MC1R and age heteroclassification of face phenotypes in the Rio Grande do Sul population.

BACKGROUND: Forensic DNA phenotyping (FDP) consists of the use of methodologies for predicting externally visible characteristics (EVCs) from the genetic material of biological samples found in crime scenes and has proven to be a promising tool in aiding human identification in police activities. Currently, methods based on multiplex assays and statistical models of prediction of EVCs related to hair, skin, and iris pigmentation using panels of SNP and INDEL biomarkers have already been developed and validated by the forensic scientific community. As well as traces of pigmentation, an individual's perceived age (PA) can also be considered an EVC and its estimation in unknown individuals can be useful for the progress of investigations. Liu and colleagues (2016) were pioneers in evidencing that, in addition to lifestyle and environmental factors, the presence of SNP and INDEL variants in the MC1R gene - which encodes a transmembrane receptor responsible for regulating melanin production - seems to contribute to an individual's PA. The group highlighted the association between these MC1R gene polymorphisms and the PA in the European population, where carriers of risk haplotypes appeared to be up to 2 years older in comparison to their chronological age (CA). PURPOSE: Understanding that genotype-phenotype relationships cannot be extrapolated between different population groups, this study aimed to test this hypothesis and verify the applicability of this variant panel in the Rio Grande do Sul admixed population. METHODS: Based on genomic data from a sample of 261 volunteers representative of gaucho population and using a multiple linear regression (MLR) model, our group was able to verify a significant association among nine intronic variants in loci adjacent to MC1R (e.g., AFG3L1P, TUBB3, FANCA) and facial age appearance, whose PA was defined after age heteroclassification of standard frontal face images through 11 assessors. RESULTS: Different from that observed in European populations, our results show that the presence of effect alleles (R) of the selected variants in our sample influenced both younger and older face phenotypes. The influence of each variant on PA is expressed as ß values. CONCLUSIONS: There are important molecular mechanisms behind the effects of MC1R locus on PA, and the genomic background of each population seems to be crucial to determine this influence.

Acid hydrolysis conditions do affect the non-extractable phenolic compounds composition from grape peel and seed.

This study aimed to evaluate the effect of hydrolysis conditions on non-extractable phenolic compounds (NEPC) composition of grape peel and seed powder. The effect of temperature (50-90 °C), hydrochloric acid concentration (0.1-15.0 %), and time (5-20 min) were evaluated to understand their impact on NEPC release/extraction and degradation. The use of 1.0 and 8.0 % of HCl concentrations (v/v) and temperatures of 65 and 80 °C produced extracts with higher concentrations and a larger set of compounds. These conditions promoted a balance between release/extraction and degradation processes, thereby maximizing the NEPC content in the extracts. Furthermore, the results suggest that hydrolysis conditions can be set to modulate the release of specific classes. Non-extractable proanthocyanidins showed higher concentrations when intermediate values of temperature and acid concentration were applied. Hydrolysable tannins and hydroxybenzoic acids, on the other hand, were better extracted using higher acid concentrations and higher temperatures. The results suggest that the concentration and composition of NEPC are influenced by the hydrolysis conditions and the type of matrix. Hence, it is crucial to account for this compositional variation when conducting research on the biological effects of NEPC and when using this fraction as supplements or food ingredients.

Haplotype distribution in a forensic full mtDNA genome database of admixed Southern Brazilians and its association with self-declared ancestry and pigmentation traits.

BACKGROUND: The advent of massively parallel sequencing (MPS) applications focused on the generation of forensic-quality full mitochondrial genome sequences led to a popularization of the technique on a global scale. However, the lack of forensic-graded population databases has refrained a wider adoption of full genome sequences as the industry standard, despite its better discrimination capacity of individual maternal lineages. PURPOSE: This work describes a forensic-oriented full mtDNA genome database comprised of 480 samples from a Southern Brazilian population. METHODS: A collection of mitochondrial sequences were obtained from low-pass, full genome DNA sequencing results. The complete sample set was evaluated regarding haplotype composition and distribution. Summary statistics and forensic parameters were calculated and are presented for the database, with detailed information concerning the impact of removing genetic information in the form of specific variants or increasingly larger genomic regions. Interpopulational analysis comparing haplotypical diversity in Brazilian and 26 worldwide populations was also performed. The association between mitochondrial genetic variability and phenotypic diversity was also evaluated in populations, with self-declared ancestry and three distinct phenotypic pigmentation traits (eyes, skin and hair colors) as parameters. RESULTS: The presented database can be used to evaluate mitochondrial-related genetic evidence, providing LR values of up to 20,465 for unobserved haplotypes. Haplotype distribution in Southern Brazil seems to be different than the remaining of the country, with a larger contribution of maternal lines with European origin. Despite association can be found between lighter and darker phenotypes or self-declared ancestry and haplotype distribution, prediction models cannot be reliably proposed due to the admixed nature of the Brazilian population. CONCLUSIONS: The proposed database provides a basis for statistical calculation and frequency estimation of full mitochondrial genomes, and can be part of an integrated, representative, national database comprising most of the genetic diversity of maternal lineages in the country.

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets.

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios.

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.

Simultaneous identification of low-molecular weight phenolic and nitrogen compounds in craft beers by HPLC-ESI-MS/MS.

Phenolic and nitrogenous compounds from different styles craft beers were identified by high performance liquid chromatography and mass spectrometry in order to stratify beer samples according to their style. For this, an exploratory assessment relying on Linear Discriminant Analysis was performed. Fifty-seven phenolic compounds were reported and twelve of them were found for the first time in beer: benzoic acids, 2,4-dihydroxybenzoic acid, 2,3-dihydroxybenzoic acid, dimethoxybenzoic acid; phenolic acid conjugates, 3-p-coumaroylquinic acid, 4-p-coumaroylquinic acid, 3-feruloylquinic acid, 4-feruloylquinic acid, 5-feruloylquinic acid; flavonoids, taxifolin hexoside, quercetin dihexoside, apigenin-6,8-dipentoside, and isofraxidin hexoside. Additionally, 11 nitrogenous compounds belonging to the phenolamide class were found. Two discriminant functions were generated and allowed a satisfactory separation among all beer styles. 3-Caffeoylquinic acid, 3-p-coumaroylquinic acid, 4-p-coumaroylquinic acid, 5-caffeoylquinic acid, coumaric acid, kaempferol-3-O-rutinoside, proanthocyanidin B dimer III and proanthocyanidin B dimer V were the compounds that showed the highest capacity of discriminate the beer styles (IPA, Lager and Weiss).

Comparison between counterfeit and authentic medicines: A novel approach using differential scanning calorimetry and hierarchical cluster analysis.

Erectile dysfunction medicines such as Cialis and Viagra are very popular worldwide and are between the most prevalent counterfeit medicines in Brazil. A range of analytical methods has been used to analyze Cialis and Viagra, such as ATR-FTIR, GCMS and UPLC-MS. Until now, there are no data available of DSC methods for analysis of counterfeit medicines of Cialis and Viagra. DSC is a thermal analysis that provides useful information of physico-chemical events, and however is almost not used for forensic purposes. In this study, thermal analysis of 25 counterfeit Viagra and Cialis seized by Brazilian Federal Police were performed by DSC and compared to their authentic medicines and analytical standards, along with chemometric tools. Authentic samples of Viagra displayed a similar thermal profile with the API, while Cialis were different with additional endothermic peaks, that could be related to excipients interference. Thermograms of Viagra counterfeit samples were similar to authentic samples, while Cialis showed an enlargement and displacement of endothermic peaks. Also, some Cialis counterfeit samples showed melting peaks attributed to sildenafil, the API of Viagra, instead tadalafil, confirming previous results obtained by UPLC-MS. Multivariate analysis with application of Hierarchical Cluster Analysis classified different groups of samples, including a cluster with counterfeit Cialis and Viagra, indicating the use of same API for both counterfeit medicines and possibly the same illicit production; and a cluster with authentic Viagra and counterfeit Cialis, confirming the addition of sildenafil instead tadalafil to Cialis counterfeit samples. Here for the first time we described the use of DSC for chemical profiling of Cialis and Viagra and showed that even when applied to a small group of samples, DSC along with chemometric tools can be considered as a good auxiliary method in forensic casework samples. DSC provided useful data to perform the identification of counterfeit and authentic medicines, with low cost and a simple method.