The ability to demonstrate the reproducibility of gene expression microarray results is a critical consideration for the use of microarray technology in clinical applications. While studies have asserted that microarray data can be "highly reproducible" under given conditions, there is little ability to quantitatively compare amongst the various metrics and terminology used to characterize and express measurement performance. Use of standardized conceptual tools can greatly facilitate communication among the user, developer, and regulator stakeholders of the microarray community. While shaped by less highly multiplexed systems, measurement science (metrology) is devoted to establishing a coherent and internationally recognized vocabulary and quantitative practice for the characterization of measurement processes.The two independent aspects of the metrological concept of "accuracy" are "trueness" (closeness of a measurement to an accepted reference value) and "precision" (the closeness of measurement results to each other). A carefully designed collaborative study enables estimation of a variety of gene expression measurement precision metrics: repeatability, several flavors of intermediate precision, and reproducibility. The three 2004 Expression Analysis Pilot Proficiency Test collaborative studies, each with 13 to 16 participants, provide triplicate microarray measurements on each of two reference RNA pools. Using and modestly extending the consensus ISO 5725 documentary standard, we evaluate the metrological precision figures of merit for individual microarray signal measurement, building from calculations appropriate to single measurement processes, such as technical replicate expression values for individual probes on a microarray, to the estimation and display of precision functions representing all of the probes in a given platform.With only modest extensions, the established metrological framework can be fruitfully used to characterize the measurement performance of microarray and other highly multiplexed systems. Precision functions, summarizing routine precision metrics estimated from appropriately repeated measurements of one or more reference materials as functions of signal level, are demonstrated and merit further development for characterizing measurement platforms, monitoring changes in measurement system performance, and comparing performance among laboratories or analysts.
We have investigated cotransformation in mammalian cells and its potential for identifying cells that have been modified by gene targeting. Selectable genes on separate DNA fragments were simultaneously introduced into cells by coelectroporation. When the introduced fragments were scored for random integration, 75% of the transformed cells integrated both fragments within the genome of the same cell. When one of the cointroduced fragments was scored for integration at a specific locus by gene targeting, only 4% of the targeted cells cointegrated the second fragment. Apparently, cells that have been modified by gene targeting with one DNA fragment rarely incorporate a second DNA fragment. Despite this limitation, we were able to use the cotransformation protocol to identify targeted cells by screening populations of colonies that had been transformed with a cointroduced selectable gene. When hypoxanthine phosphoribosyltransferase (hprt) targeting DNA was coelectroporated with a selectable neomycin phosphotransferase (neo) gene into embryonic stem (ES) cells, hprt-targeted colonies were isolated from the population of neo transformants at a frequency of 1 per 70 G418-resistant colonies. In parallel experiments with the same targeting construct, hprt-targeted cells were found at a frequency of 1 per 5,500 nonselected colonies. Thus, an 80-fold enrichment for targeted cells was observed within the population of colonies transformed with the cointroduced DNA compared with the population of nonselected colonies. This enrichment for targeted cells after cotransformation should be useful in the isolation of colonies that contain targeted but nonselectable gene alterations.
Vasovagal reactions (VVRs) are common but complex donor adverse reactions (DAEs) in blood donations. VVRs have been extensively studied with a multitude of risk factors identified including young age, female gender and first-time donor status. How they may interplay remains obscure.A total of 1,984,116 blood donations and 27,952 immediate VVRs (iVVRs) and 1,365 delayed VVRs (dVVRs) reported between 2011 and 2021 in NZ were used in multivariate logistic regression analyses each concerning donations with iVVRs as cases and those free of DAEs as controls. For each analysis stepwise selection was used to identify the best model and risk factors carrying significant main effects and/or interactions. Identified interactions informed further in-depth regression analyses to dissect iVVR risk patterns.Over 95% of VVRs were iVVRs that had lower female preponderance and deferrals than dVVRs. iVVRs had a school seasonal pattern in whole blood donations driven by first-time donors from schools/colleges, and interactions between gender and age group differentiating the first-time from repeat donations. Subsequent regression analyses identified the known and novel risk factors of year and mobile collection sites and their interactions. iVVR rates were roundly elevated in 2020 and 2021 probably because of COVID-19 restrictions like facemask wearing. Exclusion of the 2020 and 2021 data removed the interactions with year, but confirmed interactions of gender with mobile collection sites (p = 6.2e-07) in first-time donations only and with age group in repeat donations only (p < 2.2e-16), together indicating young female donors at the highest risk of iVVRs. Our results also revealed that donation policy changes contributed to the year effects; donors had a lower iVVR risk at mobile sites than well-medicalized donation centers probably because of under-reporting.Modeling statistical interactions is valuable in identifying odds and revealing novel iVVR risk patterns and insights into blood donations.
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