The low ketogenic capacity of pigs correlates with a low activity of mitochondrial 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) synthase. To identify the molecular mechanism controlling such activity, we isolated the pig cDNA encoding this enzyme and analysed changes in mRNA levels and mitochondrial specific activity induced during development and starvation. Pig mitochondrial synthase showed a tissue-specific expression pattern. As with rat and human, the gene is expressed in liver and large intestine; however, the pig differs in that mRNA was not detected in testis, kidney or small intestine. During development, pig mitochondrial HMG-CoA synthase gene expression showed interesting differences from that in the rat: (1) there was a 2–3 week lag in the postnatal induction; (2) the mRNA levels remained relatively abundant through the suckling–weaning transition and at maturity, in contrast with the fall observed in rats at similar stages of development; and (3) the gene expression was highly induced by fasting during the suckling, whereas no such change in mitochondrial HMG-CoA synthase mRNA levels has been observed in rat. The enzyme activity of mitochondrial HMG-CoA synthase increased 27-fold during starvation in piglets, but remained one order of magnitude lower than rats. These results indicate that post-transcriptional mechanism(s) and/or intrinsic differences in the encoded enzyme are responsible for the low activity of pig HMG-CoA synthase observed throughout development or after fasting.

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Background COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. Conclusions Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

It is known that Cannabis in Brazil could either originate from Paraguay or be cultivated in Brazil. While consumer markets in the North and Northeast regions are maintained by national production, the rest of the country is supplied with Cannabis from Paraguay. However, the Brazilian Federal Police (BFP) has exponentially increased the seizure number of Cannabis seeds sent by mail. For this reason, the aim of the study was to assess the 13-loci short tandem repeat (STR) multiplex system proposed by Houston et al. (2015) to evaluate the power of such markers in individualization and origin differentiation of Cannabis sativa samples seized in Brazil by the BFP. To do so, 72 Cannabis samples seized in Brazil by BFP were analyzed. The principal coordinate analysis (PCoA) and probability identity (PI) analysis were computed. Additionally, the Cannabis samples' genotypes were subjected to comparison by Kruskal-Wallis H, followed by a multiple discriminant analysis (MDA). All samples analyzed revealed a distinct genetic profile. PCoA clearly discriminated the seizure sets based on their geographic origin. A combination of seven loci was enough to differentiate samples' genotypes, and the PI for a random sample is approximately one in 50 billion. The Cannabis samples were 100% correct as classified by Kruskal-Wallis H, followed by an MDA. The results of this study demonstrate that the 13-loci STR multiplex system successfully achieved the aim of sample individualization and origin differentiation and suggest that it could be a useful tool to help BFP intelligence in tracing back-trade routes.

Studies show that genetic polymorphisms in apolipoproteins, which are in charge of lipid transport, predispose to atherogenic dyslipidemia. This study aimed to investigate the impact of apolipoprotein E, A5, and B genotypes and dietary intake on lipid profile in a sample of elderly women in Brazil. Two hundred and fifty-two women (60 years or older) living in the outskirts of the Brazilian Federal District underwent clinical and laboratory assessments to characterize glycemic and lipidemic variables, and also to exclude confounding factors (smoking, drinking, hormone replacement, cognitive impairment, physical activity). Three-day food records were used to determine usual dietary intake, whereas genotypic evaluations were in accordance to established methodologies. Genotype frequencies were consistent with the Hardy-Weinberg equilibrium. Prior to adjustment, individuals carrying the epsilon2 allele showed higher serum levels of triglycerides (P<0.05) and VLDL (P<0.005) compared to epsilon4 carriers, whereas LDL levels were considerably elevated in epsilon4 compared to epsilon2 carriers. In the presence of high intake of total fat or a low ratio of polyunsaturated to saturated fatty acid, epsilon4 carriers lost protection against hypertriglyceridemia. There was no association of the apolipoprotein A5 and B genotypes with lipidemic levels independently of the fat intake regimen. Results are suggestive of a dysbetalipoproteinemic-like phenotype in postmenopausal women, with remarkable gene-diet interaction.

We present epigenetic methylation data for two genetic loci, GRIA2, and NPTX2, which were tested for prediction of age from different donors of biofluids. We analyzed 44 saliva samples and 23 blood samples from volunteers with ages ranging from 5 to 72 years. DNA was extracted and bisulfite modified using commercial kits. Specific primers were used for amplification and methylation profiles were determined by pyrosequencing. Methylation data from both markers and their relationship with age were determined using linear regression analysis, which indicates a positive correlation between methylation and age. Older individuals tend to have increased methylation in both markers compared to younger individuals and this trend was more pronounced in the GRIA2 locus when compared to NPTX2. The epigenetic predicted age, calculated using a GRIA2 regression analysis model, was strongly correlated to chronological age (R(2) = 0.801), with an average difference of 6.9 years between estimated and observed ages. When using a NPTX2 regression model, we observed a lower correlation between predicted and chronological age (R(2) = 0.654), with an average difference of 9.2 years. These data indicate these loci can be used as a novel tool for age prediction with potential applications in many areas, including clinical and forensic investigations.