BackgroundThis study compared event rates of diabetic ketoacidosis (DKA) and severe hypoglycemia, as well as glycemic control, among children, adolescents, and young adults with type 1 diabetes mellitus (T1DM) receiving basal-bolus or premixed insulin therapy.MethodsA total of 825 individuals aged ≤ 20 years with T1DM, using either basal-bolus or premixed insulin regimens, were retrospectively recruited from 2001 to 2015. Rates of DKA after diagnosis, severe hypoglycemia, and the level of glycated hemoglobin A1c (HbA1c) improvement during the follow-up period were analyzed.ResultsOf the 825 patients, 226 receiving a premixed regimen were matched to the same number of patients receiving a basal-bolus regimen. In the matched cohort, DKA (10.62% vs. 5.31%; p = 0.037) and severe hypoglycemic episodes (25.22% vs. 10.62%; p < 0.001) were significantly higher in patients receiving a premixed regimen than those receiving a basal-bolus regimen. The median reduction of HbA1c, compared to the treatment-naive level, was better with the basal-bolus regimen than with the premixed regimen in both matched (2.2 vs. 2.1; p = 0.034) and the entire (3.1 vs. 1.9; p < 0.001) cohorts. Regardless of insulin regimen, a higher HbA1c level was significantly linked to higher risk of DKA development (hazard ratio [HR] 1.35 per 1% increase; p < 0.001) once the HbA1c level was ≥7.5%.ConclusionsA premixed insulin regimen may increase the DKA occurrence rate and severe hypoglycemic risk in children, adolescents, and young adults with TIDM, compared to a basal-bolus regimen. Tight glycemic control with HbA1c < 7.5% may prevent the increased risk of DKA.

Differentiated thyroid cancer (DTC) from follicular epithelial cells is the most common form of thyroid cancer. Beyond the common papillary thyroid carcinoma (PTC), there are a number of rare but difficult-to-diagnose pathological classifications, such as follicular thyroid carcinoma (FTC). We employed deep convolutional neural networks (CNNs) to facilitate the clinical diagnosis of differentiated thyroid cancers. An image dataset with thyroid ultrasound images of 421 DTCs and 391 benign patients was collected. Three CNNs (InceptionV3, ResNet101, and VGG19) were retrained and tested after undergoing transfer learning to classify malignant and benign thyroid tumors. The enrolled cases were classified as PTC, FTC, follicular variant of PTC (FVPTC), Hürthle cell carcinoma (HCC), or benign. The accuracy of the CNNs was as follows: InceptionV3 (76.5%), ResNet101 (77.6%), and VGG19 (76.1%). The sensitivity was as follows: InceptionV3 (83.7%), ResNet101 (72.5%), and VGG19 (66.2%). The specificity was as follows: InceptionV3 (83.7%), ResNet101 (81.4%), and VGG19 (76.9%). The area under the curve was as follows: Incep-tionV3 (0.82), ResNet101 (0.83), and VGG19 (0.83). A comparison between performance of physicians and CNNs was assessed and showed significantly better outcomes in the latter. Our results demonstrate that retrained deep CNNs can enhance diagnostic accuracy in most DTCs, including follicular cancers.