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 β 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.

Counterfeit medicines: a pilot study for chemical profiling employing a different proposal of an usual technique

Gas chromatography (GC) is a gold standard technique used in forensic laboratories, including for the characterization of counterfeit medicines. When coupled simultaneously to flame ionization (FID) and mass detector (MS) allow the identification and quantification of medicines and drugs employing a single method, besides permitting the application of chemometric tools for forensic intelligence purposes. Here is presented a pilot project that developed and applied a qualitative method for the analysis of counterfeit medicines comprised by amphetamine-type stimulants and antidepressants, through a simple extraction procedure followed by GC-FID/MS analysis, with application of exploratory tools by Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). The main purpose was to identify similarities between the all compounds detected in the irregular medicines allowing the traceability of illicit producers with the creation of a common data base. Through the analyses it was verified that different producers of counterfeit medicines labeled as Sibutramine, added a mixture of Caffeine and Benzocaine in their formulation, respecting the same ratio of 2.2:1. HCA was able to confirm these results, showing the presence of both falsifications in the same cluster, representing the best tool to identify similar characteristics among the samples – when compared to PCA. Other interesting finding was the use of Fluoxetine as a falsification of counterfeit medicines labeled as Sibutramine and Diethylpropion. Another seized sample labeled as “Nobesio Forte”, marketed as a mix of stimulants, showed only Caffeine and Lidocaine in its formulation. The pilot project applied primarily to 45 samples of counterfeit medicines containing amphetamine-type stimulants and antidepressants, showed the capability of perform the chemical profiling of counterfeit medicines in the solid form - powder, capsules and tablets. Further analysis can be performed for different types of medicines in solid form using the developed method, allowing the construction of a single database to perform the chemical profiling of counterfeit medicines, enabling the traceability of illicit producers.

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.

Full mtDNA genome sequencing of Brazilian admixed populations: A forensic-focused evaluation of a MPS application as an alternative to Sanger sequencing methods

The use of Massive Parallel Sequencing (MPS) techniques have been proposed by the forensic community as an alternative to Sanger sequencing methods in routine forensic casework analysis regarding mitochondrial DNA (mtDNA). Interesting features of MPS include high throughput, ability to simultaneously genotype a significant number of samples by barcoding techniques, processing automation, reduced time and costs, among others. Advantages include the capability of generating full mtDNA genome sequences versus usual techniques, usually limited to hypervariable or control regions exclusively. In this work, 96 reference single-source samples from three different Brazilian cities were subjected to full mtDNA genome sequencing by MPS techniques using an early-access version of Precision ID mtDNA Whole Genome Panel on an Ion Torrent PGM platform (Thermo Fisher Scientific, Waltham, MA, USA). Complete, high-quality sequences were obtained and sequencing performance was evaluated via four different metrics. As a subset of evaluated samples have been previously submitted for Sanger sequencing of the control region, a comparative analysis of both methods' results was conducted in order to compare technique adequacy within a forensic context. Even though this study is one of the first to report full mtDNA genome sequences for Brazilian admixed populations, the observed haplotypes exhibit a predominance of Native American and African maternal lineages in the studied sample set, reproducing results described in the literature for control regions only. Interpopulation analysis among Brazilian and 26 worldwide populations was also carried out. The results indicate that MPS-generated full mtDNA genome sequences may have great utility in forensic real casework applications, with a pronounced gain of genetic information and discrimination power provided by coding region evaluation and the enhanced capacity of heteroplasmies determination. Database construction and other relevant factors concerning implementation of such techniques in Brazilian forensic laboratories are also discussed.

Heurística para minimização do atraso total de tarefas baseada em curvas de aprendizado e aspectos ergonômicos

Ambientes produtivos apoiados em Customização em Massa são caracterizados por elevada variedade de modelos de produtos e tamanhos reduzidos de lotes, exigindo rápida adequação dos recursos aos novos modelos em produção. Tais características dificultam a programação de tarefas que demandam procedimentos manuais com distintos níveis de complexidade e repetitividade. Este artigo propõe uma abordagem que integra curvas de aprendizado, ferramentas de programação de tarefas e aspectos ergonômicos com vistas ao sequenciamento de tarefas em equipes de trabalhadores. Para tanto, propõe-se a regra ATCE (Apparent Tardiness Cost with Ergonomics Factors), a qual busca simultaneamente reduzir o atraso total ponderado e a alocação sucessiva de tarefas com complexidades idênticas a uma mesma equipe (medido através do percentual de saturação). A aplicação da ATCE em duas linhas produtivas da indústria calçadista apresentou desempenho satisfatório do ponto de vista ergonômico, reduzindo o percentual de saturação, quando comparado ao resultado gerado pela regra original ATC (Apparent Tardiness Cost). Em termos produtivos, a função objetivo pouco aumentou em todos os casos.