RNA sequencing and gene co-expression network of in vitro matured oocytes and blastocysts of buffalo.

In reproductive technologies, uncovering the molecular aspects of oocyte and embryo competence under different conditions is crucial for refining protocols and enhancing efficiency. RNA-seq generates high-throughput data and provides transcriptomes that can undergo additional computational analyses. This study presented the transcriptomic profiles of in vitro matured oocytes and blastocysts produced in vitro from buffalo crossbred (Bubalus bubalis), coupled with gene co-expression and module preservation analysis. Cumulus Oophorus Complexes, obtained from slaughterhouse-derived ovaries, were subjected to in vitro maturation to yield metaphase II oocytes (616) or followed in vitro fertilization and culture to yield blastocysts for sequencing (526). Oocyte maturation (72%, ±3.34 sd) and embryo development (21.3%, ±4.18 sd) rates were obtained from three in vitro embryo production routines following standard protocols. Sequencing of 410 metaphase II oocytes and 70 hatched blastocysts (grade 1 and 2) identified a total of 13,976 genes, with 62% being ubiquitously expressed (8,649). Among them, the differentially expressed genes (4,153) and the strongly variable genes with the higher expression (fold-change above 11) were highlighted in oocytes (BMP15, UCHL1, WEE1, NLRPs, KPNA7, ZP2, and ZP4) and blastocysts (APOA1, KRT18, ANXA2, S100A14, SLC34A2, PRSS8 and ANXA2) as representative indicators of molecular quality. Additionally, genes exclusively found in oocytes (224) and blastocysts (2,200) with specific biological functions were identified. Gene co-expression network and module preservation analysis revealed strong preservation of functional modules related to exosome components, steroid metabolism, cell proliferation, and morphogenesis. However, cell cycle and amino acid transport modules exhibited weak preservation, which may reflect differences in embryo development kinetics and the activation of cell signaling pathways between buffalo and bovine. This comprehensive transcriptomic profile serves as a valuable resource for assessing the molecular quality of buffalo oocytes and embryos in future in vitro embryo production assays.

Tools and methodology to in silico phage discovery in freshwater environments.

Freshwater availability is essential, and its maintenance has become an enormous challenge. Due to population growth and climate changes, freshwater sources are becoming scarce, imposing the need for strategies for its reuse. Currently, the constant discharge of waste into water bodies from human activities leads to the dissemination of pathogenic bacteria, negatively impacting water quality from the source to the infrastructure required for treatment, such as the accumulation of biofilms. Current water treatment methods cannot keep pace with bacterial evolution, which increasingly exhibits a profile of multidrug resistance to antibiotics. Furthermore, using more powerful disinfectants may affect the balance of aquatic ecosystems. Therefore, there is a need to explore sustainable ways to control the spreading of pathogenic bacteria. Bacteriophages can infect bacteria and archaea, hijacking their host machinery to favor their replication. They are widely abundant globally and provide a biological alternative to bacterial treatment with antibiotics. In contrast to common disinfectants and antibiotics, bacteriophages are highly specific, minimizing adverse effects on aquatic microbial communities and offering a lower cost-benefit ratio in production compared to antibiotics. However, due to the difficulty involving cultivating and identifying environmental bacteriophages, alternative approaches using NGS metagenomics in combination with some bioinformatic tools can help identify new bacteriophages that can be useful as an alternative treatment against resistant bacteria. In this review, we discuss advances in exploring the virome of freshwater, as well as current applications of bacteriophages in freshwater treatment, along with current challenges and future perspectives.

SATIN: a micro and mini satellite mining tool of total genome and coding regions with analysis of perfect repeats polymorphism in coding regions.

BACKGROUND: Tandem repeats are specific sequences in genomic DNA repeated in tandem that are present in all organisms. Among the subcategories of TRs we have Satellite repeats, that is divided into macrosatellites, minisatellites, and microsatellites, being the last two of specific interest because they can identify polymorphisms between organisms due to their instability. Currently, most mining tools focus on Simple Sequence Repeats (SSR) mining, and only a few can identify SSRs in the coding regions. RESULTS: We developed a microsatellite mining software called SATIN (Micro and Mini SATellite IdentificatioN tool) based on a new sliding window algorithm written in C and Python. It represents a new approach to SSR mining by addressing the limitations of existing tools, particularly in coding region SSR mining. SATIN is available at https://github.com/labgm/SATIN.git . It was shown to be the second fastest for perfect and compound SSR mining. It can identify SSRs from coding regions plus SSRs with motif sizes bigger than 6. Besides the SSR mining, SATIN can also analyze SSRs polymorphism on coding-regions from pre-determined groups, and identify SSRs differentially abundant among them on a per-gene basis. To validate, we analyzed SSRs from two groups of Escherichia coli (K12 and O157) and compared the results with 5 known SSRs from coding regions. SATIN identified all 5 SSRs from 237 genes with at least one SSR on it. CONCLUSIONS: The SATIN is a novel microsatellite search software that utilizes an innovative sliding window technique based on a numerical list for repeat region search to identify perfect, and composite SSRs while generating comprehensible and analyzable outputs. It is a tool capable of using files in fasta or GenBank format as input for microsatellite mining, also being able to identify SSRs present in coding regions for GenBank files. In conclusion, we expect SATIN to help identify potential SSRs to be used as genetic markers.

Comparative genetic and pathogenic approaches of Escherichia coli isolated simultaneously from pyometra and urine of bitches.

Escherichia coli (E. coli) are widely related to pyometra and cystitis in dogs, and these infections can occur simultaneously. The goal of this study was to determine genetic and pathogenic insights of 14 E. coli isolated simultaneously from pyometra content and bladder urine of seven bitches. To achieve this, in silico and in vitro comparative analyses were conducted. Whole-genome comparisons demonstrated that E. coli isolated from pyometra and urine of the same animal were predominantly genetic extraintestinal E. coli clones belonging to the same Sequence Type and phylogroup. The E. coli clones identified in this study included ST372, ST457, ST12, ST127, ST646, and ST961. Five isolates (35.7%) belonged to the ST12 complex. Except for two E. coli, all other isolates belonged to the B2 Clermont phylogroup. Interestingly, some genomes of E. coli from urine carried more virulence genes than those E. coli from pyometra. Both pyometra and urine E. coli isolates demonstrated a strong affinity for adhering to HeLa and T24 cells, with a low affinity for invading them. However, certain isolates from urine exhibited a greater tendency to adhere to T24 cells in qualitative and quantitative assays compared to isolates from pyometra. In conclusion, this study revealed the high genomic similarity between pyometra and urine E. coli isolates, as well as the virulent capacity of both to colonize endometrial and urothelial cells. The findings of this study underscore the importance of concurrently managing both infections clinically and could potentially contribute to future resources for the prevention of cystitis and pyometra.

EasySSR: a user-friendly web application with full command-line features for large-scale batch microsatellite mining and samples comparison.

Microsatellites, also known as SSRs or STRs, are polymorphic DNA regions with tandem repetitions of a nucleotide motif of size 1-6 base pairs with a broad range of applications in many fields, such as comparative genomics, molecular biology, and forensics. However, the majority of researchers do not have computational training and struggle while running command-line tools or very limited web tools for their SSR research, spending a considerable amount of time learning how to execute the software and conducting the post-processing data tabulation in other tools or manually-time that could be used directly in data analysis. We present EasySSR, a user-friendly web tool with command-line full functionality, designed for practical use in batch identifying and comparing SSRs in sequences, draft, or complete genomes, not requiring previous bioinformatic skills to run. EasySSR requires only a FASTA and an optional GENBANK file of one or more genomes to identify and compare STRs. The tool can automatically analyze and compare SSRs in whole genomes, convert GenBank to PTT files, identify perfect and imperfect SSRs and coding and non-coding regions, compare their frequencies, abundancy, motifs, flanking sequences, and iterations, producing many outputs ready for download such as PTT files, interactive charts, and Excel tables, giving the user the data ready for further analysis in minutes. EasySSR was implemented as a web application, which can be executed from any browser and is available for free at https://computationalbiology.ufpa.br/easyssr/. Tutorials, usage notes, and download links to the source code can be found at https://github.com/engbiopct/EasySSR.

Genomic Characterization of Aeromonas veronii Provides Insights into Taxonomic Assignment and Reveals Widespread Virulence and Resistance Genes throughout the World.

Aeromonas veronii is a Gram-negative bacterial species that causes disease in fish and is nowadays increasingly recurrent in enteric infections of humans. This study was performed to characterize newly sequenced isolates by comparing them with complete genomes deposited at the NCBI (National Center for Biotechnology Information). Nine isolates from fish, environments, and humans from the São Francisco Valley (Petrolina, Pernambuco, Brazil) were sequenced and compared with complete genomes available in public databases to gain insight into taxonomic assignment and to better understand virulence and resistance profiles of this species within the One Health context. One local genome and four NCBI genomes were misidentified as A. veronii. A total of 239 virulence genes were identified in the local genomes, with most encoding adhesion, motility, and secretion systems. In total, 60 genes involved with resistance to 22 classes of antibiotics were identified in the genomes, including mcr-7 and cphA. The results suggest that the use of methods such as ANI is essential to avoid misclassification of the genomes. The virulence content of A. veronii from local isolates is similar to those complete genomes deposited at the NCBI. Genes encoding colistin resistance are widespread in the species, requiring greater attention for surveillance systems.

Genomic analysis of Klebsiella pneumoniae high-risk clone ST11 co-harbouring MCR-1.27 and KPC-2 recovered at a paediatric oncologic hospital in the Brazilian Amazon region.

OBJECTIVES: The horizontal transfer of antibiotic resistance genes in Gram-negative bacteria, mainly through plasmids, is one of the greatest concerns for health systems worldwide and has been a growing threat in hospitals related to healthcare-associated infections by multidrug-resistant bacteria. Here we present p henotypic and genomic characterization of a KPC-2 and MCR-1.27-producing Klebsiella pneumoniae strain isolated from a paediatric patient at an oncologic hospital in Belém, Pará State, Brazilian Amazon region. METHODS: Antibiotic susceptibility test, whole genome sequencing, and in silico analysis were used to characterize the bacterial isolate (IEC48020) received in the Evandro Chagas Institute. RESULTS: The isolate was resistant to carbapenems, colistin, polymyxin B, and several other antimicrobials and was susceptible in vitro just to tigecycline, classified as an extensively drug-resistant phenotype. Genomic analysis revealed IEC48020 strain belonged to sequence type 11, clonal complex 258 high-risk clone and the presence of eight plasmids, two of them harbouring mcr-1.27 and blaKPC-2 genes, and the presence of virulence-related genes encoding yersiniabactin, phospholipase D, and traT genes. CONCLUSIONS: The presence and dissemination of high-risk clone bacteria with high disseminating plasmids containing antibiotic resistance genes for last resource antibiotics treatment options is a threat to the healthcare system and demands efforts in surveillance and epidemiological research for better knowledge of the actual situation of antibiotic resistance in the healthcare system, especially in the Amazon region, Brazil.

Immunoexpression of stem cell markers SOX-2, NANOG AND OCT4 in ameloblastoma.

Background: Ameloblastoma (AME) is characterized by a locally invasive growth pattern. In an attempt to justify the aggressiveness of neoplasms, the investigation of the role of stem cells has gained prominence. The SOX-2, NANOG and OCT4 proteins are important stem cell biomarkers. Methodology: To verify the expression of these proteins in tissue samples of AME, dentigerous cyst (DC) and dental follicle (DF), immunohistochemistry was performed and indirect immunofluorescence were performed on the human AME (AME-hTERT) cell line. Results: Revealed expression of SOX-2, NANOG and OCT4 in the tissue samples and AME-hTERT lineage. Greater immunostaining of the studied proteins was observed in AME compared to DC and DF (p < 0.001). Conclusions: The presence of biomarkers indicates a probable role of stem cells in the genesis and progression of AME.

Aeromonas allosaccharophila Strain AE59-TE2 Is Highly Antagonistic towards Multidrug-Resistant Human Pathogens, What Does Its Genome Tell Us?

Multidrug-resistant bacteria are of critical importance and a problem for human health and food preservation; the discovery of new antimicrobial substances to control their proliferation is part of the solution. This work reports on 57 antagonistic Aeromonas strains, of which 38 strains were antagonistic towards problematic human pathogens. The genome of the most antagonistic strain was sequenced and identified as Aeromonas allosaccharophila. Its genome was fully annotated and mined for genes that might explain that activity. Strain AE59-TE was antagonistic toward clinically relevant gram-negative and gram-positive multidrug-resistant bacteria, including Klebsiella pneumoniae KPC, Escherichia coli ESBL, Salmonella typhimurium, and Staphylococcus aureus MRSA. Strain AE59-TE2 was identified by multilocus sequence analysis. Genome mining identified four genes homologous to the bacteriocin, zoocin A from Streptococcus equi and a gene 98% similar to cvpA linked to colicin V production. A. allosaccharophila strain AE59-TE2 produced antimicrobial activity against a broad range of bacteria, including important gram-negative bacteria, not typically targeted by bacteriocins. Herewere described novel zoocin genes that are promising for industrial applications in the food and health sectors. Interesting and important antagonistic activity is described combined with the first detailed genomic analysis of the species Aeromonas allosaccharophila.

In silico approach to identify microsatellite candidate biomarkers to differentiate the biovar of Corynebacterium pseudotuberculosis genomes.

Corynebacterium pseudotuberculosis is the causative bacterial agent of the zoonotic disease known as caseous lymphadenitis, and it presents several mechanisms of response to host defenses, including the presence of virulence factors (VFs). The genomes of these bacteria have several polymorphic markers known as microsatellites, or simple sequence repeats (SSRs), that can be used to characterize the genome, to study possible polymorphisms existing among strains, and to verify the effects of such polymorphic markers in coding regions and regions associated with VFs. In this study, several SSRs were identified within coding regions throughout the 54 genomes of this species, revealing possible polymorphisms associated with coding regions that could be used as strain-specific or serotype-specific identifiers of C. pseudotuberculosis. The similarities associated with SSRs amongst the different serum variants of C. pseudotuberculosis, biovars equi and ovis, were also evaluated, and it was possible to identify SSRs located in coding regions responsible for a VF enrolled in pathogenesis known to mediate bacterial adherence (SpaH-type pili virulence factor). Phylogenetic analyses revealed that strains sharing SSR patterns, including the possible polymorphisms identified in the same position of gene-coding regions, were displayed by strains with a common ancestor, corroborating with the Genome Tree Report of the NCBI. Statistical analysis showed that the microsatellite groups belonging to equi and ovis biovars have a significance of 0.006 (p-value) in similarity, thus indicating them as good biomarker candidates for C. pseudotuberculosis.

Impacts of soybean agriculture on the resistome of the Amazonian soil.

The soils of the Amazon are complex environments with different organisms cohabiting in continuous adaptation processes; this changes significantly when these environments are modified for the development of agricultural activities that alter the chemical, macro, and microbiological compositions. The metagenomic variations and the levels of the environmental impact of four different soil samples from the Amazon region were evaluated, emphasizing the resistome. Soil samples from the organic phase from the different forest, pasture, and transgenic soybean monocultures of 2-14 years old were collected in triplicate at each site. The samples were divided into two groups, and one group was pre-treated to obtain genetic material to perform sequencing for metagenomic analysis; another group carried out the chemical characterization of the soil, determining the pH, the content of cations, and heavy metals; these were carried out in addition to identifying with different databases the components of the microbiological communities, functional genes, antibiotic and biocide resistance genes. A greater diversity of antibiotic resistance genes was observed in the forest soil. In contrast, in monoculture soils, a large number of biocide resistance genes were evidenced, highlighting the diversity and abundance of crop soils, which showed better resistance to heavy metals than other compounds, with a possible dominance of resistance to iron due to the presence of the acn gene. For up to 600 different genes for resistance to antibiotics and 256 genes for biocides were identified, most of which were for heavy metals. The most prevalent was resistance to tetracycline, cephalosporin, penam, fluoroquinolone, chloramphenicol, carbapenem, macrolide, and aminoglycoside, providing evidence for the co-selection of these resistance genes in different soils. Furthermore, the influence of vegetation cover on the forest floor was notable as a protective factor against the impact of human contamination. Regarding chemical characterization, the presence of heavy metals, different stress response mechanisms in monoculture soils, and the abundance of mobile genetic elements in crop and pasture soils stand out. The elimination of the forest increases the diversity of genes for resistance to biocides, favoring the selection of genes for resistance to antibiotics in soils.

Unraveling the Genomic Potential of the Thermophilic Bacterium Anoxybacillus flavithermus from an Antarctic Geothermal Environment.

Antarctica is a mosaic of extremes. It harbors active polar volcanoes, such as Deception Island, a marine stratovolcano having notable temperature gradients over very short distances, with the temperature reaching up to 100 °C near the fumaroles and subzero temperatures being noted in the glaciers. From the sediments of Deception Island, we isolated representatives of the genus Anoxybacillus, a widely spread genus that is mainly encountered in thermophilic environments. However, the phylogeny of this genus and its adaptive mechanisms in the geothermal sites of cold environments remain unknown. To the best of our knowledge, this is the first study to unravel the genomic features and provide insights into the phylogenomics and metabolic potential of members of the genus Anoxybacillus inhabiting the Antarctic thermophilic ecosystem. Here, we report the genome sequencing data of seven A. flavithermus strains isolated from two geothermal sites on Deception Island, Antarctic Peninsula. Their genomes were approximately 3.0 Mb in size, had a G + C ratio of 42%, and were predicted to encode 3500 proteins on average. We observed that the strains were phylogenomically closest to each other (Average Nucleotide Identity (ANI) > 98%) and to A. flavithermus (ANI 95%). In silico genomic analysis revealed 15 resistance and metabolic islands, as well as genes related to genome stabilization, DNA repair systems against UV radiation threats, temperature adaptation, heat- and cold-shock proteins (Csps), and resistance to alkaline conditions. Remarkably, glycosyl hydrolase enzyme-encoding genes, secondary metabolites, and prophage sequences were predicted, revealing metabolic and cellular capabilities for potential biotechnological applications.

Viral Metagenomics Reveals Widely Diverse Viral Community of Freshwater Amazonian Lake.

Despite the importance of understanding the ecology of freshwater viruses, there are not many studies on the subject compared to marine viruses. The microbiological interactions in these environments are still poorly known, especially between bacteriophages and their host bacteria and between cyanophages and cyanobacteria. Lake Bologna, Belém, capital of the Brazilian State of Pará, is a water source that supplies the city and its metropolitan region. However, it remains unexplored regarding the contents of its virome and viral diversity composition. Therefore, this work aims to explore the taxonomic diversity of DNA viruses in this lake, especially bacteriophages and cyanophages, since they can act as transducers of resistance genes and reporters of water quality for human consumption. We used metagenomic sequencing data generated by previous studies. We analyzed it at the taxonomic level using the tools Kraken2, Bracken, and Pavian; later, the data was assembled using Genome Detective, which performs the assembly of viruses. The results observed here suggest the existence of a widely diverse viral community and established microbial phage-regulated dynamics in Lake Bolonha. This work is the first ever to describe the virome of Lake Bolonha using a metagenomic approach based on high-throughput sequencing, as it contributes to the understanding of water-related public health concerns regarding the spreading of antibiotic resistance genes and population control of native bacteria and cyanobacteria.

The experience of teaching introductory programming skills to bioscientists in Brazil.

Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year's experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants' assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners' feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term.

COVID-PA Bulletin: reports on artificial intelligence-based forecasting in coping with COVID-19 pandemic in the state of Pará, Brazil. / Boletim COVID-PA: relatos sobre projeções baseadas em inteligência artificial no enfrentamento da pandemia de COVID-19 no estado do Pará.

OBJECTIVE: To report the university extension research result entitled 'The COVID-PA Bulletin', which presented forecasts on the behavior of the pandemic in the state of Pará, Brazil. METHODS: The artificial intelligence technique also known as 'artificial neural networks' was used to generate 13 bulletins with short-term forecasts based on historical data from the State Department of Public Health information system. RESULTS: After eight months of predictions, the technique generated reliable results, with an average accuracy of 97% (observed for147 days) for confirmed cases, 96% (observed for 161 days) for deaths and 86% (observed for 72 days) for Intensive Care Unit bed occupancy. CONCLUSION: These bulletins have become a useful decision-making tool for public managers, assisting in the reallocation of hospital resources and optimization of COVID-19 control strategies in various regions of the state of Pará.

Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon.

The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.

HIF-1α Is Associated with Resistance to Hypoxia-Induced Apoptosis in Ameloblastoma.

BACKGROUND: Ameloblastoma (AMB) is a benign odontogenic tumour, with an aggressive local behaviour and a high rate of recurrence. Previous studies have demonstrated that hypoxia-induced factor alpha 1 (HIF-1α) and activated caspase-3 contribute to tumour invasiveness and cytogenesis in ameloblastoma. Hypoxia increases HIF-1α levels, which triggers a number of signalling pathways. This paper aimed to present data in the study of hypoxia-activated signalling pathways that modulate proapoptotic and antiapoptotic events in AMB. METHODS: Twenty cases of AMB and ten cases of dental follicle (DF) were used to analyse the immunoexpression of HIF-1α, p53, BNIP3, Bcl-2, IAP-2, GLUT1, and Bax. To contribute to the study, an analysis of expression and genetic interaction was performed using the cell line AME-1. RESULTS: AMB and DF expressed the studied proteins. These proteins showed significantly greater immunoexpression in AMB compared with the DF (p < 0.05). HIF-1α showed an important association with GLUT1, and a positive correlation was observed among p53, Bcl-2, and IAP-2. Transcriptomic analysis showed the significant expression of the studied proteins, and the network generated showed a direct association of HIF-1αF with GLUT1 (SLC2A1), TP53, and LDHA. Interestingly, GLUT1 also exhibited direct interaction with TP53 and LDHA. CONCLUSION: In AMB tumorigenesis, hypoxia is possibly related to antiapoptotic events, which suggests an important role for HIF-1α, GLUT1, Bcl-2, IAP-2, and possibly p53.

Boletim COVID-PA: relatos sobre projeções baseadas em inteligência artificial no enfrentamento da pandemia de COVID-19 no estado do Pará / Boletín COVID-PA: informes de proyecciones basadas en inteligencia artificial para enfrentar la pandemia COVID-19 en el estado de Pará, Brasil / COVID-PA Bulletin: reports on artificial intelligence-based forecasting in coping with COVID-19 pandemic in the state of Pará, Brazil

Objetivo: Relatar o produto de pesquisa e extensão universitária denominado Boletim COVID-PA, que apresentou projeções sobre o comportamento da pandemia no estado do Pará, Brasil. Métodos: Utilizou-se da técnica de inteligência artificial conhecida como 'redes neurais artificiais', para geração de 13 boletins com projeções de curto prazo baseadas nos dados históricos do sistema da Secretaria de Estado de Saúde Pública. Resultados: Após oito meses de projeções, a técnica gerou resultados confiáveis, com precisão média de 97% (147 dias observados) para casos confirmados, 96% (161 dias observados) para óbitos e 86% (72 dias observados) para ocupação de leitos de unidade de terapia intensiva. Conclusão: Esses boletins tornaram-se um instrumento útil para a tomada de decisão de gestores públicos, auxiliando na realocação de recursos hospitalares e otimização das estratégias de controle da COVID-19 nas diversas regiões do estado do Pará.

Objetivo: Reporte el resultado de la investigación y extensión universitaria denominada 'Boletim COVID-PA' que presentó proyecciones sobre el comportamiento de la pandemia en el estado de Pará, con un enfoque práctico y computacionalmente eficiente. Métodos: Fue utilizada una técnica de inteligencia artificial denominadas Redes Neurales para generar trece boletines con proyecciones basado en datos históricos del sistema de la Secretaría de Salud Pública. Resultados: Después de ocho meses de previsiones, la técnica genero resultados confiables con una precisión promedio de 97% (147 días observados) para casos confirmados, 96% (161 días observados) para los fallecimientos y 86% (72 días observados) para la ocupación de camas en las unidades de cuidados intensivos. Conclusión: Estos boletines se convirtieron en una herramienta para la toma de decisiones, auxiliando en la redistribución de recursos en los hospitales en el estado de Pará.

Objective: To report the university extension research result entitled 'The COVID-PA Bulletin', which presented forecasts on the behavior of the pandemic in the state of Pará, Brazil. Methods: The artificial intelligence technique also known as 'artificial neural networks' was used to generate 13 bulletins with short-term forecasts based on historical data from the State Department of Public Health information system. Results: After eight months of predictions, the technique generated reliable results, with an average accuracy of 97% (observed for147 days) for confirmed cases, 96% (observed for 161 days) for deaths and 86% (observed for 72 days) for Intensive Care Unit bed occupancy. Conclusion: These bulletins have become a useful decision-making tool for public managers, assisting in the reallocation of hospital resources and optimization of COVID-19 control strategies in various regions of the state of Pará.

Metallothionein Expression and its Influence on the In Vitro Biological Behavior of Mucoepidermoid Carcinoma.

Mucoepidermoid carcinoma (MEC) is the most common tumor in the salivary glands, often presenting with recurrence and metastasis due to its high invasive capacity. Metallothionein (MT), a zinc storage protein that supplies this element for protease activity, is probably related to mucoepidermoid carcinoma behavior. This prompted us to characterize a cell line derived from mucoepidermoid carcinoma and to correlate metallothionein expression with transforming growth factor-α (TGF-α), tumor necrosis factor-α (TNF-α) and matrix metalloproteinases (MMPs). Transcriptomic analysis and cytogenetic assays were performed to detect the expression of genes of interest and cellular chromosomal alterations, respectively. MEC cells with a depleted metallothionein 2A (MT2A) gene were subjected to Western blot to correlate metallothionein expression with growth factors and MMPs. Additionally, cells with depleted MT were subjected to migration and invasion assays. The transcriptomic study revealed reads mapped to cytokeratins 19 and AE1/AE3, α-smooth muscle actin, vimentin, and fibronectin. Cytogenetic evaluation demonstrated structural and numerical alterations, including the translocation t(11;19)(q21;p13), characteristic of MEC. Metallothionein depletion was correlated with the decreased expression of TGF-α and MMP-9, while TNF-α protein levels were augmented. Migration and invasion activity were diminished after metallothionein silencing. Our findings suggest an important role of MT in MEC invasion, through the regulation of proteins involved in this process.

Comparative genomic analysis and identification of pathogenicity islands of hypervirulent ST-17 Streptococcus agalactiae Brazilian strain.

Streptococcus agalactiae are important pathogenic bacteria that cause severe infections in humans, especially neonates. The mechanism by which ST-17 causes invasive infections than other STs is not well understood. In this study, we sequenced the first genome of a S. agalactiae ST-17 strain isolated in Brazil using the Illumina HiSeq 2500 technology. S. agalactiae GBS90356 ST-17 belongs to the capsular type III and was isolated from a neonatal with a fatal case of meningitis. The genome presented a size of 2.03 Mbp and a G + C content of 35.2%. S. agalactiae has 706 genes in its core genome and an open pan-genome with a size of 5.020 genes, suggesting a high genomic plasticity. GIPSy software was used to identify 10 Pathogenicity islands (PAIs) which corresponded to 15% of the genome size. IslandViewer4 corroborated the prediction of six PAIs. The pathogenicity islands showed important virulence factors genes for S. agalactiae e.g. neu, cps, dlt, fbs, cfb, lmb. SignalP detected 20 proteins with signal peptides among the 352 proteins found in PAIs, which 60% were located in the SagPAI_5. SagPAI_2 and 5 were mainly detected in ST-17 strains studied. Moreover, we identified 51 unique genes, 9 recombination regions and a large number of SNPs with an average of 760.3 polymorphisms, which can be related with high genomic plasticity and virulence during host-pathogen interactions. Our results showed implications for pathogenesis, evolution, concept of species and in silico analysis value to understand the epidemiology and genome plasticity of S. agalactiae.