Objective To evaluate the effectiveness of three neurological assessments in experimental autoim?mune encephalomyelitis. Methods Thirty-two female C57BL/6 mice (18-20g) were randomly divided into normal group (n=15) and immune group (n=17). Immune group were induced subcutaneously in the flank by emulsion consisted of MOG33-35 and complete Freund’s adjuvant. Mice were assessed by two investigators for 35 days after immunization in a blinded manner using Kono’s 5-point criterion, Weaver’s 15-point criterion and improved 15-point criterion. Based on H&E staining, we analyzed the accuracy, sensibility and dependency of three assessments. Results 35-day clini?cal-score plots revealed that the fluctuation of symptoms was more obvious in improved 15-point criterion compared with the other two criteria. 15-point criterion and improved 15-point criterion are more accurate than 5-point criterion in atypical-onset EAE. 15-point criterion and improved 15-point criterion could detect the neurological deficits much earli?er than 5-point criterion (P<0.001). Neurological scores assessed by improved 15-point were well correlated with the pathological findings during the peak and remission stage. Conclusion Improved 15-point criterion is better neurological score system than Kono’s 5-point criterion and Weaver’s 15-point criterion because of its accurate assessment of the neurological deficits in experimental autoimmune encephalomyelitis.

Background::Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them.Methods::This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated.Results::When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899–0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796–0.995) and 0.870 (95% CI, 0.769–0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model.Conclusions::DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo.