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BackgroundTo investigate the relationship 25-hydroxy vitamin D (25OHD) level among children and in children with type 1 diabetes mellitus (T1DM).MethodsA case–control study was conducted to compare the serum 25OHD levels between cases and controls. This study recruited 296 T1DM children (106 newly diagnosed T1DM patients and 190 established T1DM patients), and 295 age- and gender-matched healthy subjects as controls.ResultsThe mean serum 25OHD in T1DM children was 48.69 ± 15.26 nmol/L and in the controls was 57.93 ± 19.03 nmol/L. The mean serum 25OHD in T1DM children was lower than that of controls (P < 0.01). The mean serum 25OHD level (50.42 ± 14.74 nmol/L) in the newly diagnosed T1DM children was higher than that (47.70 ± 15.50 nmol/L) in the established T1DM children but the difference was not statistically significant (P = 0.16). HbA1c values were associated with 25OHD levels in established T1DM children (r = 0.264, P < 0.01), and there was no association between 25OHD and HbA1c in newly diagnosed T1DM children (r = 0.164; P > 0.05).ConclusionVitamin D deficiency is common in T1DM children, and it should be worthy of attention on the lack of vitamin D in established T1DM children.

BackgroundIt is reported that radiomic features extracted from quantitative susceptibility mapping (QSM) had promising clinical value for the diagnosis of Parkinson’s disease (PD). We aimed to explore the usefulness of radiomics features based on magnitude images to distinguish PD from non-PD controls.MethodsWe retrospectively recruited PD patients and controls who underwent brain 3.0T MR including susceptibility-weighted imaging (SWI). A total of 396 radiomics features were extracted from the SN of 95 PD patients and 95 non-PD controls based on SWI. Intra-/inter-observer correlation coefficients (ICCs) were applied to measure the observer agreement for the radiomic feature extraction. Then the patients were randomly grouped into training and validation sets in a ratio of 7:3. In the training set, the maximum correlation minimum redundancy algorithm (mRMR) and the least absolute shrinkage and selection operator (LASSO) were conducted to filter and choose the optimized subset of features, and a radiomics signature was constructed. Moreover, radiomics signatures were constructed by different machine learning models. Area under the ROC curves (AUCs) were applied to evaluate the predictive performance of the models. Then correlation analysis was performed to evaluate the correlation between the optimized features and clinical factors.ResultsThe intro-observer CC ranged from 0.82 to 1.0, and the inter-observer CC ranged from 0.77 to 0.99. The LASSO logistic regression model showed good prediction efficacy in the training set [AUC = 0.82, 95% confidence interval (CI, 0.74–0.88)] and the validation set [AUC = 0.81, 95% CI (0.68–0.91)]. One radiomic feature showed a moderate negative correlation with Hoehn-Yahr stage (r = −0.49, P = 0.012).ConclusionRadiomic predictive features based on SWI magnitude images could reflect the Hoehn-Yahr stage of PD to some extent.

The Newton's cradle motion-like triboelectric nanogenerator, which utilizes elastic deformation to recycle energy is designed and fabricated. With this new design, the output current of this TENG is 5.7 times as much as that of the common contact-separation TENG and 2.3 times as much as that of similar structure TENG without using elasticity.