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1.
PLoS One ; 17(2): e0263888, 2022.
Article in English | MEDLINE | ID: covidwho-1690705

ABSTRACT

BACKGROUND: The COVID Stress Scales (CSS) assess health- and contamination-related distress in the face of a medical outbreak like the ongoing COVID-19 pandemic. Though the CSS is translated into 21 languages, it has not been validated in a Swedish national sample. AIM: Our general objective is to provide a translation, replication, and validation of the CSS and test its convergent- and discriminant validity in relation to anxiety, health anxiety, depression, and stress in the general Swedish population. We also present latent psychometric properties by modelling based on item response theory. METHODS: Participants consisted of 3044 Swedish adults (> 18 years) from a pre-stratified (gender, age, and education) sample from The Swedish Citizen Panel. Mental health status was assessed by validated instruments, including the CSS, PHQ-4, SHAI-14, and PSS-10. RESULTS: Results indicate that our Swedish translation of CSS has good psychometric properties and consists of 5 correlated factors. DISCUSSION: The CSS is useful either as a unidimensional or multidimensional construct using the CSS scales to measure key facets of pandemic-related stress. CONCLUSIONS: The findings support the cross-cultural validity of the CSS and its potential utility in understanding many of the emotional challenges posed by the current and future pandemics.


Subject(s)
COVID-19/psychology , Psychiatric Status Rating Scales , Stress, Psychological/psychology , Adolescent , Adult , Aged , Aged, 80 and over , Discriminant Analysis , Factor Analysis, Statistical , Female , Humans , Least-Squares Analysis , Male , Middle Aged , Regression Analysis , Socioeconomic Factors , Sweden , Young Adult
2.
PLoS One ; 17(2): e0263597, 2022.
Article in English | MEDLINE | ID: covidwho-1677591

ABSTRACT

The test-trace-isolate-quarantine (TTIQ) strategy, where confirmed-positive pathogen carriers are isolated from the community and their recent close contacts are identified and pre-emptively quarantined, is used to break chains of transmission during a disease outbreak. The protocol is frequently followed after an individual presents with disease symptoms, at which point they will be tested for the pathogen. This TTIQ strategy, along with hygiene and social distancing measures, make up the non-pharmaceutical interventions that are utilised to suppress the ongoing COVID-19 pandemic. Here we develop a tractable mathematical model of disease transmission and the TTIQ intervention to quantify how the probability of detecting and isolating a case following symptom onset, the fraction of contacts that are identified and quarantined, and the delays inherent to these processes impact epidemic growth. In the model, the timing of disease transmission and symptom onset, as well as the frequency of asymptomatic cases, is based on empirical distributions of SARS-CoV-2 infection dynamics, while the isolation of confirmed cases and quarantine of their contacts is implemented by truncating their respective infectious periods. We find that a successful TTIQ strategy requires intensive testing: the majority of transmission is prevented by isolating symptomatic individuals and doing so in a short amount of time. Despite the lesser impact, additional contact tracing and quarantine increases the parameter space in which an epidemic is controllable and is necessary to control epidemics with a high reproductive number. TTIQ could remain an important intervention for the foreseeable future of the COVID-19 pandemic due to slow vaccine rollout and highly-transmissible variants with the potential for vaccine escape. Our results can be used to assess how TTIQ can be improved and optimised, and the methodology represents an improvement over previous quantification methods that is applicable to future epidemic scenarios.


Subject(s)
COVID-19/epidemiology , Contact Tracing , Quarantine , Basic Reproduction Number , COVID-19/transmission , Discriminant Analysis , Humans
3.
Sci Rep ; 12(1): 1614, 2022 01 31.
Article in English | MEDLINE | ID: covidwho-1661979

ABSTRACT

As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.


Subject(s)
COVID-19/blood , COVID-19/diagnosis , Immunity , Lasers , Pandemics , SARS-CoV-2/immunology , Spectrophotometry, Atomic/methods , Barium/analysis , COVID-19/epidemiology , COVID-19/virology , Data Accuracy , Discriminant Analysis , False Negative Reactions , False Positive Reactions , Humans , Machine Learning , Reverse Transcriptase Polymerase Chain Reaction/methods , SARS-CoV-2/genetics , Sensitivity and Specificity , Zinc/analysis
4.
Anal Chem ; 94(5): 2425-2433, 2022 02 08.
Article in English | MEDLINE | ID: covidwho-1650031

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.


Subject(s)
COVID-19 , Saliva , Discriminant Analysis , Humans , Least-Squares Analysis , Multivariate Analysis , SARS-CoV-2 , Spectroscopy, Fourier Transform Infrared
5.
Gac. méd. Méx ; 157(3): 240-245, may.-jun. 2021. tab, graf
Article in Spanish | WHO COVID, LILACS (Americas) | ID: covidwho-1552050

ABSTRACT

Resumen Introducción: La escasez de aplicaciones centradas en la persona y con vistas al desarrollo de la conciencia del riesgo que representa la pandemia de COVID-19 estimula la exploración y creación de herramientas de carácter preventivo accesibles a la población. Objetivo: Elaboración de un modelo predictivo que permita evaluar el riesgo de letalidad ante infección por el virus SARS-CoV-2. Métodos: Exploración de datos públicos de 16 000 pacientes positivos a COVID-19, para generar un modelo discriminante eficiente, valorado con una función score y que se expresa mediante un cuestionario autocalificado de interés preventivo. Resultados: Se obtuvo una función lineal útil con capacidad discriminante de 0.845; la validación interna con bootstrap y la externa, con 25 % de los pacientes de prueba, mostraron diferencias marginales. Conclusión: El modelo predictivo, basado en 15 preguntas accesibles puede convertirse en una herramienta de prevención estructurada.


Abstract Introduction: The scarcity of person-centered applications aimed at developing awareness on the risk posed by the COVID-19 pandemic, stimulates the exploration and creation of preventive tools that are accessible to the population. Objective: To develop a predictive model that allows evaluating the risk of mortality in the event of SARS-CoV-2 virus infection. Methods: Exploration of public data from 16,000 COVID-19-positive patients to generate an efficient discriminant model, evaluated with a score function and expressed by a self-rated preventive interest questionnaire. Results: A useful linear function was obtained with a discriminant capacity of 0.845; internal validation with bootstrap and external validation, with 25 % of tested patients showing marginal differences. Conclusion: The predictive model with statistical support, based on 15 accessible questions, can become a structured prevention tool.


Subject(s)
Humans , Male , Female , Infant , Child, Preschool , Child , Adolescent , Adult , Middle Aged , Aged , Young Adult , Models, Statistical , COVID-19/prevention & control , Discriminant Analysis , Linear Models , Risk , COVID-19/mortality
6.
Cells ; 10(12)2021 11 25.
Article in English | MEDLINE | ID: covidwho-1542428

ABSTRACT

Acute respiratory distress syndrome (ARDS) is a serious lung condition characterized by severe hypoxemia leading to limitations of oxygen needed for lung function. In this study, we investigated the effect of anandamide (AEA), an endogenous cannabinoid, on Staphylococcal enterotoxin B (SEB)-mediated ARDS in female mice. Single-cell RNA sequencing data showed that the lung epithelial cells from AEA-treated mice showed increased levels of antimicrobial peptides (AMPs) and tight junction proteins. MiSeq sequencing data on 16S RNA and LEfSe analysis demonstrated that SEB caused significant alterations in the microbiota, with increases in pathogenic bacteria in both the lungs and the gut, while treatment with AEA reversed this effect and induced beneficial bacteria. AEA treatment suppressed inflammation both in the lungs as well as gut-associated mesenteric lymph nodes (MLNs). AEA triggered several bacterial species that produced increased levels of short-chain fatty acids (SCFAs), including butyrate. Furthermore, administration of butyrate alone could attenuate SEB-mediated ARDS. Taken together, our data indicate that AEA treatment attenuates SEB-mediated ARDS by suppressing inflammation and preventing dysbiosis, both in the lungs and the gut, through the induction of AMPs, tight junction proteins, and SCFAs that stabilize the gut-lung microbial axis driving immune homeostasis.


Subject(s)
Arachidonic Acids/therapeutic use , Endocannabinoids/therapeutic use , Gastrointestinal Microbiome , Gastrointestinal Tract/pathology , Lung/pathology , Polyunsaturated Alkamides/therapeutic use , Respiratory Distress Syndrome/drug therapy , Respiratory Distress Syndrome/microbiology , Animals , Arachidonic Acids/pharmacology , Butyrates/metabolism , Cecum/pathology , Cell Separation , Colon/drug effects , Colon/pathology , Discriminant Analysis , Dysbiosis/complications , Dysbiosis/microbiology , Endocannabinoids/pharmacology , Enterotoxins , Female , Gastrointestinal Tract/drug effects , Lymph Nodes/drug effects , Lymph Nodes/pathology , Lymphocyte Activation/drug effects , Mice, Inbred C57BL , Pneumonia/drug therapy , Pneumonia/microbiology , Polyunsaturated Alkamides/pharmacology , Respiratory Distress Syndrome/complications , T-Lymphocytes/drug effects
7.
Gac Med Mex ; 157(3): 231-236, 2021.
Article in English | MEDLINE | ID: covidwho-1535078

ABSTRACT

INTRODUCTION: The scarcity of person-centered applications aimed at developing awareness on the risk posed by the COVID-19 pandemic, stimulates the exploration and creation of preventive tools that are accessible to the population. OBJECTIVE: To develop a predictive model that allows evaluating the risk of mortality in the event of SARS-CoV-2 virus infection. METHODS: Exploration of public data from 16,000 COVID-19-positive patients to generate an efficient discriminant model, evaluated with a score function and expressed by a self-rated preventive interest questionnaire. RESULTS: A useful linear function was obtained with a discriminant capacity of 0.845; internal validation with bootstrap and external validation, with 25 % of tested patients showing marginal differences. CONCLUSION: The predictive model with statistical support, based on 15 accessible questions, can become a structured prevention tool.


INTRODUCCIÓN: La escasez de aplicaciones centradas en la persona y con vistas al desarrollo de la conciencia del riesgo que representa la pandemia de COVID-19 estimula la exploración y creación de herramientas de carácter preventivo accesibles a la población. OBJETIVO: Elaboración de un modelo predictivo que permita evaluar el riesgo de letalidad ante infección por el virus SARS-CoV-2. MÉTODOS: Exploración de datos públicos de 16 000 pacientes positivos a COVID-19, para generar un modelo discriminante eficiente, valorado con una función score y que se expresa mediante un cuestionario autocalificado de interés preventivo. RESULTADOS: Se obtuvo una función lineal útil con capacidad discriminante de 0.845; la validación interna con bootstrap y la externa, con 25 % de los pacientes de prueba, mostraron diferencias marginales. CONCLUSIÓN: El modelo predictivo, basado en 15 preguntas accesibles puede convertirse en una herramienta de prevención estructurada.


Subject(s)
COVID-19/prevention & control , Models, Statistical , Adolescent , Adult , Aged , COVID-19/mortality , Child , Child, Preschool , Discriminant Analysis , Female , Humans , Infant , Linear Models , Male , Middle Aged , Risk , Young Adult
8.
Sci Rep ; 11(1): 20793, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479813

ABSTRACT

In Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.


Subject(s)
COVID-19/blood , COVID-19/epidemiology , Cytokines/blood , Aged , Biomarkers/blood , COVID-19 Testing , Case-Control Studies , Cytokines/metabolism , Discriminant Analysis , Female , Humans , Interleukin-6/metabolism , Interleukin-8/metabolism , Italy/epidemiology , Machine Learning , Male , Middle Aged , Pandemics , Regression Analysis , SARS-CoV-2
9.
Sci Rep ; 11(1): 18444, 2021 09 16.
Article in English | MEDLINE | ID: covidwho-1415956

ABSTRACT

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.


Subject(s)
Methicillin Resistance , Staphylococcus aureus/classification , Staphylococcus aureus/growth & development , Deep Learning , Discriminant Analysis , Humans , Metal Nanoparticles/chemistry , Microbial Sensitivity Tests , Neural Networks, Computer , Signal-To-Noise Ratio , Silver/chemistry , Spectrum Analysis, Raman , Staphylococcus aureus/drug effects , Support Vector Machine
10.
Int J Mol Sci ; 22(17)2021 Sep 02.
Article in English | MEDLINE | ID: covidwho-1390657

ABSTRACT

COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.


Subject(s)
Biomarkers/blood , Carbon/metabolism , Liver/metabolism , Metabolome , Nitrogen/metabolism , Amino Acids/metabolism , COVID-19/blood , COVID-19/pathology , COVID-19/virology , Cytokines/blood , Discriminant Analysis , Humans , Least-Squares Analysis , Metabolic Networks and Pathways/genetics , Metabolomics/methods , SARS-CoV-2/isolation & purification , Severity of Illness Index
11.
J Chem Theory Comput ; 17(9): 5896-5906, 2021 Sep 14.
Article in English | MEDLINE | ID: covidwho-1354072

ABSTRACT

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.


Subject(s)
Angiotensin-Converting Enzyme 2/chemistry , Spike Glycoprotein, Coronavirus/chemistry , Discriminant Analysis , Machine Learning , Molecular Dynamics Simulation , Protein Binding , Protein Conformation
12.
Vet Med Sci ; 7(5): 1980-1988, 2021 09.
Article in English | MEDLINE | ID: covidwho-1351271

ABSTRACT

OBJECTIVES: This research aims to explore the factors motivate consumers to eat game meat during a multi-state disease outbreak. METHODS: It proposes a segmentation of consumers based on their attitudes toward and reveals the consumers' food beliefs that motivate their actions. Three segments of game meat consumers were identified: identity seekers, health seekers, and taste seekers. RESULTS: A survey of the potential impact that the COVID-19 crisis has on these three clusters' future food choices showed that the identity and health seekers are more open to a change in food choices. However, the taste seekers are less likely to be influenced by external factors. CONCLUSIONS: This research indicates that for the policymakers, the key is to take game meat consumers as an effective intervention entry point. It is crucial to facilitate healthy food choices and to promote socially- and culturally-appropriate food beliefs by improving public awareness of the risks of game meat, and invest in organic food. RESEARCH IMPLICATIONS: This research provides new insights into the food beliefs of game meat consumers via motivation-based segmentation.


Subject(s)
Animals, Wild/virology , COVID-19/psychology , Meat/standards , Motivation , Adult , Analysis of Variance , Animals , Anxiety , COVID-19/etiology , China , Choice Behavior , Cluster Analysis , Discriminant Analysis , Educational Status , Female , Food, Organic , Health Behavior , Humans , Income , Male , Middle Aged , Surveys and Questionnaires , Taste
13.
Anal Chem ; 93(30): 10391-10396, 2021 08 03.
Article in English | MEDLINE | ID: covidwho-1316694

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic continues to ravage the world, with many hospitals overwhelmed by the large number of patients presenting during major outbreaks. A rapid triage for COVID-19 patient requiring hospitalization and intensive care is urgently needed. Age and comorbidities have been associated with a higher risk of severe COVID-19 but are not sufficient to triage patients. Here, we investigated the potential of attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy as a rapid blood test for classification of COVID-19 disease severity using a cohort of 160 COVID-19 patients. A simple plasma processing and ATR-FTIR data acquisition procedure was established using 75% ethanol for viral inactivation. Next, partial least-squares-discriminant analysis (PLS-DA) models were developed and tested using data from 130 and 30 patients, respectively. Addition of the ATR-FTIR spectra to the clinical parameters (age, sex, diabetes mellitus, and hypertension) increased the area under the ROC curve (C-statistics) for both the training and test data sets, from 69.3% (95% CI 59.8-78.9%) to 85.7% (78.6-92.8%) and 77.8% (61.3-94.4%) to 85.1% (71.3-98.8%), respectively. The independent test set achieved 69.2% specificity (42.4-87.3%) and 94.1% sensitivity (73.0-99.0%). Diabetes mellitus was the strongest predictor in the model, followed by FTIR regions 1020-1090 and 1588-1592 cm-1. In summary, this study demonstrates the potential of ATR-FTIR spectroscopy as a rapid, low-cost COVID-19 severity triage tool to facilitate COVID-19 patient management during an outbreak.


Subject(s)
COVID-19 , Ataxia Telangiectasia Mutated Proteins , Discriminant Analysis , Humans , Least-Squares Analysis , SARS-CoV-2 , Spectroscopy, Fourier Transform Infrared
14.
Angew Chem Int Ed Engl ; 60(31): 17102-17107, 2021 07 26.
Article in English | MEDLINE | ID: covidwho-1245354

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 subjects testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Herein, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , Saliva/chemistry , Animals , Chlorocebus aethiops , Cohort Studies , Discriminant Analysis , Humans , Least-Squares Analysis , Monte Carlo Method , Point-of-Care Testing , Proof of Concept Study , SARS-CoV-2 , Sensitivity and Specificity , Specimen Handling , Spectrophotometry, Infrared , Vero Cells
15.
Anal Chem ; 93(11): 4782-4787, 2021 03 23.
Article in English | MEDLINE | ID: covidwho-1114675

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.


Subject(s)
COVID-19/diagnosis , Peptides/blood , SARS-CoV-2/metabolism , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods , Area Under Curve , COVID-19/metabolism , COVID-19/virology , Case-Control Studies , Discriminant Analysis , High-Throughput Screening Assays , Humans , Least-Squares Analysis , Machine Learning , Principal Component Analysis , ROC Curve , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Tuberculosis/metabolism , Tuberculosis/pathology
16.
Sci Rep ; 11(1): 2941, 2021 02 03.
Article in English | MEDLINE | ID: covidwho-1062774

ABSTRACT

In recent months, Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread throughout the world. COVID-19 patients show mild, moderate or severe symptoms with the latter ones requiring access to specialized intensive care. SARS-CoV-2 infections, pathogenesis and progression have not been clearly elucidated yet, thus forcing the development of many complementary approaches to identify candidate cellular pathways involved in disease progression. Host lipids play a critical role in the virus life, being the double-membrane vesicles a key factor in coronavirus replication. Moreover, lipid biogenesis pathways affect receptor-mediated virus entry at the endosomal cell surface and modulate virus propagation. In this study, targeted lipidomic analysis coupled with proinflammatory cytokines and alarmins measurement were carried out in serum of COVID-19 patients characterized by different severity degree. Serum IL-26, a cytokine involved in IL-17 pathway, TSLP and adiponectin were measured and correlated to lipid COVID-19 patient profiles. These results could be important for the classification of the COVID-19 disease and the identification of therapeutic targets.


Subject(s)
COVID-19/pathology , Lipid Metabolism/physiology , Alarmins/blood , COVID-19/virology , Cytokines/blood , Discriminant Analysis , Female , Humans , Least-Squares Analysis , Lipids/blood , Male , Middle Aged , SARS-CoV-2/isolation & purification , Severity of Illness Index
17.
Anal Chem ; 93(5): 2950-2958, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1041144

ABSTRACT

There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15-30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results (n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.


Subject(s)
Algorithms , COVID-19/diagnosis , SARS-CoV-2/physiology , Spectroscopy, Fourier Transform Infrared/methods , Virion/chemistry , COVID-19/virology , Discriminant Analysis , Gamma Rays , Humans , Point-of-Care Testing , Principal Component Analysis , SARS-CoV-2/isolation & purification , Saliva/virology , Sensitivity and Specificity , Signal-To-Noise Ratio , Virion/radiation effects , Virus Inactivation
18.
Anal Chem ; 93(4): 2191-2199, 2021 02 02.
Article in English | MEDLINE | ID: covidwho-1019731

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) has led to substantial infections and mortality around the world. Fast screening and diagnosis are thus crucial for quick isolation and clinical intervention. In this work, we showed that attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FT-IR) can be a primary diagnostic tool for COVID-19 as a supplement to in-use techniques. It requires only a small volume (∼3 µL) of the serum sample and a shorter detection time (several minutes). The distinct spectral differences and the separability between normal control and COVID-19 were investigated using multivariate and statistical analysis. Results showed that ATR-FT-IR coupled with partial least squares discriminant analysis was effective to differentiate COVID-19 from normal controls and some common respiratory viral infections or inflammation, with the area under the receiver operating characteristic curve (AUROC) of 0.9561 (95% CI: 0.9071-0.9774). Several serum constituents including, but not just, antibodies and serum phospholipids could be reflected on the infrared spectra, serving as "chemical fingerprints" and accounting for good model performances.


Subject(s)
COVID-19/diagnosis , Spectroscopy, Fourier Transform Infrared/methods , Case-Control Studies , Diagnosis, Differential , Discriminant Analysis , Feasibility Studies , Humans
19.
EBioMedicine ; 63: 103154, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-957021

ABSTRACT

BACKGROUND: Early diagnosis of coronavirus disease 2019 (COVID-19) is of the utmost importance but remains challenging. The objective of the current study was to characterize exhaled breath from mechanically ventilated adults with COVID-19. METHODS: In this prospective observational study, we used real-time, online, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of expired air from adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS). FINDINGS: Between March 25th and June 25th, 2020, we included 40 patients with ARDS, of whom 28 had proven COVID-19. In a multivariate analysis, we identified a characteristic breathprint for COVID-19. We could differentiate between COVID-19 and non-COVID-19 ARDS with accuracy of 93% (sensitivity: 90%, specificity: 94%, area under the receiver operating characteristic curve: 0·94-0·98, after cross-validation). The four most prominent volatile compounds in COVID-19 patients were methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal. INTERPRETATION: The real-time, non-invasive detection of methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal in exhaled breath may identify ARDS patients with COVID-19. FUNDING: The study was funded by Agence Nationale de la Recherche (SoftwAiR, ANR-18-CE45-0017 and RHU4 RECORDS, Programme d'Investissements d'Avenir, ANR-18-RHUS-0004), Région Île de France (SESAME 2016), and Fondation Foch.


Subject(s)
COVID-19/pathology , Metabolomics/methods , Volatile Organic Compounds/analysis , Aged , Area Under Curve , Breath Tests , COVID-19/virology , Critical Illness , Discriminant Analysis , Female , Humans , Least-Squares Analysis , Male , Middle Aged , Pilot Projects , Principal Component Analysis , Prospective Studies , ROC Curve , Respiration, Artificial , Respiratory Distress Syndrome/pathology , SARS-CoV-2/isolation & purification , Volatile Organic Compounds/metabolism
20.
J Breath Res ; 15(1): 011001, 2020 10 22.
Article in English | MEDLINE | ID: covidwho-889456

ABSTRACT

Infectious pathogens are a global issue. Global air travel offers an easy and fast opportunity not only for people but also for infectious diseases to spread around the world within a few days. Also, large public events facilitate increasing infection numbers. Therefore, rapid on-site screening for infected people is urgently needed. Due to the small size and easy handling, ion mobility spectrometry coupled with a multicapillary column (MCC-IMS) is a very promising, sensitive method for the on-site identification of infectious pathogens based on scents, representing volatile organic compounds (VOCs). The purpose of this study was to prospectively assess whether identification of Influenza-A-infection based on VOCs by MCC-IMS is possible in breath. Nasal breath was investigated in 24 consecutive persons with and without Influenza-A-infection by MCC-IMS. In 14 Influenza-A-infected patients, infection was proven by PCR of nasopharyngeal swabs. Four healthy staff members and six patients with negative PCR result served as controls. For picking up relevant VOCs in MCC-IMS spectra, software based on cluster analysis followed by multivariate statistical analysis was applied. With only four VOCs canonical discriminant analysis was able to distinguish Influenza-A-infected patients from those not infected with 100% sensitivity and 100% specificity. This present proof-of-concept-study yields encouraging results showing a rapid diagnosis of viral infections in nasal breath within 5 min by MCC-IMS. The next step is to validate the results with a greater number of patients with Influenza-A-infection as well as other viral diseases, especially COVID-19. Registration number at ClinicalTrials.gov NCT04282135.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Influenza A virus/isolation & purification , Influenza, Human/diagnosis , Pneumonia, Viral/diagnosis , Aged , Breath Tests , COVID-19 , Coronavirus Infections/complications , Discriminant Analysis , Female , Humans , Influenza, Human/complications , Ion Mobility Spectrometry , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Prospective Studies , SARS-CoV-2 , Sensitivity and Specificity
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