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Asparagus Sp.: Phytochemicals and Marketed Herbal Formulations
Published in Amit Baran Sharangi, K. V. Peter, Medicinal Plants, 2023
Vikas Bajpai, Pratibha Singh, Preeti Chandra, Brijesh Kumar
The developed UPLC-MS/MS method was applied for the quantitative analysis of 7 major active compounds in twig of A. racemosus, A. officinalis and A. adscendens and other plant parts of A. racemosus, i.e., leaf, stem, and root also. The contents were calculated with external standard methods based on the respective calibration curve. The results demonstrated a successful application of the developed method for the quantitation of the major saponins, sapogenins, flavonoids, and phenolic acid in different samples of Asparagus species. Furthermore, developed technique was applied to evaluate different marketed formulations of Asparagus species.
Does my model predict accurately?
Published in Thomas A. Gerds, Michael W. Kattan, Medical Risk Prediction, 2021
Thomas A. Gerds, Michael W. Kattan
A model is well-calibrated (in the large) if 17% of the subjects have the event when the predicted risk for all of them is 17%. The value 17% is just an example. This should hold for all values that the predicted risk can take, i.e., all values between 0% and 100%. The calibration curve is defined over the range of values that the model is possibly predicting. For any such value the calibration is the observed event frequency among patients that have a predicted risk equal or at least close to that value. There are many different ways to define “close,” and a popular method is to simply categorize the predicted risks according to deciles [123]. This yields the histogram type of the calibration plot. Figure 5.7 shows a calibration diagram based on 10 groups of the predicted risks that are obtained using deciles. The model is well-calibrated (in the small) if the light gray bars are as high as the dark gray bars.
Drug Products with Multiple Components—Development of TCM
Published in Shein-Chung Chow, Innovative Statistics in Regulatory Science, 2019
By fitting an appropriate statistical model between these standards (well-established clinical endpoints) and their corresponding responses (TCM scores), an estimated calibration curve can be obtained. The estimated calibration curve is also known as the standard curve. For a given patient, his/her unknown measurement of well-established clinical endpoint can be determined based on the standard curve by replacing the dependent variable with its TCM score.
Novel model predicts diastolic cardiac dysfunction in type 2 diabetes
Published in Annals of Medicine, 2023
Mingyu Hao, Xiaohong Huang, Xueting Liu, Xiaokang Fang, Haiyan Li, Lingbo Lv, Liming Zhou, Tiecheng Guo, Dewen Yan
The calibration curve is the consistency between the frequency of observed results and prediction probability. Research calibration is expressed by following the relationship between the frequency of the effect and the predicted probability. A sensible calibration measure is a likelihood ratio statistic testing the null hypothesis that intercept = 0 and slope = 1. The statistic has a χ2 distribution with 2 degrees of freedom (unreliability U-statistic) [20]. We also evaluated average (E-aver) and maximal errors (E-max) between predictions and observations obtained from a calibration curve. Plotted the calibration curve to assess the calibration of the nomogram, and the nonsignificant test statistics show that the model has been perfectly calibrated [21]. Decision curve analysis (DCA) was used to evaluate the clinical value of the predictive model. Decision curve analysis was conducted to determine the clinical usefulness of the nomogram by quantifying the net benefits at different threshold probabilities in the validation dataset [22].
Liquid chromatography–tandem mass spectrometry (LC–MS/MS) method for determination of free and total dabigatran in human plasma and its application to a pharmacokinetic study
Published in Drug Development and Industrial Pharmacy, 2021
Khurshid Shaikh, Ashish Mungantiwar, Supriya Halde, Nancy Pandita
The linearity of calibration curve standards was determined by the accuracy of standards from the nominal concentration and evaluating the slope, intercept, and goodness-of-fit measure for linear regression (r2) of the weighting factor (1/concentration2). The expected calibration curve range consisting of Blank, a zero standard and eight non-zero concentration standards ranged 1.04–406.49 ng/mL, established to cover the concentrations obtained from unknown samples of the pharmacokinetic study. Three precision and accuracy batches were run consisting of linearity standards and four levels of quality control samples in five replicates. Intra-day, within batch and Inter day accuracy and precision, were determined by repeated analyses performed on two different days.
Resveratrol loaded glycyrrhizic acid-conjugated human serum albumin nanoparticles for tail vein injection II: pharmacokinetics, tissue distribution and bioavailability
Published in Drug Delivery, 2020
Mingfang Wu, Chen Zhong, Yiping Deng, Qian Zhang, Xiaoxue Zhang, Xiuhua Zhao
Plasma samples obtained from blank plasma and 1 h after tail vein administration of GL-HSA-RES-NPs (contains 6 mg/kg RES) were detected by HPLC. The results obtained are shown in Figure 3. Blank plasma endogenous substances did not interfere with RES and internal standard determination. RES retention time was 5.5 min, retention time of internal standard 9.7 min. The chromatographic separation is good and the method has a high specificity. Low, medium and high amounts of RES are added to the plasma or tissue samples to determine that the relative recovery was acceptable (Table 1). Table 2 shows the results of the standard equation of linear regression. The correlation coefficient of calibration curve obtained by each sample is more than 0.99. The results of accuracy and precision are shown in Table 3, indicating that this method was reliable and repeatable. Table 4 shows the results of the stability of RES. The stability results indicated that the concentration of RES in plasma was unchanged at –20 °C for 60 days. The prepared sample was stable at 20 °C for 24 h, and there was no change in rat plasma RES after three freeze-thaw cycles. RES was stably stored in post-preparation samples saved at 4 °C for 24 h.