Objective To establish a prediction model for geriatric frailty syndrome(GFS)in elder-ly patients with acute ischemic stroke(AIS)after treatment.Methods Clinical data of 156 elderly AIS patients admitted to our hospital from January 2020 to December 2022 were collected and ret-rospectively analyzed.According to occurrence of GFS or not,they were divided into GFS group(n=57)and control group(n=99).The differences of clinical features were recorded and com-pared between the two groups of elderly AIS patients.Multivariate logistic regression model was used to analyze the risk factors for GFS in the elderly AIS patients.And a prediction model for GFS was constructed.Results Larger proportions of aged ≥80 years,diabetes,massive cerebral infarction and dysphagia were observed in the GFS group than the control group(P＜0.05,P＜0.01).Multivariate logistic regression analysis showed that aged ≥80 years(OR=2.890,95％CI:1.306-6.395,P=0.009),diabetes(OR=4.892,95％CI:2.172-11.018,P=0.000),massive cere-bral infarction(OR=3.363,95％CI:1.418-7.977,P=0.006)and dysphagia(OR=2.772,95％CI:1.123-6.844,P=0.027)were independent risk factors for GFS in the elderly AIS patients after treatment.A nomogram prediction model was constructed.Then the dataset was randomly divided into a training set and a validation set in a ratio of 7∶3.The AUC value was 0.840(95％CI:0.754-0.927)in the training set,and 0.676(95％CI:0.518-0.833)in the validation set.Hos-mer-Lemeshow Goodness-of-Fit test indicated that when the model was subjected to the valida-tion set,a Chi-square value of 14.394 and a P value of 0.072 were obtained.Conclusion Our no-mogram prediction model has good value in predicting the occurrence of GFS in elderly AIS pa-tients after treatment.

Objective To compare the detection results of three kinds of automated nucleic acid purifiers, and to evaluate the detection performance of the domestic 2019-nCoV RNA purifier. Methods Three automated nucleic acid purifiers, namely A (imported), B (domestic), and C (domestic) automated nucleic acid extraction instruments, were used to purify nucleic acid. The Conchestan 2019-nCoV RNA (Liquid) quality control product S5 (batch number 202007002, reference level 1000cp/ml) was chosen as the experimental object. The quality control product was diluted in a series of 10 to 1000 times to prepare experimental samples of different concentrations. Among them, the A nucleic acid purifier used its own matching reagents, and the B and C purifiers belonged to a same manufacturer with different models and used their own supporting reagents as well as third-party reagents, to evaluate the anti-pollution ability, precision, accuracy, repeatability, detection limit and linear correlation. Results Using the imported brand A as a reference standard for comparison, when using reagents from B, the linear correlation between the two domestic nucleic acid purifiers and the imported equipment were 0.999, 0.915 (N-terminal), and 0.997, 0.825 (ORF1ab-terminal), respectively; when using the third-patty reagents, the linear correlation between the two domestic nucleic acid purifiers and the imported equipment were 0.999, 0.915 (N-terminal) and 0.997, 0.825 (ORF1ab-terminal), respectively. Conclusion The extraction of 2019-NCOV RNA by domestic nucleic acid purifiers can be fully automated with good correlation. The system performance is comparable to international standards. Moreover, the extraction time of the domestic nucleic acid purifiers is shorter than the imported one, which offers obvious advantages when the number of samples is large.