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Simulation-based methods such as Approximate Bayesian Computation (ABC) are well adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. RF allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated datasets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of two example datasets corresponding to pool-sequencing and individual-sequencing SNP datasets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP datasets to make inferences about complex population genetic histories.
Aims: Our study aimed to investigate the relationships between elevated carotid-intima media thickness (CIMT) and serum uric acid (SUA) levels in hypertensive patients attending primary care clinics. Methods: We conducted a cross-sectional study on 140 hypertensive patients attending out-patient follow-up in two primary care clinics in Sungai Buloh, Malaysia, using a convenient sampling method. Serum uric acid levels were measured and divided into 4-quartile. Two radiologist specialists performed B mode ultrasonography to assess the right and left CIMT in all participants. Results: Participants’ mean SUA level was 355.75 ± 0.13. Their mean age was 53.44 (± 9.90), with a blood pressure control of 137.09 ± 13.22 / 81.89 ± 8.95. Elevated CIMT taken at ≥75th percentile was 0.666 for the left and 0.633 for the right common carotid arteries. Using multiple logistic regression, compared with the first quartile of the SUA level, the odd of elevated CIMT in quartile four in the common carotid artery was (OR=2.00; 95% CI= 0.64-6.27, p=0.576) for the right and (OR=0.62; 95% CI= 0.20-2.00, p=0.594) for the left. Waist circumference (p = 0.001), body mass index (p=0.013), triglycerides (p<0.001) and high-density lipoprotein cholesterol (p=0.001) were significantly associated with the SUA quartiles. Conclusion: Although there was an increasing trend in the odd of elevated right CIMT across the SUA quartiles, this association, however, was not significant. Preventive effort to tackle the clustering effect of metabolic markers within this study population is needed to reduce the future risk of developing cardiovascular disease.

Amit Bansal

and 2 more

Background: Pregnant women and young children are at high risk for influenza complications and, therefore, recommended for annual influenza vaccination. However, most studies investigating the safety, immunogenicity and effectiveness of inactivated influenza vaccines (IIV) were conducted in healthy adults. Therefore, the safety, immunogenicity and effectiveness of IIV in pregnant women and young children are underexplored. Objective: In this review, we evaluate the safety profile, immunogenicity, and effectiveness of IIV in healthy pregnant women and children <5 years old. Methods: We searched the electronic databases PubMed, Google Scholar, MEDLINE, Embase, WHO International Clinical Trials Registry Platform (ICTRP), and UpToDate up until 8th June 2020. Selection criteria included publications assessing safety, immunogenicity and effectiveness of IIV in healthy pregnant women and children <5 years. Results: A total of 60 studies were selected for the review: 9 on IIV safety, 17 and 11 on immunogenicity, and 13 and 10 on effectiveness of IIV in pregnant women and children, respectively. Most randomized controlled trials in pregnant women included in this review were conducted in low- and middle-income countries while observation studies were conducted in high-income countries. Conclusion: IIV were found to be a safe preventive strategy with moderate immunogenicity and effectiveness estimates for pregnant women and children <5 years old. However, the effect sizes depended upon the study design, individual factors, vaccine type and manufacturing practices, and the antigenic match between the influenza vaccine strains and the circulating strains.

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