Senecavirus A (SVA) is a major viral pathogen causing disease in pigs, and effective monitoring of SVA infection is critical for disease control. In this study, we aimed to develop a reliable ELISA method for rapidly detecting neutralizing antibodies against SVA. We used HEK293F cells to express an SVA-specific porcine Fab antibody and verified the biological activity of the Fab antibody by indirect ELISA, immunofluorescence assay, virus neutralization test, and Western blotting. The Fab antibody was biotinylated and used as a competitive antibody to establish a competitive ELISA (C-ELISA) for detecting neutralizing antibodies against SVA. We then evaluated the C-ELISA in terms of sensitivity, specificity, repeatability, and result agreement rate with the VNT. The results showed that we successfully prepared an SVA-specific porcine Fab antibody, which showed high affinity for SVA. We named this antibody 1M33Fab and designated it as Bio-1M33Fab after biotin labeling. The assay conditions were optimized as follows: the coating concentration of SVA particles being 1 μg/mL, the working concentration of Bio-1M33Fab being 0.5 μg/mL, the optimal serum dilution of 1:10, and the optimal dilution of enzyme-labeled avidin being 1:30 000. At a percent inhibition (PI) of 47%, the assay demonstrated the highest sensitivity (96.88%) and specificity (100%), with no cross-reactivity observed with the positive sera of major porcine viral diseases. The intra-assay coefficient of variation ranged from 1.12% to 7.34%, while the inter-assay coefficient of variation ranged from 1.10% to 8.97%, indicating good repeatability. In the detection of 224 clinical pig serum samples, C-ELISA and VNT showed a result agreement rate of 93.75%. In conclusion, we successfully develop a C-ELISA method for detecting neutralizing antibodies against SVA by using a porcine-derived Fab antibody, which lays a foundation for the development of detection kits.
Animals ; Swine ; Antibodies, Neutralizing/immunology* ; Enzyme-Linked Immunosorbent Assay/methods* ; Immunoglobulin Fab Fragments/immunology* ; Antibodies, Viral/immunology* ; Picornaviridae/immunology* ; Humans ; HEK293 Cells ; Swine Diseases/diagnosis* ; Picornaviridae Infections/diagnosis*

This study aims to establish an antibody detection method for porcine deltacoronavirus (PDCoV). The recombinant proteins PDCoV-N1 and PDCoV-N2 were expressed via the prokaryotic plasmid pColdII harboring the N gene sequence of the PDCoV strain CH/XJYN/2016. The reactivity and specificity of PDCoV-N1 and PDCoV-N2 with anti-PEDV sera were analyzed after the recombinant proteins were analyzed by SDS-PAGE and purified by the Ni-NTA Superflow Cartridge. Meanwhile, Western blotting and indirect immunofluorescence assay were carried out separately to validate the recombinant proteins PDCoV-N1 and PDCoV-N2. Finally, we established an indirect ELISA method based on the recombinant protein PDCoV-N2 after optimizing the conditions and tested the sensitivity, specificity, and reproducibility of the method. Then, the established method was employed to examine 102 clinical serum samples. The recombinant protein PDCoV-N2 showed low cross-reactivity with anti-PEDV sera. The optimal conditions of the indirect ELISA method based on PDCoV-N2 were as follows: the antigen coating concentration of 1.25 μg/mL and coating at 37 ℃ for 1 h; blocking by BSA overnight at 4 ℃; serum sample dilution at 1:50 and incubation at 37 ℃ for 1 h; secondary antibody dilution at 1:80 000 and incubation at 37 ℃ for 1 h; color development with TMB chromogenic solution at 37 ℃ for 10 min. The S/P value ≥ 0.45, ≤0.38, and between 0.45 and 0.38 indicated that the test sample was positive, negative, and suspicious, respectively. The testing results of the antisera against porcine epidemic diarrhea virus (PEDV), porcine circovirus 2 (PCV2), transmissible gastroenteritis virus (TGEV), foot-and-mouth disease virus (FMDV), and African swine fever virus (ASFV) showed that the S/P values were all less than 0.38. The testing results of the 800-fold diluted anti-PDCoV sera were still positive. The results of the inter- and intra-batch tests showed that the coefficients of variation of this method were less than 10%. Clinical serum sample test results showed the coincidence rate between this method and neutralization test was 94.12%. In this study, an ELISA method for the detection of anti-PDCoV antibodies was successfully established based on the truncated N protein of PDCoV. This method is sensitive, specific, stable, and reproducible, serving as a new method for the clinical diagnosis of PDCoV.
Animals ; Enzyme-Linked Immunosorbent Assay/methods* ; Swine ; Antibodies, Viral/blood* ; Recombinant Proteins/genetics* ; Deltacoronavirus/isolation & purification* ; Coronavirus Infections/virology* ; Swine Diseases/diagnosis* ; Coronavirus Nucleocapsid Proteins ; Sensitivity and Specificity

Objective To explore the value of dual-phase enhanced CT radiomics in predicting post-acute pancreatitis diabetes mellitus(PPDM-A).Methods A total of 145 patients with acute pancreatitis(AP)were retrospectively collected,including 62 patients in PPDM-A group and 83 patients in non-PPDM-A group.The patients were randomly divided into training set and test set at a ratio of 7︰3,the pancreatic parenchyma in arterial phase and venous phase was delineated and the radiomics features were extracted.Vari-ance threshold method,univariate selection method and least absolute shrinkage and selection operator(LASSO)were used to screen radiomics features.The prediction performance of the model was evaluated by the area under the curve(AUC).The DeLong test was used to compare the prediction efficiency between the models,and the calibration curve and decision curve were used to evaluate the prediction efficiency of the model.Results The AUC of arterial phase model,venous phase model,combined arterial venous phase model,clinical model and radiomics combined clinical model in the training set were 0.845,0.792,0.829,0.656 and 0.862,respec-tively.The DeLong test results showed that only the difference between the radiomics combined clinical model and the clinical model in the training set and the test set was statistically significant(P<0.05).The decision curve showed that the radiomics combined clinical model had high clinical practicability in a certain range,and the calibration curve showed that the radiomics combined clinical model had the best fitting degree with the actual observation value.Conclusion Based on the dual-phase enhanced CT radiomics combined clinical model,PPDM-A can be predicted more accurately,and it can provide a certain reference value for the clinical development of per-sonalized treatment programs.

[Objective] To investigate the clinical features and treatment of renal cell carcinoma unclassified with medullary phenotype (RCCU-MP), so as to improve the clinical understanding of this disease. [Methods] The clinical data of 2 patients with pathological diagnosis of renal medullary carcinoma (RMC) in Zhujiang Hospital during 2019 and 2023 were retrospectively analyzed, and relevant literature was reviewed. [Results] Both patients had symptoms of backache, and imaging examination indicated renal space-occupying lesions.Case 1 was diagnosed as RMC by renal biopsy, and case 2 was pathologically diagnosed as RMC after surgery.Both cases lacked evidence of sickle cell trait or sickle cell disease, and were finally diagnosed as RCCU-MP.Case 1 did not receive antineoplastic therapy and died 5 months after diagnosis.Case 2 underwent laparoscopic nephrectomy, and then received gemcitabine + paclitaxel chemotherapy + immunotherapy.The patient's tumor progressed gradually after first-line treatment was abandoned due to concurrent hematologic infection, and he eventually died 7 months after surgery. [Conclusion] The clinical features of RCCU-MP are partially similar to those of RMC.The diagnosis of RCCU-MP requires pathological examinations and should exclude sickle cell trait or sickle cell disease.Due to the aggressive nature of the tumor, the prognosis of patients is poor.

Objective:To explore the value of dual-layer detector spectral CT quantitative parameters in evaluating the treatment response of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).Methods:The study was a cross-sectional study. From May 2021 to March 2023, a total of 52 patients with LARC who received complete nCRT and were pathologically confirmed rectal adenocarcinoma at the Guangdong Province Hospital of Traditional Chinese Medicine were retrospectively enrolled. Each patient underwent spectral CT examination before and after nCRT, including plain scan, arterial phase (AP), and venous phase (VP) scans. According to the tumor regression grade, the patients were divided into the good response ( n=20) and the poor response group ( n=32). Measurements of the primary tumor′s spectral CT parameters, including effective atomic number (Z eff) at plain scan, iodine concentration (IC), CT values of 40 keV and 100 keV virtual monochromatic image (VMI) at dual-enhanced phases, were taken before and after nCRT. Additionally, the normalized iodine concentration (NIC), spectral curve slope (λHU), and the change rate of the above parameters before and after nCRT were calculated. The independent sample t-test or Mann-Whitney U test was used to compare the differences between the two groups. The receiver operating characteristic (ROC) curve was used to assess the efficacy of various metrics in evaluating the tumor treatment response of nCRT. A binary logistic regression analysis of combined parameter results was performed for the parameters with the areas under curve (AUC)>0.75, and the AUC of the combined parameter was evaluated. Results:There were significant differences in NIC AP and λHU VP before nCRT, NIC VP and λHU VP after nCRT, and the change rates of Z eff, NIC AP, NIC VP and λHU AP between the good response group and the poor response group ( P<0.05). The remaining parameters showed no statistically significant difference ( P>0.05). The ROC curve results showed that the AUCs of the above 8 parameters for evaluating tumor treatment response of nCRT were 0.702, 0.655, 0.695, 0.769, 0.738, 0.807, 0.791, and 0.677, respectively. The AUC of the combined model of the three parameters with AUC>0.75 (λHU VP after nCRT, the change rate of NIC AP and NIC VP) was 0.869, with 80.0% sensitivity and 84.4% specificity. Conclusion:The quantitative parameters derived from spectral CT may provide new markers for evaluating the response to nCRT treatment in patients with LARC. The multi-parameter combined model can improve diagnostic efficacy.

Objective To explore the clinical value of models based on clinical features and enhanced MRI radiomics for predicting the occurrence of post-acute pancreatitis diabetes mellitus(PPDM-A).Methods A retrospective selection of 161 acute pancreatitis(AP)patients was conducted,comprising 99 in the non-PPDM-A group and 62 in the PPDM-A group.They were randomly divided into training set and test set in a ratio of 7∶3.Region of interest(ROI)were delineated and radiomics features were extracted on the late arterial phase MRI images.Optimal radiomics features were selected by maximum relevance and minimum redundancy(mRMR)and least absolute shrinkage and selection operator(LASSO).Support vector machine(SVM)was used to develop three predictive models.The efficacy of the models in predicting PPDM-A was evaluated,the receiver operating characteristic(ROC)curve was drawn,and the DeLong test was employed to assess the difference in predictive capability among the models.Results In the training set,the area under the curve(AUC)of the clinical model,radiomics model,and combined model were 0.702,0.810 and 0.901,respectively,and in the test set were 0.678,0.797 and 0.830,respectively.The DeLong test revealed a statistically significant difference in the predictive capability of the combined model compared to the clinical model both in the training and test sets(training set:P＜0.001;test set:P=0.019).Conclusion The combined model based on clinical features and enhanced MRI radiomics features demonstrates good predictive effi-cacy and can provide valuable insights for clinical interventions aimed at preventing PPDM-A.

Objective To screen mitochondria-related genes in Crohn's disease (CD) based on the Gene Expression Omnibus (GEO) database, construct an artificial neural network diagnostic model and evaluate its performance. Methods The CD-related datasets GSE186582 and GSE102133 were downloaded from the GEO database for differential expression genes (DEGs) screening. The intersection of DEGs and mitochondrial genes from the MitoCarta 3.0 database was obtained. Least absolute shrinkage and selection operator regression and random forest algorithms were used to identify CD-specific genes and construct an artificial neural network diagnostic model. The model was further validated by the validation set GSE95095, and the diagnostic performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. The immune cell infiltration in CD was assessed by the CIBERSORT algorithm, and the relationship between biomarkers and infiltrated immune cells was investigated. Results A total of 551 DEGs were obtained, including 275 upregulated and 276 downregulated genes. There were 20 mitochondria-related genes associated with CD. A total of 9 mitochondria-related feature genes (SOD2, MTHFD2, BPHL, PXMP2, RMND1, AGXT2, MAOA, HMGCS2, MAOB) were screened by two machine learning algorithms. An artificial neural network diagnostic model was constructed by the selected feature genes. The values of AUC of the model in the training and validation groups were 0.956 and 0.736 respectively. Immune cell infiltration analysis showed that the feature genes were associated with resting memory CD4⁺ T cells, activated memory CD4⁺ T cells, activated dendritic cells, neutrophils, and CD8⁺ T cells. Conclusion The artificial neural network diagnostic model for CD based on 9 mitochondrial genes has good predictive performance.

Objective To construct a prognostic prediction model for hepatocellular carcinoma (HCC) based on immune and metabolism related genes, analyze the prognostic immune response of HCC patients, and screen potential drugs for HCC treatment through drug sensitivity analysis. Methods HCC expression profiling and clinical data were obtained from The Cancer Genome Atlas (TCGA) database, and a list of immune-related genes was obtained from the Immport database; the Perl language was used to extract metabolism-related pathway gene sets from the Molecular Signatures Database(MSig DB), and co-expression related genes were found through differential analysis and co-expression analysis; the univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were used to screen prognosis-related genes and construct a risk prognosis model for HCC, and risk scores for all HCC samples were calculated. Using the median risk score as the critical value, the reliability of the prognostic model was evaluated through risk curves, Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, independent prognostic analysis, and Nomograms. The correlations between risk scores and pathway enrichment analysis as well as immune cell infiltration were analyzed. Drug sensitivity analysis was used to identify potential therapeutic drugs for HCC. Results Five immune and metabolic genes with independent prognostic value were obtained, and a prognostic model based on immune and metabolic genes was constructed. Survival analysis showed that in the total dataset, training group and validation group, the survival rate of the low-risk group was significantly higher than that ofthe high-risk group (P < 0.05). The areas under the ROC curves of the prognostic model for the training group at 1, 3 and 5 years were 0.780, 0.699 and 0.706 respectively. Cox regression analysis showed that grading and risk score could be used as independent prognostic factors for HCC (P < 0.05), with a concordance index of 0.734 (95%CI, 0.669 to 0.798), indicating good model performance. Immune cell infiltration results showed significant differences in resting NK cells, monocytes, M0 macrophages, and M1 macrophages between the high-risk and low-risk groups (P < 0.05). Drug sensitivity analysis screened 12 drugs that may have potential therapeutic effects in HCC patients (P < 0.01). Conclusion The prognostic model of HCC based on five immune and metabolic genes has good predictive performance, and can be used as a new indicator for prognosis evaluation; the 12 drugs screened out have potential efficacy for HCC.

Objective: To investigate whether the cyclic adenosine monophosphate (cAMP) / exchange proteins directly activated by cAMP (Epac) / ras-related protein 1 ( Rap1 ) signalling pathway is involved in the intervening mechanisms of interleukin-1 β (IL-1 β) ，tumor necrosis factor-α (TNF-α) ，brain-derived neurotrophic factor (BDNF) and Jujuboside A(JuA) secretion by NG2 cells． Methods: NG2 cells were cultured in vitro and the experiment was divided into control group ，pertussis toxin ( PTX) group ，ESI-09 group，JuA group and positive drug group．The effect of different concentrations of JuA on the survival rate of NG2 cells was detected by CCK-8 method，and the expression of IL-1 β , TNF-α , BDNF，cAMP，Epac，Rap1 mRNA and protein in each group was detected by RT-PCR and Western blot. Results: Compared with the control group，the PTX group decreased the expression of IL-1 β and TNF-α mRNA and protein (P＜0. 01) and increased the expression of cAMP and BDNF mRNA and protein (P＜0. 01) ; the ESI-09 group increased the expression of IL-1 β and TNF-α mRNA and protein (P < 0. 05) and decreased the expression of BDNF，Epac and Rap1 mRNA and protein expression (P＜0. 01) ; the JuA group and positive drug group increased IL-1 β , TNF-α , BDNF，cAMP，Epac，Rap1 mRNA and protein expression (P＜0. 01) ． Conclusion The cAMP / Epac / Rap1 signaling pathway is involved in the secretion of IL-1 β , TNF- α , and BDNF by NG2 cells．JuA may act on cAMP / Epac / Rap1 signaling pathway to affect the secretion of BDNF by NG2 cells.

Objective:To analyze the characteristics of smoking network among smokers in Kunming smoking cessation outpatient service.Methods:A multicenter, cross-sectional study was carried out, and smoking cessation outpatients from 8 tertiary hospitals (the Second Affiliated Hospital of Kunming Medical University, the First Affiliated Hospital of Kunming Medical University, Yan′an Hospital, Ganmei Hospital, the 920 Hospital of the Chinese People′s Liberation Army Joint Logistic Support Force, Kunming First People′s Hospital, the First People′s Hospital of Yunnan Province and the Second People′s Hospital of Yunnan Province) in Kunming, Yunnan Province were included to receive a questionnaire survey. The questionnaire mainly consisted of four parts: (1) demographic characteristics of smokers; (2) the specific situation of smoking; (3) social support network; (4) smoking network. A total of 360 questionnaires were distributed in this survey, 351 were recovered, and 351 were valid, the collected data from valid questionnaire were statistically analyzed to analyze the social demographic characteristics, smoking status and smoking network characteristics of smokers.Results:In this survey, the demographic characteristics of the 351 patients in the smoking cessation clinic were as follows: there were 342 male cases (97.4%), 249 cases (70.9%) were Han, 233 patients (66.4%) were non-religious, there were 236 cases (67.2%) with college degree or above, and 51 cases (14.5%) were unemployed. The analysis of smoking status showed that 167 patients (47.6%) maintained daily smoking, 247 patients (70.4%) had plans to quit smoking, at least one parent of 258 patients (73.5%) had a history of smoking, 188 patients (53.6 %) smoked at home. In the smoking network of the surveyed patients, there were 304 patients (86.6%) who smoked together with two or more people, and more than 90 percent of the patients smoked with the same sex.Conclusions:The smoking network of patients in Kunming smoking cessation clinic is small, and a third of smokers are friends. It is suggested to use the characteristics of smoking network to carry out smoking cessation intervention activities, so as to improve the success rate of smoking cessation.