Development and validation of a health information system for assistance and research in gestational trophoblast disease.

BACKGROUND: Gestational Trophoblastic Disease (GTD) comprises pathological forms of placental trophoblastic tissue proliferation. When benign, they present with hydatidiform moles, and when malignant, they are called Gestational Trophoblastic Neoplasia. With the growth of the practice of digital health, allied to updated therapeutic approaches, the Outpatient Clinic for Gestational Trophoblastic Disease has built a Health Information System (HIS), contributing to the teaching-learning binomial, as well as to self-care. METHODS: This is a cross-sectional and blind technological assessment research for developing SIS-Mola (Website for the medical team and the Application "MolaApp" aimed at patients with GTD). We used the Praxis management approach to manage the application creation project. In the tasks involving real-time chat, a WebSocket layer was created and hosted together with the project's web services, which use the Arch Linux operating system. For the evaluations, we provided questionnaires developed based on the System Usability Scale (SUS), to determine the degree of user satisfaction, with objective questions on the Likert scale. We invited 28 participants for the evaluations, among ABDTG specialist physicians, doctors from the DTG Outpatient Clinic team, and the patients. The study was systematized according to the rules of treatment and follow-up in treating the disease. RESULTS: The tests were conducted from November 2021 to February 2022. The responses obtained on a Likert scale indicated reliability and credibility to the HIS, since the total usability score, measured by the ten questions of the SUS instrument, had a mean of 81.1 (clinicians), 80 (patients) and median of 77.5 for both groups. The sample was characterized according to the variables: age, gender, education, computer knowledge, and profession. CONCLUSION: Developing a HIS in the GTD Outpatient Clinic met the objectives regarding the rules of treatment and follow-up of patients. With these digital tools, it is possible to obtain data about the patient's health, sending information through exams performed and appropriate treatments. The connectivity capacity allows agile care, saving time, costs and solving the displacement problem. The TICs generate natural efficiency for the organization in the flow of service and the formation of a database, improving the quality of the assistance.

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice.

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

Sensitivity Analysis by the PROMETHEE-GAIA method: Algorithms evaluation for COVID-19 prediction.

With the expansion of coronavirus in the World, the search for technology solutions based on the analysis and prospecting of diseases has become constant. The paper addresses a machine learning algorithms analysis used to predict and identify infected patients. For analysis, we use a multicriteria approach using the PROMETHEE-GAIA method, providing the structuring of alternatives respective to a set of criteria, thus enabling the obtaining of their importance degree under the perspective of multiple criteria. The study approaches a sensitivity analysis, evaluating the alternatives using the PROMETHEE I and II methods, along with the GAIA plan, both implemented by the Visual PROMETHEE computational tool, exploring numerical and graphical resources. The analysis model proves to be effective, guaranteeing the ranking of alternatives by inter criterion evaluation and local results with intra criterion evaluation, providing a transparent analysis concerning the selection of prediction algorithms to combat the COVID-19 pandemic.

Prediction of SARS-CoV-2 hosts among Brazilian mammals and new coronavirus transmission chain using evolutionary bioinformatics.

Severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 are thought to transmit to humans via wild mammals, especially bats. However, evidence for direct bat-to-human transmission is lacking. Involvement of intermediate hosts is considered a reason for SARS-CoV-2 transmission to humans and emergence of outbreak. Large biodiversity is found in tropical territories, such as Brazil. On the similar line, this study aimed to predict potential coronavirus hosts among Brazilian wild mammals based on angiotensin-converting enzyme 2 (ACE2) sequences using evolutionary bioinformatics. Cougar, maned wolf, and bush dogs were predicted as potential hosts for coronavirus. These indigenous carnivores are philogenetically closer to the known SARS-CoV/SARS-CoV-2 hosts and presented low ACE2 divergence. A new coronavirus transmission chain was developed in which white-tailed deer, a susceptible SARS-CoV-2 host, have the central position. Cougar play an important role because of its low divergent ACE2 level in deer and humans. The discovery of these potential coronavirus hosts will be useful for epidemiological surveillance and discovery of interventions that can contribute to break the transmission chain. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s44149-021-00020-w.

Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates (30%, 50%, and 80%). The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning.