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Artículos de revistas sobre el tema "Analytical variability"

Autor: Grafiati

Publicado: 30 de noviembre de 2024

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1

Banfi, G., L. Drago y G. Lippi. "Analytical Variability in Athletes Haematological Testing". International Journal of Sports Medicine 31, n.º 03 (marzo de 2010): 218. http://dx.doi.org/10.1055/s-0030-1248327.

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2

Rosenthal-Allieri, Maria Alessandra, Marie-Line Peritore, Albert Tran, Philippe Halfon, Sylvia Benzaken y Alain Bernard. "Analytical variability of the Fibrotest proteins". Clinical Biochemistry 38, n.º 5 (mayo de 2005): 473–78. http://dx.doi.org/10.1016/j.clinbiochem.2004.12.012.

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3

Nicas, Mark, Barton P. Simmons y Robert C. Spear. "ENVIRONMENTAL VERSUS ANALYTICAL VARIABILITY IN EXPOSURE MEASUREMENTS". American Industrial Hygiene Association Journal 52, n.º 12 (diciembre de 1991): 553–57. http://dx.doi.org/10.1080/15298669191365199.

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4

Budyak, Ivan L., Kristi L. Griffiths y William F. Weiss. "Estimating analytical variability in two-dimensional data". Analytical Biochemistry 513 (noviembre de 2016): 36–38. http://dx.doi.org/10.1016/j.ab.2016.08.021.

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5

Fourier, Anthony, Erik Portelius, Henrik Zetterberg, Kaj Blennow, Isabelle Quadrio y Armand Perret-Liaudet. "Pre-analytical and analytical factors influencing Alzheimer's disease cerebrospinal fluid biomarker variability". Clinica Chimica Acta 449 (septiembre de 2015): 9–15. http://dx.doi.org/10.1016/j.cca.2015.05.024.

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6

Tayyarah, Rana, Michael J. Morton y Jason W. Flora. "HPHC Testing of Tobacco and Smoke to Examine Cigarette Temporal Variability". Contributions to Tobacco & Nicotine Research 31, n.º 2 (1 de julio de 2022): 112–26. http://dx.doi.org/10.2478/cttr-2022-0012.

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Summary Commercial cigarettes were analyzed for harmful and potentially harmful constituents (HPHCs) in tobacco and smoke to investigate temporal product variability independent of analytical variability over one week, one year, and three years. Cigarettes from the worldwide market with various design features were collected over a 3-year period, stored, and tested concurrently for HPHCs to minimize analytical variability; repeat testing of reference cigarette 3R4F was included as an analytical control for the study design. Physical parameters were found to be relatively consistent. No trends in variability were noted based on blend type, smoke analyte matrix, or magnitude of an HPHC's yield. Combustion-related HPHCs generally showed low variation. Long-term batch-to-batch variability was found to be higher than short-term variability for tobacco-related compounds that have the potential to vary over time due to weather and agronomic practices. “Tar”, nicotine, and carbon monoxide were tested in multiple labs and showed greater lab-to-lab variability than batch-to-batch variability across all phases. Based on the results of this study, commercial cigarette products appear to have relatively low product variability. The low analyte variability noted in this study with products tested under unconventionally controlled analytical conditions serves to indicate that analytical variability may be a significant contributor to overall variability for general product testing over time and in interlaboratory studies. Laboratory controls and using a matched reference product across studies and between laboratories are important to assess testing differences and variability.

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7

Eller, Peter M., H. Amy Feng, Ruiguang S. Song, Rosa J. Key-Schwartz, Curtis A. Esche y Jensen H. Groff. "Proficiency Analytical Testing (PAT) Silica Variability, 1990–1998". AIHAJ 60, n.º 4 (julio de 1999): 533–39. http://dx.doi.org/10.1202/0002-8894(1999)060<0533:patsv>2.0.co;2.

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8

Bentley, C., J. J. Crawford y C. A. Broderius. "Analytical and Physiological Variability of Salivary Microbial Counts". Journal of Dental Research 67, n.º 11 (noviembre de 1988): 1409–13. http://dx.doi.org/10.1177/00220345880670111001.

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9

Eller, Peter M., H. Amy Feng, Ruiguang S. Song, Rosa J. Key-Schwartz, Curtis A. Esche y Jensen H. Groff. "Proficiency Analytical Testing (PAT) Silica Variability, 1990–1998". American Industrial Hygiene Association Journal 60, n.º 4 (1 de julio de 1999): 533–39. http://dx.doi.org/10.1080/00028899908984475.

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10

Lim, Chun Yee, Shin Ow Yang, Corey Markus y Tze Ping Loh. "Calibration frequency and analytical variability of laboratory measurements". Clinica Chimica Acta 539 (enero de 2023): 87–89. http://dx.doi.org/10.1016/j.cca.2022.12.006.

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11

Percy, Andrew J., Carol E. Parker y Christoph H. Borchers. "Pre-analytical and analytical variability in absolute quantitative MRM-based plasma proteomic studies". Bioanalysis 5, n.º 22 (noviembre de 2013): 2837–56. http://dx.doi.org/10.4155/bio.13.245.

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12

Whitaker, Thomas, William Horwitz, Richard Albert y Stanley Nesheim. "Variability Associated with Analytical Methods Used To Measure Aflatoxin in Agricultural Commodities". Journal of AOAC INTERNATIONAL 79, n.º 2 (1 de marzo de 1996): 476–86. http://dx.doi.org/10.1093/jaoac/79.2.476.

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Abstract A total of 1019 analytical precision estimates obtained from method-performance (collaborative) studies for mycotoxins published through 1991 were sortedby type of variance measurement, type of analytical method, and type of agricultural commodity. Precision estimates for total aflatoxin were sorted into 2 precision measurements (among-labo ratories and with in-laboratory), 3 analytical methods (thin-layer chromatography [TLC], liquid chromatography [LC], and enzyme-linked immunosorbent assay [ELISA]), and 11 agricultural commodities. Sufficient data existed to study the analytical variability (precision) associated with 36 sorted combinations (of a possible 66). In all but one combination (within-laboratory, barley, and TLC), the variance (V) was a function of totalafla- toxin concentration (C). A power function of the form V = aCb, where a and b are constants, describes the relationship between variance and aflatoxin concentration. The coefficients a and b were determined from regression analysis. When results were pooled across all agricultural commodities, LC had the lowest analytical variability while ELISA had the highest. For a given method, among-labora tories variability was approximately double the with in-laboratory variability. These analytical variability estimates can be coupled with previously determined variability estimates of sampling and sample preparation to determine the performance associated with specific test procedures used to inspect agricultural commodities for aflatoxin.

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13

Fraser, C. G., P. Hyltoft Peterson y M. L. Larsen. "Setting analytical goals for random analytical error in specific clinical monitoring situations". Clinical Chemistry 36, n.º 9 (1 de septiembre de 1990): 1625–28. http://dx.doi.org/10.1093/clinchem/36.9.1625.

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Abstract Strategies abound for the setting of analytical goals in clinical chemistry. Many, especially those more recently proposed for particular clinical situations, are concerned with tests used in diagnosis. We suggest a general theory for the setting of goals in situations that specifically involve the monitoring of individuals. Goals are calculated from the formula CVA less than [(delta c 2/2Z2)-CVB2]1/2, where CVA is the analytical imprecision (as coefficient of variation, CV); delta c is the percentage change in serial results that is considered clinically significant; Z is the Z-statistic, which depends only on the probability selected for statistical significance; and CVB is the average inherent within-subject biological variation (as CV). Examples given show applications in hematology and in monitoring diabetes mellitus, chronic renal failure, and hepatitis. The derived goals are for total random analytical error (imprecision and intermittent systematic variation), and provide objective criteria that should be achieved in practice. The effect of analytical variability on both variability in test results and the probability that a stated change can be considered significant should be calculated whether or not the goals are attained.

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14

Mikita, G. y B. Schigol. "ANALYTICAL SPECTRAL METHOD FOR PROCESSING CARDIAC PERIOD VARIABILITY DATA". Transport Business of Russia, n.º 2 (2022): 79–83. http://dx.doi.org/10.52375/20728689_2022_2_79.

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15

Longnecker, Matthew P. "Pharmacokinetic Variability and the Miracle of Modern Analytical Chemistry". Epidemiology 17, n.º 4 (julio de 2006): 350–51. http://dx.doi.org/10.1097/01.ede.0000222510.59457.7b.

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16

Martin, Mike y Scott M. Hofer. "Intraindividual Variability, Change, and Aging: Conceptual and Analytical Issues". Gerontology 50, n.º 1 (10 de diciembre de 2003): 7–11. http://dx.doi.org/10.1159/000074382.

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17

Zhou, Hongtao, Xing Zhou y Francis Benistant. "Analytical compact modeling and statistical variability study of LDMOS". Microelectronics Reliability 54, n.º 6-7 (junio de 2014): 1096–102. http://dx.doi.org/10.1016/j.microrel.2013.10.019.

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18

Frustaci, Fabio, Pasquale Corsonello y Stefania Perri. "Analytical Delay Model Considering Variability Effects in Subthreshold Domain". IEEE Transactions on Circuits and Systems II: Express Briefs 59, n.º 3 (marzo de 2012): 168–72. http://dx.doi.org/10.1109/tcsii.2012.2184377.

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19

Strobel, Natalie A., Robert G. Fassett, Susan A. Marsh y Jeff S. Coombes. "Importance of understanding pre-analytical variability in biomarker development". International Journal of Cardiology 150, n.º 2 (julio de 2011): 223–24. http://dx.doi.org/10.1016/j.ijcard.2011.05.014.

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20

Snoj Tratnik, Janja, Darja Mazej y Milena Horvat. "Analytical Quality Requirements in Human Biomonitoring Programs: Trace Elements in Human Blood". International Journal of Environmental Research and Public Health 16, n.º 13 (28 de junio de 2019): 2287. http://dx.doi.org/10.3390/ijerph16132287.

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Human biomonitoring (HBM) programs consist of several interrelated and equally important steps. Of these steps, the study design must answer a specific question: How many individuals must be recruited in order to define the spatial or temporal trends of exposure to environmental pollutants in a given HBM study? Two components must be considered at this stage: the population variability of the expected exposure and the performance characteristics of the analytical methods used. The objective of the present study was to quantify the contribution to the required sample size arising from (i) measurement uncertainty and (ii) inter-laboratory measurement variability. For this purpose, the sample size was calculated using the measurement uncertainty of one laboratory, inter-laboratory comparison exercise data, and population variability for commonly studied metals (mercury, cadmium, and lead) in blood. Measurement uncertainty within one laboratory proved to have little influence on the sample size requirements, while the inter-laboratory variability of the three metals increased the requirements considerably, particularly in cases of low population variability. The multiple laboratories approach requires that laboratory variability be considered as early as the planning stage; a single-laboratory approach is thus a cost-effective compromise in HBM to reduce variability due to the participation of different laboratories.

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21

Schultze, A. E. y A. R. Irizarry. "Recognizing and Reducing Analytical Errors and Sources of Variation in Clinical Pathology Data in Safety Assessment Studies". Toxicologic Pathology 45, n.º 2 (15 de noviembre de 2016): 281–87. http://dx.doi.org/10.1177/0192623316672945.

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Veterinary clinical pathologists are well positioned via education and training to assist in investigations of unexpected results or increased variation in clinical pathology data. Errors in testing and unexpected variability in clinical pathology data are sometimes referred to as “laboratory errors.” These alterations may occur in the preanalytical, analytical, or postanalytical phases of studies. Most of the errors or variability in clinical pathology data occur in the preanalytical or postanalytical phases. True analytical errors occur within the laboratory and are usually the result of operator or instrument error. Analytical errors are often ≤10% of all errors in diagnostic testing, and the frequency of these types of errors has decreased in the last decade. Analytical errors and increased data variability may result from instrument malfunctions, inability to follow proper procedures, undetected failures in quality control, sample misidentification, and/or test interference. This article (1) illustrates several different types of analytical errors and situations within laboratories that may result in increased variability in data, (2) provides recommendations regarding prevention of testing errors and techniques to control variation, and (3) provides a list of references that describe and advise how to deal with increased data variability.

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22

Fu, Liping y Bruce Hellinga. "Delay Variability at Signalized Intersections". Transportation Research Record: Journal of the Transportation Research Board 1710, n.º 1 (enero de 2000): 215–21. http://dx.doi.org/10.3141/1710-25.

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Delays that individual vehicles may experience at a signalized intersection are usually subject to large variation because of the randomness of traffic arrivals and interruption caused by traffic signal controls. Although such variation may have important implications for the planning, design, and analysis of signal controls, currently no analytical model is available to quantify it. The development of an analytical model for predicting the variance of overall delay is described. The model is constructed on the basis of the delay evolution patterns under two extreme traffic conditions: highly undersaturated and highly oversaturated conditions. A discrete cycle-by-cycle simulation model is used to generate data for calibrating and validating the proposed model. The practical implications of the model are demonstrated through its use in determining optimal cycle times with respect to delay variability and in assessing level of service according to the percentiles of overall delay.

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23

Lavoine, Etienne, Philippe Davy, Caroline Darcel y Romain Le Goc. "On the Density Variability of Poissonian Discrete Fracture Networks, with application to power-law fracture size distributions". Advances in Geosciences 49 (3 de septiembre de 2019): 77–83. http://dx.doi.org/10.5194/adgeo-49-77-2019.

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Abstract. This paper presents analytical solutions to estimate at any scale the fracture density variability associated to stochastic Discrete Fracture Networks. These analytical solutions are based upon the assumption that each fracture in the network is an independent event. Analytical solutions are developed for any kind of fracture density indicators. Those analytical solutions are verified by numerical computing of the fracture density variability in three-dimensional stochastic Discrete Fracture Network (DFN) models following various orientation and size distributions, including the heavy-tailed power-law fracture size distribution. We show that this variability is dependent on the fracture size distribution and the measurement scale, but not on the orientation distribution. We also show that for networks following power-law size distribution, the scaling of the three-dimensional fracture density variability clearly depends on the power-law exponent.

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24

Biniasch, Marieke, Ruediger Paul Laubender, Martin Hund, Katharina Buck y Christian De Geyter. "Intra- and inter-cycle variability of anti-Müllerian hormone (AMH) levels in healthy women during non-consecutive menstrual cycles: the BICYCLE study". Clinical Chemistry and Laboratory Medicine (CCLM) 60, n.º 4 (29 de octubre de 2021): 597–605. http://dx.doi.org/10.1515/cclm-2021-0698.

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Abstract Objectives Determine variability of serum anti-Müllerian hormone (AMH) levels during ovulatory menstrual cycles between different women (inter-participant), between non-consecutive cycles (inter-cycle) and within a single cycle (intra-cycle) in healthy women. Methods Eligible participants were women aged 18–40 years with regular ovulatory menstrual cycles. Serum samples were collected every second day during two non-consecutive menstrual cycles. AMH levels were measured in triplicate using the Elecsys® AMH Plus immunoassay (Roche Diagnostics). AMH level variability was evaluated using mixed-effects periodic regression models based on Fourier series. The mesor was calculated to evaluate inter-participant and inter-cycle variability. Inter- and intra-cycle variability was evaluated using peak-to-peak amplitudes. Separation of biological and analytical coefficients of variation (CVs) was determined by analysing two remeasured AMH levels (with and without original AMH levels). Results A total of 47 women were included in the analysis (42 assessed over two cycles; five one cycle only). CV of unexplained biological variability was 9.61%; analytical variability was 3.46%. Inter-participant variability, given by time-series plots of AMH levels, was greater than inter-cycle variability. Between individual participants, both mesor and peak-to-peak amplitudes proved variable. In addition, for each participant, intra-cycle variability was higher than inter-cycle variability. Conclusions Inter-participant and intra-cycle variability of AMH levels were greater than inter-cycle variability. Unexplained biological variability was higher than analytical variability using the Elecsys AMH Plus immunoassay. Understanding variability in AMH levels may aid in understanding differences in availability of antral ovarian follicles during the menstrual cycle, which may be beneficial in designing gonadotropin dosage for assisted reproductive technology.

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25

Schectman, G. y E. Sasse. "Variability of lipid measurements: relevance for the clinician". Clinical Chemistry 39, n.º 7 (1 de julio de 1993): 1495–503. http://dx.doi.org/10.1093/clinchem/39.7.1495.

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Abstract Decreasing the large test variability associated with measurements of blood cholesterol, triglyceride, and high-density lipoprotein (HDL)- and low-density lipoprotein (LDL)-cholesterol is likely to improve the classification of coronary heart disease (CHD) risk and allow improved monitoring of lipid-lowering treatments. However, improving test precision will benefit the clinician only if (a) the analytical test variability is high relative to the biological test variability and (b) detecting subtle responses to diet or drug therapy is clinically important. Improving HDL- and LDL-cholesterol test precision can be expected to increase the clinical usefulness of these measurements because values for HDL- and LDL-cholesterol correlate closely with CHD risk; are associated with small, yet clinically important, changes in response to diet and (or) drug therapy; and have substantial analytical test variability relative to biological variability. On the other hand, measurements of both blood cholesterol and triglyceride have high biological relative to analytical variability, and do not correlate as closely with CHD risk. Therefore, further improvements in precision for these measurements are less likely to be useful to the clinician.

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Makris, K. "Are stroke biomarkers ready for research and clinical use? Focus on the pre-analytical, analytical and post-analytical variability". Clinica Chimica Acta 493 (junio de 2019): S743. http://dx.doi.org/10.1016/j.cca.2019.03.1444.

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27

Kumar, Aditi, Kristin Gravdal, Jonathan P. Segal, Helen Steed, Matthew J. Brookes y Hafid O. Al-Hassi. "Variability in the Pre-Analytical Stages Influences Microbiome Laboratory Analyses". Genes 13, n.º 6 (15 de junio de 2022): 1069. http://dx.doi.org/10.3390/genes13061069.

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Introduction: There are numerous confounding variables in the pre-analytical steps in the analysis of gut microbial composition that affect data consistency and reproducibility. This study compared two DNA extraction methods from the same faecal samples to analyse differences in microbial composition. Methods: DNA was extracted from 20 faecal samples using either (A) chemical/enzymatic heat lysis (lysis buffer, proteinase K, 95 °C + 70 °C) or (B) mechanical and chemical/enzymatic heat lysis (bead-beating, lysis buffer, proteinase K, 65 °C). Gut microbiota was mapped through the 16S rRNA gene (V3–V9) using a set of pre-selected DNA probes targeting >300 bacteria on different taxonomic levels. Apart from the pre-analytical DNA extraction technique, all other parameters including microbial analysis remained the same. Bacterial abundance and deviations in the microbiome were compared between the two methods. Results: Significant variation in bacterial abundance was seen between the different DNA extraction techniques, with a higher yield of species noted in the combined mechanical and heat lysis technique (B). The five predominant bacteria seen in both (A) and (B) were Bacteroidota spp. and Prevotella spp. (p = NS), followed by Bacillota (p = 0.005), Lachhnospiraceae (p = 0.0001), Veillonella spp. (p < 0.0001) and Clostridioides (p < 0.0001). Conclusion: As microbial testing becomes more easily and commercially accessible, a unified international consensus for optimal sampling and DNA isolation procedures must be implemented for robustness and reproducibility of the results.

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Kaysi, Isam y Nadine Hage Ali. "Analytical Modeling of Driver-Guidance Schemes with Flow Variability Considerations". Transportation Research Record: Journal of the Transportation Research Board 1717, n.º 1 (enero de 2000): 55–65. http://dx.doi.org/10.3141/1717-08.

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The role that advanced traveler information systems (ATISs) are likely to play in alleviating traffic congestion is explored. The impacts of and benefits from traveler guidance systems that are based on instantaneous as well as predictive information are assessed by developing an analytical formulation for a simple prototypical network. Previous research introducing day-to-day flow variability with both compliance and market-penetration considerations is reviewed, and the case in which traffic flow varies within the day is developed. Two strategies for determining route-guidance directives are considered in the case of predictive information. Also, the integration of ATIS and traffic control through open-loop coordination measures is introduced, with the objective of finding the optimal signal control to maintain user equilibrium on alternate routes. The superiority of predictive information in maintaining guidance validity and mitigating the potential adverse impacts of information is demonstrated.

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Paltrinieri, Saverio, Veronica Paciletti y Jari Zambarbieri. "Analytical variability of estimated platelet counts on canine blood smears". Veterinary Clinical Pathology 47, n.º 2 (junio de 2018): 197–204. http://dx.doi.org/10.1111/vcp.12604.

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30

Bolker, Benjamin M. "Combining endogenous and exogenous spatial variability in analytical population models". Theoretical Population Biology 64, n.º 3 (noviembre de 2003): 255–70. http://dx.doi.org/10.1016/s0040-5809(03)00090-x.

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31

Hennebert, Pierre, Anne-Françoise Stoffel, Mathieu Hubner, Daniel Fortmann, Patricia Merdy y Giovanni Beggio. "The inherent variability of some environmental analytical methods hampers the circular economy of materials". Detritus, n.º 21 (30 de noviembre de 2022): 17–26. http://dx.doi.org/10.31025/2611-4135/2022.16225.

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This paper is the third part of three papers on sampling by the number of particles, focusing on analytical variability. The objective is to propose a target variability of waste and contaminated soil analyses (extraction and quantification), that can be used for calculation of the size of a representative sample. Data of intra- and inter-laboratory variability are presented. As the variability of the quantification step (after extraction) is limited in waste and soil analyses to about 0.01, the analytical variability stems from three main sources: (i) non-homogeneous test portions; (ii) for partial extraction methods, variable extraction rate, due to presence of options in the method or insufficient time for equilibrium (leaching or percolation test, biotests); and (iii) ill-defined solid/liquid separation (leaching or percolation tests), critical since there are colloids and nanoparticles in the leachates, representing from 0 to 100% of the element fraction in the leachate. Counter-intuitively, the centrifugation (annex E of EN 12457) series before the 450 nm-filtration delivers leachates more concentrated in particles (median size 150 nm, 1 sample) and statistically more concentrated in elements (+13%, 27 samples, 287 paired data). Without centrifugation, the filter cake that builds up on the membrane is an additional filter. A target intra-laboratory variability of CVr = 0.10 (10%) and inter-laboratory variability of CVR = 0.20 (20%) is proposed for all analytical methods. The methods with higher CVr and CVR should be revisited to not jeopardise the sampling and characterisation efforts of waste and soil, particularly for valorisation in the circular economy.

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Řehák, Jan. "Variability of Spatial Frequency Distribution". Geografie 95, n.º 3 (1990): 186–94. http://dx.doi.org/10.37040/geografie1990095030186.

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A measure of spatial variability (called geostructural variance) is defined for a frequency distribution on a finite set of places in a space whose geographical relations are assessed by a matrix of (generally conceived) distances. A set of measures stemming from the same approach describe the positions and properties of individual places in the geostructure. This complex of characteristics provides a clear-cut way of an analytical diagnostic reflection of the spatial properties of frequency distributions.

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Boyer, Kenneth W., William Horwitz y Richard Albert. "Interlaboratory Variability in Trace Element Analysis". Analytical Chemistry 57, n.º 2 (febrero de 1985): 454–59. http://dx.doi.org/10.1021/ac50001a031.

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McDonald, Jennifer S., Dragana Milosevic, Honey V. Reddi, Stefan K. Grebe y Alicia Algeciras-Schimnich. "Analysis of Circulating MicroRNA: Preanalytical and Analytical Challenges". Clinical Chemistry 57, n.º 6 (1 de junio de 2011): 833–40. http://dx.doi.org/10.1373/clinchem.2010.157198.

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BACKGROUND There is great interest in circulating microRNAs (miRNAs) as disease biomarkers. Translating promising miRNAs into validated clinical tests requires the characterization of many preanalytical and analytical parameters. METHODS miRNAs were extracted from serum and plasma samples of healthy volunteers, and miRNAs known to be present in serum and plasma (miR-15b, miR-16, miR-24, and miR-122) were amplified by reverse-transcription quantitative PCR. Stability and the effects of hemolysis were determined. Assay variation and its components, including the effect of adding control miRNA, were assessed by nested ANOVA. RESULTS miRNA concentrations were higher in plasma than in serum. Processing of plasma to remove subcellular/cellular components reduced miRNA concentrations to those of serum. The miRNAs analyzed were stable refrigerated or frozen for up to 72 h and were stable at room temperature for 24 h. Hemolysis increased the apparent concentration of 3 of the miRNAs. The total variability of replicate miRNA concentrations was <2.0-fold, with most of the variability attributable to the extraction process and interassay imprecision. Normalizing results to those of spiked exogenous control miRNAs did not improve this variability. CONCLUSIONS Detailed validation of the preanalytical steps affecting miRNA detection and quantification is critical when considering the use of individual miRNAs as clinical biomarkers. Unless these causes of imprecision are considered and mitigated, only miRNAs that are extremely up- or downregulated will be suitable as clinical biomarkers.

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Gallagher, S. K., L. K. Johnson y D. B. Milne. "Short- and Long-Term Variability of Selected Indices Related to Nutritional Status. II. Vitamins, Lipids, and Protein Indices". Clinical Chemistry 38, n.º 8 (1 de agosto de 1992): 1449–53. http://dx.doi.org/10.1093/clinchem/38.8.1449.

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Abstract Five free-living women (ages 23-38 years) who consumed a self-selected diet and five women (ages 23-44 years) residing in a metabolic unit who were fed constant diet were assessed for variation in vitamin and general chemistry indices. Blood was drawn from these women once a month for five months, once a week for five weeks, and once a day for five days to assess analytical and biological variability of the indices. Analytical variability was determined by concurrently analyzing control samples prepared from plasma and serum pools. All samples were analyzed in duplicate. Of the measured indices, vitamins and lipids seemed to be the most variable. Diet had a significant effect only on ascorbic acid. We were unable to show any seasonal change for these analytes. Estimations of analytical variability, along with estimates of biological variability, and knowledge of dietary practices are essential when interpreting differences in analytes.

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Bielohuby, Maximilian, Martin Bidlingmaier y Uwe Schwahn. "Control of (pre)-analytical aspects in immunoassay measurements of metabolic hormones in rodents". Endocrine Connections 7, n.º 4 (abril de 2018): R147—R159. http://dx.doi.org/10.1530/ec-18-0035.

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The measurement of circulating hormones by immunoassay remains a cornerstone in preclinical endocrine research. For scientists conducting and interpreting immunoassay measurements of rodent samples, the paramount aim usually is to obtain reliable and meaningful measurement data in order to draw conclusions on biological processes. However, the biological variability between samples is not the only variable affecting the readout of an immunoassay measurement and a considerable amount of unwanted or unintended variability can be quickly introduced during the pre-analytical and analytical phase. This review aims to increase the awareness for the factors ‘pre-analytical’ and ‘analytical’ variability particularly in the context of immunoassay measurement of circulating metabolic hormones in rodent samples. In addition, guidance is provided how to gain control over these variables and how to avoid common pitfalls associated with sample collection, processing, storage and measurement. Furthermore, recommendations are given on how to perform a basic validation of novel single and multiplex immunoassays for the measurement of metabolic hormones in rodents. Finally, practical examples from immunoassay measurements of plasma insulin in mice address the factors ‘sampling site and inhalation anesthesia’ as frequent sources of introducing an unwanted variability during the pre-analytical phase. The knowledge about the influence of both types of variability on the immunoassay measurement of circulating hormones as well as strategies to control these variables are crucial, on the one hand, for planning and realization of metabolic rodent studies and, on the other hand, for the generation and interpretation of meaningful immunoassay data from rodent samples.

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37

Shilo, Elad, Yosef Ashkenazy, Alon Rimmer, Shmuel Assouline y Yitzhaq Mahrer. "Wind Spatial Variability and Topographic Wave Frequency". Journal of Physical Oceanography 38, n.º 9 (1 de septiembre de 2008): 2085–96. http://dx.doi.org/10.1175/2008jpo3886.1.

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Abstract The association of topographic waves with wind action has been documented in several natural lakes throughout the world. However, the influence of the wind’s spatial variability (wind stress curl) on the frequency of topographic waves has only been partially investigated. Here the role of wind stress curl on the frequency of topographic waves in an idealized elliptic paraboloid basin has been studied both analytically and numerically. It is shown that the analytical solution is the sum of an elliptic rotation determined by the wind stress curl and two counterrotating circulation cells, which propagate cyclonically after the wind ceases. Furthermore, it is shown that cyclonic elliptical rotation (associated with positive wind stress curl) increases the rotation frequency of the double-gyre pattern while anticyclonic elliptical rotation (associated with negative wind stress curl) decreases the oscillatory mode frequency. It is also shown that bottom friction has some effect on the structure of the double-gyre pattern but hardly affects the oscillatory frequency. Numerical solutions of the depth-integrated nonlinear shallow-water equations confirmed that the frequency of the topographic wave increases (decreases) when forcing the model with cyclonic (anticyclonic) wind curl.

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38

Couchman, Lewis y Cajetan F. Moniz. "Analytical considerations for the biochemical assessment of vitamin D status". Therapeutic Advances in Musculoskeletal Disease 9, n.º 4 (13 de febrero de 2017): 97–104. http://dx.doi.org/10.1177/1759720x17692500.

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The most widely used and clinically accepted biochemical marker for assessing vitamin D status is the total serum 25-hydroxyvitamin D [25(OH)D] concentration. Despite the analysis of 25(OH)D dating back to the early 1970s, modern analytical techniques still exhibit significant interassay variability due to varying concentrations of other related vitamin D metabolites and sample-to-sample matrix differences. It is important for clinicians requesting 25(OH)D analyses to understand these issues and limitations, and where necessary to confront laboratories for details of analytical methods used. The availability of reference measurement procedures for 25(OH)D based on liquid chromatography and tandem mass spectrometry, whilst not intended for routine clinical sample analysis, should be utilized to improve assay harmonization and reduce interlaboratory variability. Laboratories should also be forthcoming with details of subscriptions to external quality assessment schemes and assay traceability. As well as discussing the reasons for ongoing assay variability for 25(OH)D, this short review will also briefly discuss other assays related to the assessment of vitamin D status, including parathyroid hormone, 24,25-dihydroxyvitamin D, 1,25-dihydroxyvitamin D and vitamin D binding proteins.

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39

Whitaker, Thomas B., Winston M. Hagler, Francis G. Giesbrecht y Anders S. Johansson. "Sampling, Sample Preparation, and Analytical Variability Associated with Testing Wheat for Deoxynivalenol". Journal of AOAC INTERNATIONAL 83, n.º 5 (1 de septiembre de 2000): 1285–92. http://dx.doi.org/10.1093/jaoac/83.5.1285.

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Abstract The variability associated with testing wheat for deoxynivalenol (DON) was measured using a 0.454 kg sample, Romer mill, 25 g comminuted subsample, and the Romer Fluoroquant analytical method. The total variability was partitioned into sampling, sample preparation, and analytical variability components. Each variance component was a function of the DON concentration and equations were developed to predict each variance component using regression techniques. The effect of sample size, subsample size, and number of aliquots on reducing the variability of the DON test procedure was also determined. For the test procedure, the coefficient of variation (CV) associated with testing wheat at 5 ppm was 13.4%. The CVs associated with sampling, sample preparation, and analysis were 6.3, 10.0, and 6.3%, respectively. For the sample variation, a 0.454 kg sample was used; for the sample preparation variation, a Romer mill and a 25 g subsample were used; for the analytical variation, the Romer Fluoroquant method was used. The CVs associated with testing wheat are relatively small compared to the CV associated with testing other commodities for other mycotoxins, such as aflatoxin in peanuts. Even when the small sample size of 0.454 kg was used, the sampling variation was not the largest source of error as found in other mycotoxin test procedures.

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40

De Munck, J., A. Mine, A. Poitevin, A. Van Ende, M. Vivan Cardoso, K. L. Van Landuyt, M. Peumans y B. Van Meerbeek. "Meta-analytical Review of Parameters Involved in Dentin Bonding". Journal of Dental Research 91, n.º 4 (14 de diciembre de 2011): 351–57. http://dx.doi.org/10.1177/0022034511431251.

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Bond-strength testing is the method most used for the assessment of bonding effectiveness to enamel and dentin. We aimed to disclose general trends in adhesive performance by collecting dentin bond-strength data systematically. The PubMed and EMBASE databases were used to identify 2,157 bond-strength tests in 298 papers. Most used was the micro-tensile test, which appeared to have a larger discriminative power than the traditional macro-shear test. Because of the huge variability in dentin bond-strength data and the high number of co-variables, a neural network statistical model was constructed. Variables like ‘research group’ and ‘adhesive brand’ appeared most determining. Weighted means derived from this analysis confirmed the high sensitivity of current adhesive approaches (especially of all-in-one adhesives) to long-term water-storage and substrate variability.

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41

Trucksess, Mary W., Thomas B. Whitaker, Andrew B. Slate, Kristina M. Williams, Vickery A. Brewer, Paul Whittaker y James T. Heeres. "Variation of Analytical Results for Peanuts in Energy Bars and Milk Chocolate". Journal of AOAC INTERNATIONAL 87, n.º 4 (1 de julio de 2004): 943–49. http://dx.doi.org/10.1093/jaoac/87.4.943.

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Abstract Peanuts contain proteins that can cause severe allergic reactions in some sensitized individuals. Studies were conducted to determine the percentage of recovery by an enzyme-linked immunosorbent assay (ELISA) method in the analysis for peanuts in energy bars and milk chocolate and to determine the sampling, subsampling, and analytical variances associated with testing energy bars and milk chocolate for peanuts. Food products containing chocolate were selected because their composition makes sample preparation for subsampling difficult. Peanut-contaminated energy bars, noncontaminated energy bars, incurred milk chocolate containing known levels of peanuts, and peanut-free milk chocolate were used. A commercially available ELISA kit was used for analysis. The sampling, sample preparation, and analytical variances associated with each step of the test procedure to measure peanut protein were determined for energy bars. The sample preparation and analytical variances were determined for milk chocolate. Variances were found to be functions of peanut concentration. Sampling and subsampling variability associated with energy bars accounted for 96.6% of the total testing variability. Subsampling variability associated with powdered milk chocolate accounted for >60% of the total testing variability. The variability among peanut test results can be reduced by increasing sample size, subsample size, and number of analyses. For energy bars the effect of increasing sample size from 1 to 4 bars, subsample size from 5 to 20 g, and number of aliquots quantified from 1 to 2 on reducing the sampling, sample preparation, and analytical variance was demonstrated. For powdered milk chocolate, the effects of increasing subsample size from 5 to 20 g and number of aliquots quantified from 1 to 2 on reducing sample preparation and analytical variances were demonstrated. This study serves as a template for application to other foods, and for extrapolation to different sizes of samples and subsamples as well as numbers of analyses.

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42

Coucke, Wim, Corine Charlier, Willy Lambert, Frank Martens, Hugo Neels, Jan Tytgat, Philippe Van de Walle et al. "Application of the Characteristic Function to Evaluate and Compare Analytical Variability in an External Quality Assessment Scheme for Serum Ethanol". Clinical Chemistry 61, n.º 7 (1 de julio de 2015): 948–54. http://dx.doi.org/10.1373/clinchem.2015.240176.

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Abstract BACKGROUND As a cornerstone of quality management in the laboratory, External Quality Assessment (EQA) schemes are used to assess laboratory and analytical method performance. The characteristic function is used to describe the relation between the target concentration and the EQA standard deviation, which is an essential part of the evaluation process. The characteristic function is also used to compare the variability of different analytical methods. METHODS We fitted the characteristic function to data from the Belgian External Quality Assessment program for serum ethanol. Data included results from headspace gas chromatography and the enzymatic methods of Abbott, Roche, Siemens, and Ortho-Clinical Diagnostics. We estimated the characteristic function with weighted nonlinear regression. By introducing dummy variables, we rewrote the original formula of the characteristic function to assess statistical inference for comparing the variability of the different analytical methods. RESULTS The characteristic function fitted the data precisely. Comparison between methods showed that there was little difference between the estimated variability for low concentrations, and that the increase in SD with increasing target concentration was slower for Abbott and Roche than for the other methods. CONCLUSIONS The characteristic function can successfully be introduced in clinical schemes, although its applicability to fit the data should always be assessed. Because of its easy parameterization, it can be used to assess differences in performance between analytical methods and to assess laboratory performance. The characteristic function also offers an alternative framework for coefficients of variation to describe variability of analytical methods.

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43

Brandt, Jon E. y Jan K. Horbaczewski. "VARIABILITY OF PHYSICOCHEMICAL ANALYTICAL RESULTS FROM MINESOILS AND QA/QC CONSIDERATIONS". Journal American Society of Mining and Reclamation 1997, n.º 1 (1997): 152–66. http://dx.doi.org/10.21000/jasmr97010152.

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44

Minobe, Shoshiro. "Analytical studies from processes to decadal scale air-sea coupled variability". Oceanography in Japan 23, n.º 5 (15 de septiembre de 2014): 147–69. http://dx.doi.org/10.5928/kaiyou.23.5_147.

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45

Maldini, Carla, Katie Druce, Neil Basu, Michael P. LaValley y Alfred Mahr. "Exploring the variability in Behçet’s disease prevalence: a meta-analytical approach". Rheumatology 57, n.º 1 (17 de febrero de 2017): 185–95. http://dx.doi.org/10.1093/rheumatology/kew486.

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46

Liu, Fang, Sule Ozev y Plamen K. Nikolov. "Parametric variability analysis for multistage analog circuits using analytical sensitivity modeling". ACM Transactions on Design Automation of Electronic Systems 13, n.º 2 (2 de abril de 2008): 1–28. http://dx.doi.org/10.1145/1344418.1344429.

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47

Bindi, L., P. Bonazzi, M. Zoppi y P. G. Spry. "Chemical variability in wakabayashilite: a real feature or an analytical artifact?" Mineralogical Magazine 78, n.º 3 (junio de 2014): 693–702. http://dx.doi.org/10.1180/minmag.2014.078.3.16.

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AbstractWakabayashilite is a rare mineral with ideal formula [(As,Sb)6S9][As4S5]. Its structure consists of an [M6S9] bundle-like unit (M = As, Sb) running along the [001] axis and [As4S5] cage-like molecules. In this study, samples of wakabayashilite from different occurrences (Khaidarkan, Kyrgyzstan; Jas Roux, France; White Caps mine, USA; Nishinomaki mine, Japan) were selected to verify the possible presence of different molecular groups replacing the As4S5 molecule. Given the chemical (electron probe microanalysis-wavelength dispersive spectroscopy), spectroscopic (micro-Raman) and structural (single-crystal X-ray diffraction) results obtained, it appears evident that only the As4S5 molecular group is present in the wakabayashilite structure and that the apparent non-stoichiometry reported in literature is actually due to unreliable chemical analyses. The structural role of the minor elements (Cu, Zn and Tl) in wakabayashilite is also discussed.

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48

Holmes, Earle W., Jean Garbincius y Kathleen M. McKenna. "Analytical Variability Among Methods for the Measurement of 25-Hydroxyvitamin D". American Journal of Clinical Pathology 140, n.º 4 (1 de octubre de 2013): 550–60. http://dx.doi.org/10.1309/ajcpu2skw1tfkswy.

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49

Gronewold, Andrew D., Craig A. Stow, Kannappan Vijayavel, Molly A. Moynihan y Donna R. Kashian. "Differentiating Enterococcus concentration spatial, temporal, and analytical variability in recreational waters". Water Research 47, n.º 7 (mayo de 2013): 2141–52. http://dx.doi.org/10.1016/j.watres.2012.12.030.

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50

Francomano, Dante, Benjamin L. Gottesman y Bryan C. Pijanowski. "Biogeographical and analytical implications of temporal variability in geographically diverse soundscapes". Ecological Indicators 112 (mayo de 2020): 105845. http://dx.doi.org/10.1016/j.ecolind.2019.105845.

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