BACKGROUND:It has been found that abnormal apoptosis of bone tissue cells induced by abnormal iron metabolism plays an important role in the progression of osteoporosis. OBJECTIVE:To investigate the effect of naringin on iron metabolism and cell apoptosis in bone tissue of rats with osteoporosis. METHODS:Fifty 2-month-old female Sprague-Dawley rats were randomly divided into five groups with 10 rats in each group:sham group,osteoporosis group,naringin low-dose group,naringin high-dose group,and naringin high-dose+DKK-1 group.Except for the sham group,rat models of osteoporosis were established by removing bilateral ovarian tissues in the other groups.At 8 weeks after modeling,rats in the naringin low-and high-dose groups were given 100 and 400 mg/kg/d naringenin by gavage,respectively,and rats in the naringenin high dose+DKK-1 group were given 400 mg/kg/d naringin by gavage and subcutaneous injection of 25 mg/kg/d DKK-1,an inhibitor of the Wnt1 signaling pathway,for 7 consecutive days.Relevant indexes were detected after administration. RESULTS AND CONCLUSION:Compared with the osteoporosis group,naringin could enhance the bone mineral density and serum calcium and superoxide dismutase levels in rats(P<0.05),and reduce the serum levels of osteocalcin,malondialdehyde,and phosphorus(P<0.05),while DKK-1 could partially inhibit the interventional effect of naringin(P<0.05).Results from Micro-CT scanning,hematoxylin-eosin and TUNEL staining showed that compared with the osteoporosis group,naringin significantly improved bone microstructure and reduced the rate of cell apoptosis,while DKK-1 partially inhibited the interventional effect of naringin.Immunofluorescence staining results showed that compared with the osteoporosis group,naringin could reduce the oxygen content,anti-tartaric acid phosphatase expression,and elevate the expression of alkaline phosphatase in active tibia tissues(P<0.05),while DKK-1 could partially inhibit the interventional effect of naringin(P<0.05).Results from Prussian blue staining and immunohistochemical staining showed that compared with the osteoporosis group,naringin reduced iron deposition in bone and liver tissues as well as the expression of transferrin receptor 1(P<0.05),and elevated the protein expression of ferroportin 1(P<0.05)in bone tissue,and DKK-1 partially inhibited the intervention of naringin(P<0.05).PCR and western blot assay of tibia specimens showed that compared with the osteoporosis group,naringin decreased the expression of anti-tartrate acid phosphatase,transferrin receptor 1 and Bax(P<0.05),and elevated the expression of alkaline phosphatase,ferroportin 1,Bcl-2,Wnt1 and β-catenin(P<0.05),while DKK-1 partially inhibited the interfering effect of naringin(P<0.05).To conclude,naringin inhibits the progression of osteoporosis by reducing iron deposition and apoptosis rate in bone tissue,which may be related to the activation of the Wnt1 signaling pathway.

Objective To investigate the differential expression genes(DEGs)related to angiogenesis in rosacea(RA)by utilizing bioinformatics analysis in order to screen the key genes and verify their mRNA expression levels.Methods The gene microarray dataset GSE65914 was retrieved from the Gene Expression Omnibus(GEO)repository.Analyzed by R programming,the dataset was refined to identify DEGs related to RA,and then cross-referenced with angiogenesis-related genes from the GeneCards database to get a subset specific to RA angiogenesis.The process of identifying key genes was augmented by employing protein-protein interaction(PPI)network analysis and Cytoscape-based computational algorithms.The mRNA expression levels of the aforementioned pivotal genes were detected by real-time fluorescent quantitative reverse transcription PCR(RT-qPCR).Results A total of 947 RA-associated DEGs were identified from GEO dataset,and then 202 genes related to RA angiogenesis were further delineated.PPI network analysis and Cytoscape algorithm finally identified 3 key genes,that is,CXCL8,IL-1B,and STAT1.The results of RT-qPCR showed that the mRNA expression levels of MIP-2,GCP-2,IL-1B and STAT1 in RA lesions were significantly higher than those in normal controls(P<0.05).Conclusion With aid of bioinformatics analysis,our study has screened and validated key genes associated with angiogenesis in RA,namely CXCL8,IL-1B,and STAT1,which providing a theoretical basis for elucidating the potential mechanisms underlying RA-induced angiogenesis and developing targeted therapeutic strategies.

Objective To establish a rapid,sensitive,and specific multiplex PCR detection method for the simultaneous detection of Cryptosporidium,Eimeria,and bovine parvovirus.Methods Specific primers targeting the SSU rRNA genes of Cryptosporidium and Eimeria,as well as the VP2 gene of bovine parvovirus were designed and the corresponding recombinant plasmid standards were constructed.To establish the multiplex PCR method,the reaction conditions were optimized using temperature gradient PCR and single-variable control methods.The sensitivity,specificity,reproducibility,and clinical application of the protocol were evaluated.Results The optimal annealing temperature was found to be 60.5℃,and the forward and reverse primer concentrations were determined to be 0.2 μmol/L for Eimeria,and 0.4 μmol/L for Cryptosporidium and bovine parvovirus.The assay demonstrated high sensitivity,with detection limits of 243,260,and 3 110 copies for the recombinant plasmid standards of Cryptosporidium,Eimeria,and bovine parvovirus,respectively.Specificity testing showed no cross-reactivity with ten common bovine pathogens,including Salmonella,bovine viral diarrhea virus,and bovine rotavirus.Consistent intra-and inter-batch results confirmed the strong reproducibility of the method.Clinical application to 81 diarrhea samples from various regions in the Ganzi Prefecture,Sichuan,revealed positivity rates of 18.52%(15/81)for Cryptosporidium,34.57%(28/81)for Eimeria,and 18.52%(15/81)forbovineparvovirus,withamixedinfectionrateof3.7%(3/81).Conclusions Themultiplex PCR method established in this study offers a reliable tool for differential diagnosis and epidemiological investigation of the three common diarrheal pathogens in yaks.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

OBJECTIVE: Several epidemiological observational studies have related particulate matter (PM) exposure to Inflammatory bowel disease (IBD), but many confounding factors make it difficult to draw causal links from observational studies. The objective of this study was to explore the causal association between PM _2.5 exposure, its absorbance, and IBD. METHODS: We assessed the association of PM _2.5 and PM _2.5 absorbance with the two primary forms of IBD (Crohn's disease [CD] and ulcerative colitis [UC]) using Mendelian randomization (MR) to explore the causal relationship. We conducted two-sample MR analyses with aggregated data from the UK Biobank genome-wide association study. Single-nucleotide polymorphisms linked with PM _2.5 concentrations or their absorbance were used as instrumental variables (IVs). We used inverse variance weighting (IVW) as the primary analytical approach and four other standard methods as supplementary analyses for quality control. RESULTS: The results of MR demonstrated that PM _2.5 had an adverse influence on UC risk (odds ratio [ OR] = 1.010; 95% confidence interval [ CI] = 1.001-1.019, P = 0.020). Meanwhile, the results of IVW showed that PM _2.5 absorbance was also causally associated with UC ( OR = 1.012; 95% CI = 1.004-1.019, P = 0.002). We observed no causal relationship between PM _2.5, PM _2.5 absorbance, and CD. The results of sensitivity analysis indicated the absence of heterogeneity or pleiotropy, ensuring the reliability of MR results. CONCLUSION Based on two-sample MR analyses, there are potential positive causal relationships between PM _2.5, PM _2.5 absorbance, and UC.
Humans ; Mendelian Randomization Analysis ; Particulate Matter/analysis* ; Polymorphism, Single Nucleotide ; Inflammatory Bowel Diseases/genetics* ; Air Pollutants/analysis* ; Crohn Disease/genetics* ; Colitis, Ulcerative/genetics* ; Genome-Wide Association Study ; Risk Factors ; Environmental Exposure

Objective: To utilize machine learning(ML) algorithms to develop accurate and effective prediction models for TAC dose/weight-adjusted trough concentration(C0/D). Methods: Data were collected on 264 TAC blood concentration monitoring data from 72 patients undergoing kidney transplantation. The effects of population statistical data, clinical features, combined medication, and ultrasound feature parameters on TAC C0/D were analyzed. Features with a significance level less than 0.05 in the univariate analysis of TAC C0/D were selected for inclusion in the random forest(RF) algorithm to identify significant features. These features were interpreted using partial dependency plots. Five ML algorithms, including RF, support vector regression(SVR), extreme gradient boosting(XGBoost), decision trees(DT) and artificial neural networks(ANN), were employed to establish the TAC C0/D prediction model. Hyper-parameter tuning was performed on the RF model that performed the best. Results : Ten characteristic variables with importance scores>5 were retained and included in the ML model: transglutaminase, red blood cell count, blood urea nitrogen, weight, serum creatinine, renal segmental arterial resistance index, renal aortic resistance index, hematocrit, renal pelvic Young′s modulus value, and time after transplantation. The partial dependence plots showed that all 10 important variables screened were positively correlated with TAC C0/D. The tuned RF model outperformed the other models with aR2of 0.81, aRMSEof 43.93, and aMAEof 29.97. Conclusion The ML models demonstrate good performance in predicting TAC C0/D and provide innovative interpretations using partial dependence plot. The optimized RF model shows optimal performance and offers a novel tool for individualized medication adjustment for TAC in renal transplant patients.

ObjectiveThe U6 promoter is an essential element for driving sgRNA expression in the clustered regularly interspaced short palindromic repeat sequences/CRISPR-associated protein 9（CRISPR/Cas9）gene editing system in dicotyledonous plants. Endogenous U6 promoters typically exhibit higher transcriptional activity， which can significantly improve gene editing efficiency. This study aims to identify endogenous U6 promoters in Artemisia annua to optimize its CRISPR/Cas9 gene editing system， which holds significant importance for its molecular breeding. MethodsOn the basis of the highly conserved U6 snRNA sequences in Arabidopsis thaliana， endogenous U6 promoters were screened in the A. annua genome. Expression vectors were constructed with candidate AaU6 promoter driving the firefly luciferase （LUC） reporter gene， and then transiently transformed into Nicotiana benthamiana. Transcriptional activities of the promoters were measured and compared by in vivo imaging and the Dual Luciferase Reporter assay. ResultsEight endogenous U6 promoters were successfully cloned from A. annua. Sequences alignment revealed that all these promoters contained the two conserved cis-acting elements， upstream sequence element （USE） and TATA-box， which affected their transcriptional activity. Dual-luciferase activity assays indicated that the transcriptional activities of AaU6-3， AaU6-1， and AaU6-5 were significantly higher than that of the Arabidopsis AtU6-26 promoter， with AaU6-3 exhibiting the highest activity. ConclusionThis study identified three endogenous AaU6 promoters with high transcriptional activity in A. annua， providing key functional elements for establishing an efficient gene editing system in A. annua. These findings will contribute to advancing precision molecular breeding and high-quality germplasm innovation in A. annua.

INTRODUCTION: During the coronavirus disease 2019 (COVID-19) pandemic, contact tracers were under immense pressure to deliver effective and timely contact tracing, raising concerns of higher susceptibility to burnout. Our study aimed to determine burnout prevalence among hospital-based contact tracers and associated risk factors, so that interventions to reduce burnout risk could be formulated. METHODS: One hundred and ninety-six active contact tracers across three hospitals within a healthcare cluster were invited to complete an anonymous online survey. To identify burntout, data such as demographics, work-related variables and contact tracing-related variables were collected using the Copenhagen Burnout Inventory. Associated factors were identified using multivariate statistics. Open-ended questions were included to understand the challenges and potential improvements through qualitative analysis. RESULTS: A total of 126 participants completed the survey, giving a completion rate of 64%, and almost half of these participants (42.9%) reported burnout. Protective factors included being on work-from-home arrangements (adjusted odds ratio [OR] 0.22, 95% confidence interval [CI] 0.08-0.56), perception of being well supported by their institution (adjusted OR 0.25, 95% CI 0.08-0.80) and being married (adjusted OR 0.28, 95% CI 0.12-0.64). Risk factors included having an administrative role pre-COVID-19 (adjusted OR 3.62, 95% CI 1.33-9.83). Work-related burnout was related to being activated for more than 1 day in the preceding week (unadjusted OR 3.25, 95% CI 1.33-7.94) and multiple activations in a day (unadjusted OR 3.54, 95% CI 1.44-4.41). Biggest challenges identified by participants were language barrier (62.7%), followed by workflow-related issues (42.1%). CONCLUSION Our study demonstrated burnout and other challenges faced by a team of mostly hospital-based administrative staff redeployed on a part-time basis to ensure timely contact tracing. To mitigate burnout, we recommend choosing staff on work-from-home arrangements and ensuring adequate manpower and rostering arrangements.
Humans ; COVID-19/epidemiology* ; Burnout, Professional/epidemiology* ; Singapore/epidemiology* ; Female ; Male ; Cross-Sectional Studies ; Adult ; Middle Aged ; Risk Factors ; Surveys and Questionnaires ; Contact Tracing/methods* ; SARS-CoV-2 ; Prevalence ; Pandemics