ObjectiveTo investigate the association between insulin resistance （IR） and cardiovascular metabolic indicators， including blood pressure， blood lipids， and serum uric acid， in a middle-aged population. MethodsBased on the baseline survey of the Hong Sifang cohort of China National Salt Industry Group， a total of 575 participants were included. According to the homeostasis model assessment of insulin resistance， the participants were divided into an IR group （n=116） and a non-IR group （n=459）. Differences between the two groups were compared using the t-test and chi-square test. Multivariate logistic regression analysis was performed to evaluate the associations between IR and abnormalities in metabolic indicators. ResultsA total of 575 participants were included， including 390 men and 185 women， with a mean age of （48.58 ± 5.14） years and a mean body mass index （BMI） of （24.16 ± 2.97） kg/m². Blood pressure， serum uric acid， and blood lipid levels were significantly higher in the IR group than in the non-IR group （P<0.001）. After adjusting for potential confounders including age， sex， and BMI， IR was independently associated with hypertension， dyslipidemia， and hyperuricemia， with odds ratios （OR） and 95% confidence intervals （CI） of 2.57 （1.61-4.09）， 3.81 （2.36-6.17）， and 2.68 （1.53-4.71）， respectively （all P<0.001）. ConclusionInsulin resistance is significantly associated with multiple cardiovascular metabolic risk factors in the middle-aged population. Monitoring IR may contribute to the early prevention and control of cardiovascular diseases.

GPR126, also known as ADGRG6, is one of the most deeply studied aGPCRs. Initially, GPR126 was thought to be a receptor associated with muscle development and was primarily expressed in the muscular and skeletal systems. With the deepening of research, it was found that GPR126 is expressed in multiple mammalian tissues and organs, and is involved in many biological processes such as embryonic development, nervous system development, and extracellular matrix interactions. Compared with other aGPCRs proteins, GPR126 has a longer N-terminal domain, which can bind to ligands one-to-one and one-to-many. Its N-terminus contains five domains, a CUB (complement C1r/C1s, Uegf, Bmp1) domain, a PTX (Pentraxin) domain, a SEA (Sperm protein, Enterokinase, and Agrin) domain, a hormone binding (HormR) domain, and a conserved GAIN domain. The GAIN domain has a self-shearing function, which is essential for the maturation, stability, transport and function of aGPCRs. Different SEA domains constitute different GPR126 isomers, which can regulate the activation and closure of downstream signaling pathways through conformational changes. GPR126 has a typical aGPCRs seven-transmembrane helical structure, which can be coupled to Gs and Gi, causing cAMP to up- or down-regulation, mediating transmembrane signaling and participating in the regulation of cell proliferation, differentiation and migration. GPR126 is activated in a tethered-stalk peptide agonism or orthosteric agonism, which is mainly manifested by self-proteolysis or conformational changes in the GAIN domain, which mediates the rapid activation or closure of downstream pathways by tethered agonists. In addition to the tethered short stem peptide activation mode, GPR126 also has another allosteric agonism or tunable agonism mode, which is specifically expressed as the GAIN domain does not have self-shearing function in the physiological state, NTF and CTF always maintain the binding state, and the NTF binds to the ligand to cause conformational changes of the receptor, which somehow transmits signals to the GAIN domain in a spatial structure. The GAIN domain can cause the 7TM domain to produce an activated or inhibited signal for signal transduction, For example, type IV collagen interacts with the CUB and PTX domains of GPR126 to activate GPR126 downstream signal transduction. GPR126 has homology of 51.6%-86.9% among different species, with 10 conserved regions between different species, which can be traced back to the oldest metazoans as well as unicellular animals.In terms of diseases, GPR126 dysfunction involves the pathological process of bone, myelin, embryo and other related diseases, and is also closely related to the occurrence and development of malignant tumors such as breast cancer and colon cancer. However, the biological function of GPR126 in various diseases and its potential as a therapeutic target still needs further research. This paper focuses on the structure, interspecies differences and conservatism, signal transduction and biological functions of GPR126, which provides ideas and references for future research on GPR126.

Cholesterol on the cell membrane plays a crucial role in regulating membrane proteins,influencing cell functions,and the occurrence and development of tumors.The CD44 receptor protein is closely associated with tumor invasion and metastasis.Therefore,developing biosensors capable of in-situ simultaneous monitoring of cholesterol and CD44 receptor protein on the cell membrane is of great significance for disease diagnosis.In this study,a spatially resolved strategy dual-signal multifunctional electrochemiluminescence(ECL)sensor was designed.The positive potential luminescent probe(ALC)was prepared by combining gold nanostars with luminol and cholesterol oxidase(ChOx),and the negative potential luminescent probe(CZA)was prepared by combining carbon quantum dots(CQD)with ZIF-8 and gold nanoparticles(AuNPs).The dual probes were fixed on dual working electrodes respectively to construct a spatial resolution dual signal sensor,and the signals were independently output with K2S2O8 as co-reactant.When the measurement signal of ALC probe was enhanced due to enzymatic catalysis of cholesterol,CZA synchronously output the internal reference signal,realizing ratio ECL detection of cholesterol,with a linear range of 50 nmol/L-1600 μmol/L and a detection limit of 20 nmol/L.Furthermore,after the double probes were respectively modified with folic acid(FA)and hyaluronic acid(HA),a spatially resolved dual-functional cell sensor was constructed by capturing human hepatoma cells HepG2 through ligand-receptor specific recognition.This sensor enabled the simultaneous monitoring of cholesterol-ChOx catalytic reaction and CD44-HA recognition on the cell membrane,as well as the quantitative detection of cancer cells and cholesterol on the cell membrane.The results showed that the linear detection range for HepG2 cell was 100-10000 cell/mL.By reducing cholesterol on the cell membrane using methyl-β-cyclodextrin(MβCD)downregulated CD44 expression,it was found that the cholesterol content was positively correlated with CD44 expression on the cell membrane.The control experiments showed that the analysis method was feasible.The designed ECL sensor could be used to detect cholesterol ratiometrically and provided a new method for the simultaneous analysis of multiple functional molecules in cells.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective:Invasive pulmonary mucormycosis(PM)is a rare but highly lethal opportunistic infection.COVID-19 associated mucormycosis(CAM)is difficult to diagnose,often leading to misdiagnosis or missed diagnosis,and has poor treatment outcomes.This study reports a case of successfully treated CAM and explores optimized diagnostic and therapeutic strategies.Methods:A retrospective analysis of the diagnosis and treatment process in a 50-year-old female patient with COVID-19 associated with diabetic ketoacidosis(DKA)and invasive pulmonary mucormycosis was conducted.Combined with a literature review,the therapeutic efficacy of local bronchoscopic instillation in conjunction with systemic treatment using liposomal Amphotericin B(L-AmB)was specifically evaluated.Results:The patient was rapidly diagnosed with Rhizopus microsporus infection through metagenomic next-generation sequencing(mNGS).She subsequently received antifungal treatment with intravenous L-AmB combined with local bronchoscopic instillation.After treatment,the patient was significantly improved,with imaging studies showing gradual absorption of the lesions.Follow-up at six months revealed no recurrence.A literature review suggests that early diagnosis and multimodal therapy are key to improving survival rates in patients with CAM.Conclusion:mNGS can significantly improve the early diagnosis rate of CAM.The combination of local and systemic treatment with L-AmB is valuable in improving prognosis.Early diagnosis,multimodal antifungal therapy,and individualized management are key to increasing the survival rate of patients with CAM.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Liver diseases have a high prevalence rate worldwide with relatively poor long-term clinical outcomes and have become one of the leading causes of disease burden and death around the world,which poses significant challenges to public health.Eosinophils(Eos)are a class of highly conserved multifunctional immune cells that play critical effector roles in allergic diseases.In recent years,an increasing amount of evidence has shown that Eos plays an important role in the pathogenesis of liver diseases,exerting a protective or harmful effect in different liver diseases,which has become a research hotspot in this field.This article elaborates on the role and potential mechanism of action of Eos in liver diseases,in order to provide a new perspective for in-depth research on the pathogenesis of liver diseases and lay the foundation for developing therapeutic strategies targeting Eos.

Objective To explore the mechanism of matrine in the treatment of uveal melanoma by network pharmacology,and the related results were verified by molecular docking and cell experiments.Methods The potential targets of matrine were obtained from Swiss Target Prediction,SuperPred and TCMSP databases.The targets related to uveal melanoma were obtained from GeneCards,OMIM,CTD and DrugBank databases.Protein-protein interaction(PPI)network was established to screen core targets.Gene ontology(GO)enrichment analysis and Kyoto encyclopedia of genes and genomes(KEGG)signal pathway analysis were carried out for potential targets.The interaction between matrine and core targets was evaluated by molecular docking technique.The effects of different concentrations of matrine(0.25 g/L,0.50 g/L,1.00 g/L,2.00 g/L)on the proliferation of uveal melanoma cells were evaluated based on CCK-8 method.Western blot was used to verify the regulatory effects of different concentrations of matrine(0.25 g/L,0.50 g/L,1.00 g/L,2.00 g/L)on PI3K-Akt signaling pathway.Results A total of 208 potential targets of matrine were identified,and 5 453 targets related to uveal melanoma were obtained.The topological analysis of PPI network revealed 8 core targets,namely interleukin-6(IL-6),tumor necrosis factor(TNF),myelocytomatosis proteins(MYC),signal transducer and activator of transcription 3(STAT3),caspase-3(CASP3),heat shock protein 90AB1(HSP90AB1),mammalian target of rapamycin(mTOR),and matrix metalloproteinase 9(MMP9).GO enrichment analysis showed that biological processes(BP)mainly included inflammatory response,protein phosphorylation and response to exogenous stimuli;cell components(CC)mainly included plasma membrane,cell surface and cytoplasm;molecular function(MF)mainly included the same protein binding,ATP binding and kinase activity.The enrichment analysis of KEGG pathway showed that the effect of matrine was mediated by viral carcinogenesis,cancer pathway,TNF signaling pathway and PI3K-Akt signaling pathway.Molecular docking showed that matrine had good binding ability with the selected core targets.The results of cell experiments showed that matrine at concentrations of 0.50 g/L,1.00 g/L and 2.00 g/L could inhibit the proliferation of MuM2B cells,and the cell survival rate gradually decreased with the increase of concentration.Matrine at concentrations of 0.50 g/L,1.00 g/L and 2.00 g/L could down-regulate the protein expression levels of p-PI3K and p-Akt,and the protein expression levels of p-PI3K and p-Akt gradually decreased with the increase of concentration.Conclusion Matrine acts on tar-gets such as IL-6,TNF,MYC,STAT3,CASP3,HSP90AB1,mTOR,MMP9,and exerts therapeutic effects on uveal melanoma by viral carcinogenesis,cancer pathway,TNF signaling pathway and PI3K-Akt signaling pathway,etc.

Objective To explore the mechanism of matrine in the treatment of uveal melanoma by network pharmacology,and the related results were verified by molecular docking and cell experiments.Methods The potential targets of matrine were obtained from Swiss Target Prediction,SuperPred and TCMSP databases.The targets related to uveal melanoma were obtained from GeneCards,OMIM,CTD and DrugBank databases.Protein-protein interaction(PPI)network was established to screen core targets.Gene ontology(GO)enrichment analysis and Kyoto encyclopedia of genes and genomes(KEGG)signal pathway analysis were carried out for potential targets.The interaction between matrine and core targets was evaluated by molecular docking technique.The effects of different concentrations of matrine(0.25 g/L,0.50 g/L,1.00 g/L,2.00 g/L)on the proliferation of uveal melanoma cells were evaluated based on CCK-8 method.Western blot was used to verify the regulatory effects of different concentrations of matrine(0.25 g/L,0.50 g/L,1.00 g/L,2.00 g/L)on PI3K-Akt signaling pathway.Results A total of 208 potential targets of matrine were identified,and 5 453 targets related to uveal melanoma were obtained.The topological analysis of PPI network revealed 8 core targets,namely interleukin-6(IL-6),tumor necrosis factor(TNF),myelocytomatosis proteins(MYC),signal transducer and activator of transcription 3(STAT3),caspase-3(CASP3),heat shock protein 90AB1(HSP90AB1),mammalian target of rapamycin(mTOR),and matrix metalloproteinase 9(MMP9).GO enrichment analysis showed that biological processes(BP)mainly included inflammatory response,protein phosphorylation and response to exogenous stimuli;cell components(CC)mainly included plasma membrane,cell surface and cytoplasm;molecular function(MF)mainly included the same protein binding,ATP binding and kinase activity.The enrichment analysis of KEGG pathway showed that the effect of matrine was mediated by viral carcinogenesis,cancer pathway,TNF signaling pathway and PI3K-Akt signaling pathway.Molecular docking showed that matrine had good binding ability with the selected core targets.The results of cell experiments showed that matrine at concentrations of 0.50 g/L,1.00 g/L and 2.00 g/L could inhibit the proliferation of MuM2B cells,and the cell survival rate gradually decreased with the increase of concentration.Matrine at concentrations of 0.50 g/L,1.00 g/L and 2.00 g/L could down-regulate the protein expression levels of p-PI3K and p-Akt,and the protein expression levels of p-PI3K and p-Akt gradually decreased with the increase of concentration.Conclusion Matrine acts on tar-gets such as IL-6,TNF,MYC,STAT3,CASP3,HSP90AB1,mTOR,MMP9,and exerts therapeutic effects on uveal melanoma by viral carcinogenesis,cancer pathway,TNF signaling pathway and PI3K-Akt signaling pathway,etc.