Content curation on virtual environments: a perspective from the university professor

The objective of this paper was to characterize the current state of content curation in university professors. Also, a diagnosis was made to establish the informational competencies that these professionals possessed. The study methodology was based on the quantitative perspective with a descriptive scope and the use of the survey as a research technique that evaluated four dimensions: search, selection, characterization and dissemination of digital content. The results presented a set of shortcomings in the explicit dimensions, with an emphasis on characterization, since in educational digital content the textual format prevailed and critical evaluations were scarce. Moreover, insufficient use of content curation tools was detected. A wider participation of subjects due to the social distancing caused by covid-19 was evidenced as a limitation. The novelty of the study consisted in analyzing content curation with an informational competence approach, transversal in the training of university professors. It was concluded that there is little training in relation to the curation of content for teaching, as some indications of informational performance were observed, in isolation that hindered a better preparation of the subject. (English) [ FROM AUTHOR]

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5, and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.

[Clinical features of COVID-19 infection among patients consulting at an emergency service]. / Evaluación de los predictores clínicos de infección respiratoria aguda por coronavirus SARS-CoV-2 en población adulta.

BACKGROUND: COVID-19 is a serious public health problem worldwide. AIM: To describe the clinical features of COVID-19 infection in adult patients consulting at an Emergency Service. MATERIAL AND METHODS: Descriptive prospective study of adult patients with suspected COVID-19 consulting between April 1 and July 31, 2020, at the Emergency Service of a clinical hospital. Clinical features, chronic comorbidities and demographic data were recorded. RESULTS: We assessed 2,958 adult patients aged 42 ± 15 years (46% males). In 54% of them, COVID-19 infection was confirmed, 40% had preexisting diseases, especially hypertension (15%), hypothyroidism (6%), diabetes (6%), asthma (5%) and obesity (6%). The main clinical manifestations associated with COVID-19 were general malaise (79%), anorexia (38%), myalgia (64%), fever (52%), headache (70%), anosmia/dysgeusia (60%), cough (56%), dyspnea (54%) and diarrhea (36%). In the multivariate analysis, the main clinical predictors of COVID-19 infection were malaise, anorexia, fever, myalgia, headache, nasal congestion, cough, expectoration, anosmia/dysgeusia, and history of close contact with a SARS-CoV-2 patient. Odynophagia and chest discomfort were negative predictors of the disease. The history of fever associated with anorexia, cough, and dyspnea or anosmia/dysgeusia and close contact with a SARS-CoV-2 patient had high specificity and positive predictive value for COVID-19 infection. CONCLUSIONS: Clinical features of COVID-19 infection were highly unspecific in these patients. Clinical diagnostic prediction models could be useful to support healthcare decision making at primary care setting.

[Clinical features and predictors of severity in 1022 adults with COVID-19]. / Manifestaciones clínicas y predictores de gravedad en pacientes adultos con infección respiratoria aguda por coronavirus SARS-CoV-2.

BACKGROUND: In December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan city and spread rapidly throughout China and the world. AIM: To describe the clinical features, risk factors, and predictors of hospitalization in adult patients treated for acute respiratory infections associated with coronavirus SARS-CoV-2. MATERIAL AND METHODS: Descriptive prospective study of ambulatory and hospitalized adult patients with confirmed COVID-19 attended between April 1 and May 31, 2020. Clinical features, chronic comorbidities and demographic data were recorded, and patients were followed for two months as outpatients. RESULTS: We assessed 1,022 adults aged 41 ± 14 years (50% men) with laboratory-confirmed COVID-19. One-third had comorbidities, specially hypertension (12.5%), hypothyroidism (6.6%), asthma (5.4%) and diabetes (4.5%). Hospital admission was required in 11%, 5.2% were admitted to critical care unit and 0.9% were connected to mechanical ventilation. Common symptoms included fatigue (55.4%), fever (52.5%), headache (68.6%), anosmia/dysgeusia (53.2%), dry cough (53.4%), dyspnea (27.4%) and diarrhea (35.5%). One third of patients reported persistence of symptoms at one-month follow-up, specially fatigue, cough and dyspnea. In the multivariate analysis, age, fever, cough, dyspnea and immunosuppression were associated with hospitalization and ICU admission. Age, male sex and moderate-severe dyspnea were associated with requirement of mechanical ventilation. The main predictors of prolonged clinical course were female sex, presence of comorbidities, history of dyspnea, cough, myalgia and abdominal pain. CONCLUSIONS: Clinical features of COVID-19 were highly unspecific. Prediction models for severity, will help medical decision making at the primary care setting.

Association of environmental and meteorological factors on the spread of COVID-19 in Victoria, Mexico, and air quality during the lockdown.

This study aims to analyze the correlation between environmental factors and confirmed cases of COVID-19 pandemic in Victoria, Mexico. The analysis is performed at the micro-level, filtering only confirmed cases of COVID-19 that are located near air quality monitoring stations, within an approximate coverage of 2.5 km, in order to identify a possible specific association between PM2.5, PM10, carbon monoxide (CO), relative humidity, temperature, absolute humidity, and total confirmed cases of COVID-19. The results evidenced that the cases of COVID-19 were very strongly associated with CO concentration. Our results also suggested that particulate matter pollution (PM2.5 and PM10) exposure have a significant correlation for confirmed cases of COVID-19. Furthermore, we studied the changes in air quality during the COVID-19 outbreak by comparing the average concentration of the four weeks before lockdown (February 16 to March 14, 2020) and the following twelve weeks during the partial lockdown (March 15 to June 06, 2020), revealing a very significant decrease of pollutants.

Association Between Pulmonary Embolism and COVID-19 in Emergency Department Patients Undergoing Computed Tomography Pulmonary Angiogram: The PEPCOV International Retrospective Study.

BACKGROUND: There have been reports of procoagulant activity in patients with COVID-19. Whether there is an association between pulmonary embolism (PE) and COVID-19 in the emergency department (ED) is unknown. The aim of this study was to assess whether COVID-19 is associated with PE in ED patients who underwent a computed tomographic pulmonary angiogram (CTPA). METHODS: A retrospective study in 26 EDs from six countries. ED patients in whom a CTPA was performed for suspected PE during a 2-month period covering the pandemic peak. The primary endpoint was the occurrence of a PE on CTPA. COVID-19 was diagnosed in the ED either on CT or reverse transcriptase-polymerase chain reaction. A multivariable binary logistic regression was built to adjust with other variables known to be associated with PE. A sensitivity analysis was performed in patients included during the pandemic period. RESULTS: A total of 3,358 patients were included, of whom 105 were excluded because COVID-19 status was unknown, leaving 3,253 for analysis. Among them, 974 (30%) were diagnosed with COVID-19. Mean (±SD) age was 61 (±19) years and 52% were women. A PE was diagnosed on CTPA in 500 patients (15%). The risk of PE was similar between COVID-19 patients and others (15% in both groups). In the multivariable binary logistic regression model, COVID-19 was not associated with higher risk of PE (adjusted odds ratio = 0.98, 95% confidence interval = 0.76 to 1.26). There was no association when limited to patients in the pandemic period. CONCLUSION: In ED patients who underwent CTPA for suspected PE, COVID-19 was not associated with an increased probability of PE diagnosis. These results were also valid when limited to the pandemic period. However, these results may not apply to patients with suspected COVID-19 in general.