BACKGROUND:Several observational studies have found a strong association between thyroid function and its related disorders and osteoporosis,but the causal relationship is unclear.OBJECTIVE:To ascertain the causal relationship between genetically predicted thyroid function and its associated disorders,as well as osteoporosis,through the Mendelian randomization analysis with extensive pooled genetic data.METHODS:Pooled data from genome-wide association studies were employed to investigate the causal relationship between thyroid function and its associated disorders and osteoporosis.This was achieved through the utilization of the inverse variance weighting method as the primary Mendelian randomization analysis method,in conjunction with the MR-Egger method,weighted median method,simple model method,and weighted model method.A two-step mediated Mendelian randomization analysis was used to calculate the mediating effect of drug-mediated thyroid dysfunction on osteoporosis and the mediating proportion.Subsequently,sensitivity analyses were conducted using the MR-Egger intercept test and MR-PRESSO to detect multiplicity,Cochran's Q test to detect heterogeneity,and leave-one-out to perform sensitivity analyses.RESULTS AND CONCLUSION:(1)The results of the inverse variance weighting method showed that thyroid function had an effect on bone mineral density,and that thyrotropin,free triiodothyronine on bone mineral density,free thyroxine,and subclinical hyperthyroidism all had a causal effect on bone mineral density.(2)In addition,mediation analyses revealed a potential mediating effect of carbimazole in the causal relationship between hyperthyroidism and the risk of developing osteoporosis,as well as a potential mediating effect of levothyroxine sodium in the causal relationship between hypothyroidism and the risk of developing osteoporosis.(3)In conclusion,thyrotropin,which is high in the normal range,has been demonstrated to increase bone mineral density.Conversely,free triiodothyronine and free thyroxine,which are also high within the normal range,as well as subclinical hyperthyroidism,have been shown to decrease bone mineral density.The risk of developing osteoporosis is partially mediated by the pathway of taking the therapeutic medication in the context of pharmacologic treatment of thyroid dysfunction.(4)The present study primarily focuses on European population data.However,given the commonality of the genetic background and the generalizability of genome-wide data analysis methods,it is of significant importance to explore the pathogenesis of osteoporosis in the Chinese population,develop effective interventions,and provide genetic counseling.

ObjectiveTo investigate whether visceral fat area （VFA） is an independent risk factor for significant liver fibrosis in patients with nonalcoholic fatty liver disease （NAFLD） based on clinical data， and to establish an effective diagnostic model. MethodsA total of 222 NAFLD patients who attended Department of Hepatology， Guangdong Provincial Hospital of Traditional Chinese Medicine， from January 2021 to April 2025 were enrolled， and according to liver stiffness measurement （≥8 kPa or not）， they were divided into significant fibrosis group and non-significant fibrosis group. Propensity score matching （PSM） was performed at a ratio of 1∶1 to balance the baseline data between the two groups. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups； the chi-square test was used for comparison of categorical data between groups. A Spearman correlation analysis was used to determine the correlation of VFA and other indicators with significant liver fibrosis； univariate and multivariate logistic regression analyses were used to identify whether VFA was an independent risk factor for significant liver fibrosis in NAFLD patients， and the receiver operating characteristic （ROC） curve was plotted to assess the predictive performance of related indicators. ResultsA total of 45 patients with significant liver fibrosis and 177 patients without significant liver fibrosis were enrolled， and after PSM， 90 patients （45 pairs） were finally included in analysis. Compared with the non-significant fibrosis group， the significant fibrosis group had significantly higher levels of body mass index （BMI）， fasting blood glucose （FBG）， glycated hemoglobin （HbA1c）， uric acid （UA）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， gamma-glutamyl transpeptidase （GGT）， controlled attenuation parameter （CAP）， and VFA， as well as a significantly higher proportion of patients with visceral fat obesity or three or more metabolic risk factors （all P<0.05）. VFA， BMI， AST， and HbA1c were strongly correlated with significant liver fibrosis （all r>0.5， all P <0.05）， and ALT， GGT， UA， FBG， and CAP were significantly positively correlated with significant liver fibrosis （r=0.3　—　0.5， all P<0.05）. VFA （odds ratio ［OR］=1.040， 95% confidence interval ［CI］： 1.018　—　1.062， P<0.05）， FBG （OR=2.372， 95%CI： 1.199　—　4.691， P<0.05）， and AST （OR=1.032， 95%CI： 1.003　—　1.058， P<0.05） were independent risk factors for significant liver fibrosis in NAFLD patients. The new diagnostic model based on VFA， FBG， and AST （with an area under the ROC curve ［AUC］ of 0.907） had a significantly better performance than aspartate aminotransferase-to-platelet ratio index （AUC=0.834）， fibrosis-4 （AUC=0.660）， triglyceride-glucose index （AUC=0.656）， and NAFLD fibrosis score （AUC=0.768） in predicting significant liver fibrosis in NAFLD patients （all P<0.05）. ConclusionVFA is an independent risk factor for significant liver fibrosis in NAFLD patients， and the noninvasive diagnostic model based on VFA， FBG， and AST can effectively predict the onset of significant liver fibrosis in NAFLD patients.

ObjectiveTo investigate the effects of Jianpi Qinghua granules on blood glucose fluctuations in patients with newly diagnosed overweight/obese type 2 diabetes mellitus （T2DM） and Qi-Yin deficiency syndrome from the perspective of skeletal muscle mass and function， while providing new insights for the treatment of diabetes. MethodsThis study employed a randomized， double-blind， placebo-controlled design. A total of 110 newly diagnosed overweight/obese T2DM patients meeting the inclusion criteria were randomly assigned to either the traditional Chinese medicine （TCM） group （54 cases） or the control group （56 cases）. Patients in the TCM group received Jianpi Qinghua Granules， while those in the control group received a placebo. Both groups underwent dietary and exercise guidance. After 12 weeks of intervention， blood glucose fluctuations were assessed using the following parameters： time in the target blood glucose range （TIR）， mean daily blood glucose （MBG）， standard deviation of mean daily blood glucose （SDBG）， mean amplitude of glycemic excursions （MAGE）， coefficient of variation of blood glucose （CV）， glycated hemoglobin （HbA1c） achievement rate， fasting plasma glucose （FPG）， and 2 hour postprandial glucose （2 hPG）. Skeletal muscle mass was measured by dual-energy X-ray absorptiometry （DXA）， while skeletal muscle function was evaluated using a handheld dynamometer for distal muscle strength and a 5-time sit-to-stand test for lower limb function. Additionally， pancreatic islet function and TCM syndrome scores were analyzed. ResultsNo significant differences were observed in baseline data between the two groups before intervention， ensuring comparability. After treatment， compared to the control group， the TCM group showed a significant increase in TIR （P<0.01）. While the SDBG and CV decreased， and MBG and MAGE increased in the TCM group， these differences were not statistically significant. Notably， the TCM group exhibited significant reductions in 2 hPG （P<0.01） and HbA1c （P<0.05）， though the decrease in FPG was not statistically significant. The HbA1c achievement rate in the TCM group was significantly higher than that in the control group （χ²=45.498， P<0.01）. In terms of skeletal muscle mass and function， the TCM group demonstrated a significant increase in handgrip strength （P<0.01） and a significant reduction in the 5-time sit-to-stand duration （P<0.05）. However， although body fat percentage increased， leading to a decrease in skeletal muscle mass and the ratio of skeletal muscle to fat， these changes were not statistically significant. For pancreatic islet function， the TCM group showed significant reductions in fasting insulin （FINS） and homeostasis model assessment of insulin resistance （HOMA-IR） （P<0.01）. Additionally， the TCM syndrome score in the TCM group was significantly reduced （P<0.01）. ConclusionJianpi Qinghua granules may reduce blood glucose fluctuations in newly diagnosed overweight/obese T2DM patients with Qi-Yin deficiency syndrome by enhancing skeletal muscle function， improving pancreatic islet function， and ameliorating related TCM syndromes.

BACKGROUND:Several observational studies have found a strong association between thyroid function and its related disorders and osteoporosis,but the causal relationship is unclear.OBJECTIVE:To ascertain the causal relationship between genetically predicted thyroid function and its associated disorders,as well as osteoporosis,through the Mendelian randomization analysis with extensive pooled genetic data.METHODS:Pooled data from genome-wide association studies were employed to investigate the causal relationship between thyroid function and its associated disorders and osteoporosis.This was achieved through the utilization of the inverse variance weighting method as the primary Mendelian randomization analysis method,in conjunction with the MR-Egger method,weighted median method,simple model method,and weighted model method.A two-step mediated Mendelian randomization analysis was used to calculate the mediating effect of drug-mediated thyroid dysfunction on osteoporosis and the mediating proportion.Subsequently,sensitivity analyses were conducted using the MR-Egger intercept test and MR-PRESSO to detect multiplicity,Cochran's Q test to detect heterogeneity,and leave-one-out to perform sensitivity analyses.RESULTS AND CONCLUSION:(1)The results of the inverse variance weighting method showed that thyroid function had an effect on bone mineral density,and that thyrotropin,free triiodothyronine on bone mineral density,free thyroxine,and subclinical hyperthyroidism all had a causal effect on bone mineral density.(2)In addition,mediation analyses revealed a potential mediating effect of carbimazole in the causal relationship between hyperthyroidism and the risk of developing osteoporosis,as well as a potential mediating effect of levothyroxine sodium in the causal relationship between hypothyroidism and the risk of developing osteoporosis.(3)In conclusion,thyrotropin,which is high in the normal range,has been demonstrated to increase bone mineral density.Conversely,free triiodothyronine and free thyroxine,which are also high within the normal range,as well as subclinical hyperthyroidism,have been shown to decrease bone mineral density.The risk of developing osteoporosis is partially mediated by the pathway of taking the therapeutic medication in the context of pharmacologic treatment of thyroid dysfunction.(4)The present study primarily focuses on European population data.However,given the commonality of the genetic background and the generalizability of genome-wide data analysis methods,it is of significant importance to explore the pathogenesis of osteoporosis in the Chinese population,develop effective interventions,and provide genetic counseling.

ObjectiveTo explore the mechanism of Yishen Qubi Tongluo Formula （益肾祛痹通络方， YQTF） in the treatment of rheumatoid arthritis（RA）. MethodsNetwork pharmacology was employed to retrieve and screen the active components and potential targets of YQTF as well as RA-related targets using databases including TCMSP， BATMAN， ETCM and GEO. The intersection of targets related to active components and RA-related targets was identified， and a protein-protein interaction （PPI） network was constructed. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed， and a drug-active component-common target network of YQTF in the treatment of RA was established. The core components of YQTF were molecularly docked with key targets. Human rheumatoid arthritis synovial fibroblast cell line MH7A was divided into blank group， model group， methotrexate group and YQTF group. The blank group was cultured with 10% fetal bovine serum， while the other three groups were stimulated with 10 μg/L of recombinant human tumor necrosis factor-α （TNF-α） for 24 h to establish the RA cell model. On this basis， the methotrexate group was treated with methotrexate suspension at a concentration of 20 μmol/L， and the YQTF group was treated with 10% YQTF-medicated serum. After 48 h of intervention， the levels of TNF-α and interleukin-17A（IL-17A）contents in cell supernatants were detected by enzyme-linked immunosorbent assay （ELISA）， and mRNA expressions of phosphatidylinositol 3-kinase（PI3K）， protein kinase B（AKT） and mammalian target of rapamycin（mTOR） were detected by real-time quantitative polymerase chain reaction （RT-qPCR）. ResultsNetwork pharmacological analysis identified 209 active components and 583 potential target genes of YQTF， as well as 818 RA-related targets. A total of 29 common targets were obtained from the intersection of drug-related targets and RA-related targets. Quercetin，β-sitosterol， kaempferol， stigmasterol and luteolin were the core active components of YQTF for the treatment of RA， while matrix metalloproteinase-9 （MMP9）， prostaglandin-endoperoxide synthase 2 （PTGS2）， Toll-like receptor 4 （TLR4）， tumor protein p53 （TP53） and transcription factor AP-1 subunit JUN were the key targets. The GO and KEGG pathway enrichment analysis showed that the involved biological processes and pathways were mainly associated with antioxidant responses， PI3K-AKT signaling pathway and Toll-like receptor （TLR） signaling pathway. Molecular docking results showed that MMP9 and PTGS2 exhibited high binding affinities with quercetin， β-sitosterol， kaempferol， stigmasterol and luteolin； TLR4 exhibited high binding activities with β-sitosterol， stigmasterol and luteolin； and TP53 showed high binding affinity with luteolin. The results of cell experiments showed that compared with the control group， the contents of TNF-α and IL-17A as well as the mRNA expressions of AKT and mTOR in the model group significantly increased （P＜0.05 or P＜0.01）. Compared with the model group， all the above indicators significantly decreased in the YQTF group， while the contents of TNF-α and the mRNA expression of AKT significantly decreased in the methotrexate group （P＜0.05 or P＜0.01）. ConclusionThe mechanism of YQTF in the treatment of RA may be associated with reducing inflammatory cytokine secretion and inhibiting the activation of the PI3K-AKT-mTOR signaling pathway.

ObjectiveAs a second messenger in intracellular signal transduction, Ca²⁺ plays an important role in cell migration. Previous studies have demonstrated that extracellular Ca²⁺ influx can promote electric field-guided cell migration, known as electrotaxis. However, the effect of intracellular Ca²⁺ flow on electrotaxis is unclear. Therefore, in this study, we investigate the effect of Ca²⁺ flux on the electrotaxis of Dictyostelium discoideum. MethodsThe electrotaxis of Dictyostelium discoideum was investigated by applying a direct current (DC) electric field. Cell migration was recorded using a real-time imaging system. Calcium channel inhibitors, the extracellular Ca²⁺ chelator EGTA, Ca²⁺-free DB buffer, and caffeine were applied to investigate the impact of intra- and extracellular Ca²⁺ flow on electrotaxis. The involvement of G proteins and ERK2 in directed cell migration mediated by endoplasmic reticulum Ca²⁺ release was explored using mutants. ResultsDictyostelium discoideum migrated toward the cathode in the electric field in a voltage-dependent manner. The intracellular Ca²⁺ concentration of the cells was significantly increased in the electric field. Inhibition of both extracellular Ca²⁺ influx and intracellular Ca²⁺ release suppressed cell electrotaxis migration. Inhibition of endoplasmic reticulum Ca²⁺ release induced by caffeine significantly impaired the electrotaxis of Dictyostelium discoideum. Deletion of Gα2, Gβ, Gγ, and Erk2 notably reduced the electrotaxis of the cells. Enhancing Ca²⁺ release mediated by caffeine restored the electrotaxis of the Gα2^-, Gβ ^-, and Erk2^- mutant cells partially or completely, but did not restore electrotaxis in the Gγ^- mutant cells. ConclusionCa²⁺ release from the endoplasmic reticulum regulates electrotaxis migration in Dictyostelium discoideum and is involved in the regulation of cell electrotaxis by G proteins and ERK2.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background/Aims: Anti-reflux mucosal ablation (ARMA) is a promising endoscopic intervention for proton pump inhibitor (PPI)-dependent gastroesophageal reflux disease (GERD). However, the effect of ARMA on esophageal motility remains unclear. Methods: Twenty patients with PPI-dependent GERD receiving ARMA were prospectively enrolled. Comprehensive self-report symptom questionnaires, endoscopy, 24-hour impedance-pH monitoring, and high-resolution impedance manometry were performed and analyzed before and 3 months after ARMA. Results: All ARMA procedures were performed successfully. Symptom scores, including GerdQ (11.16 ± 2.67 to 9.11 ± 2.64, P = 0.026) and reflux symptom index (11.63 ± 5.62 to 6.11 ± 3.86, P = 0.001), improved significantly, while 13 patients (65%) reported discontinuation of PPI. Total acid exposure time (5.84 ± 4.63% to 2.83 ± 3.41%, P = 0.024) and number of reflux episodes (73.05 ± 19.34 to 37.55 ± 22.71, P < 0.001) decreased significantly after ARMA. Improved esophagogastric junction (EGJ) barrier function, including increased lower esophageal sphincter resting pressure (13.89 ± 10.78 mmHg to 21.68 ± 11.5 mmHg, P = 0.034), 4-second integrated relaxation pressure (5.75 ± 6.42 mmHg to 9.99 ± 5.89 mmHg, P = 0.020), and EGJ-contractile integral(16.42 ± 16.93 mmHg · cm to 31.95 ± 21.25 mmHg · cm, P = 0.016), were observed. Esophageal body contractility also increased significantly (distal contractile integral, 966.85 ± 845.84 mmHg · s · cm to 1198.8 ± 811.74 mmHg · s · cm, P = 0.023). Patients with symptom improvement had better pre-AMRA esophageal body contractility. Conclusions ARMA effectively improves symptoms and reflux burden, EGJ barrier function, and esophageal body contractility in patients with PPIdependent GERD during short-term evaluation. Longer follow-up to clarify the sustainability of ARMA is needed.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.