A case of sudden cardiac death due to mitochondrial disease

A 25-year-old, emaciated man without medical treatment was found to have died suddenly at home by his mother. At autopsy, there were no injuries to his body, but significant circulatory insufficiency was observed. Electron microscopy revealed abnormal mitochondria in cells of the cardiac conduction system. The conduction system was filled with mitochondrial size abnormalities and mitochondrial cristae abnormalities. No notable abnormal findings were observed in other organs. Genetic examination of the blood revealed the mitochondrial pathogenetic variant m.3243A>G. Epileptic seizures, diabetic ketoacidosis, and hyperosmolar hyperglycemic state were unlikely to be the cause of sudden death. The cause of death was diagnosed as arrhythmia possibly induced by the failure of the cardiac conduction system due to mitochondrial disease. This is a rare case of sudden death caused by an accumulation of abnormal mitochondria in the cardiac conduction system.     

A Proposal for an Alternative Approach to Particle Size Method Development During Early-Stage Small Molecule Pharmaceutical Development

Particle size analysis in the pharmaceutical industry has long been a source of debate regarding how best to define measurement accuracy; the degree to which the result of a measurement or calculation conforms to the true value. Defining a “true” value for the size of a particle can be challenging as the output of its measurement will differ because of variations in measurement approaches, instrumental differences and calculation methods. Consequently, for “real” particles, a universal “true” value does not exist and accuracy is therefore not a definable characteristic. Accordingly, precision is then a measure of the ability to reproducibly achieve a measurement of unknown relevance.This article proposes, in place of accuracy, a means to define the “appropriateness” of a measurement in line with the critical quality attributes (CQA) of the material being characterized. The decision as to whether the measurement is correct should involve a link to the CQA; that is, correlation should be demonstrated, without which the measured particle size cannot be defined as a critical material attribute.Correspondingly, methods should also be able to provide sufficient precision to demonstrate discrimination relating to variation in the CQA. The benefits and challenges of this approach are discussed.     

P-Values and Power in Orthopedic Research: Myths and Reality

The results of statistical tests in orthopedic studies are typically reported using P-values. If a P-value is smaller than the pre-determined level of significance (eg, < .05), the null hypothesis is rejected in support of the alternative. This automaticity in interpreting statistical results without consideration of the power of the study has been denounced over the years by statisticians, since it can potentially lead to misinterpretation of the study conclusions. In this paper, we review fundamental misconceptions and misinterpretations of P-values and power, along with their connection with confidence intervals, and we provide guidelines to orthopedic researchers for evaluating and reporting study results. We provide real-world orthopedic examples to illustrate the main concepts. Please visit the following https://youtu.be/bdPU4luYmF0 for videos that explain the highlights of the paper in practical terms.     

Genetic variation of 17 autosomal STR loci in the Lahu population from Yunnan and phylogenetic structure exploration among 28 populations

This study evaluated the genetic variation of 17 autosomal short tandem repeat (STR) loci included in the PowerPlex® 18D Kit. Samples of 562 unrelated healthy Lahu individuals living in Yunnan Province in southwestern China were investigated. The data were analyzed to provide information on allele frequencies and other statistical parameters relevant to the forensic population. Of the 17 loci, 16 reached the Hardy–Weinberg equilibrium after Bonferroni correction. A total of 176 alleles were identified in 17 STR loci, and allele frequencies ranged from 0.000 890 to 0.578 292. The combined discrimination power (CPD) and probability of excluding paternity (CPE) of the 17 STR loci were 0.999 999 999 999 999 999 489 and 0.999 998 301 753 122. The genetic relationships among 28 populations were also estimated.     

Sample size re-estimation for clinical trials with longitudinal negative binomial counts including time trends

In some diseases, such as multiple sclerosis, lesion counts obtained from magnetic resonance imaging (MRI) are used as markers of disease progression. This leads to longitudinal, and typically overdispersed, count data outcomes in clinical trials. Models for such data invariably include a number of nuisance parameters, which can be difficult to specify at the planning stage, leading to considerable uncertainty in sample size specification. Consequently, blinded sample size re-estimation procedures are used, allowing for an adjustment of the sample size within an ongoing trial by estimating relevant nuisance parameters at an interim point, without compromising trial integrity. To date, the methods available for re-estimation have required an assumption that the mean count is time-constant within patients. We propose a new modeling approach that maintains the advantages of established procedures but allows for general underlying and treatment-specific time trends in the mean response. A simulation study is conducted to assess the effectiveness of blinded sample size re-estimation methods over fixed designs. Sample sizes attained through blinded sample size re-estimation procedures are shown to maintain the desired study power without inflating the Type I error rate and the procedure is demonstrated on MRI data from a recent study in multiple sclerosis.     

Tumour volume distribution can yield information on tumour growth and tumour control

BackgroundIt is shown that tumour volume distributions can yield information on two aspects of cancer research: tumour induction and tumour control.Materials and methodsFrom the hypothesis that the intrinsic distribution of breast cancer volumes follows an exponential distribution, firstly the probability density function of tumour growth time was deduced via a mathematical transformation of the probability density functions of tumour volumes. In a second step, the distribution of tumour volumes was used to model the variation of the clonogenic cell number between patients in order to determine tumour control probabilities for radiotherapy patients.ResultsDistribution of lag times, i.e. the time from the appearance of the first fully malignant cell until a clinically observable cancer, can be used to deduce the probability of tumour induction as a function of patient age. The integration of the volume variation with a Poisson-TCP model results in a logistic function which explains population-averaged survival data of radiotherapy patients.ConclusionsThe inclusion of tumour volume distributions into the TCP formalism enables a direct link to be deduced between a cohort TCP model (logistic) and a TCP model for individual patients (Poisson). The TCP model can be applied to non-uniform tumour dose distributions.     

Evidence from a BreastScreen cohort does not support a longer inter-screen interval in women who have no conventional risk factors for breast cancer

ObjectivesTo determine screening outcomes in women who have no recorded risk factors for breast cancer.MethodsA retrospective population-based cohort study included all 1,026,137 mammography screening episodes in 323,082 women attending the BreastScreen Western Australia (part of national biennial screening) program between July 2007 and June 2017. Cancer detection rates (CDR) and interval cancer rates (ICR) were calculated in screening episodes with no recorded risk factors for breast cancer versus at least one risk factor stratified by age. CDR was further stratified by timeliness of screening (<27 versus ≥27 months); ICR was stratified by breast density.ResultsAmongst 566,948 screens (55.3%) that had no recorded risk factors, 2347 (40.9%) screen-detected cancers were observed. In screens with no risk factors, CDR was 50 (95%CI 48–52) per 10,000 screens and ICR was 7.9 (95%CI 7.4–8.4) per 10,000 women-years, estimates that were lower than screens with at least one risk factor (CDR 83 (95%CI 80–86) per 10,000 screens, ICR 12.2 (95%CI 11.5–13.0) per 10,000 women-years). Compared to timely screens with risk factors, delayed screens with no risk factors had similar CDR across all age groups and a higher proportion of node positive cancers (26.1% vs 20.7%). ICR was lowest in screens that had no risk factors nor dense breasts in all age groups.ConclusionsMajority of screens had no recorded breast cancer risk factors, hence a substantial proportion of screen-detected cancers occur in these screening episodes. Our findings may not justify less frequent screening in women with no risk factors.