[Validation of a symptom scale for COVID-19 patients in ambulatory care]

Background: A symptom scale can be useful for the standardization of clinical evaluations and follow-up of COVID-19 patients in ambultaroy care. Scale development should be accompanied by an assessment of its reliablility and validity. Objective: To develop and measure the psychometric characteristics of a COVID-19 symptom scale to be answered by either healthcare personnel or adult patients in ambulatory care. Material and methods: The scale was developed by an expert panel using the Delphi method. We evaluated inter-rater reliability, where we defined a good correlation if Spearman's Rho was >= 0.8;test-retest, where we defined a good correlation if Spearman's Rho was >= 0.7;factor analysis using principal component methodology;and discriminant validity using Mann-Whitney's U test. A p < 0.05 was considered statistically significant. Results: We obtained an 8 symptom scale, each symptom is scored from 0-4, with a total minimum score of 0 and a maximum of 32 points. Inter-rater reliability was 0.995 (n = 31), test-retest showed correlation of 0.88 (n = 22), factor analysis detected 4 factors (n = 40) and discriminant capacity of healthy versus sick adults was significant (p < 0.0001, n = 60). Conclusions: We obtained a reliable and valid Spanish (from Mexico) symptom scale for COVID-19 ambulatory care, answerable by patients and health care staff. Copyright © 2023 Revista Medica del Instituto Mexicano del Seguro Social.

Perception of diet, health habits, personal agency during COVID-19

Analyzing the effect of the variables Eating Habits, Emotional Condition and Physical Activity (PA) Agency on Diet Perception and PA Time, in Colombian university students under COVID-19 confinement conditions. Preliminary correlational research was conducted through a comparative survey with both exploratory and explanatory scope. It was applied to 389 students who voluntarily completed the instrument on a Google Form. The structural model explains respectively 38% and 53% of the variability of the students' diet perception and PA time. The model shows both statistical (X2 = 84 [47 gl p = 0,09]) and practical (IBBAN = 96;IBBANN = 99;IAC = 0,99 and RMSEA = 0,02 [0,00, 0,04]) goodness of fit. Hence, it can be stated that this inclusive model has the same explanatory power as the saturated one, which relates all variables to each other. Eating habits and intention were found to have a direct effect on the university students' diet perception. Just as well, eating habits, intention and diet perception were observed to increase PA time.

TFM a Dataset for Detection and Recognition of Masked Faces in the Wild

Droplet transmission is one of the leading causes of the spread of respiratory infections, such as coronavirus disease (COVID-19). The proper use of face masks is an effective way to prevent the transmission of such diseases. Nonetheless, different types of masks provide various degrees of protection. Hence, automatic recognition of face mask types may benefit the control access to facilities where a specific protection degree is required. In the last two years, several deep learning models have been proposed for face mask detection and properly wearing mask recognition. However, the current publicly available datasets do not consider the different mask types and occasionally lack real-world elements needed to train robust models. In this paper, we introduce a new dataset named TFM with sufficient size and variety to train and evaluate deep learning models for face mask detection and recognition. This dataset contains more than 135,000 annotated faces from about 100,000 photographs taken in the wild. We consider four mask types (cloth, respirators, surgical and valved) as well as unmasked faces, of which up to six can appear in a single image. The photographs were mined from Twitter within two years since the beginning of the COVID-19 pandemic. Thus, they include diverse scenes with real-world variations in background and illumination. With our dataset, the performance of four state-of-the-art object detection models is evaluated. The experimental results show that YOLOv5 can achieve about 90% of mAP@0.5, demonstrating that the TFM dataset can be used to train robust models and may help the community step forward in detecting and recognizing masked faces in the wild. Our dataset and pre-trained models used in the evaluation will be available upon the publication of this paper. © 2022 ACM.

Twitter Face Image Mining for Recognition of Different Face Mask Types

In the current pandemic of coronavirus disease (COVID-19), an effective way to prevent the transmission and infection of the virus is the proper use of face masks. However, the different types of masks provide different degrees of protection. For instance, valved masks protect the user but do not help to stop the transmission. Hence, the automatic recognition of face mask types may benefit applications that control access to facilities where a certain facepiece is required. In this paper, we propose a Twitter mining framework to gather a large-scale dataset of masked faces suitable to train deep learning-based models for face mask recognition. We employ a keyword-based selection where non-face images are discarded by an efficient face detector (Retinaface). Finally, we train a state-of-the-art CNN architecture (ConvNeXt) for recognizing the wearing mask. We also present a brief analysis of more than two million image-based tweets acquired over two years since the beginning of the pandemic. The code of the proposed framework and a preliminary dataset of more than 10K faces (manually annotated into unmasked, surgical, cloth, respirators, and valved masks) are available on github.com/GibranBenitez/FaceMask Twitter. © 2022 The authors and IOS Press. All rights reserved.

Poor Survival in COVID-19 Associated with Lymphopenia and Higher Neutrophile-Lymphocyte Ratio

BACKGROUND: Immune cell counts in blood in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection may be useful prognostic biomarkers of disease severity, mortality, and response to treatment. OBJECTIVES: To analyze sub-populations of lymphocytes at hospital admission in survivors and deceased from severe pneumonia due to coronavirus disease-2019 (COVID-19). METHODS: We conducted a cross-sectional study of healthcare workers confirmed with SARS-CoV-2 in convalescents (control group) and healthy controls (HC) diagnosed with severe COVID-19. Serum samples were taken at hospital admission and after recovery. Serum samples ≥ 25 days after onset of symptoms were analyzed for lymphocyte subpopulations through flow cytometry. Descriptive statistics, Kruskall-Wallis test, receiver operating characteristic curve, calculation of sensitivity, specificity, predictive values, and Kaplan-Meier analysis were performed. RESULTS: We included 337 patients: 120 HC, 127 convalescents, and 90 severe COVID-19 disease patients (50 survivors, 40 deceased). For T cells, total lymphocytes ≥ 800/μL, CD3+ ≥ 400/μL, CD4+ ≥ 180/μL, CD8+ ≥ 150/μL, B cells CD19+ ≥ 80/μL, and NK ≥ 34/μL subsets were associated with survival in severe COVID-19 disease patients. All subtypes of lymphocytes had higher concentrations in survivors than deceased, but similar between HC and convalescents. Leukocytes ≥ 10.150/μL or neutrophils ≥ 10,000/μL were associated with increased mortality. The neutrophil-to-lymphocyte ratio (NLR) ≥ 8.5 increased the probability of death in severe COVID-19 (odds ratio 11.68). CONCLUSIONS: Total lymphocytes;NLR;and levels of CD3+, CD4+, CD8+, and NK cells are useful as biomarkers of survival or mortality in severe COVID-19 disease and commonly reach normal levels in convalescents.