The objective of this study was to develop a rapid and precise detection technique for por-cine Senecavirus A(SVA)employing reverse transcription recombinase polymerase amplification(RT-RAA)in conjunction with CRISPR Cas13a technology.Additionally,the study aimed to opti-mize the assay's reaction conditions to enhance amplification efficiency.Eight RT-RAA primer sets were designed based on the conserved gene sequence of porcine SVA,and a series of reaction condi-tions were evaluated to refine the RT-RAA reaction system.Subsequently,CRISPR-derived RNA(crRNA)sequences were developed and selected to construct the RT-RAA-CRISPR reaction sys-tem.The method's specificity was determined by examining six prevalent porcine pathogenic nucleic acids,while its sensitivity was assessed using SVA cRNA standards quantified by digital PCR.The method's stability and the consistency of clinical sample analysis were also evaluated.The findings revealed that the optimized RT-RAA and CRISPR reaction systems exhibited the highest amplifi-cation efficiency at a reaction temperature of 37 ℃.Among the eight crRNAs,five were identified as exhibiting the strongest detection signals.The formulated RT-RAA-CRISPR Cas13a method demonstrated exceptional specificity,showing no cross-reactivity with other common porcine disea-ses,including ASFV,PRRSV,PEDV,PCV2,CSFV,and PRV.The method achieved high sensitivi-ty,detecting as low as 0.86 copies/μL of SVA,and exhibited stable fluorescence output,robust re-producibility,and the ability to complete clinical sample analysis within 50 minutes.Consistency e-valuation with six positive and 58 negative samples indicated 100％agreement in outcomes.These results substantiate that the study successfully established a rapid and specific RT-RAA-CRISPR Cas13a detection method for the on-site identification of porcine Senecavirus A,demonstrating high specificity and sensitivity,and holds promise for application in SVA monitoring and control initia-tives.

The objective of this study was to develop a rapid and precise detection technique for por-cine Senecavirus A(SVA)employing reverse transcription recombinase polymerase amplification(RT-RAA)in conjunction with CRISPR Cas13a technology.Additionally,the study aimed to opti-mize the assay's reaction conditions to enhance amplification efficiency.Eight RT-RAA primer sets were designed based on the conserved gene sequence of porcine SVA,and a series of reaction condi-tions were evaluated to refine the RT-RAA reaction system.Subsequently,CRISPR-derived RNA(crRNA)sequences were developed and selected to construct the RT-RAA-CRISPR reaction sys-tem.The method's specificity was determined by examining six prevalent porcine pathogenic nucleic acids,while its sensitivity was assessed using SVA cRNA standards quantified by digital PCR.The method's stability and the consistency of clinical sample analysis were also evaluated.The findings revealed that the optimized RT-RAA and CRISPR reaction systems exhibited the highest amplifi-cation efficiency at a reaction temperature of 37 ℃.Among the eight crRNAs,five were identified as exhibiting the strongest detection signals.The formulated RT-RAA-CRISPR Cas13a method demonstrated exceptional specificity,showing no cross-reactivity with other common porcine disea-ses,including ASFV,PRRSV,PEDV,PCV2,CSFV,and PRV.The method achieved high sensitivi-ty,detecting as low as 0.86 copies/μL of SVA,and exhibited stable fluorescence output,robust re-producibility,and the ability to complete clinical sample analysis within 50 minutes.Consistency e-valuation with six positive and 58 negative samples indicated 100％agreement in outcomes.These results substantiate that the study successfully established a rapid and specific RT-RAA-CRISPR Cas13a detection method for the on-site identification of porcine Senecavirus A,demonstrating high specificity and sensitivity,and holds promise for application in SVA monitoring and control initia-tives.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To evaluate the diagnostic value of native T1 mapping in differentiating Oxford classification (MEST-C) scores in patients with IgA nephropathy.Methods:In this prospective study, patients who underwent both T1 mapping and renal biopsy at the First Medical Center of the Chinese PLA General Hospital between April 2023 and October 2024 were consecutively enrolled. Two radiologists, blinded to clinical and pathological information, measured renal T1 mapping parameters, including cortical T1 (cT1), medullary T1 (mT1), the corticomedullary difference (ΔT1), and the corticomedullary ratio (T1 ratio). Clinical and renal biopsy data based on the Oxford classification from patients with IgA nephropathy were collected. The Oxford classification includes five indicators: Mesangial hypercellularity (M), Endocapillary hypercellularity (E), Segmental glomerulosclerosis or adhesion (S), Tubular atrophy/interstitial fibrosis (T), and Cellular or fibrocellular crescents (C). Spearman correlation analysis was applied to evaluate the associations between MEST-C scores and T1 parameters. The diagnostic performance of T1 parameters for discriminating among scores of the Oxford classification was analyzed using the receiver operating characteristic (ROC) curve.Results:A total of 124 patients with IgA nephropathy were included in this study [66 males, 58 females; age 19-70 years, 39 (30, 51) years]. Except for the E indicator, M, S, T, and C were significantly correlated with renal T1 values ( ρ=0.177-0.414, all P<0.05). cT1 showed the best diagnostic efficacy for the S score, with an area under the curve (AUC) of 0.798, a sensitivity of 68.7%, and a specificity of 88.0%. The best T1 parameter for differentiating the T score was the T1 ratio, with an AUC of 0.687, a sensitivity of 57.9%, and a specificity of 79.1%. Conclusion:Native T1 mapping can be used for the non-invasive assessment of the S and T scores in the Oxford classification of patients with IgA nephropathy.

Objective:To evaluate the diagnostic value of native T1 mapping in differentiating Oxford classification (MEST-C) scores in patients with IgA nephropathy.Methods:In this prospective study, patients who underwent both T1 mapping and renal biopsy at the First Medical Center of the Chinese PLA General Hospital between April 2023 and October 2024 were consecutively enrolled. Two radiologists, blinded to clinical and pathological information, measured renal T1 mapping parameters, including cortical T1 (cT1), medullary T1 (mT1), the corticomedullary difference (ΔT1), and the corticomedullary ratio (T1 ratio). Clinical and renal biopsy data based on the Oxford classification from patients with IgA nephropathy were collected. The Oxford classification includes five indicators: Mesangial hypercellularity (M), Endocapillary hypercellularity (E), Segmental glomerulosclerosis or adhesion (S), Tubular atrophy/interstitial fibrosis (T), and Cellular or fibrocellular crescents (C). Spearman correlation analysis was applied to evaluate the associations between MEST-C scores and T1 parameters. The diagnostic performance of T1 parameters for discriminating among scores of the Oxford classification was analyzed using the receiver operating characteristic (ROC) curve.Results:A total of 124 patients with IgA nephropathy were included in this study [66 males, 58 females; age 19-70 years, 39 (30, 51) years]. Except for the E indicator, M, S, T, and C were significantly correlated with renal T1 values ( ρ=0.177-0.414, all P<0.05). cT1 showed the best diagnostic efficacy for the S score, with an area under the curve (AUC) of 0.798, a sensitivity of 68.7%, and a specificity of 88.0%. The best T1 parameter for differentiating the T score was the T1 ratio, with an AUC of 0.687, a sensitivity of 57.9%, and a specificity of 79.1%. Conclusion:Native T1 mapping can be used for the non-invasive assessment of the S and T scores in the Oxford classification of patients with IgA nephropathy.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To investigate the impact of the interval period between biopsy and MR examination on tumor detection and extraprostatic extension (EPE) assessment for prostate cancer (PCa) using multi-parametric MRI (mpMRI).Methods:The study was cross-sectional and retrospectively included 130 patients with PCa who underwent RP and preoperative systematic biopsies followed by mpMRI between January 2021 and December 2022 in the First Medical Center of Chinese PLA General Hospital. Patients were divided into 3 groups according to interval following biopsy (group A,<3 weeks, 31 cases; group B, 3-6 weeks, 67 cases; group C,>6 weeks, 32 cases). The percentages of hemorrhage volume in the total prostate were drawn on T 1WI and calculated. The junior, senior and expert radiologists independently localized the index lesions and calculated the accuracy for tumor detection, in addition to assessing the probabilities of EPE according to EPE grade. The correlation between the hemorrhage extent and interval was analyzed using the Spearman correlation coefficient. The accuracy for tumor detection was compared using χ2 test among groups. The diagnostic performance of the radiologists for EPE prediction was assessed using the receiver operating characteristic curve, and the differences between the corresponding area under the curve (AUC) were compared using the DeLong test. Results:The percentage of hemorrhage was correlated with the interval between biopsy and MR examination ( r=-0.325, P<0.001). The detection accuracy of junior radiologist was 83.9% (26/31), 76.1% (51/67), and 78.1% (25/32) in group A, B and C, respectively; no differences were observed in the detection accuracy among three groups ( χ2=0.76, P=0.685). The detection accuracy of senior radiologist was 83.9% (26/31), 80.6% (54/67), and 71.9% (23/32) in 3 groups with no differences ( χ2=1.53, P=0.464). The detection accuracy of expert radiologist was 80.6% (25/31), 77.6% (52/67), and 93.8% (30/32) with no differences ( χ2=3.95, P=0.139). The AUC (95% CI) for predicting EPE were 0.830 (0.652-0.940), 0.704 (0.580-0.809), 0.800 (0.621-0.920) in the group A, B and C for junior radiologist; 0.876 (0.708-0.966), 0.768 (0.659-0.863), 0.896 (0.736-0.975) for senior radiologist; and 0.866 (0.695-0.961), 0.813 (0.699-0.895), 0.852 (0.682-0.952) for expert radiologist, respectively. No differences were observed among the subgroups in each radiologist ( P>0.05). Conclusion:The interval period does not significantly affect the detection accuracy and EPE assessment of PCa using mpMRI. There is probably no necessity for prolonged intervals following systematic biopsy to preserve the clarity of MRI interpretation for PCa.

Objective:To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in different regions of the kidney in distinguishing IgA nephropathy (IgAN) patients from healthy volunteers.Methods:This study was a cross-sectional study. Eighty-four patients diagnosed with IgAN (IgAN group) who underwent renal biopsy (lower pole of the left kidney) and were pathologically confirmed at the First Medical Center of PLA General Hospital from February 2022 to September 2023 and thirty-four healthy volunteers (control group) were included prospectively. The regions of interest were outlined in the right renal cortex, medulla, and parenchyma for all subjects, and the apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D *), and perfusion fraction (f) were measured in the corresponding regions. The differences in IVIM-DWI parameters between the IgAN group and the control group were compared using the student′s t-test or the Mann-Whitney U test. Receiver operating characteristic curve analysis was performed on the parameters with statistically significant differences, and the area under the curve (AUC) was calculated. Results:There were statistically significant differences in renal cortical ADC, renal parenchymal ADC, renal cortical D, renal parenchymal D, and renal medullary f values between the IgAN group and the control group ( Z=-3.03, -2.21, -2.62, -2.03, -2.03; P=0.002, 0.027, 0.009, 0.043, 0.042). The AUCs (95% CI) for diagnosing IgAN using renal cortical ADC, renal parenchymal ADC, renal cortical D, renal parenchymal D, and renal medullary f values were 0.679 (0.586-0.762), 0.630 (0.537-0.717), 0.654 (0.535-0.774), 0.619 (0.497-0.742), and 0.620 (0.495-0.745), respectively. There were no statistically significant differences in renal medullary ADC, D, renal cortex, medulla and parenchyma D *, renal cortical and renal parenchymal f values between the two groups ( P>0.05). Conclusion:The quantitative parameters of renal IVIM-DWI are influenced by different measurement regions, among which the ADC, D of renal cortex and parenchyma, and f of renal medulla can be used for the initial diagnosis of IgAN.

Objective:To investigate the intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in the differential diagnosis of diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) among patients with type 2 diabetes mellitus (T2DM).Methods:A diagnostic test. In this prospective study, patients with T2DM who underwent both IVIM-DWI and renal biopsy at the First Medical Center of Chinese PLA General Hospital between October 2017 and September 2021 were consecutively enrolled. IVIM-DWI parameters including perfusion fraction (f), pure diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were measured in the renal cortex, medulla, and parenchyma. Patients were divided into the DN group and NDRD group based on the renal biopsy results. IVIM-DWI parameters, clinical information, and diabetes-related biochemical indicators between the two groups were compared using Student′s t-test or Mann-Whitney U test. The correlation of IVIM-DWI parameters with diabetic nephropathy histological scores were analyzed using Spearman′s correlation analyzes. The diagnostic efficiency of IVIM-DWI parameters for distinguishing between DN and NDRD were assessed using the receiver operating characteristic (ROC) curves. Results:A total of 27 DN patients and 23 NDRD patients were included in this study. The DN group comprised 19 male and 8 female patients, with an average age of 52±9 years. The NDRD group comprised 16 male and 7 female patients, with an average age of 49±10 years. The DN group had a higher D* value in the renal cortex and a lower f value in the renal medulla than the NDRD group (9.84×10 -3 mm 2/s vs. 7.35×10 -3 mm 2/s, Z=-3.65; 41.01% vs. 46.74%, Z=-2.29; all P<0.05). The renal medulla D* value was negatively correlated with DN grades, interstitial lesion score, and interstitial fibrosis and tubular atrophy (IFTA) score ( r=-0.571, -0.409, -0.409; all P<0.05) while the renal cortex f value was positively correlated with vascular sclerosis score ( r=0.413, P=0.032). The renal cortex D* value had the highest area under the curve (AUC) for discriminating between the DN and NDRD groups (AUC=0.802, sensitivity 91.3%, specificity 55.6%). Conclusion:IVIM-derived renal cortex D* value can be used non-invasively to differentiate DN from NDRD in patients with T2DM that can potentially facilitate individualized treatment planning for diabetic patients.