This study was aimed at understanding the Babesia species makeup and distribution in small mammals in Jinsha River Basin of Yunnan Province,and the Babesia carriage status in small mammals in this area,to provide a scientific basis for the preven-tion and control of Babesia disease.A total of 1 493 small mammals belonging to 5 orders,10 families,25 genera,and 54 species were captured from 10 counties(cities)in the Jinsha River Basin of Yunnan Province in various agricultural and forest environments.DNA was extracted from liver and tick tissues,and 150 bp fragments of Babesia 18S rRNA were detected through molecular biological methods.The positive samples showed amplification of a 1 600 bp target fragment of 18S rRNA.Species characteristics were assessed through sequence comparison and phylogenetic analysis.A total of 14 small mammals infected with Babesia were detected in six coun-ties(cities)of Jinsha River Basin,Yunnan Province,with a positivity rate of 0.93%(14/1 493).The Otsu and Kobe types of Babesia voles were analyzed,and their sequences were compared with the sequences from human Babesia cases with high similarity and close evolutionary relationships.The positivity rates were 2.34%(3/128)in Qiaojia County,2.06%(2/97)in Yongshan County,1.88%(4/213)in Yuanmou County,1.03%(3/291)in Deqin County,0.95%(1/105)in Shangri-La City,and 0.78%(1/128)in Shuifu County.The positive small mammals belonged to one order,two families,six genera,and the following eight species:P.leucurus 5.56%(1/18),R.brunneusculus 3.36%(4/119),M.minutus 3.33%(1/30),E.custos 2.94%(1/34),N.confucianus 2.65%(3/113),N.fulvescens 2.35%(2/85),A.latronum 1.16%(1/86),and A.draco 0.98%(1/102).The detection of Babesia in M.minutus was re-poorted first time.Small animals infected with Babesia were detected in all three habitats and altitudes,and higher infection rates were observed in forest regions between 1 500 and 2 500 meters and high-altitude residential areas.Babesia infection was found in many small mammals in several counties(cities)along Jinsha River in Yunnan Province,and the epidemic status of Babesia in these areas warrants attention.

A prediction model for the risk of childhood allergic asthma was established through the analysis of public datasets. By using bioinformatics analysis methods, two datasets, GSE40732 and GSE40888, were selected, which included the whole-genome expression profile data of 222 children. Among them, GSE40732 was used as the training dataset to detect differentially expressed genes in peripheral blood mononuclear cells of children with the disease, and the master regulator analysis (MRA) algorithm was used to screen the master regulator genes in the inflammation-related pathway (GO: 0006954). After obtaining the master regulator genes, the expression of these master regulator genes in the GSE40732 and GSE40888 datasets was detected, and a prediction model was constructed through logistic regression, based on which risk scores were assigned to children. By comparing the risk scores of healthy children and children with the disease, the area under the curve (AUC) was used to evaluate the classification performance of the model. The average value of the risk scores of all children with the disease output by the model was calculated as the threshold. According to this threshold, the children with the disease in the two datasets were divided into high-risk and low-risk groups. The CIBERSORT algorithm was applied to analyze the infiltration of immune cells in the high-risk and low-risk groups, and the enrichment analysis of signaling pathways was completed using the msigdbr package in R software. The results showed that compared with healthy children, there were 377 up-regulated genes and 255 down-regulated genes in the peripheral blood mono-nuclear cells of children with the disease. The MRA algorithm analysis showed that there were five genes ( MUC5B, CST4, CCR7, TNF-α, and THBS1) that were the master regulator genes in the regulatory network. Risk score= MUC5B×3.47 +CST4×2.17 +CCR7×0.59 +TNF- α×0.54 +THBS1×1.67. The AUC in the GSE40732 and GSE40888 datasets were 0.874 and 0.682, respectively. Compared with the low-risk group, the resting memory CD4 +T cells and regulatory T cells in the peripheral blood of children with the disease in the high-risk group significantly decreased ( P<0.05), and both the IL-33 and IL-13 pathways were highly enriched. In conclusion, the model constructed in this study has a good predictive efficiency for the risk of allergic asthma and also has a certain effect on risk stratification.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

A prediction model for the risk of childhood allergic asthma was established through the analysis of public datasets. By using bioinformatics analysis methods, two datasets, GSE40732 and GSE40888, were selected, which included the whole-genome expression profile data of 222 children. Among them, GSE40732 was used as the training dataset to detect differentially expressed genes in peripheral blood mononuclear cells of children with the disease, and the master regulator analysis (MRA) algorithm was used to screen the master regulator genes in the inflammation-related pathway (GO: 0006954). After obtaining the master regulator genes, the expression of these master regulator genes in the GSE40732 and GSE40888 datasets was detected, and a prediction model was constructed through logistic regression, based on which risk scores were assigned to children. By comparing the risk scores of healthy children and children with the disease, the area under the curve (AUC) was used to evaluate the classification performance of the model. The average value of the risk scores of all children with the disease output by the model was calculated as the threshold. According to this threshold, the children with the disease in the two datasets were divided into high-risk and low-risk groups. The CIBERSORT algorithm was applied to analyze the infiltration of immune cells in the high-risk and low-risk groups, and the enrichment analysis of signaling pathways was completed using the msigdbr package in R software. The results showed that compared with healthy children, there were 377 up-regulated genes and 255 down-regulated genes in the peripheral blood mono-nuclear cells of children with the disease. The MRA algorithm analysis showed that there were five genes ( MUC5B, CST4, CCR7, TNF-α, and THBS1) that were the master regulator genes in the regulatory network. Risk score= MUC5B×3.47 +CST4×2.17 +CCR7×0.59 +TNF- α×0.54 +THBS1×1.67. The AUC in the GSE40732 and GSE40888 datasets were 0.874 and 0.682, respectively. Compared with the low-risk group, the resting memory CD4 +T cells and regulatory T cells in the peripheral blood of children with the disease in the high-risk group significantly decreased ( P<0.05), and both the IL-33 and IL-13 pathways were highly enriched. In conclusion, the model constructed in this study has a good predictive efficiency for the risk of allergic asthma and also has a certain effect on risk stratification.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To evaluate the respective or synergistic value of cytoplasmic tryptophan-tRNA ligase(WARS1/SYWC)and adenosine deaminase(ADA)in diagnosing tuberculous pleural effusion(TPE).Methods A retrospective analysis was conducted on 120 patients with pleural effusion(64 cases of TPE,56 cases of non-TPE)admitted to the First Affiliated Hospital of Jinan University and its affiliated Shunde Hospital from January 2020 to December 2024.Pleural fluid SYWC levels were identified using enzyme-linked immunosorbent assay(ELISA).Univariate and multivariate logistic regression analyses were performed to identify diagnostic predictors,while receiver operating characteristic(ROC)curves were plotted to assess the diagnostic perfor-mance of individual and combined biomarkers.Results Compared to the non-TPE group,TPE group exhibited significantly younger age,lower pleural CEA,less serum CEA,and lower neutrophil-to-lymphocyte ratio(NLR),but significantly higher levels of pleural ADA,total protein,SYWC,and serum CRP(all P<0.05).Univariate analysis identified age,pleural CEA,carbohydrate antigen 199,ADA,SYWC,serum CEA,and NLR as potential predictors.Multivariate analysis confirmed pleural ADA(OR=1.064,95%CI:1.017～1.228)and SYWC(OR=6.695,95%CI:2.794～16.04)as independent diagnostic factors.At optimal cutoffs,SYWC(16.94 μg/L)demonstrated a sensitivity of 71.80%and specificity of 98.21%,while ADA(36.5 U/L)showed a sensitivity of 93.75%and a specificity of 89.29%.Combined detection increased the sensitivity to 95.56%,the specificity to 98.0%,and the accuracy to 97.87%.ROC analysis revealed an AUC of 0.973(95%CI:0.943～1.000)for the combination,outperforming ADA(0.897)and SYWC(0.938)alone.Conclusion The combi-nation of SYWC and ADA notably enhances diagnostic efficacy for TPE,providing high sensitivity and specificity as a reliable tool for clinical differentiation.