Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems.

In November 2015, a catastrophic rupture of the Fundão dam in Mariana (Brazil), resulted in extensive socio-economic and environmental repercussions that persist to this day. In response, several reforestation programs were initiated to remediate the impacted regions. However, accurately assessing soil health in these areas is a complex endeavor. This study employs machine learning techniques to predict soil quality indicators that effectively differentiate between the stages of recovery in these areas. For this, a comprehensive set of soil parameters, encompassing 3 biological, 16 chemical, and 3 physical parameters, were evaluated for samples exposed to mining tailings and those unaffected, totaling 81 and 6 samples, respectively, which were evaluated over 2 years. The most robust model was the decision tree with a restriction of fewer levels to simplify the tree structure. In this model, Cation Exchange Capacity (CEC), Microbial Biomass Carbon (MBC), Base Saturation (BS), and Effective Cation Exchange Capacity (eCEC) emerged as the most pivotal factors influencing model fitting. This model achieved an accuracy score of 92% during training and 93% during testing for determining stages of recovery. The model developed in this study has the potential to revolutionize the monitoring efforts conducted by regulatory agencies in these regions. By reducing the number of parameters that necessitate evaluation, this enhanced efficiency promises to expedite recovery monitoring, simultaneously enhancing cost-effectiveness while upholding the analytical rigor of assessments.

Clinical demographic characteristics of total knee arthroplasty in a university hospital

ABSTRACT Objective: To assess socio-demographic characteristics of patients undergoing total knee arthroplasty (TKA) in a public university hospital, evaluating the outcome infection and associated factors. Method: A retrospective study was carried out with 78 patients undergoing TKA, from 2013 to 2014. The socio-demographic and clinical characteristics of the patients were collected. Comparison between infected and non-infected patients was performed to find out which variables were possibly associated to this complication. Result: Of 81 arthroplasties performed, patients were older (mean age 64 years), women (79%), with primary osteoarthritis as main etiology (87.6%) and most had comorbidities (82.7%). Infection occurred in 16% of patients, and this outcome associated with age older than 65 years (p=0.023) and the occurrence of deep vein thrombosis (p=0.027). Conclusion: Patients undergoing TKA are mostly elderly women with primary osteoarthritis in the knee and comorbidities who developed infection in 16% of cases. More studies need to be conducted aimed at creating specific protocols in order to improve the quality of clinical practice. Level of Evidence III, Retrospective Comparative Study.

CLINICAL DEMOGRAPHIC CHARACTERISTICS OF TOTAL KNEE ARTHROPLASTY IN A UNIVERSITY HOSPITAL.

OBJECTIVE: To assess socio-demographic characteristics of patients undergoing total knee arthroplasty (TKA) in a public university hospital, evaluating the outcome infection and associated factors. METHOD: A retrospective study was carried out with 78 patients undergoing TKA, from 2013 to 2014. The socio-demographic and clinical characteristics of the patients were collected. Comparison between infected and non-infected patients was performed to find out which variables were possibly associated to this complication. RESULT: Of 81 arthroplasties performed, patients were older (mean age 64 years), women (79%), with primary osteoarthritis as main etiology (87.6%) and most had comorbidities (82.7%). Infection occurred in 16% of patients, and this outcome associated with age older than 65 years (p=0.023) and the occurrence of deep vein thrombosis (p=0.027). CONCLUSION: Patients undergoing TKA are mostly elderly women with primary osteoarthritis in the knee and comorbidities who developed infection in 16% of cases. More studies need to be conducted aimed at creating specific protocols in order to improve the quality of clinical practice. Level of Evidence III, Retrospective Comparative Study.

As redes neurais artificiais e o ensino da medicina / Artificial neural networks and medical education

As transformações da prática médica nos últimos anos - sobretudo com a incorporação de novas tecnologias da informação - apontam a necessidade de ampliar as discussões sobre o processo ensino-aprendizagem na educação médica. A utilização de novas tecnologias computacionais no ensino médico tem demonstrado inúmeras vantagens no processo de aquisição de habilidades para a identificação e a resolução de problemas, o que estimula a criatividade, o senso crítico, a curiosidade e o espírito científico. Nesse contexto, ganham destaque as Redes Neurais Artificiais (RNA) - sistemas computacionais cuja estrutura matemática é inspirada no funcionamento do cérebro humano -, as quais têm sido úteis no processo ensino-aprendizagem e na avaliação de estudantes de Medicina. Com base nessas ponderações, o escopo da presente comunicação é revisar aspectos da aplicação das RNA na educação médica.

The transformations that medical practice has undergone in recent years - especially with the incorporation of new information technologies - point to the need to broaden discussions on the teaching-learning process in medical education. The use of new computer technologies in medical education has shown many advantages in the process of acquiring skills in problem solving, which encourages creativity, critical thinking, curiosity and scientific spirit. In this context, it is important to highlight artificial neural networks (ANN) - computer systems with a mathematical structure inspired by the human brain - which proved to be useful in the evaluation process and the acquisition of knowledge among medical students. The purpose of this communication is to review aspects of the application of ANN in medical education.