Clinical Usefulness of a Short Version of the Internet Addiction Test to Screen for Probable Internet Addiction in Adolescents with Autism Spectrum Disorder.

Internet addiction (IA) is defined as the condition of being addicted to all sorts of activities on the Internet. Individuals with neurodevelopmental disorders, including autism spectrum disorder (ASD), may be susceptible to IA. Early detection and intervention for probable IA are important to prevent severe IA. In this study, we investigated the clinical usefulness of a short version of the Internet Addiction Test (s-IAT) for the screening of IA among autistic adolescents. The subjects were 104 adolescents with a confirmed diagnosis of ASD. They were requested to answer 20 questions from the original Internet Addiction Test (IAT). In the data analysis process, we comparatively calculated the sum of scores to the 12 questions of s-IAT. In total, 14 of the 104 subjects were diagnosed as having IA based on the face-to-face clinical interview that was regarded as the gold standard. Statistical analysis suggested that the optimal cut-off for s-IAT was at 35. When we applied the cut-off of 70 on the IAT, only 2 of 14 subjects (14.3%) with IA were screened positive, whereas 10 (71.4%) of them were screened by using the cut-off point of 35 on s-IAT. The s-IAT might be useful for the screening of IA in adolescents with ASD.

Instability of non-REM sleep in older women evaluated by sleep-stage transition and envelope analyses.

Study objective: Traditionally, age-related deterioration of sleep architecture in older individuals has been evaluated by visual scoring of polysomnographic (PSG) recordings with regard to total sleep time and latencies. In the present study, we additionally compared the non-REM sleep (NREM) stage and delta, theta, alpha, and sigma wave stability between young and older subjects to extract features that may explain age-related changes in sleep. Methods: Polysomnographic recordings were performed in 11 healthy older (72.6 ± 2.4 years) and 9 healthy young (23.3 ± 1.1 years) females. In addition to total sleep time, the sleep stage, delta power amplitude, and delta, theta, alpha, and sigma wave stability were evaluated by sleep stage transition analysis and a novel computational method based on a coefficient of variation of the envelope (CVE) analysis, respectively. Results: In older subjects, total sleep time and slow-wave sleep (SWS) time were shorter whereas wake after sleep onset was longer. The number of SWS episodes was similar between age groups, however, sleep stage transition analysis revealed that SWS was less stable in older individuals. NREM sleep stages in descending order of delta power were: SWS, N2, and N1, and delta power during NREM sleep in older subjects was lower than in young subjects. The CVE of the delta-band is an index of delta wave stability and showed significant differences between age groups. When separately analyzed for each NREM stage, different CVE clusters in NREM were clearly observed between young and older subjects. A lower delta CVE and amplitude were also observed in older subjects compared with young subjects in N2 and SWS. Additionally, lower CVE values in the theta, alpha and sigma bands were also characteristic of older participants. Conclusion: The present study shows a decrease of SWS stability in older subjects together with a decrease in delta wave amplitude. Interestingly, the decrease in SWS stability coincided with an increase in short-term delta, theta, sigma, and alpha power stability revealed by lower CVE. Loss of electroencephalograms (EEG) variability might be a useful marker of brain age.

Risk factors of hikikomori among office workers during the COVID-19 pandemic: A prospective online survey.

The global pandemic of COVID-19 has forced people to restrict their outings. In Japan, self-restraint behavior (SRB) has been requested by the government, and some of those decreasing their outings may shift to pathological social withdrawal; hikikomori. The purpose of this study was to examine the risk factors of hikikomori conducting an online prospective survey. An online survey was conducted in June 2020 and December 2020; (1) SRB-related indicators (degree of SRB, motivation for SRB, stigma and self-stigma toward COVID-19, anxiety and depressive feelings toward COVID-19) and (2) general mental health (hikikomori tendency, depressive symptoms, modern type depression (MTD) tendency, internet addiction) were collected. A cross-lagged effects model was performed to examine the association between these variables. Lack of emotional support and lack of socialization in June 2020 increased isolation in December 2020. Besides, MTD and hikikomori interacted with each other. Interestingly, although hikikomori tendency increased depressive tendencies, SRB itself did not have a significant path on any mental health-related variables. Poor interpersonal relationships, rather than SRB per se, are suggested to be a risk factor for increased isolation among office workers in the COVID-19 pandemic. Appropriate early interventions such as interpersonal or emotional support may prevent the transition to pathological hikikomori. The association between MTD and hikikomori seems to reveal the interesting possibility that MTD is a gateway to increased risk of hikikomori, and that hikikomori is a gateway to MTD as well. Future research is required to elucidate the relationship between hikikomori and MTD.

Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability.

Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved high accuracy but are not widely applied in clinical practice. In our understanding, the existing methods have failed to establish the trust of sleep experts (e.g., physicians and clinical technologists) due to a lack of ability to explain the evidences/clues for scoring. In this study, we developed a deep-learning-based scoring model with a reasoning mechanism called class activation mapping (CAM) to solve this problem. This mechanism explicitly shows which portions of the signals support our model's sleep stage decision, and we verified that these portions overlap with the "characteristic waves," which are evidences/clues used in the manual scoring process. In exchange for the acquisition of explainability, employing CAM makes it difficult to follow some scoring rules. Although we concerned the negative effect of CAM on the scoring accuracy, we have found that the impact is limited. The evaluation experiment shows that the proposed model achieved a scoring accuracy of [Formula: see text]. It is superior to those of some existing methods and the inter-rater reliability among the sleep experts. These results suggest that Sleep-CAM achieved both explainability and required scoring accuracy for practical usage.

Sarcopenia assessed by skeletal muscle mass volume is a prognostic factor for oncological outcomes of rectal cancer patients undergoing neoadjuvant chemoradiotherapy followed by surgery.

INTRODUCTION: Recently, sarcopenia has been reported to be associated with poor postoperative outcomes in various cancers. However, its clinical significance for rectal cancer patients undergoing neoadjuvant chemoradiotherapy (NACRT) followed by surgery remains unknown. MATERIALS AND METHODS: This study included 46 patients with locally advanced rectal cancer who underwent curative surgery after NACRT. Sarcopenia was assessed by measuring the cross-sectional psoas muscle area (PA) at L3 and total bilateral psoas muscle volume (PV). Patients with a lower PV or PA value than the median were assigned to the sarcopenia group while others were assigned to the non-sarcopenia group. Clinical outcomes were then compared between groups. RESULTS: The sarcopenia group included 22 patients. The rate of overall postoperative complications did not differ between groups. Five-year relapse-free survival (RFS) was significantly lower in the sarcopenia group when sarcopenia was assessed by PV after NACRT (44.0% vs. 82.6%, P = 0.00494). In contrast, RFS did not differ between groups when sarcopenia was assessed by PA. Multivariable analysis identified PV after NACRT as the most significant risk factor for RFS (hazard ratio 4.00; 95% CI 1.27-12.66, P = 0.018). CONCLUSION: Sarcopenia assessed by total PV after NACRT may be an accurate and reliable predictor of poor oncological outcomes in rectal cancer patients.

GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm.

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse's data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.

Thoracoscopic retrosternal gastric conduit resection in the supine position for gastric tube cancer.

INTRODUCTION: Recent advances in the treatment for esophageal cancer have improved the prognosis after esophagectomy, but they have led to an increased incidence of gastric tube cancer. In most patients who underwent retrosternal reconstruction, median sternotomy is performed; it is associated with a risk of postoperative bleeding and osteomyelitis, and pain often negatively affects respiration. Here, we report the first case of thoracoscopic retrosternal gastric conduit resection in the supine position (TRGR-S). MATERIALS AND SURGICAL TECHNIQUE: A 75-year-old male patient was placed in the supine position. Four ports were placed in the left chest wall. The gastric tube was separated from the epicardium, sternum, and left brachiocephalic vein. Because of adhesions between the gastric tube and the right pleura, combined resection of the right pleura was performed. The dorsal side of the gastric tube was dissected before the ventral side, enabling the gastric tube to be suspended from the back of the sternum and, thus, making it easier to expose the surgical field. Next, pedicled jejunal reconstruction via the presternal route was performed. There were no postoperative complications. The pathological diagnosis was signet ring cell carcinoma (pT1b, pN0, M0, pStage I), indicating R0 resection. DISCUSSION: TRGR-S does not require sternotomy, reducing the risk of postoperative bleeding and osteomyelitis. In the presence of adhesions, TRGR-S is safe and provides a good surgical view. It is also reliable procedure for resection of retrosternal gastric tube cancer, and it is ergonomic for surgeons.

MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks.

Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such methods has generally been evaluated using rather small-scale datasets, and their robustness against individual differences and noise has not been adequately verified. This research proposes a novel automated scoring method named "MC-SleepNet", which combines two types of deep neural networks. Then, we evaluate its performance using a large-scale dataset that contains 4,200 biological signal records of mice. The experimental results show that MC-SleepNet can automatically score sleep stages with an accuracy of 96.6% and kappa statistic of 0.94. In addition, we confirm that the scoring accuracy does not significantly decrease even if the target biological signals are noisy. These results suggest that MC-SleepNet is very robust against individual differences and noise. To the best of our knowledge, evaluations using such a large-scale dataset (containing 4,200 records) and high scoring accuracy (96.6%) have not been reported in previous related studies.