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Novel molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood non-coding RNA (ncRNA) signature in diagnosing ASD. One hundred eighty six blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profile was re-annotated into the expression profile of 4143 ncRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 20-ncRNA signature, and a diagnostic score was calculated for each sample according to the ncRNA expression levels and the model coefficients. The score demonstrated an excellent diagnostic ability for ASD in the training set (area under receiver operating characteristic curve [AUC] = 0.96), validation set (AUC = 0.97) and the overall (AUC = 0.96). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.96). In subgroup analysis, the signature was also robust when considering the potential confounders of sex, age, and disease subtypes. In comparison with a 55-gene signature deriving from the same dataset, the ncRNA signature showed an obviously better diagnostic ability (AUC: 0.96 vs 0.68, P

Supplemental Digital Content is available in the text, Pioglitazone may have potential benefits in the treatment of cutaneous and metabolic derangements of psoriasis, but its role in the treatment of psoriasis remains in debate. We therefore conducted a meta-analysis to evaluate the clinical efficacy and safety of pioglitazone in psoriasis vulgaris (PsV). We performed a comprehensive search in database of PubMed, Web of Science, Cochrane library, Embase and China National Knowledge Infrastructure (CNKI), and Wan fang database through March 2019 to identify eligible studies. Randomized controlled trials that have evaluated the effect and safety of pioglitazone in PsV were included. Treatment success was defined as ≥75% reduction in psoriasis area and severity index (PASI) score after treatment. Weighted mean differences (WMD), relative risks (RRs) and the corresponding 95% confidence intervals (CIs) were pooled to compare the clinical efficacy and safety between different groups. Six randomized controlled trials (n = 270) were included. Meta-analysis showed that pioglitazone was associated with a remarkable reduction in PASI score in patients with PsV (weight mean difference: 2.68, 95% CI 1.41–3.94, P

Reliable molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood small nuclear RNA (snRNA) signature in diagnosing ASD. 186 blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profiles were re-annotated into the expression profiles of 1253 snRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 9-snRNA signature (RNU1-16P, RNU6-1031P, RNU6-258P, RNU6-335P, RNU6-485P, RNU6-549P, RNU6-98P, RNU6ATAC26P, and RNVU1-15), and a diagnostic score was calculated for each sample according to the snRNA expression levels and the model coefficients. The score demonstrated a good diagnostic ability for ASD in the training set (area under receiver operating characteristic curve (AUC) = 0.90), validation set (AUC = 0.87), and the overall (AUC = 0.88). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.88). In subgroup analysis, the signature was robust when considering the confounders of gender, age, and disease subtypes, and displayed a significantly better performance among the female and younger cases (P = .039; P = .002). In comparison with a 55-gene signature deriving from the same dataset, the snRNA signature showed a better diagnostic ability (AUC: 0.88 vs 0.80, P = .049). In conclusion, this study identified a novel and robust blood snRNA signature in diagnosing ASD, which might help improve the diagnostic accuracy for ASD in clinical practice. Nevertheless, a large-scale prospective study was needed to validate our results.