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DNA methylation (DNAm)-based cell mixture deconvolution (CMD) has become a quintessential part of epigenome-wide association studies where DNAm is profiled in heterogeneous tissue types. Despite being introduced over a decade ago, detection limits, which represent the smallest fraction of a cell type in a mixed biospecimen that can be reliably detected, have yet to be determined in the context of DNAm-based CMD. Moreover, there has been little attention given to approaches for quantifying the uncertainty associated with DNAm-based CMD. Here, analytical frameworks for determining both cell-specific limits of detection and quantification of uncertainty associated with DNAm-based CMD are described. This work may contribute to improved rigor, reproducibility and replicability of epigenome-wide association studies involving CMD.

Objective: To estimate the minimum iodine concentrations detectable in simulated vessels of various diameters for both subtraction computed tomography (CT) and dual‐energy CT systems. Methods: Fillable tubes (diameters: 1, 3, and 5 mm) were filled with a variety of iodine concentrations (range: 0–20 mg/ml), placed in the center of 28‐mm cylindrical rods and surrounded with water. Rods with and without fillable tubes were placed in a 20‐cm cylindrical solid‐water phantom to simulate administration of iodine in blood vessels. The phantom was scanned with clinical subtraction CT (SCT) and dual‐energy CT (DECT) head protocols to assess the detection of minimum iodine concentrations in both systems. The SCT and DECT images were evaluated quantitatively with a MATLAB script to extract regions of interest (ROIs) of each simulated vessel. ROI measurements were used to calculate the limit of detectability (LOD) and signal‐to‐noise ratio of Rose criteria for the assessment of the contrast thresholds. Results: Both SNRRose and LOD methods agreed and determined the minimum detectable iodine concentration to be 0.4 mg/ml in the 5‐mm diameter vessel for SCT. However, the minimum detectable concentration in the 5‐mm vessel with DECT was 1 mg/ml. The 3‐mm vessel had a minimum detectable concentration of 0.8 mg/ml for SCT and 2 mg/ml for DECT. Lastly, the minimum detectable iodine concentration for the 1‐mm vessel was 10 mg/ml for SCT and 10 mg/ml for DECT. Conclusion: In this phantom study, SCT showed the capability to detect lower iodine concentrations compared to DECT. Contrast thresholds varied for vessels of different diameters and the smaller vessels required a higher iodine concentration for detection. Based on this knowledge, radiologists can modify their protocols to increase contrast enhancement. [ABSTRACT FROM AUTHOR]

Noroviruses cause acute gastroenteritis with symptoms of diarrhoea and vomiting, and their high infectivity allows outbreaks to readily occur. Quickly identifying and isolating potential contaminants is an effective method to prevent the spread of outbreaks. A total of 376 samples collected from nine outbreaks were categorized as either patient, asymptomatic individual, cook or environmental samples, according to the source of contamination. Using real‐time PCR and sequencing analysis, norovirus GII genotypes were detected in 34·9% of samples from patients, 19·2% from asymptomatic individuals, 2·4% from the environment and 1·4% from cooks. Our findings showed contrasting results in samples categories quantified based on the limit of blank and detection limit by reverse transcription droplet digital PCR, which is a more sensitive testing method than real‐time‐PCR. [ABSTRACT FROM AUTHOR]

Faecal immunochemical tests for haemoglobin (FIT) are widely used in asymptomatic population screening for colorectal (bowel) cancer. FIT are also used to assist with the assessment of patients presenting with lower abdominal symptoms. Quantitative FIT allow the generation of numerical estimates of faecal haemoglobin (f-Hb) concentrations. There is now great interest in "low" f-Hb concentrations in these clinical settings: in consequence, knowledge of the detection capability is very important for f-Hb concentration examinations. There are a number of current problems associated with the reporting of low f-Hb concentrations and wide misunderstanding of the metrological aspects of examinations of f-Hb at low concentrations. These would be solved if the detectability characteristics of f-Hb concentration examinations, namely, the limit of blank (LoB), limit of detection (LoD) and limit of quantitation (LoQ), were generated, validated and used in reporting systems exactly as recommended in the EP17-A2 guideline of the Clinical Laboratory Standards Institute. LoB and LoD are statistical concepts, but the LoQ depends on definition of analytical performance specifications (APS). In this Opinion Paper proposals for interim APS are made, based on the current state of the art achieved with examinations of faecal samples. It is proposed that LoQ is determined at an examination imprecision of CV≤10% using faecal samples naturally positive for Hb rather than faeces spiked with haemolysate. Detailed proposals for reporting f-Hb data at low concentrations are also made. [ABSTRACT FROM AUTHOR]

Abstract Background The detection of an increase and/or decrease of cardiac troponin (cTnI) values, with at least one value above the 99th percentile of the upper reference limit (URL) have a central role in acute myocardial infarction (AMI) diagnosis. The employment of sex specific 99th percentile URLs and High-sensitivity (Hs) assays are recommended. We assessed sex specific 99th percentile URL for Access Hs-cTnI and AccuTnI3+ (Beckman Coulter) using European donor reference population following recent International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) recommendations. Methods 300 males and 300 females plasma samples were collected. Both chemiluminescent immunoenzymatic assays were performed on UniCel DxI 800 platform (Beckman Coulter). Results For Access hsTnI, the observed sex-specific 99th percentile URLs were 5.5 (90% CI: 4.4–7.6) for females and 13.9 ng/L (90% CI: 7.4–17.4) for males. For AccuTnI+3 we could not establish them because the assay couldn't report detectable values of troponin for most of the analyzed samples. Conclusion The sex-specific 99th percentile URLs established for Access hsTnI assay were significantly lower than those declared by the manufacturer caused by the different choice of population selection, age groups and sample types: for those reasons, we maintain the 99th URLs provided by manufacturer. Highlights • Sex specific 99th upper reference limits for troponins are recommended by guidelines. • Study on 300 males and 300 females from European donor reference population. • We gave sex specific cut-offs for high-sensitivity troponin; not for contemporary one. • Our sex-specific reference limits were lower than those provided by the manufacturers. • We maintain the 99th upper reference limits declared by the manufacturer. [ABSTRACT FROM AUTHOR]

Background: Implementing International Organization for Standardization definitions of limit of blank and limit of detection requires precision estimates from specimens devoid of analyte (blank specimens) and also from specimens located close to zero. Calculations are straightforward if errors are constant over the relevant concentration range but estimation of the relationship between variability and concentration (variance function) is necessary in the general case when errors are not constant. This study investigated the efficacy of incorporating the variance function into estimation of limit of blank, limit of detection and their confidence intervals.Methods: Simulated data, designed to encompass the range of properties that would typically be observed in practice, consisted of four distinct relationships between variance and concentration, in combination with large and small variances and three concentration ranges. Four methods of estimating limit of blank were evaluated together with the accuracy of variance function derived estimates of limit of detection and the accuracy of symmetrical 95% confidence intervals constructed from limit of blank and limit of detection constituent variables.Results: Most limit of blank estimates and all limit of detection estimates showed systematic negative bias but, provided the data concentration range is not too small, the biases were consistently <1% with confidence interval coverages ranging from 92% to 95%. Estimating limit of blank by extrapolating the variance function to zero lost little in comparison with methods based on blank specimen data.Conclusions: The variance function provides a convenient and reliable way of analyzing data from experiments evaluating detection capability and, provided certain assumptions are tenable, of estimating limit of blank and limit of detection as part of routine internal quality control. [ABSTRACT FROM AUTHOR]

Background A recent study used simulated internal quality control data (4 specimens × 40 replicates) to investigate the use of variance functions in estimating limit of blank and limit of detection, as per ISO definitions. Small systematic negative biases were found (typically <1%), but subsequent investigation has shown that these estimates had unacceptably large uncertainties because of an inadequate simulation size. Methods The previous data generation and variance function estimations were repeated 25 times using a different random number generator seed on each occasion. The study was further extended by increasing data quantities 100-fold and by reducing the number of replicates per specimen (40 through 20, 10, 5 and 2). Results The previously reported negative biases were shown to be an artefact, and this was confirmed by simulations using 100-fold more data. Biases were <|0.1%| throughout with replication ≥20, but positive biases were found at lower replication; up to + 1.23% in the case of duplicates and large variances (e.g. some immunoassays) and up to + 0.2% in the case of duplicates and small variances. Conclusions The variance function provides essentially unbiased estimates of limit of blank and limit of detection at data replication ≥20 (bias: <1 part in 1000) and minimal biases at lower replication when measurement errors are small. [ABSTRACT FROM AUTHOR]

Background Proper malaria diagnosis depends on the detection of asexual forms of Plasmodium spp. in the blood. Thick blood smear microscopy is the accepted gold standard of malaria diagnosis and is widely implemented. Surprisingly, diagnostic performance of this method is not well investigated and many clinicians in African routine settings base treatment decisions independent of microscopy results. This leads to overtreatment and poor management of other febrile diseases. Implementation of quality control programmes is recommended, but requires sustained funding, external logistic support and constant training and supervision of the staff. This study describes an easily applicable method to assess the performance of thick blood smear microscopy by determining the limit of blank and limit of detection. These two values are representative of the diagnostic quality and allow the correct discrimination between positive and negative samples. Methods Standard-conform methodology was applied and adapted to determine the limit of blank and the limit of detection of two thick blood smear microscopy methods (WHO and Lambaréné method) in a research centre in Lambaréné, Gabon. Duplicates of negative and low parasitaemia thick blood smears were read by several microscopists. The mean and standard deviation of the results were used to calculate the limit of blank and subsequently the limit of detection. Results The limit of blank was 0 parasites/μL for both methods. The limit of detection was 62 and 88 parasites/μL for the Lambaréné and WHO method, respectively. Conclusion With a simple, back-of-the-envelope calculation, the performance of two malaria microscopy methods can be measured. These results are specific for each diagnostic unit and cannot be generalized but implementation of a system to control microscopy performance can improve confidence in parasitological results thereby strengthen malaria control. [ABSTRACT FROM AUTHOR]

Abstract: Biomarkers typically evolve from a research setting to use in clinical care as evidence for their independent contribution to patient management accumulates. This evidence relies heavily on knowledge of the preanalytical, analytical, and postanalytical characteristics of the biomarker''s measurement. For the preanalytical phase, considerations such specimen type, acceptable anticoagulants for blood samples, biologic variation and stability of the biomarker under various conditions are key. The analytical phase entails critical details for development and maintenance of assays having performance characteristics that are “fit for service” for the clinical application at hand. Often, these characteristics describe the ability to measure minute quantities in the biologic matrix used for measurement. Although techniques such as mass spectrometry are used effectively for biomarker discovery, routine quantification often relies on use of immunoassays; early in development, the most common immunoassay used is the enzyme-linked immunosorbent assay format. As biomarkers evolve successfully, they will be adapted to large main laboratory platforms or, depending on the need for speed, point-of-care devices. Users must pay particular attention to performance parameters of assays they are considering for clinical implementation. These parameters include the limit of blank, a term used to describe the limit of analytical noise for an assay; limit of detection, which describes the lowest concentration that can reliably be discriminated from analytical noise; and perhaps most importantly, the limit of quantitation, which is the lowest concentration at which a biomarker can be reliably measured within some predefined specifications for total analytical error that is based on clinical requirements of the test. The postanalytical phase involves reporting biomarker values, which includes reporting units, any normalization factors, and interpretation. Standardization, a process that involves metrological traceability to a primary reference material and definitive measurement method, is important to assure that all biomarker values are transferable in the literature and across institutions. In the absence of standardization, assays can be harmonized using secondary reference materials so that biomarker values can be combined for meta-analysis and interpreted clinically with common reference and decision limit values. [Copyright &y& Elsevier]

A recently proposed procedure for estimating the limit of detection (LoD) of an assay using a partly non-parametric approach is applied to five HPLC drug assays. The non-parametrically determined 95th percentile of the blank measurements (LoB) is obtained as the value of the N (95/100)+0.5 ordered observations. The LoD is the lowest level of analyte that is likely to yield a measured result exceeding the LoB. The LoD equals LoB+c β ×SD S , where SD S is the analytical SD at low concentrations, and c β = z 1–β /(1–1/(4× f )) ( f =degrees of freedom). c β ≈1.65 for a type II error of 5%. The blank distributions deviated significantly from normality for four of the five assays because of skewness to the right. The estimated LoB ranged from 3.3 to 10.2 nmol/L. The LoDs were in the interval from 7.8 to 17.2 nmol/L. A new procedure for estimation of the standard error (SE) of the estimated LoD that is partly based on the bootstrap principle showed good performance in simulation studies. In conclusion, the partly non-parametric procedure for estimation of the LoD of typical HPLC drug assays was found to be a suitable approach. [ABSTRACT FROM AUTHOR]

Current commercially available digital PCR (dPCR) systems and assays are capable of detecting individual target molecules with considerable reliability. As tests are developed and validated for use on clinical samples, the need to understand and develop robust statistical analysis routines increases. This chapter covers the fundamental processes and limitations of detecting and reporting on single molecule detection. We cover the basics of quantification of targets and sources of imprecision. We describe the basic test concepts: sensitivity, specificity, limit of blank, limit of detection, and limit of quantification in the context of dPCR. We provide basic guidelines how to determine those, how to choose and interpret the operating point, and what factors may influence overall test performance in practice.