Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering.

Chronic kidney disease is a major public health problem around the world and this disease early diagnosis is still a great challenge as it is asymptomatic in its early stages. Thus, in order to identify variables capable of assisting CKD diagnosis and monitoring, machine learning techniques and statistical analysis use has shown itself to be extremely promising. For this work, unsupervised machine learning, statistical analysis techniques and discriminant analysis were used.Clinical Relevance - Discriminating variables characterization assist to differentiate groups of patients in different stages of Chronic Kidney Disease and it has important outcomes in the development of future models to aid clinical decision-making, as they can generate models with a greater predictive capacity for Chronic Kidney Disease, predominantly aiding the early diagnosis capacity of this pathology.

HIV-1 subtypes and drug resistance in children during antiretroviral therapy in Brazil.

We evaluate the genetic characterization of 132 HIV-1 pol sequences from children and adolescents undergoing antiretroviral therapy in Northeast Brazil. Phylogenetic and recombination analyses were performed using the maximum likelihood method using SeaView version 4 and SIMPLOT software. Most individuals harbored HIV-1 B (84.8%) and BF recombinants (9.8%), although other non-B subtypes were detected: HIV-1 C (1.5%), HIV-1 F (2.4%), and BC recombinants (1.5%). Antiretroviral resistance was 47% (95% confidence interval [CI]: 38.7%-55.4%). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) showed higher frequencies of primary mutations, with 40.9% (95% CI: 32.9%-49.4%), followed by nucleoside reverse transcriptase inhibitors (NRTI) and protease inhibitors (PIs) with 34.8% (95% CI: 27.3-43.3) and 6.1% (95% CI: 3.1%-11.5%), respectively. Among NRTIs, higher resistance levels were observed for abacavir, emtricitabine, and lamivudine; for NNRTI, nevirapine and efavirenz. The most common primary mutations found were M184V (29.5%), K103N (25%), M41L (9.8%), T215Y (8.3%), and G190A (8.3%). Our findings highlight the importance of surveillance of resistance mutations, which contributes to the continuous updating and implementation of preventive measures to decrease mother-to-child-transmission and transmitted drug resistance.