Brachial plexus block is a common regional anesthesia method widely used in upper limb surgery. Nowadays, ultrasound-guided brachial plexus block has been extensively used in clinical anesthesia. However, accurate brachial plexus block is highly dependent on the physician’s experience, and a physician without extensive clinical experience may cause nerve injury when performing a nerve block. With the development of artificial intelligence technology, the deep learning method can automatically identify the brachial plexus in ultrasound images and assist doctors in completing the brachial plexus block accurately and quickly. In this paper, we aim to evaluate the performance of different deep learning models in identifying brachial plexus (i.e., segmentation of brachial plexus) from ultrasonic images to explore the best models and training strategies for this task. To this end, we use a new dataset containing 340 brachial plexus ultrasound images annotated by three experienced clinicians. Among the 12 deep learning models we evaluated, U-Net achieves the best segmentation accuracy, with an intersection over union (IoU) of 68.50%. However, the number of U-Net parameters is very large, and it can only process 15 images per second. Compared to U-Net, LinkNet can process 142 images per second and achieve the second-best segmentation accuracy with an IoU of 66.27%. It achieves the balance between segmentation accuracy and processing efficiency, which has a good potential for the brachial plexus’s real-time segmentation task.

Aim: There is currently no consensus on the best risk assessment technique for predicting complications after hip surgery in the elderly, which is hindering the accuracy of surgical risk assessment. The goal of this study was to build a risk assessment model and evaluate its predictive value using the modified frailty index (5-mFI) and the prognostic nutritional index (PNI). Methods: A retrospective investigation was undertaken on 150 patients (aged ≥60 years) who had hip fracture surgery. Using univariate and multivariate logistic regression models, the relationship between combined 5-mFI and PNI and the evaluation of postoperative unfavorable outcomes such as infection and unscheduled intensive care unit (ICU) admission was investigated. Finally, utilizing receiver operating characteristic (ROC) curve analysis, the model’s predictive value for adverse outcomes following hip fracture surgery in elderly patients was assessed. Results: Univariate and multivariate logistic analyses revealed that preoperative PNI, 5-mFI, ASA, and gender acted as independent predictors of adverse outcomes after hip fracture surgery in the elderly. According to the ROC curve analysis, the predictive model demonstrated a high predictive value for total postoperative complications (AUC: 0.788; 95%CI: 0.715–0.860; p<0.01), infectious complications (AUC: 0.798; 95% CI: 0.727–0.868; P<0.001), and unplanned ICU admission (AUC: 0.783; 95% CI: 0.705–0.861; P<0.001). Conclusions: The multivariable evaluation model, which included 5-mFI and PNI, showed a high predictive value and can hence be applied to predict the adverse outcomes in elderly patients undergoing hip fracture surgery.

The purpose of this article is to provide a succinct summary of the sleep monitoring efforts that have been used in nocturnal enuresis (NE) and an overview of the knowledge that has accrued. This is not intended to be a comprehensive review, but rather is intended to highlight how polysomnography (PSG), a common sleep detection tool, has contributed to our understanding of NE, as arousal disorder is considered to be one of the important mechanisms. The authors have organized this report by analysis and display of different ingredients of PSG, starting with comparing the electroencephalogram (EEG) of controls and the enuretic children and then moving to evaluation of respiratory patterns of NE and comorbid disease obstructive sleep apnea (OSA). In addition, the authors' goal is to better understand the mechanism of NE by integrating various levels of sleep monitoring; those sleep-related clinical scale scores for NE are presented to date. Finally, we propose further research of NE to explore the microstructure alterations via PSG combined with EEG–fMRI or to use novel technology like portable device internet and deep learning strategy.

Introduction Virtual reality (VR) is already being used for cognitive or emotional rehabilitation. However, its role in postoperative cognitive dysfunction (POCD) has not been fully recognised. Due to the lack of an effective postoperative follow-up system, the incidence of POCD in China is not clear, and although many drugs have been proposed to improve POCD in the animal study, their clinical applications are limited, while VR provides an innovative method to provide non-pharmacological management. Methods and design This is a single-centre, randomised, double-blind, sham-controlled clinical trial. In this study, 600 patients over 55 years old undergoing laparoscopic surgery will be recruited. Participants will be randomly assigned to receive biophilic VR or sham VR (1:1 ratio), all patients have 20 min of exposure per day during the hospital stay. The primary outcome is the impact of VR on the incidence of POCD. Secondary outcomes include perioperative anxiety and instrumental activities of daily living. Changes in the performance of the neurocognitive batteries are measured by a local resident doctor. Serum samples will be collected on the day before surgery and 7 days after surgery. Ethics and dissemination This trial has ethical approval from the Medical Ethics Committee of the Affiliated Hospital of Medical School of Ningbo University (KY20210302). The study is sponsored by Ningbo University and Ningbo Science and Technology Bureau. Contact: Dr. Mao Haijiao, Chair of the hospital medical Ethics committee (ndfylunli@126.com). Trial results will be submitted for publication in peer-reviewed journals, patient recruitment began in April 2021. Written informed consent is obtained for all participants. All information acquired will be disseminated via national or international conferences and published in peer-reviewed journals. Trial registration number ChiCTR2000040919.

Background: So far, the psychological impact of COVID-19 epidemic among family members of Health care workers (HCWs) in China has been neglected. The present cross-sectional study aimed to investigate the mental health status and related factors of families of HCWs in Designated Hospitals in Ningbo, China. Method: Family members of HCWs working in five designated hospitals in Ningbo, China were recruited between February 10th and 20th, 2020. Information on demographic variables, the COVID-19-related events in the lives, knowledge of COVID-19 and the working status of family members (that is, HCWs) was collected using online self-administered questionnaires. The mental health status were assessed using the Chinese version of Patient Health Questionnare-9 (PHQ-9) and Chinese version of Generalized Anxiety Disorder-7 (GAD-7). Multivariable logistic regression analyses were conducted to identify the main factors associated with the mental health conditions. Results: A total of 822 participants completed questionnaires correctly. (response rate of 95.80%). The overall prevalence of GAD and depressive symptoms were 33.73%, and 29.35%, respectively. More times (hours) to focus on the COVID-19 (Odd ratio (OR)=1.215, 95% confidence interval (CI):1.061-1.391), family members (that is, HCWs) directly contact with confirmed or suspected COVID-19 patients (OR=1.477, 95%CI:1.069-2.040) were risk factors for GAD, while higher participants self-reported safety score for protective equipment of HCWs (OR=0.807, 95%CI:0.700-0.930) was a protective factor. More times (hours) to focus on the COVID-19 (OR=1.215, 95%CI:1.061-1.391), longer average working times per week for family members (that is, HCWs) (OR=1.017, 95%CI:1.005-1.029), being parents and other next of kin of HCWs were risk factors for depressive symptoms (OR=3.526, 95%CI:1.609-7.728 and OR=1.639, 95%CI:1.096-2.451, respectively). In addition, compared with participants who were HCWs, participants who were enterprise workers and were more likely to develop depressive symptoms(OR=1.750, 95%CI:1.104-2.776), while who were government employees or institutions employees were less likely to suffer depressive symptoms (OR=0.529, 95%CI:0.286-0.977). Conclusions: Psychological responses to COVID-19 have been dramatic among family members of HCWs during the rising phase of the outbreak. Our findings provide strong evidence to pay more attention on the mental health status of this vulnerable but often unseen populations during COVID-19 epidemic.