MAURA SALAROLI DE OLIVEIRA

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PAHC, Hospital das Clínicas, Faculdade de Medicina
LIM/49 - Laboratório de Protozoologia, Hospital das Clínicas, Faculdade de Medicina

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Autor: OLIVEIRA, Maura Salaroli de ×

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Agora exibindo 1 - 10 de 24
  • bookPart
    Infecções por Pseudomonas spp.
    (2015) LEVIN, Anna Sara Shafferman; ARRUDA, Érico Antonio Gomes de; OLIVEIRA, Maura Salaroli de
  • bookPart
    Bacteremias e infeccōes de cateter venoso central
    (2018) MENDES, Elisa Teixeira; GIRãO, Evelyne Santana; OLIVEIRA, Maura Salaroli de
  • article 7 Citação(ões) na Scopus
    Surveillance of post-cataract endophthalmitis at a tertiary referral center: a 10-year critical evaluation
    (2021) KATO, Juliana Mika; TANAKA, Tatiana; OLIVEIRA, Luiza Manhezi Shin de; OLIVEIRA, Maura Salaroli de; ROSSI, Flavia; GOLDBAUM, Mauro; PIMENTEL, Sergio Luis Gianotti; ALMEIDA JUNIOR, Joao Nobrega de; YAMAMOTO, Joyce Hisae
    BackgroundAcute post-cataract endophthalmitis (APE) is a rare complication potentially causing irreversible visual loss. A 10-year study of APE was conducted to determine its incidence, microbiological spectra and antibiotic resistance profile of APE-related pathogens at a major tertiary referral center in Brazil.MethodsAPE cases reported between January 2010 and December 2019 were included. Phacoemulsification and extracapsular cataract techniques were eligible; combined procedures, traumatic and congenital cataract were excluded. Vitreous samples were cultured and antimicrobial resistance was compared for the periods of 2010-2014 and 2015-2019. The results were analyzed with Fisher's exact test.ResultsOur sample consisted of 40,491 cataract surgeries and 51 (0.126%) APE cases. Culture was positive in 35 cases (71.4%), of which 31 (88.6%) Gram-positive, 3 (8.6%) Gram-negative, and 1 (2.9%) fungal. The most frequently isolated organism was Staphylococcus epidermidis (n=17/35, 48.6%), followed by Staphylococcus aureus (n=4/35, 11.4%). From 2010-2014 to 2015-2019, antimicrobial resistance increased against moxifloxacin (11.1-54.5%, p=0.07), ciprofloxacin (54.5-72.7%, p=0.659) and oxacillin (66.7-93.3%, p=0.13).ConclusionsThe observed incidence and microbial spectra were compatible with previous studies. A trend towards growing moxifloxacin and ciprofloxacin resistance was observed. Surveillance remains crucial to prevent treatment failure from antimicrobial resistance.
  • bookPart
    Infeçção no Paciente em Terapia intensiva
    (2016) GIRãO, Evelyne Santana; PERDIGãO NETO, Lauro; LOBO, Renata Desordi; MENDES, Elisa Teixeira; OLIVEIRA, Maura Salaroli de; COSTA, Silvia Figueiredo
  • bookPart
    Bacteremias e infecções de cateter venoso central
    (2022) VINHOLE, Ana Rubia Guedes; MENDES, Elisa Teixeira; GIRãO, Evelyne Santana; OLIVEIRA, Maura Salaroli de; LOBO, Renata Desordi
  • article 7 Citação(ões) na Scopus
    Management of acute stroke and urgent neurointerventional procedures during COVID-19 pandemic: recommendations on the Scientific Department on Cerebrovascular Diseases of the Brazilian Academy of Neurology, Brazilian Society of Cerebrovascular Diseases and Brazilian Society of Neuroradiology
    (2020) MONT'ALVERNE, Francisco Jose Arruda; LIMA, Fabricio Oliveira; NOGUEIRA, Raul Gomes; FREITAS, Carlos Clayton Macedo de; PONTES NETO, Octavio Marques; SILVA, Gisele Sampaio; OLIVEIRA, Maura Salaroli de; FRUDIT, Michel; CALDAS, Jose Guilherme Mendes Pereira; ABUD, Daniel Giansante; CONFORTO, Adriana Bastos; CARVALHO, Fernanda Martins Maia; DIAS, Francisco Antunes; BAZAN, Rodrigo; AVELAR, Wagner Mauad; MORO, Carla Heloisa Cabral; MAGALHAES, Pedro Silva Correa de; MIRANDA, Maramelia; BARBOSA, Leandro de Assis; FIOROT JUNIOR, Jose Antonio; CARDOSO, Fabricio Buchdid; REBELLO, Leticia Costa; PARENTE, Bruno de Sousa Mendes; FARIA, Mario de Barros; FREITAS, Gabriel Rodriguez de; ZETOLA, Viviane de Hiroki Flumignan; OLIVEIRA-FILHO, Jamary; BEZERRA, Daniel da Cruz; RODRIGUES, Jorge Luis Nobre; KUSTER, Gustavo; MARTINS, Sheila; CARVALHO, Joao Jose Freitas de
    Introduction: Although the 2019 severe acute respiratory syndrome coronavirus 2 infection (SARS-CoV-2, COVID-19) pandemic poses new challenges to the healthcare system to provide support for thousands of patients, there is special concern about common medical emergencies, such as stroke, that will continue to occur and will require adequate treatment. The allocation of both material and human resources to fight the pandemic cannot overshadow the care for acute stroke, a time-sensitive emergency that with an inefficient treatment will further increase mortality and long-term disability. Objective: This paper summarizes the recommendations from the Scientific Department on Cerebrovascular Diseases of the Brazilian Academy of Neurology, the Brazilian Society of Cerebrovascular Diseases and the Brazilian Society of Neuroradiology for management of acute stroke and urgent neuro-interventional procedures during the COVID-19 pandemic, including proper use of screening tools, personal protective equipment (for patients and health professionals), and patient allocation.
  • article 1 Citação(ões) na Scopus
    Correlating drug prescriptions with prognosis in severe COVID-19: first step towards resource management
    (2022) LEVIN, Anna S.; FREIRE, Maristela P.; OLIVEIRA, Maura Salaroli de; NASTRI, Ana Catharina S.; HARIMA, Leila S.; PERDIGAO-NETO, Lauro Vieira; MAGRI, Marcello M.; FIALKOVITZ, Gabriel; FIGUEIREDO, Pedro H. M. F.; SICILIANO, Rinaldo Focaccia; SABINO, Ester C.; CARLOTTI, Danilo P. N.; RODRIGUES, Davi Silva; NUNES, Fatima L. S.; FERREIRA, Joao Eduardo
    Background Optimal COVID-19 management is still undefined. In this complicated scenario, the construction of a computational model capable of extracting information from electronic medical records, correlating signs, symptoms and medical prescriptions, could improve patient management/prognosis. Methods The aim of this study is to investigate the correlation between drug prescriptions and outcome in patients with COVID-19. We extracted data from 3674 medical records of hospitalized patients: drug prescriptions, outcome, and demographics. The outcome evaluated was hospital outcome. We applied correlation analysis using a Logistic Regression algorithm for machine learning with Lasso and Matthews correlation coefficient. Results We found correlations between drugs and patient outcomes (death/discharged alive). Anticoagulants, used very frequently during all phases of the disease, were associated with good prognosis only after the first week of symptoms. Antibiotics very frequently prescribed, especially early, were not correlated with outcome, suggesting that bacterial infections may not be important in determining prognosis. There were no differences between age groups. Conclusions In conclusion, we achieved an important result in the area of Artificial Intelligence, as we were able to establish a correlation between concrete variables in a real and extremely complex environment of clinical data from COVID-19. Our results are an initial and promising contribution in decision-making and real-time environments to support resource management and forecasting prognosis of patients with COVID-19.
  • article 0 Citação(ões) na Scopus
    Evaluation of eleven immunochromatographic assays for SARS-CoV-2 detection: investigating the dengue cross-reaction
    (2022) OLIVEIRA, Beatriz Araujo; OLIVEIRA, Lea Campos de; OLIVEIRA, Franciane Mendes de; PEREIRA, Geovana Maria; SOUZA, Regina Maia de; MANULI, Erika Regina; MARCHINI, Fabricio Klerynton; ESPINOZA, Evelyn Patricia Sanchez; PARK, Marcelo; TANIGUCHI, Leandro; MENDES, Pedro Vitale; FRANCO, Lucas Augusto Moyses; NASTRI, Ana Catharina; OLIVEIRA, Maura Salaroli de; VIEIRA JUNIOR, Jose Mauro; KALLAS, Esper Georges; LEVIN, Anna Sara; SABINO, Ester Cerdeira; COSTA, Silvia Figueiredo
    COVID-19 disease is spread worldwide and diagnostic techniques have been studied in order to contain the pandemic. Immunochromatographic (IC) assays are feasible and a low-cost alternative especially in low and middle-income countries, which lack structure to perform certain diagnostic techniques. Here we evaluate the sensitivity and specificity of eleven different IC tests in 145 serum samples from confirmed cases of COVID-19 using RT-PCR and 100 negative serum samples from blood donors collected in February 2019. We also evaluated the cross-reactivity with dengue using 20 serum samples from patients with confirmed diagnosis for dengue collected in early 2019 through four different tests. We found high sensitivity (92%), specificity (100%) and an almost perfect agreement (Kappa 0.92) of IC assay, especially when we evaluated IgG and IgM combined after 10 days from the onset of symptoms with RT-PCR. However, we detected cross-reactivity between dengue and COVID-19 mainly with IgM antibodies (5 to 20% of cross-reaction) and demonstrated the need for better studies about diagnostic techniques for these diseases.
  • article 9 Citação(ões) na Scopus
    Performance of a qualitative rapid chromatographic immunoassay to diagnose COVID-19 in patients in a middle-income country
    (2020) COSTA, Silvia Figueiredo; BUSS, Lewis; ESPINOZA, Evelyn Patricia Sanchez; JR, Jose Mauro Vieira; SILVA, Lea Campos de Oliveira da; SOUZA, Regina Maia de; NETO, Lauro Perdigao; PORTO, Ana Paula Matos; LAZARI, Carolina; SANTOS, Vera Aparecida dos; DUARTE, Alberto da Silva; NASTRI, Ana Catharina; LEITE, Gabriel Fialkovitz da Costa; MANULI, Erika; OLIVEIRA, Maura Salaroli de; ZAMPELLI, Daniella Bosco; PASTORE JUNIOR, Laerte; SEGURADO, Aluisio Cotrim; LEVIN, Anna S.; SABINO, Ester
    Objectives: We evaluated a rapid chromatographic immunoassay (IgG/IgM antibodies) and an ELISA assay to diagnose COVID-19 in patient sat two Brazilian hospitals. Methods: A total of 122 subjects with COVID-19 were included: 106 SARS-COV-2 RT-PCR-positive patients and 16 RT-PCR-negative patients with symptoms and chest computed tomography (CT) consistent with COVID-19. Ninety-six historical blood donation samples were used as controls. Demographic and clinical characteristics were retrieved from electronic records. Sensitivity and specificity were calculated, as were their 95% binomial confidence intervals using the Clopper-Pearson method. All analyses were performed in R version 3.6.3. Results: The sensitivity of the chromatographic immunoassay in all RT-PCR-positive patients, irrespective of the timing of symptom onset, was 85.8% (95% binomial CI 77.7% to 91.9%). This increased with time after symptom onset, and at >14 days was 94.9% (85.9% to 98.9%). The specificity was 100% (96.4% to 100%). 15/16 (94%) RT- PCR-negative cases tested positive. The most frequent comorbidities were hypertension and diabetes mellitus and the most frequent symptoms were fever, cough, and dyspnea. All RT-PCR-negative patients had pneumonia. The most frequent thoracic CT findings were ground glass changes (n = 11, 68%), which were bilateral in 9 (56%) patients, and diffuse reticulonodular infiltrates (n = 5, 31%). Conclusions: The COVID-19 rapid chromatographic immunoassay evaluated in this study had a high sensitivity and specificity using plasma, particularly after 14 days from symptom onset. ELISA and qualitative rapid chromatographic immunoassays can be used for the diagnosis of RT-PCR-negative patients.
  • article 7 Citação(ões) na Scopus
    Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records
    (2022) RODRIGUES, Davi Silva; NASTRI, Ana Catharina S.; MAGRI, Marcello M.; OLIVEIRA, Maura Salaroli de; SABINO, Ester C.; FIGUEIREDO, Pedro H. M. F.; LEVIN, Anna S.; FREIRE, Maristela P.; HARIMA, Leila S.; NUNES, Fatima L. S.; FERREIRA, Joao Eduardo
    Background COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. Methods We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clinicas (Sao Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient's outcome. Results Time series-based machine learning models are capable of predicting a COVID-19 patient's outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). Conclusions Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.