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Objective Based on quantile regression analysis, the influencing factors of relapse hospitalization expenses of adult leukemia patients were analyzed. Methods Analyze the composition and influencing factors of hospitalization expenses for leukemia recurrence patients in our hospital. Results From 2017 to 2022 , the per capita hospitalization cost for leukemia patients with recurrence showed an increasing trend year by year. The results of quantile regression model showed that age, payment method , length of stay, times of stay, operation and complications had an impact on the hospitalization expenses of patients at different quantiles, and the difference between different quantiles was statistically significant (P<0.05). Conclusions The quantile regression method can more clearly reflect the distribution of the variables of each factor , we can reduce the hospitalization expenses of patients by improving the coverage rate of medical insurance and controlling the length of stay.
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Objective: To develop a new method for the determination of anti-oxidant activity of drugs by using the peroxidase activity of graphene nanoenzyme, and apply it for the determination of the anti-oxidant activity of the national medicine Achnatherum inebrians. Methods: The anti-oxidant activity of 18 batches of samples was measured after optimizing H2O2 concentration and reaction time. At the same time, the anti-oxidative activity was measured again using the DPPH method. The Bland-Altman and Passing-Bablok regression methods were used to compare the detection data of the two methods. Results: Using the nanoenzyme method, the inhibition rates of 18 batches of A. inebrians were between 37.28% and 71.58%, with an average of 50.87% and a median of 47.09%. Free radical scavenging rates of 18 batches of A. inebrians measured by the DPPH method were between 36.06% and 83.11%, with an average of 54.89% and a median of 50.83%. The statistical results showed that the measured values of nanoenzyme method and DPPH method were similar; There was a linear relationship between the two methods, and different methods can be transformed by Passing-Bablok regression method. Conclusion: The graphene nanoenzyme was successfully applied to the detection of anti-oxidant activity, and the anti-oxidant activity of A. inebrians was also found. The nanoenzyme method can avoid the problem of light sensitivity of the reagent, shorten the reaction time, reduce the amount of medicinal solution, and the results are consistent with the DPPH method, which is suitable for the determination of antioxidant activity.
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This study mainly discusses the contents, methods and characteristics of the collection of operation and maintenance data, as well as the establishment and evaluation methods of the distribution model of the failure time of medical electrical equipment. The distribution models of failure time at three levels of medical electrical equipment are established by linear regression method and goodness of fit test:The first is the device level MTBF distribution model, the second is the failure rate distribution model of the failure mode of key components, the third is the calculation model of the influence coefficient of influence factor on the failure mode of key components. This study presents a method of establishing MTBF segment model and implements a calculation model of influence coefficient varying with time.
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The concept and pathogenesis of acute respiratory distress syndrome (ARDS)were introduced,and the methods for identifying ARDS based on noninvasive parameters in recent years were retrospectively reviewed including regression method, nonlinear fitting method and multi parameter method.The above methods had their advantages and disadvantages summarized. It's suggested that multi noninvasive parameters and machine learning algorithms such as neural network, support vector machine and decision tree be involved in model construction to promote PaO2/FiO2assessment based on noninvasive parameters,so that the rapid diagnosis and real-time monitoring of ARDS can be realized based on noninvasive parameters while there were no need for blood gas analysis.
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Objective To explore the dilution regression method of coagulation detection(PT ,APTT) in fat blood samples . Methods We collected 40 normal blood coagulation specimens (no fat blood ,no jaundice ,no hemolysis) in Yan′an hospital of Kun-ming ,then we detected the PT and APTT of the original plasma and 3-fold diluted plasma and 5-fold diluted plasma ,the we used the data both of before dilution and diluted to do the linear regression analysis ,and finally we got the regression equations of each index .we also collected 33 fat blood samples in Yanan hospital of Kunming ,which be divide into three groups through the severity of triglycerides :mild fat blood group(1 .7 mmol/L≤TG0 .05) .Conclusion Dilution regression method can be used to detect the fat blood samples in the clinical coagulation detection .
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Objective: To rapidly and quantitatively analyze the monoester alkaloids (MAs) in the extracting and concentrating process of Aconiti Radix Cocta (ARC) by near infrared spectroscopy (NIR). Methods: Using NIR to collect the data of 103 ARC samples and using partial least squares (PLS) regression method to establish the quantitative analysis model of MAs between the information of NIR and MAs. Results: The spectral range of MAs model of ARC was 9 264.35-7 274.11 cm-1. The root mean square error of cross validation (RMSECV) was 1.171 and correlation coefficient (r) of the calibration model was 0.999 4.Through the external validation, the root mean square error of prediction (RMSEP) was 1.321 and r of the validation model was 0.992 1. According to the results of statistical analysis, r of the predicted value and the reference value of MAs was 0.999 0. The value of P is less than 0.001. This revealed that the NIR and HPLC methods had a good correlation in determining MAs and could accurately predict the amount of MAs in the covered range. Conclusion: This method is convenient, rapid, accurate, and environment protective, and could be used for the on-line determination of MAs in the extracting and concentrating process of ARC and for the determination of the extraction and concentration endpoint of ARC.
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Objective: To establish a method for the quality control of pharmaceutical cocrystal based on NIRS, using rhein-lysine (lysirein) cocrystal as a case study. Methods: Integrating sphere diffuse reflectance accessory combining partial least squares regression algorithm to establish an analytical method for the content of rhein-lysine cocrystal, and the model was used to study environmental factors, as well as wet mixing and granulating process eutectic degradation conditions. Results: The correlation coefficients (r), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and root-mean-squares error of cross-validation (RMSECV) of the proposed models were 0.9995, 0.0093, 0.0110, and 0.0120; Under high temperature and light conditions, the content of rhein-lysine cocrystal remained roughly constant (content change < 1%). Under high humidity and wet granulation process, there were some changes in cocrystal concentration, content change < 3%, but it was not significant. Conclusion: The proposed method is fast, non-destructive, simple, and accurate; Rhein-lysine cocrystal in environmental factors and modeling granulation process remains stable.
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The linear regression method of multiwavelength data and orthogonal functions spectropho-tomtry have been used for the determination of sulfamethoxazole and triemethoprim in co-trimo-xazole tablets without any preliminary separation.The methods are simple and rapid.Good results have been obtained. The average recovery of sulfamethoxazole was 99.72-100.5%, with coefficient of variation below 0.48%, and the average recovery of triemethoprim was 99.26-99.46%, with coefficient of variation below 0.83%. The accuracy and precision meet the requirements of analysis of the pharmaceutical preparations