core targets,such as NLRP3 and CXCR2,and regulating the key signaling pathways,such as the TNF signaling pathway,the MAPK signaling pathway,the Toll-like signaling pathway,and the mTOR pathway.