Meio ambiente e gestão dos resíduos sólidos : estudo sobre o consumo sustentável a partir da lei 12.305/2010

Orientador : Jean Carlos PadilhaMonografia (especialização) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Curso de Especialização em Economia e Meio Ambiente.Inclui referência

Methemoglobinemia and dapsone levels in patients with leprosy

The objective of this work was to determine the methemoglobinemia and correlate with dapsone levels in multibacillary leprosy patients under leprosy multi-drug therapy. Thirty patients with laboratory and clinical diagnosis of multibacillary leprosy were enrolled. Dapsone was analyzed by high performance liquid chromatography and methemoglobinemia by spectrophotometry. The mean dapsone concentrations in male was 1.42 g/mL and in female was 2.42 g/mL. The mean methemoglobin levels in male was 3.09 µg/mL; 191%, and in female was 2.84 ± 1.67%. No correlations were seen between dapsone levels and methemoglobin in male and female patients. Our results demonstrated that the dosage of dapsone in leprosy treatment does not promote a significant methemoglobinemia

Influence of reaction time and carbon disulfide amount on sulfophosphorylation reaction of phosphorylated resins

In this work, we studied the sulfophosphorylation reaction of phosphorylated resins by varying the reaction time and CS2 amount and characterized these materials thoroughly by thermogravimetry (TG) and infrared spectroscopy (FTIR). The thermal stability and the FTIR spectra of the Sty-DVB copolymers were significantly changed after the phosphorylation and sulfophosphorylation reactions. The unmodified copolymers presented one degradation stage while the sulfophosphorylated copolymers showed two degradation stages, due to the presence of phosphorous and sulfur in the polymeric matrix. There was a tendency for increasing content of sulfophosphoryl groups incorporated into the Sty-DVB copolymers with increasing CS2 amount and reaction time. However, for high CS2 amounts, a long reaction time was not necessary to transform the phosphoryl groups into sulfophosphoryl groups. The best conversion degree of phosphoryl into sulfophosphoryl groups was obtained by using reaction time of 144 h and 0.25 mol of CS2

Diagnostic of Chormosome 7Q11,23 Duplication Syndrome - a case report

Resumo: a Síndrome da Duplicação do Cromossomo 7q11.23, também conhecida como Síndrome da duplicação de WBS, é uma desordem rara causada pela duplicação de um segmento de 1.5 Mb e caracterizada por deficiência intelectual, dificuldade na fala e anomalias craniofaciais moderadas. Ao realizar a técnica de CMA em amostras de uma família que tem uma criança com deficiência intelectual, foi observado uma microduplicação de novo de 1.428,9 Kbp no cromossomo 7q11.3. Palavras-chave: CNV. Deficiência Intelectual. Microarranjo. Síndrome da Duplicação de Williams-Beuren

Characterization and modulation of microglial phenotypes in an animal model of severe sepsis

We aim to characterize the kinetics of early and late microglial phenotypes after systemic inflammation in an animal model of severe sepsis and the effects of minocycline on these phenotypes. Rats were subjected to CLP, and some animals were treated with minocycline (10 ug/kg) by i.c.v. administration. Animals were killed 24 hours, 5, 10 and 30 days after sepsis induction, and serum and hippocampus were collected for subsequent analyses. Real-time PCR was performed for M1 and M2 markers. TNF-α, IL-1β, IL-6, IL-10, CCL-22 and nitrite/nitrate levels were measured. Immunofluorescence for IBA-1, CD11b and arginase was also performed. We demonstrated that early after sepsis, there was a preponderant up-regulation of M1 markers, and this was not switched to M2 phenotype markers later on. We found that up-regulation of both M1 and M2 markers co-existed up to 30 days after sepsis induction. In addition, minocycline induced a down-regulation, predominantly, of M1 markers. Our results suggest early activation of M1 microglia that is followed by an overlap of both M1 and M2 phenotypes and that the beneficial effects of minocycline on sepsis-associated brain dysfunction may be related to its effects predominantly on the M1 phenotype

Stroke outcome measurements from electronic medical records : cross-sectional study on the effectiveness of neural and nonneural classifiers

Background: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations