Variations In Semen Sample Parameters Among Men In A Fertility Clinic: Implications For Reproducibility In Epidemiologic Studies

BACKGROUND: In population studies, one semen sample is usually collected per individual but in clinical settings it is recommended that multiple semen samples are collected per individual for analysis. The goal of this study is to estimate the size of within-person variability in semen quality parameters with the ultimate goal of figuring out how many repeat samples are needed in a semen quality study to represent this. We will also investigate how accurately one can predict semen parameter values for an individual using the long-term average as a standard. HYPOTHESIS: We hypothesize that a maximum of 2 semen samples has enough reliability to allow us to characterize an individual as fertile or infertile in a clinical or research setting. METHOD: This study consists of 287 men who provided a total of 654 semen samples, (range 1 to 9). Semen samples were collected over a period of about 2 years. Within-person and between-person variability was analyzed using semen parameters: sperm concentration, total sperm count, ejaculate volume, sperm morphology (% normal) and motility (% motile). PRELIMINARY RESULTS: There were no significant differences in demographics or reproductive history according to the number of samples collected. Semen sample variation between individuals is substantial but variation within individuals ranged from 14% to 28%. Intraclass correlation values ranged from 0.72 to 0.86 signifying high reproducibilty of semen parameter values. Correlation did not diminish with time. First samples given by each individual was highly similar to their long-term within-person average. CONCLUSIONS: Based on the results of this study, there is high reproducibility of semen parameter values and so 1 sample can provide a true representation of an individual\u27s long-term average

Non-Adherence Tree Analysis (NATA) - an adherence improvement framework: a COVID-19 case study

Poor medication adherence is a global phenomenon that has received a significant amount of research attention yet remains largely unsolved. Medication non-adherence can blur drug efficacy results in clinical trials, lead to substantial financial losses, increase the risk of relapse and hospitalisation, or lead to death. The most common methods of measuring adherence are post-treatment measures; that is, adherence is usually measured after the treatment has begun. What the authors are proposing in this multidisciplinary study is a new technique for predicting the factors that are likely to cause non-adherence before or during medication treatment, illustrated in the context of potential non-adherence to COVID-19 antiviral medication. Fault Tree Analysis (FTA), allows system analysts to determine how combinations of simple faults of a system can propagate to cause a total system failure. Monte Carlo simulation is a mathematical algorithm that depends heavily on repeated random sampling to predict the behaviour of a system. In this study, the authors propose a new technique called Non-Adherence Tree Analysis (NATA), based on the FTA and Monte Carlo simulation techniques, to improve adherence. Firstly, the non-adherence factors of a medication treatment lifecycle are translated into what is referred to as a Non-Adherence Tree (NAT). Secondly, the NAT is coded into a format that is translated into the GoldSim software for performing dynamic system modelling and analysis using Monte Carlo. Finally, the GoldSim model is simulated and analysed to predict the behaviour of the NAT. NATA is dynamic and able to learn from emerging datasets to improve the accuracy of future predictions. It produces a framework for improving adherence by analysing social and non-social adherence barriers. Novel terminologies and mathematical expressions have been developed and applied to real-world scenarios. The results of the application of NATA using data from six previous studies in relation to antiviral medication demonstrate a predictive model which suggests that the biggest factor that could contribute to non-adherence to a COVID-19 antiviral treatment is a therapy-related factor (the side effects of the medication). This is closely followed by a condition-related factor (asymptomatic nature of the disease) then patient-related factors (forgetfulness and other causes). From the results, it appears that side effects, asymptomatic factors and forgetfulness contribute 32.44%, 22.67% and 18.22% respectively to discontinuation of medication treatment of COVID-19 antiviral medication treatment. With this information, clinicians can implement relevant interventions and measures and allocate resources appropriately to minimise non-adherence

tRNA is a new target for cleavage by a MazF toxin

Toxin-antitoxin (TA) systems play key roles in bacterial persistence, biofilm formation and stress responses. The MazF toxin from the Escherichia coli mazEF TA system is a sequence- and single-strand-specific endoribonuclease, and many studies have led to the proposal that MazF family members exclusively target mRNA. However, recent data indicate some MazF toxins can cleave specific sites within rRNA in concert with mRNA. In this report, we identified the repertoire of RNAs cleaved by Mycobacterium tuberculosis toxin MazF-mt9 using an RNA-seq-based approach. This analysis revealed that two tRNAs were the principal targets of MazF-mt9, and each was cleaved at a single site in either the tRNAPro14 D-loop or within the tRNALys43 anticodon. This highly selective target discrimination occurs through recognition of not only sequence but also structural determinants. Thus, MazF-mt9 represents the only MazF family member known to target tRNA and to require RNA structure for recognition and cleavage. Interestingly, the tRNase activity of MazF-mt9 mirrors basic features of eukaryotic tRNases that also generate stable tRNA-derived fragments that can inhibit translation in response to stress. Our data also suggest a role for tRNA distinct from its canonical adapter function in translation, as cleavage of tRNAs by MazF-mt9 downregulates bacterial growth

Antiretroviral Therapy-Induced Mitochondrial Toxicity: Potential Mechanisms Beyond Polymerase-γ Inhibition

We hypothesized that competition between NRTI-triphosphate and endogenous deoxyribonucleoside triphosphate (dNTP) may lead to depletion of dNTP pools and mitochondrial dysfunction independent of Pol-γ inhibition. We collected peripheral blood mononuclear cells from 75 adults (25 cases: HIV-infected with mitochondrial toxicity, 25 HIV-infected positive controls, and 25 HIV-negative controls). We observed statistically significant individual and group differences in ribonucleotide (RN) and deoxyribonucleotide (dRN) pools. The median RN pool was 10062 (IQR, 7090 – 12590), 4360 (IQR, 3058 –6838), and 2968 (IQR, 2538 – 4436) pmol/106 cells for negative controls, positive controls, and cases, respectively. Cases had significantly higher absolute mtDNA copy number compared to negative controls (p<0.05). Cases had significantly higher expression of Pol-γ, nucleoside transporters, cellular kinases, and ABC compared to controls. Antiretroviral therapy perturbs ribonucleotide and deoxyribonucleotide pools. Depletion of RN and dRN pools may be associated with ART-induced mitochondrial toxicity independent of Pol-γ inhibition