Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza’s relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.

Health care workers (HCW) are at high risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The incidence of SARS-CoV-2 infection in HCW has been examined in cross-sectional studies by quantitative polymerase chain reaction (qPCR) tests, which may lead to underestimating exact incidence rates. We thus investigated the incidence of SARS-CoV-2 infection in a group of HCW at a dedicated coronavirus disease 2019 (COVID-19) hospital in a six-month follow-up period. We conducted a prospective cohort study on 109 participants of both sexes working in areas of high, moderate, and low SARS-CoV-2 exposure. qPCR tests in nasopharyngeal swabs and anti-SARS-CoV-2 IgG serum antibodies were assessed at the beginning and six months later. Demographic, clinical, and laboratory parameters were analyzed according to IgG seropositivity by paired Student’s T-test or the chi-square test. The incidence rate of SARS-CoV-2 infection was considerably high in our cohort of HCW (58%), among whom 67% were asymptomatic carriers. No baseline risk factors contributed to the infection rate, including the workplace. It is still necessary to increase hospital safety procedures to prevent virus transmissibility from HCW to relatives and non-COVID-19 patients during the upcoming waves of contagion.

The difficulty in predicting fatal outcomes in patients with coronavirus disease 2019 (COVID‐19) impacts the general morbidity and mortality due to severe acute respiratory syndrome‐coronavirus 2 infection, as it wears out the hospital services that care for these patients. Unfortunately, in several of the candidates for prognostic biomarkers proposed, the predictive power is compromised when patients have pre‐existing comorbidities. A cohort of 147 patients hospitalized for severe COVID‐19 was included in a descriptive, observational, single‐center, and prospective study. Patients were recruited during the first COVID‐19 pandemic wave (April–November 2020). Data were collected from the clinical history whereas immunophenotyping by multiparameter flow cytometry analysis allowed us to assess the expression of surface markers on peripheral leucocyte. Patients were grouped according to the outcome in survivors or non‐survivors. The prognostic value of leucocyte, cytokines or HLA‐DR, CD39, and CD73 was calculated. Hypertension and chronic renal failure but not obesity and diabetes were conditions more frequent among the deceased patient group. Mixed hypercytokinemia, including inflammatory (IL‐6) and anti‐inflammatory (IL‐10) cytokines, was more evident in deceased patients. In the deceased patient group, lymphopenia with a higher neutrophil‐lymphocyte ratio (NLR) value was present. HLA‐DR expression and the percentage of CD39+ cells were higher than non‐COVID‐19 patients but remained similar despite the outcome. Receiver operating characteristic analysis and cutoff value of NLR (69.6%, 9.4), percentage NLR (pNLR; 71.1%, 13.6), and IL‐6 (79.7%, 135.2 pg/mL). The expression of HLA‐DR, CD39, and CD73, as many serum cytokines (other than IL‐6) and chemokines levels do not show prognostic potential, were compared to NLR and pNLR values.

Background The difficulty to predict fatal outcomes in COVID-19 patients, impacts in the general morbidity and mortality due to SARSCoV2 infection, as it wears out the hospital services that care for these patients. Unfortunately, in several of the candidates for prognostic biomarkers proposed, the predictive power is compromised when patients have pre-existing co-morbidities. Methods A cohort of one hundred and forty-seven patients hospitalized for severe COVID19 was included in a descriptive, observational, single-center, and prospective study. Patients were recruited during the first COVID-19 pandemic wave (April-Nov, 2020). Data were collected from the clinical history while immunophenotyping by multiparameter flow cytometry analysis allowed us to assess the expression of surface markers on peripheral leukocytes. Patients were grouped according to the outcome in survivor or decease. The prognostic value of leukocytes, cytokines or HLA-DR, CD39, and CD73 was calculated. Results Hypertension and chronic renal failure but not obesity and diabetes were conditions more frequent among the decease group. Mixed hypercitokinemia, including inflammatory (IL-6) and anti-inflammatory (IL-10) cytokines, was more evident in deceased patients. In the decease group, lymphopenia with a higher NLR value was present. HLA-DR expression and the percentage of CD39+ cells were higher than non COVID-19 patients, but remain similar despite outcome. ROC analysis and cut-off value of NLR (69.6%, 9.4), pNLR (71.1%, 13.6), IL-6 (79.7%, 135.2 pg/mL). Conclusion The expression of HLA-DR, CD39, and CD73, as many serum cytokines (other than IL-6) and chemokines levels do not show prognostic potential compared to NLR and pNLR values.