Dose-dependent incidence of agranulocytosis in patients treated with methimazole and propylthiouracil.

Agranulocytosis is a serious adverse effect of methimazole (MMI) and propylthiouracil (PTU), and although there have been reports suggesting a dose-dependent incidence in relation to both drugs, the evidence has not been conclusive. The objective of our study was to determine whether the incidences of agranulocytosis induced by MMI and PTU exhibit dose-dependency. The subjects were 27,784 patients with untreated Graves' disease, 22,993 of whom were on an antithyroid drug treatment regimen for more than 90 days. Within this subset, 18,259 patients had been treated with MMI, and 4,734 had been treated with PTU. The incidence of agranulocytosis according to dose in the MMI group was 0.13% at 10 mg/day, 0.20% at 15 mg/day, 0.32% at 20 mg/day, and 0.47% at 30 mg/day, revealing a significant dose-dependent increase. In the PTU group, there were 0 cases of agranulocytosis at doses of 125 mg/day and below, 0.33% at 150 mg/day, 0.31% at 200 mg/day, and 0.81% at 300 mg/day, also revealing a significant dose-dependent increase. The incidence of agranulocytosis at MMI 15 mg and PTU 300 mg, i.e., at the same potency in terms of hormone synthesis inhibition, was 0.20% and 0.81%, respectively, and significantly higher in the PTU group. Our findings confirm a dose-dependent increase in the incidence of agranulocytosis with both drugs, but that at comparable thyroid hormone synthesis inhibitory doses PTU has a considerably higher propensity to induce agranulocytosis than MMI does.

Depth of cure, water absorption, and solubility of indirect composites polymerized by light-emitting diode laboratory units.

This study investigated the usefulness of a dental laboratory polymerization unit with light-emitting diode (LED) as a light source. The depth of cure (n=15), water absorption and solubility (n=9) of two indirect composite materials (Cesead N and Solidex Hardura) were evaluated by five dental laboratory polymerization units (LED Cure Master, Twinkle LED, α-Light V, α-Light II, and Hyper LII). Statistical analysis was performed by one-way ANOVA and Tukey test or non-parametric tests. Comparison of light sources for curing depth showed that metal halide had the highest value, followed by the LED group with similar values, and halogen lamps with the lowest value. The water absorption and solubility of the composite specimens polymerized with the three LED laboratory polymerization units were within the ISO recommended limit.

Body Roundness Index Is Better Correlated with Insulin Sensitivity than Body Shape Index in Young and Middle-Aged Japanese Persons.

Aims: The present study aimed to clarify the relationships between novel and traditional anthropometric indices and insulin sensitivity (SI) in young and middle-aged Japanese persons with normal glucose tolerance (NGT), and middle-aged Japanese persons with NGT and glucose intolerance. Methods: Plasma glucose and insulin levels were measured in 1270 young (age <40 years) and 2153 middle-aged persons with NGT (n = 1531) and glucose intolerance (n = 622) during a 75-g oral glucose tolerance test. Height (Ht), weight, and waist circumference (WC) were measured. The body mass index (BMI), WC, and the WC/Ht ratio were used as traditional anthropometric indices. A body shape index (ABSI) and the body roundness index (BRI) were calculated as novel indices. Indices of SI (Matsuda index and 1/homeostasis model assessment of insulin resistance) were calculated and compared with anthropometric indices. Results: The ABSI showed a weak correlation with SI indices in all groups. The BRI showed almost the same correlation with SI indices as the BMI, WC, and WC/Ht in all groups. The inverse correlation between each of the anthropometric indices other than ABSI and SI indices was weak in young persons, at 0.16-0.27 (Spearman's ρ values), but strong in middle-aged persons, at 0.38-1.00. On receiver-operating characteristic (ROC) curve analysis for detection of insulin resistance, the ABSI had a lower area under the ROC curve (AUC) than the other anthropometric indices, and the BRI and the WC/Ht ratio showed similar AUCs. The AUCs for the BRI and WC/Ht ratio were the highest in middle-aged men with NGT and glucose intolerance. Conclusions: The BRI, not the ABSI, was better correlated with SI in young and middle-aged Japanese persons. The BRI and WC/Ht ratio were comparable in their correlations with SI and the detection of insulin resistance in the participants of the present study.

An Evaluation of the Efficacy of Machine Learning in Predicting Thyrotoxicosis and Hypothyroidism: A Comparative Assessment of Biochemical Test Parameters Used in Different Health Checkups.

Objective This study assessed the efficacy of machine learning in predicting thyrotoxicosis and hypothyroidism [thyroid-stimulating hormone >10.0 mIU/L] by leveraging age and sex as variables and integrating biochemical test parameters used by the Japan Society of Health Evaluation and Promotion (JHEP) and the Japan Society of Ningen Dock (JND). Methods Our study included 20,653 untreated patients with Graves' disease, 3,435 untreated patients with painless thyroiditis, 4,266 healthy individuals, and 18,937 untreated patients with Hashimoto's thyroiditis. Machine learning was conducted using Prediction One on three distinct datasets: the Ito dataset (age, sex, and 30 blood tests and biochemical test data), the JHEP dataset (age, sex, and total protein,total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γGTP), alkaline phosphatase, creatinine (CRE), uric acid (UA), and T-Cho test data), and the JND dataset (age, sex, and AST, ALT, γGTP, CRE, and UA test data). Results The results for distinguishing thyrotoxicosis patients from the healthy control group showed that the JHEP dataset yielded substantial discriminative capacity with an area under the curve (AUC) of 0.966, sensitivity of 92.2%, specificity of 89.1%, and accuracy of 91.7%. The JND dataset displayed similar robustness, with an AUC of 0.948, sensitivity of 92.0%, specificity of 81.3%, and accuracy of 90.4%. Differentiating hypothyroid patients from the healthy control group yielded similarly robust performances, with the JHEP dataset yielding AUC, sensitivity, specificity, and accuracy values of 0.864, 84.2%, 72.1%, and 77.4%, respectively, and the JND dataset yielding values of 0.840, 83.2%, 67.2%, and 74.3%, respectively. Conclusion Machine learning is a potent screening tool for thyrotoxicosis and hypothyroidism.