Traditional treatment for type 1 diabetes mellitus （T1DM） primarily involves insulin replacement， yet some patients encounter issues such as significant blood glucose fluctuations， high risk of hypoglycemia， and weight gain. In recent years， the adjuvant therapeutic role of non-insulin hypoglycemic drugs in T1DM has gradually gained attention. This article reviews the mechanisms of action and clinical research progress of five types of non-insulin hypoglycemic drugs in the treatment of T1DM： amylin analogues （pramlintide）， biguanides （metformin）， sodium-glucose co-transporter 2 inhibitor， dipeptidyl peptidase-4 inhibitor， and glucagon-like peptide-1 receptor agonist. It is found that these drugs can enhance clinical benefits for T1DM patients by improving insulin sensitivity， delaying gastric emptying， promoting urinary glucose excretion， and regulating incretin levels， thereby reducing glycated hemoglobin levels， decreasing insulin dosage， and managing body weight. Simultaneously， these drugs also present limitations such as low patient compliance due to complex dosing regimens， increased risk of diabetic ketoacidosis， and heterogeneity in glycemic control. Future research could focus on developing individualized treatment strategies， combining pharmacogenomics with novel biomarkers to precisely identify subpopulations of patients who may benefit， and delving into the potential value of these drugs in delaying diabetic vascular complications and improving patients’ quality of life.

Background Microparticles (MPs) are one of the main medium of inflammatory reaction with an important role in atherosclerotic progression. Studies on association of air pollution exposure and levels of peripheral blood MPs are limited among human. Objective To evaluate the effects of short-term exposure to air pollution on levels of peripheral blood MPs. Method A panel of 73 healthy adults was followed with 4 repeated follow-ups in Beijing, China, from November 2014 to January 2016. During each visit, we collected questionnaire information, fasting venous blood, urine, and exposures to fine particulate matter (PM_2.5), black carbon, nitric oxide, nitrogen dioxide, nitrogen oxide, sulfur dioxide, carbon monoxide, and ozone. We used linear mixed-effect models to analyze associations of air pollution exposure with levels of total MPs (TMPs) and MPs derived from various cells. Stratified analysis was conducted by levels of C-reactive protein (CRP) and malondialdehyde (MDA). Results The results showed significant associations between air pollution exposure and peripheral blood TMPs at 2 h-6 d prior to the follow-ups (P<0.05), while no statistical associations were found for MPs derived from different cell types. Significant increases in TMPs of 7.8% (95%CI: 0.7%, 15.3%) and 14.3% (95%CI: 2.8%, 27.2%) were observed with each interquartile range (IQR) increase in PM_2.5 (IQR=64.9 μg·m⁻³) at prior 18 h and NO (IQR=40.5 μg·m⁻³) at prior 48 h. Among participants with low levels of CRP and MDA, significantly positive associations were observed between air pollution exposure and levels of TMPs (P<0.05). Conclusion Short-term exposure to air pollution is significantly associated with increased levels of circulating MPs in healthy adults, and in people with lower systemic inflammation, peripheral blood MPs levels are more easily affected after exposure to air pollutants.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Guided by the theory of "the kidney storing essence, governing the bones, and producing marrow", this study explored the mechanism of icariin(ICA) in regulating the osteogenic differentiation of rat bone mesenchymal stem cells(BMSCs) through caveolin-1(Cav1) via in vitro and in vivo experiments, aiming to provide a theoretical basis for the prevention and treatment of postmenopausal osteoporosis with traditional Chinese medicine(TCM). Primary cells were obtained from 4-week-old female SD rats using the whole bone marrow adherent method. Flow cytometry was used to detect the expression of surface markers CD29, CD90, CD11b, and CD45. The potential for osteogenic and adipogenic differentiation was assessed. The effect of ICA on cell viability was determined using the CCK-8 assay, and the impact of ICA on the formation of mineralized nodules was verified by alizarin red staining. A stable Cav1-silenced cell line was constructed using lentivirus. The effect of Cav1 silencing on osteogenic differentiation was observed via alizarin red staining. Western blot analysis was conducted to detect the expression of Cav1, Hippo/TAZ, and osteogenic markers such as Runt-related transcription factor 2(RUNX2) and alkaline phosphatase(ALP). The results showed that primary cells were successfully obtained using the whole bone marrow adherent method, positively expressing surface markers of rat BMSCs and possessing the potential for both osteogenic and adipogenic differentiation. The CCK-8 assay and alizarin red staining results indicated that 1×10~(-7) mol·L~(-1) was the optimal concentration of ICA for intervention in this experiment(P<0.05). During osteogenic induction, ICA inhibited Cav1 expression(P<0.05) while promoting TAZ expression(P<0.05). Alizarin red staining demonstrated that Cav1 silencing significantly promoted the osteogenic differentiation of BMSCs. After ICA intervention, TAZ expression was activated, and the expression of osteogenic markers ALP and RUNX2 was increased. In conclusion, Cav1 silencing significantly promotes the osteogenic differentiation of BMSCs, and ICA promotes this differentiation by inhibiting Cav1 and regulating the Hippo/TAZ signaling pathway.
Animals ; Mesenchymal Stem Cells/metabolism* ; Caveolin 1/genetics* ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; Cell Differentiation/drug effects* ; Female ; Signal Transduction/drug effects* ; Flavonoids/administration & dosage* ; Protein Serine-Threonine Kinases/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Cells, Cultured ; Humans

Objective To analyze the characteristics and risk factors associated with postoperative deep vein thrombosis(DVT)of the lower extremities in patients undergoing surgery for lumbar degenerative diseases.Methods A retrospective analysis was conducted on clinical data from 298 patients who were hospitalized for lumbar degenerative diseases and underwent lumbar spine surgery treatment in the First Affiliated Hospital of Nanchang University from October 1,2022 to April 15,2023.Patients were divided into DVT group(n=71)and non-DVT group(n=227)according to whether DVT of the lower limbs occurred within 1 week postoperatively.The incidence and distribution characteristics of postoperative DVT were analyzed.Univariate and binary logistic regression analyses were performed to identify risk factors for DVT,and receiver operating characteristic(ROC)curves were used to determine cut-off values for relevant risk factors.Results A total of 298 patients were included,among whom 159 were males(53.4%)and 139 were females(46.6%),with an average age of(64.5±9.8)years.DVT occurred in 71 patients,and the incidence of lower extremity DVT was 23.8%.In the DVT group,there were 49 cases(69.0%)of intermuscular vein thrombosis,and 22 cases of other types of thrombosis(7 cases of peroneal vein thrombosis,4 cases of posterior tibial vein thrombosis,3 cases of common femoral vein thrombosis,1 case of anterior tibial vein thrombosis,and 7 cases of multiple thrombosis);58 cases(81.7%)had DVT in one lower extremity,and 13 cases(18.3%)had DVT in both lower extremities.Univariate analysis results showed that age,body mass index(BMI),length of hospital stay,history of hypertension,operative time,and intraoperative blood loss were associated with the occurrence of lower extremity DVT after surgery for lumbar degenerative diseases(P<0.05).Binary logistic regression analysis results indicated that older age(OR=1.079,P<0.01),higher BMI(OR=1.130,P=0.01),history of hypertension(OR=2.992,P<0.01),and larger intraoperative blood loss(OR=1.002,P=0.03)were independent risk factors for the occurrence of lower extremity DVT.ROC curve analysis demonstrated that patients with age>58.5 years,BMI>24.01 kg/m2,history of hypertension,and intraoperative blood loss>550 ml had a significantly increased risk of postoperative lower limb DVT.Conclusions The incidence of lower extremity DVT after surgery for lumbar degenerative disease is high,and intermuscular venous thrombosis is more common.Older age,higher BMI,history of hypertension,and larger intraoperative blood loss are independent risk factors for the occurrence of lower extremity DVT after surgery.

Objective To screen the influencing factors associated with adverse clinical out-comes in hospitalized elderly patients with Alzheimer's disease(AD)using LASSO-Logistic regression analysis and to construct a nomogram prediction model.Methods A retrospective selection of 214 hospitalized elderly patients with AD who visited the Department of Geriatric Medicine in the hospital from February 2021 to March 2023 was conducted,and clinical data of all patients were collected.Patients were divided into adverse events group(n=53)and non-adverse events group(n=161)based on the occurrence of adverse clinical outcomes.After variable screening using LASSO regres-sion,multivariate Logistic regression analysis was performed to identify independent factors influen-cing adverse clinical outcomes in hospitalized elderly patients with AD.A Nomogram model for pre-dicting adverse clinical outcomes in these patients was established based on the results of multivariate analysis.The predictive performance,calibration,and clinical utility of the Nomogram model were evaluated using the concordance index,calibration curve,and decision curve analysis(DCA).The diagnostic performance of the Nomogram model for adverse clinical outcomes in hospitalized elderly patients with AD was assessed using the receiver operating characteristic(ROC)curve and the area under the curve(AUC).Results LASSO-Logistic regression analysis revealed that the Mini-Men-tal State Examination(MMSE)score was an independent protective factor against adverse clinical outcomes in hospitalized elderly patients with AD(P＜0.05),while the Charlson Comorbidity In-dex(CCI score),creatinine,urea,and fasting blood glucose(FBG)levels were all independent risk factors for adverse clinical outcomes in these patients(P＜0.05).The Nomogram model con-structed based on the influencing factors screened by LASSO-Logistic regression analysis showed a concordance index of 0.994(95％CI,0.958 to 1.000)for predicting adverse clinical outcomes in hospitalized elderly patients with AD.The Hosmer-Lemeshow test results indicated x2=1.909,P=0.984,suggesting good model fit.The DCA result demonstrated that the model had favorable threshold probabilities and net clinical benefits.Conclusion The Nomogram model for predicting clinical outcomes in elderly inpatients with AD constructed based on LASSO-Logistic regression anal-ysis exhibits high predictive value,and can be used to forecast the occurrence of adverse clinical outcomes in these patients.

OBJECTIVE: To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.