Gamma Spectrometry
Michael Ljungberg in Handbook of Nuclear Medicine and Molecular Imaging for Physicists, 2022
The sum of the considered uncertainties in the right-hand side of Eq. 8.12 is then u2(Ax,corr) = 6.67·10-5 + 0.000234 + 0 + 0.01532(=0.000234) + 0 + 0.0172(=0.000289) = 0.000758. The relative standard deviation s(Ax,corr) is thus the square root of this number, that is, 0.028. This means that the total relative uncertainty, expressed in one standard deviation, is 2.8 per cent. Expressed with on 99 per cent confidence level, the relative uncertain becomes 1.96·0.028=5.4 per cent. To communicate this result, the activity concentration value should thus be reported as being Ax,corr(±1.96·σ)=4799.6±259 Bq kg-1.
Case Studies
Nicholas Stergiou in Nonlinear Analysis for Human Movement Variability, 2018
Recently, in addition to the study of various gait parameters, Amende et al. (2005) made an attempt to examine stride-to-stride variability in mouse models of Parkinson’s disease and Huntington’s disease. Measures of stride-to-stride variability were determined as the standard deviation and the coefficient of variation. The standard deviation reflects the dispersion about the average value for a parameter. The coefficient of variation was calculated from the equation: 100 × standard deviation/mean value. The motor speed was set to 34 cm/s for all mice. Approximately, 3 s of videography was collected for each walking mouse to provide more than seven sequential strides. Parkinson’s mice demonstrated significant gait disturbances, including shortened stride length, increased stride frequency, and increased stride-to-stride variability, symptoms characteristic of patients with Parkinson’s disease. Huntington’s mice demonstrated an increased forelimb stride-to-stride variability and a more open paw placement angle of the hindlimbs. Gait failure in Huntington’s mice resulted from an ability of the hindlimbs to engage in stepping while forelimb gait remained intact. Findings of Amende et al.’s study provide a basis for additional studies of gait measurements and their variability in mouse models of neurodegenerative diseases.
Numerical Summary Measures
Marcello Pagano, Kimberlee Gauvreau in Principles of Biostatistics, 2018
While it may not make sense to compare standard deviations, it is possible to compare the variability among two or more sets of data representing different quantities with different units of measurement using a numerical summary measure known as the coefficient of variation. The coefficient of variation relates the standard deviation of a set of values to its mean; it is the ratio of s to multiplied by 100 and is, therefore, a measure of relative variability. Because the standard deviation and the mean share the same units of measurement, the units cancel out and leave the coefficient of variation a dimensionless number. The coefficient of variation for the FEV1 data is
Nitrous anhydrase activity of carbonic anhydrase II: cysteine is required for nitric oxide (NO) dependent phosphorylation of VASP in human platelets
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2021
Dimitrios Tsikas, Stepan Gambaryan
The previously reported GC-MS method for nitrite and nitrate had been originally validated for 100-µL sample aliquots15,16. This method was adapted to 10-µL aliquots and validated for the CA microassay which involves the use of H218O needed to prepare the aqueous buffer3. For highest derivatization yield of nitrite, the incubation time was 5 min. Under these conditions, nitrate cannot be quantified accurately15. We compared in parallel the methods using 100-µL (in quadruplicate) and 10-µL aliquots (in duplicate) of nitrite solutions in 100 mM Tris-HCl buffer, pH 7.4, in H216O at added nitrite concentrations of 0, 2.5, and 5.0 µM. These concentrations were chosen because they were expected to be formed in experiments in H218O-Tris buffer. The relative standard deviation values were 2.9% and 2.4%, 0.5% and 1.0%, and 3.1% and 4.3%, respectively. The regression equations obtained from linear regression analyses of measured nitrite (y) versus added nitrite (x) were y = 0.752 + 0.645x, r2=0.9945 for the 100-µL samples and y = 0.800 + 0.668x, r2=0.9998 for the 10-µL. The y-axis intercepts reveal nitrite concentrations in the Tris-HCl buffer of 0.75 µM and 0.80 µM, respectively. The slope values of the regression equations of 0.645 and 0.668 are very close indicating almost complete agreement (96.5%) between the procedures in the investigated concentration range.
Development and pharmacokinetic evaluation of alginate-pectin polymeric rafts forming tablets using box behnken design
Published in Drug Development and Industrial Pharmacy, 2018
The concentration of PSS in raft forming tablets were analyzed according to BP Pharmacopoeia using a HPLC system (PerkinElmer 710 Bridgeport avenue Shelton, Connecticut USA) with RHS C18 column (1.5 cm ×4.6 mm, 5 μm) Agilent technologies Santa Clara, California, United States. Twenty tablets were taken, grounded, and an amount equal to average tablet weight was transferred to a conical flask having mobile phase. Solution was sonicated for 10 min for complete dissolution of active material. The analysis was performed using a mobile phase consisting of acetonitrile and phosphate buffer pH 4 in ratio of 50:50% v/v at a flow rate of 1 ml/min. Detection wavelength was set at 285 nm. The retention time of PSS was 3.398 ± 0.5 min. The linearity parameter was studied in the range of 3.125–25 ppm with a correlation coefficient (R2) of 0.999. Percentage recovery was calculated as 100.5%. The percent relative standard deviation of precision value was found less than 2%.
A scanning and image processing system with integrated design for automated micronucleus scoring
Published in International Journal of Radiation Biology, 2020
Tímea Hülber, Zsuzsa S. Kocsis, Enikő Kis, Géza Sáfrány, Csilla Pesznyák
The MN yield corresponding to 2 Gy dose is in the range of 300 MN per 1000 BN cells. The theoretical relative standard deviation of this value is 7%. The range of the uncertainty examined in the context of other contributors should be compared to this 7% since this is the theoretical minimum of the error of dose estimation. All the other contributors can be reduced with the finetuning of the sample preparation and scoring process, except the individual variability (see the last column of Table 6). The four contributors were analyzed in regard to the three scoring types (manual, semi-automatic and full-automatic). Some of the error factors affect all the three scoring methods in the same way, but for example, the difference between the results of involving multiple human scorers is only an issue in manual and semi-automatic cases. It is a fair assumption that the factors are independent from each other, so their combined variance can be calculated as the sum of the variances of the individual factors. When all of the errors are summed up for the different scoring methods, the final values do not significantly differ. The exact values of the discussed uncertainties can differ for doses higher or lower than the examined 2 Gy, but their range relative to each other remains the same.
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