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With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here, we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation. We find that the new method improves the specificity and sensitivity of lists of regions and accurately controls the false discovery rate., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Hemoglobin A1c (HbA1c) is now considered to be the marker of choice in diagnosis and management of diabetes mellitus, based on the results of certain landmark clinical trials. Herein, we report the case of a 52-year-old ethnic Southeast Asian Indian man with impaired glucose tolerance whose glycated hemoglobin (ie, HbA1c) levels, as measured via Bio-Rad D10 high-performance liquid chromatography (HPLC) and Roche Tina-quant immunoassay were 47.8% and 44.0%, respectively. No variant hemoglobin (Hb) peak was observed via the D10 chromatogram. We assayed the patient specimen on the Sebia MINICAP capillary electrophoresis platform; the HbA1c level was 6.8%, with a large variant Hb peak of 42.0%. This finding suggested the possible presence of the heterozygous Hb Hope, which can result in spuriously elevated HbA1c results on HPLC and turbidimetric immunoassays. Although the capillary electrophoresis system was able to identify the variant, the A1c results should not be considered accurate due to overlapping of the variant and adult Hb peaks on the electrophoretogram reading. Hb Hope is usually clinically silent but can present such analytical challenges. Through this case study, we critically discuss the limitations of various HbA1c assay methods, highlighting the fact that laboratory professionals need to be aware of occurrences of Hb Hope, to help ensure patient safety.

Vitamin D toxicity also known as hypervitaminosis D was previously believed to be rare. But with an increase in vitamin D supplementation several cases have been reported in literature. Fat soluble vitamins like Vitamin D, due to their ability to accumulate in the body, have a higher potential for toxicity than water soluble vitamins. The main clinical consequence of vitamin D toxicity is hypercalcemia. In this report we describe an adult female patient who developed very high serum Vitamin D levels (746 ng/mL, RI: 20 to 50) as a result of medication error. Inspite of such high serum concentrations the patient was without any clinical symptoms and had normal serum calcium. We critically discuss the mechanism of toxicity and hypothesize the possible molecular/metabolic factors which might have been responsible for this nontoxic presentation. This case study highlights the fact that physicians need to consider the risk of medication errors while prescribing Vitamin D therapy. Clinical trials to study Vitamin D toxicity in humans is not possible ethically. Thus the evidence base regarding the safety profile of Vitamin D supplementation in humans has been build through case reports. This review of the paradoxical clinico-laboratory manifestation of hypervitaminosis D could possibly contribute to existing literature., (Copyright© by the American Society for Clinical Pathology (ASCP).)

Motivation: In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard analysis of variance (ANOVA)/regression is implemented to identify the relative effects of these genes over the two types of samples from their respective arrays of expression levels. But, this technique becomes fundamentally flawed when there are unaccounted sources of variability in these arrays (latent variables attributable to different biological, environmental or other factors relevant in the context). These factors distort the true picture of differential gene expression between the two tissue types and introduce spurious signals of expression heterogeneity. As a result, many genes which are actually differentially expressed are not detected, whereas many others are falsely identified as positives. Moreover, these distortions can be different for different genes. Thus, it is also not possible to get rid of these variations by simple array normalizations. This both-way error can lead to a serious loss in sensitivity and specificity, thereby causing a severe inefficiency in the underlying multiple testing problem. In this work, we attempt to identify the hidden effects of the underlying latent factors in a gene expression profiling study by partial least squares (PLS) and apply ANCOVA technique with the PLS-identified signatures of these hidden effects as covariates, in order to identify the genes that are truly differentially expressed between the two concerned tissue types., Results: We compare the performance of our method SVA-PLS with standard ANOVA and a relatively recent technique of surrogate variable analysis (SVA), on a wide variety of simulation settings (incorporating different effects of the hidden variable, under situations with varying signal intensities and gene groupings). In all settings, our method yields the highest sensitivity while maintaining relatively reasonable values for the specificity, false discovery rate and false non-discovery rate. Application of our method to gene expression profiling for acute megakaryoblastic leukemia shows that our method detects an additional six genes, that are missed by both the standard ANOVA method as well as SVA, but may be relevant to this disease, as can be seen from mining the existing literature.