MIGUEL JOSE FRANCISCO NETO
Projetos de Pesquisa
Unidades Organizacionais
Instituto de Radiologia, Hospital das Clínicas, Faculdade de Medicina - Médico
7 resultados
Resultados de Busca
Agora exibindo 1 - 7 de 7
bookPart Aplicação da ultrassonografia nos pacientes em choque circulatório protocolos de atendimento e algoritmo de manejo dos pacientes(2016) BARROS, Dalton de Souza; SILVA, Paulo Savoia Dias da; FRANCISCO NETO, Miguel JosebookPart Pâncreas(2017) FRANCISCO NETO, Miguel José; QUEIROZ, Marcos Roberto Gomes de; MAURANO, Alexandre; GARCIA, Rodrigo Gobbo; FRANCISCO, Luciana Dias Rodrigues; MARCELINO, Antonio Sergio Zafred; TAKAHASHI, Marcelo StrausbookPart Métodos Diagnósticos por Imagem em Doenças Respiratórias(2016) FUNARI, Marcelo Buarque de Gusmão; FRANCISCO NETO, Miguel José; CHATE, Rodrigo Caruso- Rational use of chest ultrasound to confront COVID-19(2020) FRANCISCO NETO, Miguel José; QUEIROZ, Marcos Roberto Gomes de
bookPart Paracentese no paciente grave(2016) MAURANO, Alexandre; SILVA, Paulo Savoia Dias da; FRANCISCO NETO, Miguel Jose- Carotid intima-media thickness and metabolic syndrome in a rural population: Results from the Baependi Heart Study(2020) GEOVANINI, G.R.; SOUSA, I. Pinheiro de; TEIXEIRA, S.K.; FRANCISCO NETO, M.J.; GóMEZ, L.M. Gómez; GUERRA, G.C. Del; PEREIRA, A.C.; KRIEGER, J.E.Background and aims: Carotid intima-media thickness (cIMT) is a strong predictor of cardiovascular events and associated with metabolic syndrome (MetS). MetS is a cluster of cardiovascular risk factors, but the association structure between specific factors and disease development is not well-established in rural populations. We described the association structure between MetS factors and cIMT in a sample from rural Brazil. Methods: We studied 1937 participants from the Baependi Heart Study who underwent carotid ultrasound exam. We used ATP–III–2001 for MetS definition and linear mixed-effects models, adjusting by the family structure, to assess independent associations between the cardiovascular risk factors which define MetS and cIMT. Results: The sample's mean age was 46 ± 16y, 61% female, 73% white, mean body-mass-index 26±5 kg/m2, mean cIMT 0.53 ± 0.16 mm, with 35% of the sample classified with MetS. As expected, cIMT demonstrated a linear relationship with increasing age, and cIMT higher values were observed for MetS (0.58 ± 0.16 mm) compared to non-MetS (0.49 ± 0.14 mm). Considering models for cIMT with MetS and all of its factors, we found that blood pressure, glucose and obesity were independently associated with cIMT, but not HDL or triglycerides. Conclusions: cIMT showed a linear relationship with increasing age. Blood pressure, obesity, and glucose were independently associated with cIMT, but not HDL-cholesterol or triglycerides. In a rural population, hypertension, diabetes and obesity play a more important role than lipids in determining cIMT interindividual variability. © 2020 The Authors
bookPart Fisíca Médica(2019) OTADUY, Maria Concepción Garcia; CHAIM, Khallil Taverna; FRANCISCO NETO, Miguel José; MORENO, Raquel Andrade