ObjectiveTo explore the effects of perioperative autologous platelet transfusion on postoperative complications and prognosis of adult cardiac surgery patient.MethodsUsing the method of systematic review of Cochrane Collaboration, we searched PubMed, Web of Science, EMbase, The Cochrane Library, CNKI and Wangfang databases, retrieving the literature from January 1970 to June 2020 to collect clinical randomized controlled trials on the effects of autologous platelet transfusion on complications and prognosis of adult cardiac surgery patients. The extracted valid data was analyzed by RevMan5.3 software.ResultsTen studies were included, with a total of 1 083 patients. The results of meta-analysis showed that there were statistical differences in the perioperative blood loss (MD=−195.15, 95%CI −320.48-−69.83, P=0.002) and perioperative blood transfusion (MD=−0.88, 95%CI −1.23-−0.52, P<0.001). There was no statistical difference in the death rate 30 days after the operation (RR=0.90, 95%CI 0.48-1.70, P=0.75), reoperations (OR=0.48, 95%CI 0.23-1.02, P=0.06), postoperative myocardial infarction (OR=1.29, 95%CI 0.48-3.51, P=0.61), postoperative infection (OR=1.71, 95%CI 0.89-3.29, P=0.11) or postoperative ICU retention time (MD=−0.31, 95%CI −0.67-0.05, P=0.09).ConclusionPerioperative autologous platelet transfusion can reduce perioperative blood loss and blood transfusion in adult cardiac surgery patients, but has no significant impact onprognosis and postoperative complications, which indicates that perioperative autologous platelet transfusion is a safe and beneficial blood protection measure for patients undergoing cardiac surgery.
ObjectiveTo investigate the effects of transcranial direct current stimulation (tDCS) combined with virtual reality (VR) on upper limb dysfunction of stroke patients.MethodsPatients with stroke who were hospitalized in the Department of Rehabilitation Medicine, the Third Affiliated Hospital of Sun Yat-Sen University from July 2018 to January 2020 were selected. The patients were divided into tDCS group, VR group and combined treatment group by random number table method. All three groups received conventional rehabilitation treatment. Based on this, tDCS group received 2.0 mA tDCS treatment, VR group received 20 min VR treatment, and combined treatment group received the same tDCS and VR treatment. Before and 4 weeks after treatment, the Fugl-Meyer assessment-upper limb (FMA-UL), Wolf motor function test (WMFT) and modified barthel index (MBI) were used to evaluate the upper limb motor function and activities of daily life (ADL) of the three groups.ResultsA total of 45 patients were included, 15 in each group. No adverse reactions or fall off occurred during the treatment. Before treatment, there were no significant difference in FMA-UL, WMFT-Times, WMFT functional ability scores (WMFT-FAS), and MBI between the three groups (P>0.05). After 4 weeks of treatment, the FMA-UL, WMFT-Times, WMFT-FAS, and MBI scores of the three groups were significantly improved compared with those before treatment (P<0.05); the MBI score of the combination treatment group was significantly better than the tDCS group and VR group, and the FMA-UL was significantly better than the tDCS group, and the differences were statistically significant (P<0.05). Also, there were no significant differences in the improvement of FMA-UL, WMFT-Times, WMFT-FAS, and MBI scores between the tDCS group and the VR group (P>0.05); the differences of FMA-UL, WMFT-Times, WMFT-FAS, and MBI scores before and after treatment in the combined treatment group, which were significantly better than those in tDCS group and VR group (P<0.05). ConclusiontDCS combined with virtual reality can significantly improve the upper limb motor function and ADL ability of stroke patients, and the effect is superior to tDCS or VR treatment solely.
Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5−9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.