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This study aimed to investigate the possible incidence of visual light perceptions (VLPs) during radiation therapy (RT). We analyzed whether VLPs could be affected by differences in the radiation energy, prescription doses, age, sex, or RT locations, and whether all VLPs were caused by radiation. From November 2016 to August 2018, a total of 101 patients who underwent head-and-neck or brain RT were screened. After receiving RT, questionnaires were completed, and the subjects were interviewed. Random forests (RF), a tree-based machine learning algorithm, and logistic regression (LR) analyses were compared by the area under the curve (AUC), and the algorithm that achieved the highest AUC was selected. The dataset sample was based on treatment with non-human units, and a total of 293 treatment fields from 78 patients were analyzed. VLPs were detected only in 122 of the 293 exposure portals (40.16%). The dataset was randomly divided into 80% and 20% as the training set and test set, respectively. In the test set, RF achieved an AUC of 0.888, whereas LR achieved an AUC of 0.773. In this study, the retina fraction dose was the most important continuous variable and had a positive effect on VLP. Age was the most important categorical variable. In conclusion, the visual light perception phenomenon by the human body during RT is induced by radiation rather than being a self-suggested hallucination or induced by phosphenes.

Thalamostriate vein (TSV) is an important tributary of the internal cerebral vein, which mainly drains the basal ganglia and deep medulla. The purpose of this study was to explore the anatomic variation and quality of TSV and its smaller tributaries using susceptibility-weighted imaging (SWI).We acquired SWI images in 40 volunteers on a 3.0T MR system using an 8-channel high-resolution phased array coil. The frequencies of the TSV and its tributaries were evaluated. We classified TSV into types I (forming a venous angle) and II (forming a false venous angle). We classified anterior caudate vein (ACV)into types 1 (1 trunk) and 2 (2 trunks) as well as into types A (joiningTSV), B (joining anterior septal vein), and C (joining the angle of both veins).The TSV drains the areas of caudate nucleus, internal capsule,lentiform nucleus, external capsule, claustrum, extreme capsule and the white matter of the frontoparietal lobes,except thalamus. The frequencies of the TSV, ACV and transverse caudate vein (ACV) were 92.5%, 87.5% and 63.8%, respectively. We found TSV types I and II in 79.7%, and 20.3% with significantly different constitution ratios (P< 0.05). The most common types of ACV were type 1 (90.0%) and type A (64.3%).The complex three-dimensional (3D) venous architecture of TSV and its small tributaries manifests great variation, with significant and practical implications for neurosurgery.

The current study aimed to compare the estimates of body fat percentage (%BF) by performing bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA) in a sample of obese or overweight Chinese adults who participated in a weight-loss randomized control trial stratified by gender to determine whether or not BIA is a valid measurement tool. Among 189 adults [73 males, 116 females; age = 41 to 74 years; mean body mass index (BMI) = 27.3 kg/m(2)], assessments of %BF at the baseline and six months from the baseline were conducted by performing BIA and DXA. Bland-Altman analyses and multiple regression analyses were used to assess the relationships between %BFBIA and %BFDXA. Compared with DXA, BIA underestimated %BF [in males: 4.6, -2.4 to 11.7 (mean biases, 95% limit of agreement) at the baseline, 1.4, -7.4 to 10.2 at the endpoint, and 3.2, -4.8 to 11.3 in changes; in females: 5.1, -2.4 to 12.7; 2.2, -6.1 to 10.4; and 3.0, -4.8 to 10.7, respectively]. For males and females, %BFDXA proved to be a significant predictor of the difference between DXA and BIA at the baseline, the endpoint, and in changes when BMI and age were considered (in males: p

BACKGROUND: A simple diabetes risk tool that does not require laboratory tests would be beneficial in screening individuals at higher risk. Few studies have evaluated the ability of these tools to identify new cases of pre-diabetes. This study aimed to assess the ability of the American Diabetes Association Risk Tool (ADART) to predict the 3-year incidence of pre-diabetes and diabetes in Taiwanese. METHODS: This was a 3-year prospective study of 1021 residents with normoglycemia at baseline, gathered from a random sample of residents aged 40-88 years in a metropolitan city in Taiwan. The areas under the curve (AUCs) of three models were compared: ADART only, ADART plus lifestyle behaviors at baseline, and ADART plus lifestyle behaviors and biomarkers at baseline. The performance of ADART was compared with that of 16 tools that had been reported in the literature. RESULTS: The AUCs and their 95% confidence intervals (CIs) were 0.60 (0.54-0.66) for men and 0.72 (0.66-0.77) for women in model 1; 0.62 (0.56-0.68) for men and 0.74 (0.68-0.80) for women in model 2; and 0.64 (0.58-0.71) for men and 0.75 (0.69-0.80) for women in model 3. The AUCs of these three models were all above 0.7 in women, but not in men. No significant difference in either women or men (p = 0.268 and 0.156, respectively) was observed in the AUC of these three models. Compared to 16 tools published in the literature, ADART had the second largest AUC in both men and women. CONCLUSIONS: ADART is a good screening tool for predicting the three-year incidence of pre-diabetes and diabetes in females of a Taiwanese population. The performance of ADART in men was similar to the results with other tools published in the literature. Its performance was one of the best among the tools reported in the literature.