A human brain atlas of χ-separation for normative iron and myelin distributions.

Iron and myelin are primary susceptibility sources in the human brain. These substances are essential for a healthy brain, and their abnormalities are often related to various neurological disorders. Recently, an advanced susceptibility mapping technique, which is referred to as χ-separation (pronounced as "chi"-separation), has been proposed, successfully disentangling paramagnetic iron from diamagnetic myelin. This method provided a new opportunity for generating high-resolution iron and myelin maps of the brain. Utilizing this technique, this study constructs a normative χ-separation atlas from 106 healthy human brains. The resulting atlas provides detailed anatomical structures associated with the distributions of iron and myelin, clearly delineating subcortical nuclei, thalamic nuclei, and white matter fiber bundles. Additionally, susceptibility values in a number of regions of interest are reported along with age-dependent changes. This atlas may have direct applications such as localization of subcortical structures for deep brain stimulation or high-intensity focused ultrasound and also serve as a valuable resource for future research.

Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.

BACKGROUND: Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can support clinical decision making. Based on a deep learning algorithm, we developed an automated ASPECTS scoring system (Heuron ASPECTS) and validated its performance in a prespecified clinical trial. METHODS: For model training, we used non-contrast computed tomography images of 487 patients with acute ischemic stroke (AIS). For the clinical trial, 326 patients (87 with AIS, 56 with other acute brain diseases, and 183 with no brain disease) were enrolled. The results of Heuron ASPECTS were compared with the consensus generated by two stroke experts using the Bland-Altman agreement. A mean difference of less than 0.35 and a maximum allowed difference of less than 3.8 were considered the primary outcome target. The sensitivity and specificity of the model for the 10 regions of interest and dichotomized ASPECTS were calculated. RESULTS: The Bland-Altman agreement had a mean difference of 0.03 [95% confidence interval (CI): -0.08 to 0.14], and the upper and lower limits of agreement were 2.80 [95% CI: 2.62 to 2.99] and -2.74 [95% CI: -2.92 to -2.55], respectively. For ASPECTS calculation, sensitivity and specificity to detect the early ischemic change for 10 ASPECTS regions were 62.78% [95% CI: 58.50 to 67.07] and 96.63% [95% CI: 96.18 to 97.09], respectively. Furthermore, in a dichotomized analysis (ASPECTS >4 vs. ≤4), the sensitivity and specificity were 94.01% [95% CI: 91.26 to 96.77] and 61.90% [95% CI: 47.22 to 76.59], respectively. CONCLUSIONS: The current trial results show that Heuron ASPECTS reliably measures the ASPECTS for use in clinical practice.

Evaluation of a Novel Trocar-Site Closure Device in Laparoscopic Surgery.

BACKGROUND AND OBJECTIVES: We evaluated the effectiveness and safety of EZ-CloseTM compared to those of hand suture for trocar-site closure according to obesity. METHODS: Fifty-four cases of laparoscopic colorectal surgery were enrolled. For the same patient, the right port site was closed using EZ-CloseTM and left port site was closed by hand suture among cases with port-site diameter ≥10 mm. Cases switched to use of a conventional fascial closure device or with closure time 120 s were considered failures. Closure time was analyzed according to body mass index (BMI) and abdominal wall thickness (AWT). RESULTS: The mean closure time was significantly shorter with EZ-CloseTM than with hand suture (87.9 ± 21.0 vs. 128.0 ± 59.0 s, p < 0.001). The number of failure cases was significantly lower with EZ-CloseTM than with hand suture (7 vs. 27, p < 0.001). The closure time of EZ-CloseTM was significantly shorter than that of hand suture in patients with BMI ≥ 25 and < 27 kg/m2 (n = 15, 85.9 ± 19.8 vs. 135.6 ± 67.9 s, p < 0.014) and ≥ 27 kg/m2 (n = 13, 85.1 ± 18.4 vs. 150.2 ± 70.6 s, p < 0.010). With respect to AWT, the closure time of EZ-CloseTM was significantly shorter than that of hand suture in patients with AWT ≥ 20 and < 26 mm (n = 12, 81.1 ± 11.5 vs. 142.3 ± 83.7 s, p = 0.023) and ≥ 26 mm (n = 17, 85.6 ± 22.6 vs. 160.2 ± 55.5, p < 0.001). No infection and herniation were detected in both trocar sites during the follow-up period (median 20.4 months). CONCLUSION: EZ-CloseTM could provide time efficiency in trocar-site closure, especially in obese patients.

Comparing the standards of one metabolic equivalent of task in accurately estimating physical activity energy expenditure based on acceleration.

The purpose of the study is to analyse how the standard of resting metabolic rate (RMR) affects estimation of the metabolic equivalent of task (MET) using an accelerometer. In order to investigate the effect on estimation according to intensity of activity, comparisons were conducted between the 3.5 ml O2 · kg-1 · min-1 and individually measured resting VO2 as the standard of 1 MET. MET was estimated by linear regression equations that were derived through five-fold cross-validation using 2 types of MET values and accelerations; the accuracy of estimation was analysed through cross-validation, Bland and Altman plot, and one-way ANOVA test. There were no significant differences in the RMS error after cross-validation. However, the individual RMR-based estimations had as many as 0.5 METs of mean difference in modified Bland and Altman plots than RMR of 3.5 ml O2 · kg-1 · min-1. Finally, the results of an ANOVA test indicated that the individual RMR-based estimations had less significant differences between the reference and estimated values at each intensity of activity. In conclusion, the RMR standard is a factor that affects accurate estimation of METs by acceleration; therefore, RMR requires individual specification when it is used for estimation of METs using an accelerometer.