Objective To observe the effect of rat bone marrow mesenchymal stem cells(BMSCs)on the apoptosis of rat peritoneal mesothelium cells(PMCs)induced by high glucose peritoneal dialysis fluid(PDF),and to explore its possible molecular mechanism.Methods The primary BMSCs and PMCs were extracted and identified.Apoptosis of PMCs was induced by high glucose PDF.Cell supernatant from BMSCs after 24 h of culture was collected as the conditioned medium(BMSCs-CM).PMCs were co-cultured with BMSCs by conditioned media or Transwell chambers.PMCs were randomly divided into the control group,the PDF group and the PDF+BMSCs-CM group.The viability of PMCs was measured by CCK-8 in each group.The depolarization of mitochondrial membrane potential was measured by JC-1 method.TUNEL staining was used to detect cell apoptosis.Western blot assay was used to detect the expression levels of apoptosis related proteins B-cell lymphoma-2(Bcl-2),Bcl-2 associated X protein(Bax),Cleaved cysteine aspartase-3(Cleaved Caspase-3)and pathway related protein serine/threonine protein kinase(Raf),mitogen-activated extracellular signal-regulated kinase(MEK),extracellular-signal regulated protein kinase(ERK)and their phosphorylated proteins in each group.Results Compared with the control group,the proliferative activity and mitochondrial membrane potential of PMCs were decreased in the PDF group,while the apoptosis rate and the ratio of Bax/Bcl-2,Cleaved Caspase-3/Caspase-3,p-Raf/Raf,p-MEK/MEK and p-ERK/ERK were increased(P＜0.05).Compared with the PDF group,the proliferative activity and mitochondrial membrane potential of PMCs were increased in the PDF+BMSCs-CM group,while the apoptosis rate and the ratio of Bax/Bcl-2,Cleaved Caspase-3/Caspase-3,p-Raf/Raf,p-MEK/MEK and p-ERK/ERK were decreased(P＜0.05).Conclusion BMSCs can reduce the apoptosis of PMCs induced by high glucose PDF,and its mechanism maybe related to inhibiting the activation of Raf/MEK/ERK signaling pathway.

Objective:To explore the expression pattern of pyroptosis-related genes(PRGs)in hepatocellular carcinoma(HCC),and analyze the relationship between its expression and tumor prog-nosis and immune microenvironment.Methods:TCGA database was used to analyze the genetic changes and expression patterns of PRGs in primary HCC cells,and cluster analysis was used to i-dentify the pyrogenic subtypes of HCC.To compare the difference of prognosis and immune mi-croenvironment among HCC pyrodeath subtypes.Scorch death score quantified the comprehensive expression of PRGs in each sample,and analyzed the correlation between scorch death score and each immune score.Results:Two pyroptosis-associated subtypes of primary HCC were identi-fied,and the expression pattern of PRGs is closely related to the prognosis of cancer patients and the tumor microenvironment.The subtype with high expression of PRGs had a poor prognosis,and functional enrichment analysis found that some tumor-promoting pathways and PD-1 checkpoint pathways were significantly enriched in this subtype.And various cells and immune checkpoints re-lated to immunosuppression were also enriched in this subtype.By constructing PYROPTO-SIS_score to quantify the comprehensive expression of pyroptosis-related genes in each sample,it was found that PYROPTOSIS_score was significantly positively correlated with tumor-infiltrating macrophages,myeloid-derived suppressor cells,and Treg cells.Conclusion:These results sug-gest that pyroptosis may play a tumor-promoting as well as immunosuppressive role in HCC,pro-viding new insights into the assessment of tumor patient prognosis and the immune microenviron-ment.

Objective To establish a rapid screening method for 34 emerging contaminants in surface water by ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS).Methods The pretreatment conditions of solid phase extraction(SPE)were op-timized by orthogonal experimental design and the surface water samples were concentrated and ex-tracted by Oasis? HLB and Oasis? MCX SPE columns in series.The extracts were separated by Kine-tex? EVO C18 column,with gradient elution of 0.1%formic acid aqueous solution and 0.1%formic acid methanol solution.Q-TOF-MS'fullscan'and'targeted MS/MS'modes were used to detect 34 emerging contaminants and to establish a database with 34 emerging contaminants precursor ion,prod-uct ion and retention times.Results The 34 emerging contaminants exhibited good linearity in the con-centration range respectively and the correlation coefficients(r)were higher than 0.97.The limit of de-tection was 0.2-10 ng/L and the recoveries were 81.2%-119.2%.The intra-day precision was 0.78%-18.70%.The method was applied to analyze multiple surface water samples and 6 emerging contaminants were detected,with a concentration range of 1.93-157.71 ng/L.Conclusion The method is simple and rapid for screening various emerging contaminants at the trace level in surface water.

Purpose/Significance Based on the typical use scenario of sensitive personal information in medical and health institu-tions,the implementation of the secure use of sensitive personal information is explored.Method/Process For user registration,internal utilization and interaction,medical device/wearable device collection and information disclosure and other scenarios,the paper analyzes the risk of sensitive personal information leakage,explores the application scenarios,advantages and disadvantages of various technical means such as identity authentication,access control,data encryption,data desensitization and detection audit.Result/Conclusion The application of technical means can assist medical and health institutions to further protect the security of patients'personal information.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

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.

Introduction::The triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, a novel biomarker for metabolic syndrome (MetS), has been validated in the general population as being significantly correlated with cardiovascular disease (CVD) risk. However, its capabilities to predict CVD in people living with human immunodeficiency virus (HIV; PLWH) remain underexplored.Methods::We conducted a retrospective cohort study of 16,081 PLWH who initiated antiretroviral therapy (ART) at the Third People’s Hospital of Shenzhen (China) from 2005 to 2022. The baseline TG/HDL-C ratio was calculated as TG (mmol/L) divided by HDL-C (mmol/L). We employed a multivariate Cox proportional hazards model to assess the association between the TG/HDL-C ratio and CVD occurrence, using Kaplan-Meier curves and log-rank tests to compare survival distributions. The increase in prediction risk upon the addition of the biomarker to the conventional risk model was examined through the assessment of changes in net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Nonlinear relationships were investigated using a restricted cubic spline plot, complemented by a two-piecewise Cox proportional hazards model to analyze threshold effects.Results::At the median follow-up of 70 months, 213 PLWH developed CVD. Kaplan-Meier curves demonstrated a significant association between the increased risk of CVD and a higher TG/HDL-C ratio (log-rank P <0.001). The multivariate-adjusted Cox proportional hazards regression model indicated that the CVD hazard ratios (HR) (95% confidence intervals [95% CIs]) for Q2, Q3, and Q4 versus Q1 of the TG/HDL-C ratio were 2.07 (1.24, 3.45), 2.17 (1.32, 3.57), and 2.20 (1.35, 3.58), respectively ( P <0.05). The consideration of the TG/HDL-C ratio in the model, which included all significant factors for CVD incidence, improved the predictive risk, as indicated by the reclassification metrics (NRI 16.43%, 95% CI 3.35%-29.52%, P = 0.014). The restriction cubic spline plot demonstrated an upward trend between the TG/HDL-C ratio and the CVD occurrence ( P for nonlinear association = 0.027, P for overall significance = 0.009), with the threshold at 1.013. Significantly positive correlations between the TG/HDL-C ratio and CVD were observed below the TG/HDL-C ratio threshold with HR 5.88 (95% CI 1.58-21.88, P = 0.008), but not above the threshold with HR 1.01 (95% CI 0.88-1.15, P = 0.880). Conclusion::Our study confirms the effectiveness of the TG/HDL-C ratio as a predictor of CVD risk in PLWH, which demonstrates a significant nonlinear association. These findings indicate the potential of the TG/HDL-C ratio in facilitating early prevention and treatment strategies for CVD among PLWH.

Background::Scleroderma is characterized by inflammation and fibrosis, predominantly occurring in the skin and extending to various parts of the body. The pathophysiology of scleroderma is multifaceted, with the current understanding including endothelial damage, inflammatory cell infiltration, and fibroblast activation in its progression. Nonetheless, the mechanism of cellular interactions and the precise spatial distribution of these cellular events within the fibrotic tissues remain elusive, highlighting a critical gap in our comprehensive understanding of scleroderma’s pathogenesis.Methods::In this study, we administered bleomycin intradermally to the dorsal skin of four individual murine models. Subsequently, skin tissues were harvested at predetermined intervals for comprehensive spatial transcriptomic analysis to determine the spatial dynamics influencing scleroderma pathogenesis. To validate the possible results from bioinformatic analysis, further in vitro and in vivo experiments were conducted. Results::Analysis of the spatial transcriptome revealed significant alterations in cell clusters during the progression of scleroderma. Gene Ontology analysis identified disruptions in lipid metabolism as the disease advanced. Pseudotime analysis provided evidence for a phenotypic transition from adipocytes to fibroblasts. In vitro studies demonstrated increased expression of Col1a1 and α-SMA as the disease progressed. These fibroblasts have been identified as key contributors to the increasing inflammation. Co-culturing TGF-β induced adipocytes with RAW264.7 cells resulted in overexpression of pro-inflammatory cytokines in the RAW264.7 cells. Both in vitro and in vivo experiments confirmed adipocyte loss and fibroblast formation, with transformed fibroblasts showing pronounced pro-inflammatory characteristics, highlighting their crucial role in the disease mechanism. Conclusions::Our study showed the spatial distribution and dynamic alterations of various cell types during scleroderma progression. Crucially, we identified the transformation of adipocytes into fibroblasts as a key factor promoting disease advancement. These emergent fibroblasts intensify inflammation, indicating that research on these cell clusters could reveal key scleroderma mechanisms and guide future therapies.

An electrochemical chemical oxygen demand(COD)sensor was proposed based on a FTO/TiO2/PbO2 electrode and a thin-layer electrochemical cell.The FTO/TiO2/PbO2 electrode was characterized by X-ray photoelectronic spectroscopy(XPS),X-ray diffraction(XRD)spectroscopy and electrochemical technique,and the results indicated that the rapid decrease in the output signals of the electrochemical COD sensor could be attributed to oxidation of PbSO4 occurring on the surface of FTO/TiO2/PbO2 electrode.The PbO2 deposition time and concentration of Na2SO4 were further optimized and then the electrochemical COD sensor was challenged by real samples including laker water sample,river water sample and wastewater sample.The evolution trend of signals of the electrochemical COD sensor in response to lake and river water samples was identical with that obtained with the standard method(HJ/T399-2007,Water quality-determination of the chemical oxygen demand-fast digestion-spectrophotometric method).The electrochemical COD sensor exhibited significant increase in the signal intensity after the samples were switched from lake water to wastewater sample,and a mean value of 32.5 mg/L with relative standard deviation(RSD)of 6.8%were obtained after measuring 45 times the wastewater with COD value of 30 mg/L under a sampling interval of 400 s.The as-prepared electrochemical COD sensor possessed good promise in regular monitoring of COD,discharge of wastewater and industrial process control,with advantages such as a small sampling interval,mild reaction conditions and no requirement of toxic and harmful chemical reagents.