6.0,and the expression of CD62P in groupⅠand Ⅱ(32% and 36%)was significantly higher than in control group(28%).However,when the three groups were stored until day 7,all in vitro parameters determined of PCs were better maintained in control group than in the other two.Conclusions The 5-day storage of PCs in additive solution with 50% or 20 % plasma is feasible in terms of the in vitro function in platelet count and pH,however,PCs storaged in additive solutions are more easily activated.

Objective:To evaluate the effect of deep learning based on DWI and fluid attenuated inversion recovery (FLAIR) to construct a prediction model of the onset time in acute stroke.Methods:A total of 324 cases of acute stroke with clear onset time, from January 2017 to May 2020 in Nanjing First Hospital, were retrospectively enrolled and analyzed. The patients were divided into a training set of 226 patients and a test set of 98 patients according to the complete randomization method using a 7∶3 ratio, and the patients were divided into ≤ 4.5 h and >4.5 h according to symptom onset time in each group. The acute infarction areas on DWI and the corresponding high signal area on FLAIR were manually outlined by physician. Using the InceptionV3 model as the basic model for image features extraction, the deep learning prediction model based on single sequence (DWI, FLAIR) and multi sequences (DWI+FLAIR) were established and verified. Then the area under curve (AUC), accuracy of human readings, single sequence model and multi sequence model in predicting the acute stroke onset time from imaging were compared.Results:DWI-FLAIR mismatch was found in 94 cases (94/207) of patients with symptom onset time from imaging ≤ 4.5 h, while in 28 cases (28/117) of patients with symptom onset time from imaging >4.5 h. ROC analysis showed that the AUC of DWI-FLAIR mismatch in predicting acute stroke onset time from imaging was 0.607, and the accuracy was 60.2%. The prediction model of deep learning based on single sequence showed that the AUC of FLAIR was 0.761 and the accuracy was 71.4%; the AUC of DWI was 0.836 and the accuracy was 81.6%. The AUC of predicting stroke onset time based on the multi-sequence (DWI+FLAIR) deep learning model was 0.852, which was significantly better than that of manual identification ( Z = 0.617, P = 0.002), FLAIR sequence deep learning model ( Z = 2.133, P = 0.006) and DWI sequence deep learning model ( Z = 1.846, P = 0.012). Conclusion:The deep learning model based on DWI and FLAIR is superior to human readings in predicting acute stroke onset time from imaging, which could provide guidance for intravenous thrombolytic therapy for acute stroke patients with unknown onset time.

Objective:To observe any improvement in hemiplegic upper limb functioning when transcranial direct current stimulation (tDCS) is combined with robot-assisted upper limb treatment, and analyze the potential mechanism of neural plasticity through diffusion tensor imaging (DTI).Methods:Twenty stroke survivors with hemiplegia were randomly divided into a treatment group and a control group, each of 10, according to a random number table. Both groups were treated with conventional medication and rehabilitation training using an upper limb robot, while the treatment group also received tDCS daily, with the current increasing from 0 to 1mA over 10 seconds, and then decreasing to 0 over twenty minutes. The experiment lasted for 15 days. The upper extremity portion of the Fugl-Meyer rating scale (UE-FMA) and the Wolf Motor Function Rating Scale (WMFT) were used to evaluate motor functioning before and after the treatment. DTI was also conducted for both groups.Results:After the treatment, the average UE-FMA and WMFT scores of the two groups were significantly higher than before the treatment, with the average UE-FMA score of the treatment group (35.32±13.25), significantly higher than that of the control group (21.80±13.93). After the treatment there were significant differences between the groups in their average FA, rFA and FAasy of the posterior limb of the internal capsule, as well as in FA and the CST length of the central anterior gyrus.Conclusion:tDCS combined with robot-assisted upper limb rehabilitation training can significantly improve the motor functioning of hemiplegic upper limbs, possibly due to neuroplasticity mechanisms that promote CST integrity and symmetry changes. tDCS can be an important adjunct therapy in clinical neurorehabilitation.

Objective To analyze the misdiagnosis reasons and CT features of adrenal gland tumors,and to improve the understanding and diagnosis of this disease.Methods The CT features of 25 cases misdiagnosis of adrenal gland tumors proved by surgically and pathologically were analyzed retrospectively.Results 21 cases were benign tumors including 1 7 cases diagnosed as other benign tumors and 4 cases diagnosed as malignant tumors.4 cases were malignant tumors diagnosed including 3 cases diagnosed as other malignant tumors and 1 case diagnosed benign tumor.Conclusion CT diagnosis of adrenal gland tumors should be closely combined with clinical history,and we should take into account the atypical manifestations of common tumors,as well as consider the possibility of rare adrenal gland tumors,so as to reduce misdiagnosis.

Objective The incidence of traumatic brain injury (TBI) has been on the rise year by year around the globe.According to the latest Guidelines for the Management of Severe Traumatic Brain Injury (Fourth Edition) released by the Brain Trauma Foundation (BTF),there is no sufficient evidence that related medicine can promote the repairment of neural injury in the treatment of central nerve damage.The clinical treatment of TBI is facing multiple difficulties.In recent years,brain computer interface (BCI) technology has developed rapidly and shown enormous potential in TBI repairment,especially in visual and auditory restoration,neural function recovery,and cognitive restoration.BCI provides a new approach to improve the quality of life for patients.This paper reviews the application and prospect of BCI in sense,motion,and cognitive function repairment after TBI,so as to provide new insights for the treatment of TBI nerve function.

【Objective】 To discuss the influence of apheresis platelets donation mode transformation, from walk-in to appointment, on apheresis platelets donation, donor retention and donation service quality. 【Methods】 The comparative research method is used to compare the number of apheresis platelets donors, blood donation units, rate of first-time blood donation, rate of repeated blood donation, conversion rate of fixed whole blood donors and satisfaction rate before and after the transformation of donation model. Questionnaires were randomly distributed to apheresis platelets blood donors before and after the transformation to study the evaluation of appointment mode. 【Results】 In comparison with walk-in mode, the number of blood donors after adopting the appointment mode was 30 193, with 41.93% (8 920/21 273) increase; number of blood donations was 119 143, with 93.66% (57 622/61 521) increase; platelet donation was 212 717 treatment units, with 103.12% (107 990/104 727) increase; rate of repeated blood donation was 53.56% (16 172/30 193), with 15.43% increase; the number of first-time donors was 15 949, with 57.93% (5 850/10 099) increase; the conversion rate of fixed whole-blood donors was 37.86% (6 039/15 949), with 8.84% increasement; the satisfaction of appointment mode reached 99.81%, with significantly improved satisfaction with blood donation environment and waiting time. 【Conclusion】 The appointment mode of apheresis platelet donation has a promoting role in the increase of apheresis platelets donation, the improvement of solid blood donors and the quality of apheresis platelets donation services.