Determination of global chemical patterns in exhaled breath for the discrimination of lung damage in postCOVID patients using olfactory technology.

The objective of this work was to evaluate the use of an electronic nose and chemometric analysis to discriminate global patterns of volatile organic compounds (VOCs) in breath of postCOVID syndrome patients with pulmonary sequelae. A cross-sectional study was performed in two groups, the group 1 were subjects recovered from COVID-19 without lung damage and the group 2 were subjects recovered from COVID-19 with impaired lung function. The VOCs analysis was executed using a Cyranose 320 electronic nose with 32 sensors, applying principal component analysis (PCA), Partial Least Square-Discriminant Analysis, random forest, canonical discriminant analysis (CAP) and the diagnostic power of the test was evaluated using the ROC (Receiver Operating Characteristic) curve. A total of 228 participants were obtained, for the postCOVID group there are 157 and 71 for the control group, the chemometric analysis results indicate in the PCA an 84% explanation of the variability between the groups, the PLS-DA indicates an observable separation between the groups and 10 sensors related to this separation, by random forest, a classification error was obtained for the control group of 0.090 and for the postCOVID group of 0.088 correct classification. The CAP model showed 83.8% of correct classification and the external validation of the model showed 80.1% of correct classification. Sensitivity and specificity reached 88.9% (73.9%-96.9%) and 96.9% (83.7%-99.9%) respectively. It is considered that this technology can be used to establish the starting point in the evaluation of lung damage in postCOVID patients with pulmonary sequelae.

Volatile Organic Compound Exhaled Breath Sensing

This book chapter presents a broad overview of the application of nanotechnology in the biomedical area, exemplified by the application of several gas sensors (electrochemical sensors, piezoelectric sensors, optical, chemoresistive, metal oxide sensors, surface acoustic wave sensors) and focusing on the study of volatile organic compounds (VOCs) in exhaled breath for the screening of diseases of worldwide interest such as breast cancer, lung cancer, COVID-19, post COVID-19 syndrome, colorectal cancer, prostate cancer, diabetes, chronic obstructive disease, among others. This document aims to provide the state of the art in disruptive technologies based on nanosensors, especially electronic noses and the advances and perspectives in this field. The present work represents an important tool for researchers who are in the field of the development of sensing disruptive technologies for the study of VOCs in biological matrices (i.e., exhaled breath). Thus, the application of gas sensors has proven to be feasible in the biomedical area and a promising area within the diagnosis of communicable and non-communicable diseases, to be applied in POC settings, clinics, hospitals, doctors’ offices, and especially in-field applications for less-favored populations where they lack the minimum resources to achieve universal health coverage.

An Analysis COVID-19 in Mexico: a Prediction of Severity.

BACKGROUND: Coronavirus disease 2019 (COVID-19) causes a mild illness in most cases; forecasting COVID-19-associated mortality and the demand for hospital beds and ventilators are crucial for rationing countries' resources. OBJECTIVE: To evaluate factors associated with the severity of COVID-19 in Mexico and to develop and validate a score to predict severity in patients with COVID-19 infection in Mexico. DESIGN: Retrospective cohort. PARTICIPANTS: We included 1,435,316 patients with COVID-19 included before the first vaccine application in Mexico; 725,289 (50.5%) were men; patient's mean age (standard deviation (SD)) was 43.9 (16.9) years; 21.7% of patients were considered severe COVID-19 because they were hospitalized, died or both. MAIN MEASURES: We assessed demographic variables, smoking status, pregnancy, and comorbidities. Backward selection of variables was used to derive and validate a model to predict the severity of COVID-19. KEY RESULTS: We developed a logistic regression model with 14 main variables, splines, and interactions that may predict the probability of COVID-19 severity (area under the curve for the validation cohort = 82.4%). CONCLUSIONS: We developed a new model able to predict the severity of COVID-19 in Mexican patients. This model could be helpful in epidemiology and medical decisions.

Chemometric analysis of the global pattern of volatile organic compounds in the exhaled breath of patients with COVID-19, post-COVID and healthy subjects. Proof of concept for post-COVID assessment.

The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC1 = 95.9%, PC2 = 1.0%, PC3 = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%-99.9%) and 100% (88.4%-100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects.

A review of Environmental risks and vulnerability factors of indigenous populations from Latin America and the Caribbean in the face of the COVID-19.

Latin America and the Caribbean (LAC) was declared a new epicentre of the coronavirus pandemic by the World Health Organization (WHO) on 22 May 2020. As of 13 January 2021, the numbers of deaths and cases caused by COVID-19 in LAC reported are 552,000 and 17'485,000 respectively. LAC concentrates the largest percentage of indigenous populations throughout the world. In this region, poverty is persistent and particularly rural indigenous peoples hold the steepest barriers to health services and experience profound discrimination based on ethnicity, poverty, and language, compared to their non-indigenous counterparts. The information regarding the health of indigenous populations, in general, is scarce, and this problem is aggravated in the face of the COVID-19 pandemic. Therefore, the main objective of this work is to address the overall scenario of indigenous peoples in the Latin American and Caribbean region from March 2020 to January 2021, in this manner gathering information regarding health problems, economic, social, cultural and environmental factors that make indigenous populations in LAC particularly vulnerable to serious health effects from the COVID-19 pandemic, as well as compiling the mitigation strategies implemented in indigenous communities.

Comparative analysis of chemical breath-prints through olfactory technology for the discrimination between SARS-CoV-2 infected patients and controls.

BACKGROUND: We identified a global chemical pattern of volatile organic compounds in exhaled breath capable of discriminating between COVID-19 patients and controls (without infection) using an electronic nose. METHODS: The study focused on 42 SARS-CoV-2 RT-qPCR positive subjects as well as 42 negative subjects. Principal component analysis indicated a separation of the study groups and provides a cumulative percentage of explanation of the variation of 98.3%. RESULTS: The canonical analysis of principal coordinates model shows a separation by the first canonical axis CAP1 (r2 = 0.939 and 95.23% of correct classification rate), the cut-off point of 0.0089; 100% sensitivity (CI 95%:91.5-100%) and 97.6% specificity (CI 95%:87.4-99.9%). The predictive model usefulness was tested on 30 open population subjects without prior knowledge of SARS-CoV-2 RT-qPCR status. Of these 3 subjects exhibited COVID-19 suggestive breath profiles, all asymptomatic at the time, two of which were later shown to be SARS-CoV-2 RT-qPCR positive. An additional subject had a borderline breath profile and SARS-CoV-2 RT-qPCR positive. The remaining 27 subjects exhibited healthy breath profiles as well as SARS-CoV-2 RT-qPCR test results. CONCLUSIONS: In all, the use of olfactory technologies in communities with high transmission rates as well as in resource-limited settings where targeted sampling is not viable represents a practical COVID-19 screening approach capable of promptly identifying COVID-19 suspect patients and providing useful epidemiological information to guide community health strategies in the context of COVID-19.