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【Objective】 To study the relationship between the key influencing factors and the short, medium and long term blood demand, so as to provide basis for building a blood demand prediction model with less prediction error and practical guidance. 【Methods】 Through literature research, the influencing factors of blood demand were preliminarily determined. Questionnaires were designed and distributed to relevant experts, and factor analysis was carried out on the survey results to obtain key influencing factors through Delphi method. 【Results】 Through literature research, 19 influencing factors of clinical blood demand were obtained, including policy factors, medical service demand, medical technology level, regional population, population characteristics, population structure, medical resource, number of beds, culture, natural environment, operation, patients outside the region, blood use in different departments, blood infusion, time trend, emergencies and disasters, the condition of disasters, hospitals in disaster area, limited diagnosis and treatment ability. Through Delphi method and data analysis, six key factors affecting blood demand were obtained, namely sudden disaster, medical resource, environmental factor, population, bed number and blood infusion. 【Conclusion】 The influence of key factors on clinical blood demand was divided into multiple hierarchies. Blood infusion and sudden disaster were short-term influencing factors. Medical resource, population and number of beds were medium influencing factors. Environmental factor was long-term influencing factor. Short, medium and long-term influencing factors were interrelated, and have different impacts on clinical blood demand. Based on the interaction relationship, a three-dimensional mathematical model of influencing factors of clinical blood demand was established, which provided a preliminary research basis for building a blood demand prediction model with less prediction error and practical guidance.
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【Objective】 To establish an ARIMA model suitable for clinical platelet demand prediction in Suzhou, which can be used as reference to predict future clinical platelet demand and provide scientific basis for platelet collection, preparation, stock management and clinical deployment for blood banks, so as to achieve the maximum balance between platelets supply and demand . 【Methods】 The data of platelet consumption in Suzhou from 2009 to 2019 were collected and analyzed by SPSS 26 software, Time series analysis method was used to establish the ARIMA model. The model was further optimized through model identification, parameter estimation and optimal model test, and then used to predict clinical platelet consumption from January to November 2020. The predicted value was compared with the actual value to verify the prediction effect of the model. 【Results】 The optimal model for the prediction of platelet clinical demand was ARIMA (0, 1, 1) (0, 1, 1) 12. The ACF autocorrelation function value and PACF partial autocorrelation function value of the residuals were within 95% CI. Meanwhile, the LJUNG BOX test was 13.982 (P>0.05), indicating that there was no autocorrelation in the residuals. The trend of the curve between the predicted and actual value was basically the same(except for February 2020), and the predicted values were within 95% CI, with the average relative error of 7.22%, which was lower than 10%, showing good prediction effect. 【Conclusion】 ARIMA model can be used for short-term prediction of clinical platelets demand in Suzhou, and can provide basis for reasonable collection, preparation and deployment of platelets.
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【Objective】 To establish a prediction model of clinical blood demand in Suzhou urban area by ARIMA model, and to predict future clinical blood demand by sorting out the historical data, so as to guide the reasonable collection and scientific deployment of blood resources, and achieve the balance of clinical blood supply and demand. 【Methods】 The monthly data of clinical use of plasma components in Suzhou city from 2009 to 2019 were obtained, and analyzed by SPSS26 software and ARIMA model. Through model identification, parameter estimation and optimal model test, the optimal model for clinical blood prediction was determined and used to predict the clinical consumption of plasma components from January to November 2020. The predicted value was compared with the actual value to verify the prediction effect of the model. 【Results】 The optimal model was ARIMA(0, 1, 1)(0, 1, 1)12. The values of ACF autocorrelation function and PACF partial autocorrelation function of residual were both within 95%CI. Meanwhile, the Yang-Box Q statistic value was 11.596, P>0.05, which passed the white noise test. The predicted values of clinical consumption of plasma components in Suzhou urban area from January to November 2020 were all within 95%CI, consistent with the trend of actual values, with small mean relative error(7.9%) and good prediction effect. 【Conclusion】 ARIMA model can be used for short-term prediction on clinical use of plasma components in Suzhou city, and provide reference for reasonable collection, preparation and scientific deployment.
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Objective To investigate the value of T2WI histogram analysis in differential diagnosis of glioblastoma multiform (GBM) from solitary metastasis.Methods Data of 103 patients with pathologically confirmed GBM (GBM group,n=57) and solitary brain metastasis (solitary brain metastasis group,n =46) were retrospectively reviewed.All patients underwent conventional MR scanning,including axial T1WI,T2WI,FLAIR and contrast-enhanced T1WI before surgery.The histogram metrics,including mean,standard deviation (SD),median,kurtosis and skewness were calculated from ROI,which were manually placed on the maximal section of the solid part of tumors on T2WI by using Image J software.ROCs were generated to evaluate differential diagnostic performance of the histogram metrics with significant difference between both groups.Results The values of mean,SD and median were significantly higher in GMB group than those in solitary brain metastasis group (P＜0.05).The areas under ROC curve of mean,SD and median was 0.772 (95％ CI [0.681,0.862],P＜0.001),0.719 (95％ CI [0.616,0.822],P＜0.001) and 0.767 (95％ CI [0.674,0.860],P＜0.001),respectively;and the diagnosis cutoff value of mean,SD and median was 509.575,58.844 and 550.500,respectively.The sensitivity of the three parameters was 0.719,0.702 and 0.719,and the specificity was 0.783,0.652,and 0.826,respectively.Conclusion The value of mean,SD and median of T2WI histogram analysis can be helpful to differentiating GBM and solitary brain metastasis,of which the mean value is the best for differential diagnosis.
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Background Researchers are paying increasing attention to the effect of cellular senescence in vascular dysfunctional diseases,and it is hypothesized that cellular senescence may also be involved in the development of diabetes related vascular complications.The outstanding feature of cellular senescence is the upregulation of beta-galactosidase.Objective This study was to investigate the effects of high glucose on cell senescent in vitro and in vivo on bovine retinal endothelial cells (BRVECs) and mouse retina.Methods BRVECs were cultured and passaged,and the seventh generation of cells were employed in this study.The cells were divided into the control group and the high glucose culture group and cultivated using M199 medium containing 5.5 mmol/L or 25.0 mmol/L glucose,respectively.5-bromine-chlorine-4-3-indole-beta D galactose glucoside (X-Gal) staining was used to examine the expression of beta-galactosidase in the cells.Diabetic models were established in the SPF male C57BL/6J mice aged 8-10 weeks by intraperitoneal injection of streptozotocin (STZ),and the age-and gendermatched normal mice served as controls.The mouse retinas were collected and starched in the 48-well plates 3 months later.X-Gal staining was employed to calculate the positive cells.Results BRVECs grew well 24 hours after culture but showed irregular arrangement.Forty-eight hours later,the cells reached confluence with a tight connect.The ratios of positive BRVECs and total cells were (51.4±5.4) ％ and (36.6-±3.8) ％ in the high glucose culture group and the control group,with a significant difference between the two groups (t =-3.204,P =0.033).The number of positive cells for X-Gal in mouse retinas was (94.0± 15.1) /field in the diabetic group,which was higher than that in the control group ([60.0 ± 5.7]/field) (t =-2.974,P =0.041).Conclusions High glucose environment accelerates senescence of retinal cells,and high glucose induces premature cell senescence,which likely plays an important role in the pathogenesis of diabetic retinopathy.