Incidence and associated factors of type 2 diabetes mellitus onset in the Brazilian HIV/AIDS cohort study.

BACKGROUND: People living with HIV (PLH) under combined antiretroviral therapy (cART) are at risk of developing type 2 diabetes mellitus (T2DM). OBJECTIVE: We examined the incidence of T2DM, associated factors and mean time to outcome in PLH under cART. METHOD: Data for this multicenter cohort study were obtained from PLH aged over 18, who started cART in 13 Brazilian sites from 2003 to 2013. Factors associated with incident T2DM were evaluated by Cox multiple regression models. RESULTS: A total of 6724 patients (30,997.93 person-years) were followed from January 2003 to December 2016. A T2DM incidence rate of 17.3/1000 person-years (95%CI 15.8-18.8) was observed. Incidence of isolated hypertriglyceridemia and impaired fasting glucose (IFG) were 84.3 (95%CI 81.1-87.6) and 14.5/1000 person-years (95%CI 13.2-15.9), respectively. Mean time to T2DM onset was 10.5 years (95%CI 10.3-10.6). Variables associated with incident T2DM were age 40-50 [Hazard Ratio (HR) 1.7, 95%CI 1.4-2.1] and ≥ 50 years (HR 2.4, 95%CI 1.9-3.1); obesity (HR 2.1, 95%CI 1.6-2.8); abnormal triglyceride/HDL-cholesterol ratio (HR 1.8, 95%CI 1.51-2.2). IFG predicted T2DM (HR 2.6, 95%CI 1.7-2.5) and occurred on average 3.3 years before diabetes onset. Exposure to stavudine for ≥ 2 years was independently associated with incident T2DM [HR 1.6, 95%CI 1.0-2.2). CONCLUSION: Brazilian PLH under cART are at significant risk of developing T2DM and share risk factors for diabetes onset with the general population, such as older age, obesity, and having metabolic abnormalities at baseline. Moreover, stavudine use was independently associated with incident T2DM. Identifying PLH at a higher risk of T2DM can help caretakers trigger health promotion and establish specific targets for implementation of preventive measures.

Incidence and associated factors of type 2 diabetes mellitus onset in the Brazilian HIV/AIDS cohort study

ABSTRACT Background: People living with HIV (PLH) under combined antiretroviral therapy (cART) are at risk of developing type 2 diabetes mellitus (T2DM). Objective: We examined the incidence of T2DM, associated factors and mean time to outcome in PLH under cART. Method: Data for this multicenter cohort study were obtained from PLH aged over 18, who started cART in 13 Brazilian sites from 2003 to 2013. Factors associated with incident T2DM were evaluated by Cox multiple regression models. Results: A total of 6724 patients (30,997.93 person-years) were followed from January 2003 to December 2016. A T2DM incidence rate of 17.3/1000 person-years (95%CI 15.8-18.8) was observed. Incidence of isolated hypertriglyceridemia and impaired fasting glucose (IFG) were 84.3 (95%CI 81.1-87.6) and 14.5/1000 person-years (95%CI 13.2-15.9), respectively. Mean time to T2DM onset was 10.5 years (95%CI 10.3-10.6). Variables associated with incident T2DM were age 40-50 [Hazard Ratio (HR) 1.7, 95%CI 1.4-2.1] and ≥ 50 years (HR 2.4, 95%CI 1.9-3.1); obesity (HR 2.1, 95%CI 1.6-2.8); abnormal triglyceride/HDL-cholesterol ratio (HR 1.8, 95%CI 1.51-2.2). IFG predicted T2DM (HR 2.6, 95%CI 1.7-2.5) and occurred on average 3.3 years before diabetes onset. Exposure to stavudine for ≥ 2 years was independently associated with incident T2DM [HR 1.6, 95%CI 1.0-2.2). Conclusion: Brazilian PLH under cART are at significant risk of developing T2DM and share risk factors for diabetes onset with the general population, such as older age, obesity, and having metabolic abnormalities at baseline. Moreover, stavudine use was independently associated with incident T2DM. Identifying PLH at a higher risk of T2DM can help caretakers trigger health promotion and establish specific targets for implementation of preventive measures.

Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy.

The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug leads to fixation of mutations capable of imparting varying degrees of resistance. The presence of these mutations is one of the most important factors in the failure of therapeutic response to medications. Thus, it is of critical to understand the resistance patterns and mechanisms associated with them, allowing the choice of an appropriate therapeutic scheme, which considers the frequency, and other characteristics of mutations. Utilizing Paraconsistents Artificial Neural Networks, seated in Paraconsistent Annotated Logic Et which has the capability of measuring uncertainties and inconsistencies, we have achieved levels of agreement above 90% when compared to the methodology proposed with the current methodology used to classify HIV-1 subtypes. The results demonstrate that Paraconsistents Artificial Neural Networks can serve as a promising tool of analysis.

Application of paraconsistent artificial neural networks as a method of aid in the diagnosis of Alzheimer disease.

The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis.

Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis.

This work presents an application of the paraconsistent artificial neural network (PANN) in the analysis of cephalometric variables and provides an orthodontic diagnosis. An expert's analysis is subject to the inherent imprecision of measurements, registers, and individual variability of physician visual analysis. Patient input cephalometric values are compared with means drawn from individuals considered normal in the cephalometric point of view by the PANN. This reference is constituted by individuals from 6 to 18 years old, both genders. The applied cephalometric analysis was targeted to measure skeletal and dental discrepancies and established a cephalometric diagnosis. The analysis results in degrees of skeletal, anteroposterior, and dental discrepancy, pertinent to upper and lower incisors. A sample of 120 orthodontic patients was processed by the proposed model and three orthodontic experts. Comparisons between the model and the human expert's performance provided kappa indexes that varied from moderate to almost perfect agreement. The agreement between the model and specialist's performance was equivalent. In addition, the model pointed out contradictions presented in the data that were not noticed by the orthodontists, which highlight the contribution that this kind of system could carry out in the orthodontics decision support.