OBJECTIVES: The core pathology of osteoporosis lies in bone resorption exceeding bone formation; thus, promoting osteogenesis is a key therapeutic strategy. The osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) forms the biological basis of bone formation. Astragaloside IV (A-IV), a major active component of Astragalus membranaceus, is known to enhance osteogenesis, but its precise molecular mechanisms remain unclear. This study aims to investigate the effects of A-IV on the proliferation and osteogenic differentiation of BMSCs from osteoporotic rats and to elucidate its molecular mechanism through the regulation of microRNA-21 (miR-21) and Notch2 expression. METHODS: After 1 week of adaptive feeding, mature female SD rats were randomly divided into a sham-operated (Sham) group (n=4) and an ovariectomized (OVX) group (n=8) to establish an osteoporosis model. Twelve weeks after surgery, BMSCs were isolated from femoral bone marrow and cultured. Cells were divided into a S-BMSCs group (from Sham), an O-BMSCs group (from OVX), and an A-BMSCs group (from OVX-derived BMSCs treated with A-IV). S-BMSCs and O-BMSCs were induced for osteogenic differentiation using osteogenic induction medium, whereas A-BMSCs were treated with A-IV before induction. Flow cytometry was used to identify mesenchymal stem cell surface markers (CD29) and hematopoietic stem cell marker (CD34) to confirm BMSC characteristics. Cell proliferation was assessed using the methyl thiazolyl tetrazolium (MTT) assay. Alizarin red staining was performed to quantify calcium nodule formation, and alkaline phosphatase (ALP) activity assays were used to evaluate osteogenic differentiation. Real-time reverse transcription PCR (real-time RT-PCR) was used to detect changes in osteogenic-related genes, runt-related transcription factor 2 (Runx2) and osteopontin (OPN), as well as miR-21 expression. Western blotting was performed to assess Runx2, OPN, and Notch2 protein expression. RESULTS: Flow cytometry confirmed that O-BMSCs retained the phenotypic characteristics of mesenchymal stem cells. A-IV significantly enhanced the proliferation of BMSCs from osteoporotic rats (P<0.05), increased ALP activity, and upregulated the mRNA and protein expression of Runx2 and OPN (P<0.05). Bioinformatic and experimental analyses demonstrated that miR-21 directly targeted Notch2. A-IV treatment increased miR-21 expression while suppressing Notch2 protein expression and inhibiting activation of the Notch signaling pathway (P<0.05). CONCLUSIONS Astragaloside IV promotes the osteogenic differentiation of BMSCs derived from osteoporotic rats by upregulating miR-21 expression and inhibiting the key Notch signaling protein Notch2, thereby relieving the Notch2-mediated suppression of osteogenesis.
Animals ; Triterpenes/pharmacology* ; Saponins/pharmacology* ; Osteogenesis/drug effects* ; MicroRNAs/metabolism* ; Rats, Sprague-Dawley ; Female ; Cell Differentiation/drug effects* ; Mesenchymal Stem Cells/drug effects* ; Signal Transduction/drug effects* ; Osteoporosis/pathology* ; Rats ; Cells, Cultured ; Receptor, Notch2/metabolism* ; Receptors, Notch/metabolism* ; Ovariectomy ; Cell Proliferation/drug effects*

Objective:This study aims to develop a laboratory-based predictive model for early neurological deterioration (END) in patients with acute cerebral infarction (ACI) using baseline data collected at hospital admission.Methods:This study was a retrospective cohort study. Clinical and baseline laboratory test data from 502 patients with ACI admitted to the Department of Neurology at our hospital between January 1, 2022 and May 31, 2025. Of these patients, 313 were male and 189 were female, with a median age of 67 years (interquartile range: 58-73). Patients were classified into an END group and a non-END group according to the occurrence of END within 7 days of admission. Subsequently, using the caret package in R (version 4.4.2), the dataset was randomly divided into a training set ( n=351) and a validation set ( n=151) at a 7∶3 ratio, with END status as the stratification variable and a fixed random seed to ensure reproducibility. Following baseline characteristic comparisons between groups, these datasets were used for model development and validation, respectively. The differences in clinical indicators between the two patients groups were assessed using the chi-square test and the Wilcoxon rank sum test. In the training group, Lasso regression was utilized to identify variables significantly associated with END. Seven machine learning algorithms-decision tree (DT), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR)-were employed to develop predictive models. The optimal hyperparameters were determined via grid search integrated with 5-fold cross-validation. The final algorithm was selected based on comprehensive model performance evaluation. Additionally, clinical data of 79 patients with ACI, collected between June 1 to August 31, 2025, were compiled as an independent test set for external validation. The cohort comprised 49 males and 30 females, with a median age of 68 years (interquartile range: 57-72). The SHapley Additive exPlanations (SHAP) method was employed to access feature importance and model interpretability. SHAP dependence plots and interaction plots were utilized to emplore the nonlinear relationships and interaction effects among the featurevariables. Results:Among the 502 patients, 166 experienced END during 7 days of hospitalization. Lasso regression identified nine significant predictors: history of hyperlipidemia, admission NIHSS score, lymphocyte-to-monocyte ratio (LMR), hemoglobin, D-dimer, albumin, neuron-specific enolase (NSE), homocysteine (HCY), and vitamin B12. The area under the receiver operating characteristic curve (AUC) for the seven machine learning models ranged from 0.709 to 0.946. The XGB model achieved the highest predictive performance, with an AUC of 0.946 (95% CI 0.924-0.960) in the training cohort and 0.867 (95% CI 0.902-0.933) in the validation cohort. SHAP analysis revealed that the top five variables contributing to END prediction were admission NIHSS score, HCY, D-dimer, history of hyperlipidemia, and vitamin B12. Conclusion:This study successfully developed a laboratory-based prediction model for END using the XGB machine learning algorithm, which demonstrated strong predictive performance.

This study aimed to investigate the associations between the post-wash total progressively motile sperm count (TPMSC) in the first intrauterine insemination (IUI) cycle and pregnancy outcomes of the second IUI cycle. Data were retrieved from the clinical database at the Reproductive Center of Peking University Third Hospital (Beijing, China) between January 2011 and December 2022. Couples were included in this retrospective cohort study if they had unexplained or mild male factor infertility and were treated with IUI for two consecutive cycles using the same protocol. A total of 8290 couples were included in the analysis. The mean ± standard deviation (s.d.) age of women was 32.0 ± 3.5 years. We categorized groups based on the post-wash TPMSC (×10 6 ) levels in the first IUI cycle: group 1 (0 < TPMSC < 1, n = 1290), group 2 (1 ≤ TPMSC < 2, n = 863), group 3 (2 ≤ TPMSC < 3, n = 800), group 4 (3 ≤ TPMSC < 4, n = 783), group 5 (4 ≤ TPMSC < 5, n = 1541), group 6 (5 ≤ TPMSC < 6, n = 522), group 7 (6 ≤ TPMSC < 7, n = 547), group 8 (7 ≤ TPMSC < 8, n = 175), group 9 (8 ≤ TPMSC < 9, n = 556), group 10 (9 ≤ TPMSC < 10, n = 192), and group 11 (TPMSC ≥ 10), n = 1021). The primary outcome was live birth rate of the second IUI cycle. Live birth rates were 7.9%, 5.8%, 7.6%, 7.4%, 7.3%, 8.4%, 7.5%, 7.4%, 8.8%, 8.9%, and 7.6% in each group, respectively. There were no statistically significant differences in clinical pregnancy rates or live birth rates between any groups and those with the post-wash TPMSC <1 × 10 6 . In an IUI program for unexplained and mild male factor infertility, the post-wash TPMSC in the first IUI cycle was not significantly associated with the live birth rate in the second IUI cycle.
Humans ; Female ; Male ; Pregnancy ; Adult ; Retrospective Studies ; Sperm Count ; Pregnancy Rate ; Sperm Motility/physiology* ; Insemination, Artificial/methods* ; Pregnancy Outcome ; Infertility, Male/therapy* ; Insemination, Artificial, Homologous ; Live Birth

Objective To investigate the clinical significance of H2A histone family member(H2AX)gene in human lung adenocarcinoma,and to explore its biological role in lung adenocarcinoma.Methods The tran-scriptome data of lung adenocarcinoma were downloaded from the Cancer Genome Atlas and Gene Expression database.The expression of H2AX mRNA in lung adenocarcinoma tissues and adjacent normal lung tissues was extracted by R 4.3.1 software,and the relationship between the expression of H2AX and the clinicopath-ological parameters of the patients was analyzed.Kaplan-Meier and Cox regression analysis were used to ana-lyze the prognostic value of H2AX gene expression.The expression of H2AX gene in lung adenocarcinoma cell lines and normal bronchial epithelial cells was verified by quantitative real-time PCR(qPCR).Gene set enrich-ment analysis,gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were used to explore the biological role of H2AX gene.Results The expression of H2AX mRNA in lung adenocarcinoma tissues was significantly higher than that in adjacent normal tissues(P<0.001).qPCR results showed that compared with normal bronchial epithelial cells,the expression levels of H2AX mRNA in lung adenocarcinoma cell lines A549 and SPC-A1 were significantly increased(P<0.05).H2AX expression was associated with tumor stage(P=0.029)and N stage(P=0.016).Kaplan-Meier curve analysis showed that high expression of H2AX was associated with a lower overall survival rate of patients(P<0.001).Cox regression analysis showed that H2AX was an independent prognostic factor for lung adenocarcinoma patients(HR=1.011,95%CI 1.003-1.020,P=0.006).The results of functional enrichment analysis showed that H2AX gene was involved in molecular functions such as cell cycle,DNA replication,mismatch repair,and P53 signaling pathway.Conclusion H2AX gene is highly expressed in human lung adenocarcinoma,which is relat-ed to the malignant progression of tumor patients.H2AX gene can affect the progression of lung adenocarcino-ma through a variety of pathways,and may be used as a molecular marker for lung adenocarcinoma patients.

Objective:This study aims to develop a laboratory-based predictive model for early neurological deterioration (END) in patients with acute cerebral infarction (ACI) using baseline data collected at hospital admission.Methods:This study was a retrospective cohort study. Clinical and baseline laboratory test data from 502 patients with ACI admitted to the Department of Neurology at our hospital between January 1, 2022 and May 31, 2025. Of these patients, 313 were male and 189 were female, with a median age of 67 years (interquartile range: 58-73). Patients were classified into an END group and a non-END group according to the occurrence of END within 7 days of admission. Subsequently, using the caret package in R (version 4.4.2), the dataset was randomly divided into a training set ( n=351) and a validation set ( n=151) at a 7∶3 ratio, with END status as the stratification variable and a fixed random seed to ensure reproducibility. Following baseline characteristic comparisons between groups, these datasets were used for model development and validation, respectively. The differences in clinical indicators between the two patients groups were assessed using the chi-square test and the Wilcoxon rank sum test. In the training group, Lasso regression was utilized to identify variables significantly associated with END. Seven machine learning algorithms-decision tree (DT), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR)-were employed to develop predictive models. The optimal hyperparameters were determined via grid search integrated with 5-fold cross-validation. The final algorithm was selected based on comprehensive model performance evaluation. Additionally, clinical data of 79 patients with ACI, collected between June 1 to August 31, 2025, were compiled as an independent test set for external validation. The cohort comprised 49 males and 30 females, with a median age of 68 years (interquartile range: 57-72). The SHapley Additive exPlanations (SHAP) method was employed to access feature importance and model interpretability. SHAP dependence plots and interaction plots were utilized to emplore the nonlinear relationships and interaction effects among the featurevariables. Results:Among the 502 patients, 166 experienced END during 7 days of hospitalization. Lasso regression identified nine significant predictors: history of hyperlipidemia, admission NIHSS score, lymphocyte-to-monocyte ratio (LMR), hemoglobin, D-dimer, albumin, neuron-specific enolase (NSE), homocysteine (HCY), and vitamin B12. The area under the receiver operating characteristic curve (AUC) for the seven machine learning models ranged from 0.709 to 0.946. The XGB model achieved the highest predictive performance, with an AUC of 0.946 (95% CI 0.924-0.960) in the training cohort and 0.867 (95% CI 0.902-0.933) in the validation cohort. SHAP analysis revealed that the top five variables contributing to END prediction were admission NIHSS score, HCY, D-dimer, history of hyperlipidemia, and vitamin B12. Conclusion:This study successfully developed a laboratory-based prediction model for END using the XGB machine learning algorithm, which demonstrated strong predictive performance.

Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangtong Animal Husbandry,and whether vectors carry any bacterial pathogens that may cause diseases to humans,to provide scientific basis for prospective pathogen discovery and disease prevention and control. Methods Using a metataxonomics approach in concert with full-length 16S rRNA gene sequencing and operational phylogenetic unit(OPU)analysis,we characterized the species-level microbial community structure of two important vector species,biting midges and ticks,including 33 arthropod samples comprising 3,885 individuals,collected around Poyang Lake. Results A total of 662 OPUs were classified in biting midges,including 195 known species and 373 potentially new species,and 618 OPUs were classified in ticks,including 217 known species and 326 potentially new species.Surprisingly,OPUs with potentially pathogenicity were detected in both arthropod vectors,with 66 known species of biting midges reported to carry potential pathogens,including Asaia lannensis and Rickettsia bellii,compared to 50 in ticks,such as Acinetobacter lwoffii and Staphylococcus sciuri.We found that Proteobacteria was the most dominant group in both midges and ticks.Furthermore,the outcomes demonstrated that the microbiota of midges and ticks tend to be governed by a few highly abundant bacteria.Pantoea sp7 was predominant in biting midges,while Coxiella sp1 was enriched in ticks.Meanwhile,Coxiella spp.,which may be essential for the survival of Haemaphysalis longicornis Neumann,were detected in all tick samples.The identification of dominant species and pathogens of biting midges and ticks in this study serves to broaden our knowledge associated to microbes of arthropod vectors. Conclusion Biting midges and ticks carry large numbers of known and potentially novel bacteria,and carry a wide range of potentially pathogenic bacteria,which may pose a risk of infection to humans and animals.The microbial communities of midges and ticks tend to be dominated by a few highly abundant bacteria.

Objective:To explore the incidence and mortality of digestive system cancers, and the trend of the disease burden attributed to different risk factors in population in China.Methods:Data were obtained from the GLOBOCAN 2020 and the Global Burden of Disease Study in 2019 databases and only the data from the Chinese population were included. Using Excel 2019 and R 4.2.1 software, indicators including age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), age-standardized disability-adjusted life year (DALY) rate and its rate of change were used to illustrate the disease burden of digestive system cancers attributed to different factors and their trends.Results:In 2020, the ASIR of digestive system cancers in China was 83.00/100 000, and the ASMR was 63.80/100 000. The numbers of digestive system cancer cases and deaths increased with age, and more cases and deaths occurred in men than in women in all age groups. The age-standardized DALY rate of esophageal cancer, gastric cancer and liver cancers showed decreasing trends in China from 1990 to 2019 (rate of change: -45.26%, -46.87%, and -65.63%, respectively), whereas the age-standardized DALY rate of pancreatic cancer, colorectal cancer and gallbladder and biliary tract cancer showed increasing trends (rate of change: 67.61%, 30.52%, and 7.21%, respectively). The trend of the mortality rate was consistent with the DALY rate. Compared with the age-standardized DALY rate attributed to behavioral factors, the annual proportion of the age-standardized DALY rate attributed to metabolic factors to the total age-standardized DALY rate of esophageal cancer, liver cancer, pancreatic cancer, and colorectal cancer increased from 1990 to 2019. There was no significant change in the rank of age-standardized DALY rate of gastric cancer, liver cancer, pancreatic cancer, and gallbladder and biliary tract cancer attributed to different risk factors in China from 1990 to 2019, but the rank of certain attributed risk factors for the age-standardized DALY rate of esophageal cancer and colorectal cancer moved ahead (esophageal cancer: high BMI; colorectal cancer: low milk intake, and low whole-grain intake).Conclusions:The incidence and mortality of digestive system cancers was serious in China in 2020, and the annual proportion of the disease burden of digestive system cancers attributed to metabolic factors increased from 1990 to 2019. The rank of attributed risk factors for several digestive system cancers changed significantly.

Objective:To evaluate efficacy of safflower polysaccharide in treatment of thymic atrophy induced by estradiol in mice.Methods:A total of 75 female ICR mice were divided into 5 groups:control group,model group,ubenimex group,SPS high dose group,SPS low dose group.Except for control group,each group was given intraperitoneal injection of estradiol benzoate every other day for 6 times.Treatment group began administration 24 h after the last parenteral administration,once a day for 10 days.Mice were sacrificed 24 hours after the last administration,and body weight,immune organ index,MDA and GST levels in plasma,periph-eral blood cells and T cells changes were observed,thymus tissue was stained by HE staining and TUNEL cell apoptosis staining,and thymus output capacity was detected.Results:Both high and low doses of safflower polysaccharide significantly improved thymus index(P<0.05)in mice,increased leukocytes level in peripheral blood(P<0.05),proportions of CD3+CD4+T,CD3+CD8+T cells(P<0.05)and CD4+T/CD8+T,improved thymus tissue damage.High dose of safflower polysaccharide could significantly reduce apoptosis in thymus tissue and enhance thymus output.Conclusion:Safflower polysaccharide has a certain therapeutic effect on estradiol-induced thymus atrophy in mice.

Objective To investigate the changes of blood related parameters and coagulation function indicators in patients with funge-mia during hospitalization,and analyze the clinical significance of these changes and their impact on the patients'prognosis.Methods The patients with positive fungus blood culture for the first time and diagnosed as fungal bloodstream infection during December 2018 and October 2022 were retrospectively analyzed.Their blood related parameters and coagulation function indicators at admission and the time of positive blood culture were collected,and the differences of these clinical parameters were analyzed.The patients were divided into the survival group and death group based on their prognosis,and the differences in clinical parameters between the two groups were compared using the Mann-Whitney U test.The Logistic regression analysis was used to analyze the risk factors related to death.Results A total of 87 patients were enrolled in the study.The patients'results of monocyte count,lymphocyte count,and platelet(PLT)at the time of positive blood culture were significantly lower than those at admission(P＜0.001),while the levels of platelet-associated pa-rameters MPV and PDW were significantly increased(P＜0.05).The levels of coagulation function indicators such as PT,APTT,and D-dimer at the time of positive blood culture were significantly higher than those at admission(P＜0.05).Compared with the the surviv-al group,the peripheral blood PLT count in the death group at the time of positive blood culture was significantly reduced(P=0.022),while the PT,APTT and TT levels were significantly increased(P＜0.05).Logistic regression analysis showed that the death of patients was significantly correlated with the lower level of PLT(OR=1.46,95％CI:1.08-1.97,P=0.01).Conclusion The fungal infection in hospitalized patients can lead to a decrease in PLT count and the prolongation of PT and APTT.Low levels of PLT are associated with the patients'prognosis,and monitoring its changes can assist in assessing the patients'conditions and prognosis.

BackgroundElderly cataract patients with depressive symptoms have heavier self-perceived burden， and worse vision-related quality of life compared to patients without depressive symptoms， resulting in the increase of family burden. Most previous studies showed that family relationship and vision were the main factors leading to depression in elderly cataract patients， and ignored the effects of self-feeling and complicated diseases on the psychological state of elderly cataract patients. ObjectiveTo explore the relationship between depressive symptoms， self-perceived burden and postoperative vision-related quality of life in elderly cataract patients， and to analyze the risk factors of depressive symptoms. So as to provide references for targeted psychological intervention in this population. MethodsA total of 104 elderly cataract patients admitted to Jiangsu Province Hospital （The First Affiliated Hospital with Nanjing Medical University） from July 1， 2020 to December 31， 2022 were included. General information， self-perceived burden and postoperative vision-related quality of life were investigated by self-designed questionnaire， Patients' Health Questionnaire depressive Scale-9 item （PHQ-9）， Self-Perceived Burden Scale （SPBS） and National Eye Institute Visual Function Questionnaire-25 （NEI-VFQ-25）. Pearson correlation analysis was performed to analyze the correlation among PHQ-9， SPBS and NEI-VFQ-25 scores. Logistic regression analysis was used to analyze the influencing factors of depressive symptoms in eldery cataract patients. ResultsA total of 100 elderly cataract patients completed an effective questionnaire survey. Among the participants， 31 cases （31.00%） were found to have depressive symptoms. The depressive group exhibited significantly higher SPBS score （t=11.062， P<0.01） and significantly lower NEI-VFQ-25 score （t=-5.235， P<0.01） than those of the non-depressive group. Pearson correlation analysis showed a positive correlation between PHQ-9 and SPBS score （r=0.485， P<0.01）， and a negative correlation between PHQ-9 and NEI-VFQ-25 score （r=-0.440， P<0.01）. Complicated with diabetes （OR=1.441， P<0.01）， osteoarthritis （OR=1.324， P<0.05） and high SPBS score （OR=1.340， P<0.05） were risk factors of depressive sympptoms in elderly cataract patients. ConclusionThe detection rate of depressive symptoms in elderly cataract patients is higher， and postoperative vision-related quality of life in elderly cataract patients complicated with depressive symptoms is poor. Risk factors include diabetes， osteoarthritis and high self-perceived burden. ［Funded by 2022 Jiangsu Province Elderly Health Scientific Research Project （number， LKM2022019）］