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Background Poor prognosis in elderly patients with acute ischemic stroke (AIS) has put great pressure on public health. Actively searching for simple and easy-to-operate clinical indicators to screen high-risk groups with poor prognosis has become a hot issue. Objective To analyze the predictive value of sarcopenia index (SI) on the prognosis of elderly patients with AIS. Methods A total of 280 elderly AIS patients hospitalized in Changzhou Jintan First People's Hospital from July 2021 to June 2022 were selected and divided into the poor prognosis group (≥3 points) and good prognosis group (≤2 points) according to the Modified Rankin Scale (mRS). Baseline data and National Institutes of Health Stroke Scale (NIHSS) scores at admission and discharge were compared between the two groups. Multivariate Logistic regression analysis was used to explore the factors affecting the prognosis of elderly patients with AIS, and a receiver operating characteristic (ROC) curve was plotted to analyze the value of SI in predicting poor prognosis in elderly patients with AIS. Results There were 212 cases in the good prognosis group and 68 cases in the poor prognosis group. There were significant differences in the history of diabetes and previous stroke, neutrophil count (NE), lymphocyte count (LY), albumin (ALB), admission NIHSS score, discharge NIHSS score, and SI between patients between the poor prognosis group and good prognosis group (P

With the rapid development of Internet technology and mobile terminals, users’ demand for high-speed networks is increasing. Mobile edge computing proposes a distributed caching approach to deal with the impact of massive data traffic on communication networks, in order to reduce network latency and improve user service quality. In this paper, a deep reinforcement learning algorithm is proposed to solve the task unloading problem of multi-service nodes. The simulation platform iFogSim and data set Google Cluster Trace are used to carry out experiments. The final results show that the task offloading strategy based on DDQN algorithm has a good effect on energy consumption and cost, it has verified the application prospect of deep reinforcement learning algorithm in mobile edge computing.

Objective: To explore the feasibility and safety of integrating the geriatric intensive care unit (GICU) into the friendly management model of the elderly critically ill patients., Methods: A prospective controlled study was conducted. Patients with elderly critically ill admitted to the GICU and the general intensive care unit (ICU) of Jintan First People's Hospital of Changzhou from December 2021 to May 2023 were enrolled. Patients in the ICU group received the traditional intensive care and nursing mode. In addition to the ICU group basic medical care measures, the patients in the GICU group were treated with friendly management models such as flexible visitation, diagnosis and treatment environment optimization, caring diagnosis and treatment, and family participation in hospice care according to their condition assessment. The gender, age, main diagnosis, and acute physiology and chronic health evaluation II (APACHE II) at admission were recorded and compared between the two groups. During the treatment period, the incidence of nosocomial infection, unplanned extubation, falling out of bed/fall, unexpected readmission to ICU/GICU, and ICU/GICU mortality, the incidence of post-intensive care syndrome (PICS), the satisfaction rate of patients/families with medical care, and the satisfaction rate of patients/families with diagnosis and treatment environment were recorded and compared between the two groups., Results: According to the admission criteria for ICU and GICU, as well as the willingness of the patients and/or their families, a total of 59 patients were finally included in the ICU group, and 48 patients were enrolled in the GICU group. There were no significantly differences in gender, age, main diagnosis and APACHE II score between the two groups, and there were comparability. There were no significantly differences in the incidence of adverse events such as nosocomial infection [13.6% (8/59) vs. 12.5% (6/48)], unplanned extubation [5.1% (3/59) vs. 6.2% (3/48)], falling out of bed/fall [3.4% (2/59) vs. 0% (0/48)], unexpected readmission to ICU/GICU [8.5% (5/59) vs. 10.4% (5/48)], and ICU/GICU mortality [6.8% (4/59) vs. 6.2 (3/48)] between the ICU group and GICU group (all P > 0.05). Compared with the ICU group, the incidence of PICS in GICU group was significantly lower [8.3% (4/48) vs. 25.4% (15/59), P < 0.05], the satisfaction rate of patients/families with medical care [89.6% (43/48) vs. 74.6% (44/59)] and satisfaction rate of patients/families with diagnosis and treatment environment [87.5% (42/48) vs. 67.8% (40/59)] were significantly increased (both P < 0.05)., Conclusions: The use GICU as a friendly management model for elderly critically ill patients is feasible and safe, and it is worthy of further exploration and research.