BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning.

Machine Learning (ML) algorithms have been important tools for the extraction of useful knowledge from biological sequences, particularly in healthcare, agriculture, and the environment. However, the categorical and unstructured nature of these sequences requiring usually additional feature engineering steps, before an ML algorithm can be efficiently applied. The addition of these steps to the ML algorithm creates a processing pipeline, known as end-to-end ML. Despite the excellent results obtained by applying end-to-end ML to biotechnology problems, the performance obtained depends on the expertise of the user in the components of the pipeline. In this work, we propose an end-to-end ML-based framework called BioPrediction-RPI, which can identify implicit interactions between sequences, such as pairs of non-coding RNA and proteins, without the need for specialized expertise in end-to-end ML. This framework applies feature engineering to represent each sequence by structural and topological features. These features are divided into feature groups and used to train partial models, whose partial decisions are combined into a final decision, which, provides insights to the user by giving an interpretability report. In our experiments, the developed framework was competitive when compared with various expert-created models. We assessed BioPrediction-RPI with 12 datasets when it presented equal or better performance than all tools in 40% to 100% of cases, depending on the experiment. Finally, BioPrediction-RPI can fine-tune models based on new data and perform at the same level as ML experts, democratizing end-to-end ML and increasing its access to those working in biological sciences.

Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.

In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.

Machine Learning of the Whole Genome Sequence of Mycobacterium tuberculosis: A Scoping PRISMA-Based Review.

Tuberculosis (TB) remains one of the most significant global health problems, posing a significant challenge to public health systems worldwide. However, diagnosing drug-resistant tuberculosis (DR-TB) has become increasingly challenging due to the rising number of multidrug-resistant (MDR-TB) cases, despite the development of new TB diagnostic tools. Even the World Health Organization-recommended methods such as Xpert MTB/XDR or Truenat are unable to detect all the Mycobacterium tuberculosis genome mutations associated with drug resistance. While Whole Genome Sequencing offers a more precise DR profile, the lack of user-friendly bioinformatics analysis applications hinders its widespread use. This review focuses on exploring various artificial intelligence models for predicting DR-TB profiles, analyzing relevant English-language articles using the PRISMA methodology through the Covidence platform. Our findings indicate that an Artificial Neural Network is the most commonly employed method, with non-statistical dimensionality reduction techniques preferred over traditional statistical approaches such as Principal Component Analysis or t-distributed Stochastic Neighbor Embedding.

Exploring the involvement of TASK-1 in the control of isolated rat right atrium function from healthy animals and an experimental model of monocrotaline-induced pulmonary hypertension.

The TASK-1 channel belongs to the two-pore domain potassium channel family. It is expressed in several cells of the heart, including the right atrial (RA) cardiomyocytes and the sinus node, and TASK-1 channel has been implicated in the pathogenesis of atrial arrhythmias (AA). Thus, using the rat model of monocrotaline-induced pulmonary hypertension (MCT-PH), we explored the involvement of TASK-1 in AA. Four-week-old male Wistar rats were injected with 50 mg/kg of MCT to induce MCT-PH and isolated RA function was studied 14 days later. Additionally, isolated RA from six-week-old male Wistar rats were used to explore the ability of ML365, a selective blocker of TASK-1, to modulate RA function. The hearts developed right atrial and ventricular hypertrophy, inflammatory infiltrate and the surface ECG demonstrated increased P wave duration and QT interval, which are markers of MCT-PH. The isolated RA from the MCT animals showed enhanced chronotropism, faster contraction and relaxation kinetics, and a higher sensibility to extracellular acidification. However, the addition of ML365 to extracellular media was not able to restore the phenotype. Using a burst pacing protocol, the RA from MCT animals were more susceptible to develop AA, and simultaneous administration of carbachol and ML365 enhanced AA, suggesting the involvement of TASK-1 in AA induced by MCT. TASK-1 does not play a key role in the chronotropism and inotropism of healthy and diseased RA; however, it may play a role in AA in the MCT-PH model.

Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity.

HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry.

The immunohistochemical (IHC) evaluation of epidermal growth factor 2 (HER2) for the diagnosis of breast cancer is still qualitative with a high degree of inter-observer variability, and thus requires the incorporation of complementary techniques such as fluorescent in situ hybridization (FISH) to resolve the diagnosis. Implementing automatic algorithms to classify IHC biomarkers is crucial for typifying the tumor and deciding on therapy for each patient with better performance. The present study aims to demonstrate that, using an explainable Machine Learning (ML) model for the classification of HER2 photomicrographs, it is possible to determine criteria to improve the value of IHC analysis. We trained a logistic regression-based supervised ML model with 393 IHC microscopy images from 131 patients, to discriminate between upregulated and normal expression of the HER2 protein. Pathologists' diagnoses (IHC only) vs. the final diagnosis complemented with FISH (IHC + FISH) were used as training outputs. Basic performance metrics and receiver operating characteristic curve analysis were used together with an explainability algorithm based on Shapley Additive exPlanations (SHAP) values to understand training differences. The model could discriminate amplified IHC from normal expression with better performance when the training output was the IHC + FISH final diagnosis (IHC vs. IHC + FISH: area under the curve, 0.94 vs. 0.81). This may be explained by the increased analytical impact of the membrane distribution criteria over the global intensity of the signal, according to SHAP value interpretation. The classification model improved its performance when the training input was the final diagnosis, downplaying the weighting of the intensity of the IHC signal, suggesting that to improve pathological diagnosis before FISH consultation, it is necessary to emphasize subcellular patterns of staining.

Utilidad del recuento de unidades formadoras de colonia de Cryptococcus sp. en líquido cefalorraquídeo de pacientes con criptococosis meníngea asociada al sida / Cryptococcus' colony forming unit count usefulness

Antecedentes: El recuento de unidades formadoras de colonia (UFC) de Cryptococcus en el líquido cefalorraquídeo (LCR) sería un marcador fiable para el pronóstico del paciente y una herramienta simple y económica. Objetivo: Evaluar la utilidad del recuento de UFC de Cryptococcus spp. y compararlo con las variaciones de antígeno capsular de Cryptococcus (AgCr) en LCR.Materiales y métodos: Se realizó la revisión de historias clínicas de pacientes con meningoencefalitis por Cryptococcus asociada con el sida en nuestro centro, entre febrero de 2016 y julio de 2020. Se evaluaron los valores de UFC y AgCr en LCR durante la evolución de la micosis. Resultados y discusión: Se analizaron datos de 94 episodios clínicos de 85 pacientes, con un total de 297 observaciones de muestras de LCR. Se evidenció el valor del recuento de UFC por ser un marcador de viabilidad y de carga fúngica. El recuento de UFC bajo no necesariamente coexistió con un nivel bajo de AgCr. Con respecto a la evolución en el tiempo, la mayoría de los pacientes fueron diagnosticados con una alta carga fúngica y su descenso ocurrió más rápido que el del AgCr, por lo que reflejaría la mejora del paciente, permitiendo tomar conductas al respecto.Palabras clave: Criptococosis, carga fúngica, ufc/mL.

Background. The Cryptococcus' colony-forming unit (CFU) count in cerebrospinal fluid (CSF) would be a reliable marker for patient prognosis and a simple and inexpensive tool. Objectives: To evaluate the usefulness of the CFU count of Cryptococcus spp. And to compare it with the variations of Cryptococcus' capsular antigen (CrAg) in CSF.Materials and methods. Clinical records of patients with aids-related meningoencephalitis caused by Cryptococcusassisted in our center between February 2016 and July 2020 were reviewed. CFU count and CrAg values in CSF were evaluated during the evolution of the mycosis.Results and Discussion. Data from 94 clinical episodes of 85 patients with a total of 297 observations of CSF samples were analyzed.The importance of using the CFU count was evidenced as it is a viability and fungal load marker.Low CFU count did not necessarily coexist with low CrAg.Regarding the evolution over time, most of the patients were diagnosed with a high fungal load and its decrease occurred faster than that the one of AgCr. This would reflect the improvement of the patient, allowing behaviors to be taken in this regard

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil.

The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.

Lasting SARS-CoV-2 specific IgG Antibody response in health care workers from Venezuela, 6 months after vaccination with Sputnik V.

BACKGROUND: Scarce information is available regarding the long-term immunogenicity of the Sputnik V vaccine. Here Sputnik V vaccinated subjects were evaluated 6 months after receiving the 2-dose prime-boost schedule. METHODS: Eighty-six hospital workers from Venezuela, 32 with a previous COVID-19 infection and 54 SARS-CoV-2 naïve subjects, were enrolled. IgG antibodies levels against the wild-type Receptor Binding Domain (RBD) were measured in an ELISA and with an in vitro ACE2-surrogate RBD binding inhibition assay at day 42 and day 180 after receiving the second dose. IgG levels were expressed in BAU/ml. Binding inhibition antibodies were expressed in IU/ml. RESULTS: On average, RBD-IgG levels decreased by approximately 50% between the two time-points in the COVID-19 naïve cohort (geometric mean concentration (GMC) 675 BAU/mL vs. 327 BAU/ml) and decreased by approximately 25% in the previously infected cohort (GMC 1209 BAU/mL vs 910 BAU/ml). Within our cohort, 94% showed a "good to excellent" neutralizing activity measured with the in vitro test 6 months after vaccination. CONCLUSIONS: The Sputnik V vaccine provided long-term and durable humoral immunity in our cohort specially if a person has been both vaccinated and had a previous infection with SARS-CoV-2.

A sensitive and inexpensive high-resolution melting-based testing algorithm for diagnosis of transient abnormal myelopoiesis and myeloid leukemia of Down syndrome.

Patients with Down syndrome (DS) are commonly affected by a pre-leukemic disorder known as transient abnormal myelopoiesis (TAM). This condition usually undergoes spontaneous remission within the first 2 months after birth; however, in children under 5, 20%-30% of cases evolve to myeloid leukemia of Down syndrome (ML-DS). TAM and ML-DS are caused by co-operation between trisomy 21 and acquired mutations in the GATA1 gene. Currently, only next-generation sequencing (NGS)-based methodologies are sufficiently sensitive for diagnosis in samples with small GATA1 mutant clones (≤10% blasts). Alternatively, this study presents research on a new, fast, sensitive, and inexpensive high-resolution melting (HRM)-based diagnostic approach that allows the detection of most cases of GATA1 mutations, including silent TAM. The algorithm first uses flow cytometry for blast count, followed by HRM and Sanger sequencing to search for mutations on exons 2 and 3 of GATA1. We analyzed 138 samples of DS patients: 110 of asymptomatic neonates, 10 suspected of having TAM, and 18 suspected of having ML-DS. Our algorithm enabled the identification of 33 mutant samples, among them five cases of silent TAM (5/110) and seven cases of ML-DS (7/18) with blast count ≤10%, in which GATA1 alterations were easily detected by HRM. Depending on the type of genetic variation and its location, our methodology reached sensitivity similar to that obtained by NGS (0.3%) at a considerably reduced time and cost, thus making it accessible worldwide.

Methodology for the identification of relevant loci for milk traits in dairy cattle, using machine learning algorithms.

Machine learning methods were considered efficient in identifying single nucleotide polymorphisms (SNP) underlying a trait of interest. This study aimed to construct predictive models using machine learning algorithms, to identify loci that best explain the variance in milk traits of dairy cattle. Further objectives involved validating the results by comparison with reported relevant regions and retrieving the pathways overrepresented by the genes flanking relevant SNPs. Regression models using XGBoost (XGB), LightGBM (LGB), and Random Forest (RF) algorithms were trained using estimated breeding values for milk production (EBVM), milk fat content (EBVF) and milk protein content (EBVP) as phenotypes and genotypes on 40417 SNPs as predictor variables. To evaluate their efficiency, metrics for actual vs. predicted values were determined in validation folds (XGB and LGB) and out-of-bag data (RF). Less than 4500 relevant SNPs were retrieved for each trait. Among the genes flanking them, signaling and transmembrane transporter activities were overrepresented. The models trained:•Predicted breeding values for animals not included in the dataset.•Were efficient in identifying a subset of SNPs explaining phenotypic variation. The results obtained using XGB and LGB algorithms agreed with previous results. Therefore, the method proposed could be applied for future association studies on milk traits.

Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.

Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they are implemented under controlled conditions. The development of a sound weed control system begins by recognizing the crop and the different weed plants presented in the field. In this work, a classification approach of Zea mays L. (Crop), narrow-leaf weeds (NLW), and broadleaf weeds (BLW) from multi-plant images are presented. Moreover, a large image dataset was generated. Images were captured in natural field conditions, in different locations, and growing stages of the plants. The extraction of regions of interest (ROI) is carried out employing connected component analysis (CCA), whereas the classification of ROIs is based on Convolutional Neural Networks (CNN) and compared with a shallow learning approach. To measure the classification performance of both methods, accuracy, precision, recall, and F1-score metrics were used. The best alternative for the weed classification task at early stages of growth and in natural corn field environments was the CNN-based approach, as indicated by the 97% accuracy value obtained.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence.

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, "big data" approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model.

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Assessing atypical brain functional connectivity development: An approach based on generative adversarial networks.

Generative Adversarial Networks (GANs) are promising analytical tools in machine learning applications. Characterizing atypical neurodevelopmental processes might be useful in establishing diagnostic and prognostic biomarkers of psychiatric disorders. In this article, we investigate the potential of GANs models combined with functional connectivity (FC) measures to build a predictive neurotypicality score 3-years after scanning. We used a ROI-to-ROI analysis of resting-state functional magnetic resonance imaging (fMRI) data from a community-based cohort of children and adolescents (377 neurotypical and 126 atypical participants). Models were trained on data from neurotypical participants, capturing their sample variability of FC. The discriminator subnetwork of each GAN model discriminated between the learned neurotypical functional connectivity pattern and atypical or unrelated patterns. Discriminator models were combined in ensembles, improving discrimination performance. Explanations for the model's predictions are provided using the LIME (Local Interpretable Model-Agnostic) algorithm and local hubs are identified in light of these explanations. Our findings suggest this approach is a promising strategy to build potential biomarkers based on functional connectivity.

Absenteeism due to mental and behavioral disorder in employees of a federal university.

Introduction: Mental and behavioral disorders (MBD) are one of the main causes of absence from work in Brazil and worldwide. Objectives: To analyze the prevalence of absence from work according to the International Classification of Diseases, 10th revision, with stratification of disease defined as "Mental and Behavioral Disorders", in permanent employees of the Federal University of Ouro Preto and its relationships with sociodemographic and occupational determinants, during the period from 2011 to 2019. Methods: An epidemiological, descriptive, and analytical study, with a cross-sectional design and quantitative approach was conducted using primary and secondary data. The population consisted of federal public sector workers who were granted ML to treat their own health during a 9-year period. Analyses were performed using descriptive and bivariate statistics. The Wilcoxon (Mann-Whitney) and Poisson tests were used to assess the existence of associations between variables. Results: 733 medical records of employees eligible according to the inclusion criteria were analyzed. There was a rising trend in ML rates over the 9-year period. Of the sample, 23.2% (n = 170) were absent from work due to mental and behavioral disorders - females accounted for 57.6% and administrative technicians in education for 62.3%. In the multivariate analysis (Poisson test), only the outcome "time of first ML due to mental and behavioral disorders" was associated with the variable "time working at the Universidade Federal de Ouro Preto". Conclusions: The high prevalence of mental and behavioral disorders found in this investigation is an alert to the magnitude of the problem, highlighting the urgency of implementing measures to detect psychosocial risk factors, whether associated with work or not.

Introdução: Os transtornos mentais e comportamentais (TMC) configuram como uma das principais causas de afastamento do trabalho no Brasil e no mundo. Objetivos: Analisar a prevalência do afastamento do trabalho de acordo com a Classificação Internacional de Doenças e Problemas Relacionados à Saúde - 10ª Revisão, com estratificação da doença definida como "Transtornos Mentais e Comportamentais" dos servidores efetivos da Universidade Federal de Ouro Preto e sua relação com os determinantes sociodemográficos e ocupacionais, no período de 2011 a 2019. Métodos: Estudo epidemiológico com delineamento transversal e abordagem quantitativa, realizado a partir de dados primários e secundários. A população foi composta por servidores públicos federais com licenças médicas para tratamento da própria saúde no período de 9 anos. A análise foi realizada por meio de estatística descritiva e bivariada. Utilizou-se o teste de Wilcoxon (Mann-Whitney) e de Poisson para avaliação de existência de associação entre as variáveis. Resultados: Foram analisados 733 prontuários de servidores elegíveis nos critérios de admissão. A tendência das taxas de afastamento foi de crescimento no período de 9 anos. Na amostra, 23,2% (n = 170) apresentaram afastamento do trabalho por transtornos mentais e comportamentais - o sexo feminino apresentou 57,6%, e os técnicos administrativos em educação apresentaram 62,3%. Na análise multivariada (teste de Poisson), apenas o desfecho "momento do primeiro afastamento por transtornos mentais e comportamentais" apresentou associação com a variável tempo de Universidade Federal de Ouro Preto. Conclusões: A alta prevalência de transtornos mentais e comportamentais encontrada nesta investigação alerta para a magnitude do problema, evidenciando-se a urgência da implantação de medidas de detecção de fatores psicossociais de risco associados ou não ao trabalho.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications.

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

Desenvolvimento de metodologia para quantificação de vírus Influenza por RT-qPCR

Influenza A viruses A H1N1, A H3N2 and type B (Victoria lineage), are part of the current trivalent Influenza vaccine composition produced by the Instituto Butantan. The WHO recommendation is based on data about viruses circulation around the globe. Due to this constant change of strains, the production time, from the announcement of the strains to be used until the start of the annual campaign, is just over 7 months. Therefore, the quest for vaccine producers to streamline any and all procedures, without compromising quality, is constant. In this context, obtaining a good dose/egg yield is a crucial point, being directly proportional to the success of the strain replication and, therefore, to the production yield. Vaccine production begins with master seed lots and working seed lots production, subsequently used for bulk batches production. The precise, accurate and quicly quantification can improve yield. In this study, the standardization of an RT-qPCR methodology for the quantification of viral nucleic acid copies number was performed following a statistical correlation between virus data determined by EID50/mL. To achieve the objectives, a standard curve of synthetic DNA was prepared for each Influenza virus (A H1N1, A H3N2 and B (Victoria)), which allowed the quantification of copies number by the standardized methodology. The methodologies were applied to the viral strains and validated by the parameters of linearity, limits of detection and quantification, precision and specificity, reaching the acceptance criteria. A good correlation was established between the number of copies and EDI50/mL, with r of ~0.99 for each of the 3 standardized methods, demonstrating the possibility of using the methodologies in the steps that need the most exact quantification of viruses, generating data for viral replication monitoring and optimizing production processes

Os vírus Influenza A H1N1, H3N2 e tipo B (Victoria) fazem parte da composição atual da vacina trivalente produzida pelo Instituto Butantan e ela é produzida com os vírus recomendadas pela Organização Mundial da Saúde (OMS). Essa recomendação é realizada anualmente baseada em dados obtidos pelo monitoramento de sua circulação ao redor do planeta. Por conta desta mudança constante de cepas, o tempo de produção, desde o anúncio das cepas a serem utilizadas até o início da campanha anual, é de pouco mais de 7 meses. Portanto, a busca pelos produtores de vacinas para agilizar todo e qualquer procedimento, sem comprometimento da qualidade, é uma constante. Dentro desse contexto, a obtenção de um bom rendimento dose/ovo é um ponto crucial, sendo diretamente proporcional ao sucesso da replicação das cepas e, portanto, ao rendimento da produção. A produção da vacina tem início com a produção de bancos de vírus semente e bancos de vírus trabalho que são utilizados, posteriormente para a produção de todos os lotes de monovalentes, e sua quantificação de maneira precisa, exata e rápida pode auxiliar na melhoria do rendimento. Neste estudo foi realizada a padronização de uma metodologia de RT-qPCR para a quantificação do número de cópias de ácido nucleico viral visando uma correlação estatística com os dados de vírus determinado por EID50/mL. Para atingir os objetivos, inicialmente foi preparada uma curva padrão de DNA sintético para cada um dos vírus Influenza (A H1N1, A H3N2 e B (Victoria)) que permitiu a quantificação exata do número de cópias pela metodologia padronizada. A seguir, as metodologias foram aplicadas às cepas virais e validadas pelos parâmetros de linearidade, limites de detecção e quantificação, precisão e especificidade, sendo atingidos os critérios de aceitação. Foi estabelecida uma boa correlação entre o número de cópias e EID50/mL, com r de ~0,99 para cada um dos 3 métodos padronizados, demonstrando a possibilidade de utilização das metodologias nas etapas que necessitem a quantificação mais exata de vírus, gerando dados para o acompanhamento da replicação viral e otimização dos processos de produção.