Montagem de fragmentos de DNA

Orientador: João MeidanisDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoMestradoMestre em Ciência da Computaçã

Erratum to: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool

Multicenter double blind trial of autologous bone marrow mononuclear cell transplantation through intracoronary injection post acute myocardium infarction – MiHeart/AMI study

Background: Myocardial infarction remains as a major cause of mortality worldwide and a high rate of survivors develop heart failure as a sequel, resulting in a high morbidity and elevated expenditures for health system resources. We have designed a multicenter trial to test for the efficacy of autologous bone marrow (ABM) mononuclear cell (MC) transplantation in this subgroup of patients. The main hypothesis to be tested is that treated patients will have a significantly higher ejection fraction (EF) improvement after 6 months than controls. Methods: A sample of 300 patients admitted with ST elevation acute myocardial infarction (STEMI) and left ventricle (LV) systolic dysfunction, and submitted to successful mechanical or chemical recanalization of the infarct-related coronary artery will be selected for inclusion and randomized to either treated or control group in a double blind manner. The former group will receive 100 x 106 MC suspended in saline with 5% autologous serum in the culprit vessel, while the latter will receive placebo (saline with 5% autologous serum). Implications: Many phase I/II clinical trials using cell therapy for STEMI have been reported, demonstrating that cell transplantation is safe and may lead to better preserved LV function. Patients with high risk to develop systolic dysfunction have the potential to benefit more. Larger randomized, double blind and controlled trials to test for the efficacy of cell therapies in patients with high risk for developing heart failure are required.Brazilian Ministry of Science and Technology (MCT)/The Financing Agency for Studies and Projects (FINEP

Manifestações orais e craniofaciais da Síndrome de Apert: uma revisão da literatura

A síndrome de Apert apresenta desafios craniofaciais complexos, exigindo procedimentos cirúrgicos como correção de cranioestenose e avanço da região médiofacial. Embora menos prevalentes do que em outras síndromes, questões como hidrocefalia e herniação tonsilar ainda são relevantes. Anomalias craniofaciais características, como hipoplasia orbital, mandibular e dental, juntamente com atrasos no desenvolvimento dentário, são comuns.   Foi realizada uma revisão sistemática de artigos com sistematização PRISMA no período de 2021 a 2024. usando como base o Pubmed, critérios de inclusão inglês, português e espanhol com descritor  “Oral, Craniofacial Apert Syndrome”. Os estudos demonstram que a intervenção ortodôntica e maxilofacial é crucial na prevenção e tratamento dessas condições. Um diagnóstico preciso e um plano de tratamento personalizado são essenciais, destacando a importância de uma equipe odontológica bem treinada na melhoria da saúde bucal e qualidade de vida dos pacientes com síndrome de Apert

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost