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1.
J Breath Res ; 17(1)2022 11 24.
Article in English | MEDLINE | ID: covidwho-2107282

ABSTRACT

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p< 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and anF1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network,k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.


Subject(s)
COVID-19 , Volatile Organic Compounds , Humans , Breath Tests/methods , Volatile Organic Compounds/analysis , Support Vector Machine , Biomarkers/analysis
2.
Sci Rep ; 12(1): 17926, 2022 Oct 26.
Article in English | MEDLINE | ID: covidwho-2087297

ABSTRACT

Being the proximal matrix, breath offers immediate metabolic outlook of respiratory infections. However, high viral load in exhalations imposes higher transmission risk that needs improved methods for safe and repeatable analysis. Here, we have advanced the state-of-the-art methods for real-time and offline mass-spectrometry based analysis of exhaled volatile organic compounds (VOCs) under SARS-CoV-2 and/or similar respiratory conditions. To reduce infection risk, the general experimental setups for direct and offline breath sampling are modified. Certain mainstream and side-stream viral filters are examined for direct and lab-based applications. Confounders/contributions from filters and optimum operational conditions are assessed. We observed immediate effects of infection safety mandates on breath biomarker profiles. Main-stream filters induced physiological and analytical effects. Side-stream filters caused only systematic analytical effects. Observed substance specific effects partly depended on compound's origin and properties, sampling flow and respiratory rate. For offline samples, storage time, -conditions and -temperature were crucial. Our methods provided repeatable conditions for point-of-care and lab-based breath analysis with low risk of disease transmission. Besides breath VOCs profiling in spontaneously breathing subjects at the screening scenario of COVID-19/similar test centres, our methods and protocols are applicable for moderately/severely ill (even mechanically-ventilated) and highly contagious patients at the intensive care.


Subject(s)
COVID-19 , Volatile Organic Compounds , Humans , Volatile Organic Compounds/analysis , COVID-19/diagnosis , SARS-CoV-2 , Breath Tests/methods , Exhalation , Biomarkers/analysis , Monitoring, Physiologic
3.
Genome Med ; 14(1): 18, 2022 02 21.
Article in English | MEDLINE | ID: covidwho-1688773

ABSTRACT

BACKGROUND: Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. METHODS: This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. RESULTS: Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. CONCLUSIONS: In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.


Subject(s)
Bacterial Infections/diagnosis , Datasets as Topic/statistics & numerical data , Host-Pathogen Interactions/genetics , Transcriptome , Virus Diseases/diagnosis , Adult , Bacterial Infections/epidemiology , Bacterial Infections/genetics , Biomarkers/analysis , COVID-19/diagnosis , COVID-19/genetics , Child , Cohort Studies , Diagnosis, Differential , Gene Expression Profiling/statistics & numerical data , Genetic Association Studies/statistics & numerical data , Humans , Publications/statistics & numerical data , SARS-CoV-2/pathogenicity , Validation Studies as Topic , Virus Diseases/epidemiology , Virus Diseases/genetics
4.
EBioMedicine ; 85: 104296, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2061073

ABSTRACT

BACKGROUND: COVID-19 is characterized by a heterogeneous clinical presentation, ranging from mild symptoms to severe courses of disease. 9-20% of hospitalized patients with severe lung disease die from COVID-19 and a substantial number of survivors develop long-COVID. Our objective was to provide comprehensive insights into the pathophysiology of severe COVID-19 and to identify liquid biomarkers for disease severity and therapy response. METHODS: We studied a total of 85 lungs (n = 31 COVID autopsy samples; n = 7 influenza A autopsy samples; n = 18 interstitial lung disease explants; n = 24 healthy controls) using the highest resolution Synchrotron radiation-based hierarchical phase-contrast tomography, scanning electron microscopy of microvascular corrosion casts, immunohistochemistry, matrix-assisted laser desorption ionization mass spectrometry imaging, and analysis of mRNA expression and biological pathways. Plasma samples from all disease groups were used for liquid biomarker determination using ELISA. The anatomic/molecular data were analyzed as a function of patients' hospitalization time. FINDINGS: The observed patchy/mosaic appearance of COVID-19 in conventional lung imaging resulted from microvascular occlusion and secondary lobular ischemia. The length of hospitalization was associated with increased intussusceptive angiogenesis. This was associated with enhanced angiogenic, and fibrotic gene expression demonstrated by molecular profiling and metabolomic analysis. Increased plasma fibrosis markers correlated with their pulmonary tissue transcript levels and predicted disease severity. Plasma analysis confirmed distinct fibrosis biomarkers (TSP2, GDF15, IGFBP7, Pro-C3) that predicted the fatal trajectory in COVID-19. INTERPRETATION: Pulmonary severe COVID-19 is a consequence of secondary lobular microischemia and fibrotic remodelling, resulting in a distinctive form of fibrotic interstitial lung disease that contributes to long-COVID. FUNDING: This project was made possible by a number of funders. The full list can be found within the Declaration of interests / Acknowledgements section at the end of the manuscript.


Subject(s)
COVID-19 , Lung Diseases, Interstitial , Humans , Lung/diagnostic imaging , Lung/pathology , Lung Diseases, Interstitial/pathology , Fibrosis , Biomarkers/analysis , Ischemia/pathology
5.
Expert Rev Respir Med ; 16(10): 1093-1099, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2051063

ABSTRACT

BACKGROUND: Residual alveolar inflammation seems to be paramount in post-COVID pathophysiology. Currently, we still lack a reliable marker to detect and track alveolar phlogosis in these patients. Exhaled Breath Condensate (EBC) pH has robust evidences highlighting its correlation with lung phlogosis in various diseases. We aim to define the reliability of alveolar and bronchial EBC pH in the assessment and in the follow up of post-COVID-related inflammation. RESEARCH DESIGN AND METHODS: We enrolled 10 patients previously hospitalized due to COVID-19 pneumonia. We performed a complete follow-up after 3 months and 6 months from discharge. Each visit included routine blood tests, arterial blood gas analysis, 6-minute walking test, spirometry, diffusing capacity and body plethysmography. Finally, bronchial and alveolar EBC were collected at the end of each visit. RESULTS: Alveolar EBC pH was significantly lower than bronchial EBC pH at T1, alveolar EBC pH tended to be more acid after 3 months from hospital discharge compared to the same sample 6 months later. Serum inflammatory biomarkers showed no significant differences from T1 to T2. Alveolar EBC pH was positively correlated with neutrophil-lymphocyte ratio. CONCLUSIONS: Collecting EBC pH could help to understand pathophysiologic mechanism as well as monitoring alveolar inflammation in the post-COVID syndrome.


Subject(s)
Breath Tests , COVID-19 , Humans , Reproducibility of Results , Hydrogen-Ion Concentration , Biomarkers/analysis , Inflammation/diagnosis , Disease Progression , Exhalation/physiology
6.
Biosensors (Basel) ; 12(8)2022 Jul 29.
Article in English | MEDLINE | ID: covidwho-2043578

ABSTRACT

Many emerging technologies have the potential to improve health care by providing more personalized approaches or early diagnostic methods. In this review, we cover smartphone-based multiplexed sensors as affordable and portable sensing platforms for point-of-care devices. Multiplexing has been gaining attention recently for clinical diagnosis considering certain diseases require analysis of complex biological networks instead of single-marker analysis. Smartphones offer tremendous possibilities for on-site detection analysis due to their portability, high accessibility, fast sample processing, and robust imaging capabilities. Straightforward digital analysis and convenient user interfaces support networked health care systems and individualized health monitoring. Detailed biomarker profiling provides fast and accurate analysis for disease diagnosis for limited sample volume collection. Here, multiplexed smartphone-based assays with optical and electrochemical components are covered. Possible wireless or wired communication actuators and portable and wearable sensing integration for various sensing applications are discussed. The crucial features and the weaknesses of these devices are critically evaluated.


Subject(s)
Biosensing Techniques , Smartphone , Biomarkers/analysis , Biosensing Techniques/methods , Delivery of Health Care , Point-of-Care Systems
7.
Methods Mol Biol ; 2511: 375-394, 2022.
Article in English | MEDLINE | ID: covidwho-1941391

ABSTRACT

Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.


Subject(s)
COVID-19 , Biomarkers/analysis , COVID-19/diagnosis , Humans , Machine Learning , Proteins , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
8.
BMJ ; 374: n2132, 2021 09 21.
Article in English | MEDLINE | ID: covidwho-1923193

ABSTRACT

OBJECTIVE: To assess whether point-of care procalcitonin and lung ultrasonography can safely reduce unnecessary antibiotic treatment in patients with lower respiratory tract infections in primary care. DESIGN: Three group, pragmatic cluster randomised controlled trial from September 2018 to March 2020. SETTING: 60 Swiss general practices. PARTICIPANTS: One general practitioner per practice was included. General practitioners screen all patients with acute cough; patients with clinical pneumonia were included. INTERVENTIONS: Randomisation in a 1:1:1 of general practitioners to either antibiotics guided by sequential procalcitonin and lung ultrasonography point-of-care tests (UltraPro; n=152), procalcitonin guided antibiotics (n=195), or usual care (n=122). MAIN OUTCOMES: Primary outcome was proportion of patients in each group prescribed an antibiotic by day 28. Secondary outcomes included duration of restricted activities due to lower respiratory tract infection within 14 days. RESULTS: 60 general practitioners included 469 patients (median age 53 years (interquartile range 38-66); 278 (59%) were female). Probability of antibiotic prescription at day 28 was lower in the procalcitonin group than in the usual care group (0.40 v 0.70, cluster corrected difference -0.26 (95% confidence interval -0.41 to -0.10)). No significant difference was seen between UltraPro and procalcitonin groups (0.41 v 0.40, -0.03 (-0.17 to 0.12)). The median number of days with restricted activities by day 14 was 4 days in the procalcitonin group and 3 days in the usual care group (difference 1 day (95% confidence interval -0.23 to 2.32); hazard ratio 0.75 (95% confidence interval 0.58 to 0.97)), which did not prove non-inferiority. CONCLUSIONS: Compared with usual care, point-of-care procalcitonin led to a 26% absolute reduction in the probability of 28 day antibiotic prescription without affecting patients' safety. Point-of-care lung ultrasonography did not further reduce antibiotic prescription, although a potential added value cannot be excluded, owing to the wide confidence intervals. TRIAL REGISTRATION: ClinicalTrials.gov NCT03191071.


Subject(s)
Anti-Bacterial Agents/therapeutic use , Point-of-Care Testing , Procalcitonin/blood , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/drug therapy , Ultrasonography/methods , Adult , Aged , Biomarkers/analysis , Cluster Analysis , Drug Prescriptions/statistics & numerical data , Female , General Practice , Humans , Intention to Treat Analysis , Lung/diagnostic imaging , Male , Middle Aged , Primary Health Care/methods
9.
J Appl Lab Med ; 7(5): 1175-1188, 2022 09 01.
Article in English | MEDLINE | ID: covidwho-1901194

ABSTRACT

BACKGROUND: COVID-19 is a highly contagious respiratory disease that can be transmitted through human exhaled breath. It has caused immense loss and has challenged the healthcare sector. It has affected the economy of countries and thereby affected numerous sectors. Analysis of human breath samples is an attractive strategy for rapid diagnosis of COVID-19 by monitoring breath biomarkers. CONTENT: Breath collection is a noninvasive process. Various technologies are employed for detection of breath biomarkers like mass spectrometry, biosensors, artificial learning, and machine learning. These tools have low turnaround time, robustness, and provide onsite results. Also, MS-based approaches are promising tools with high speed, specificity, sensitivity, reproducibility, and broader coverage, as well as its coupling with various chromatographic separation techniques providing better clinical and biochemical understanding of COVID-19 using breath samples. SUMMARY: Herein, we have tried to review the MS-based approaches as well as other techniques used for the analysis of breath samples for COVID-19 diagnosis. We have also highlighted the different breath analyzers being developed for COVID-19 detection.


Subject(s)
Breath Tests , COVID-19 Testing , COVID-19 , Biomarkers/analysis , COVID-19/diagnosis , COVID-19 Testing/methods , Humans , Reproducibility of Results
10.
Rev Med Virol ; 32(5): e2356, 2022 09.
Article in English | MEDLINE | ID: covidwho-1802575

ABSTRACT

Early diagnosis and treatment of diseases are crucial research areas of human health. For early diagnosis, one method that has proven efficient is the detection of biomarkers which can provide real-time and accurate biological information. Most biomarker detection is currently carried out at localised dedicated laboratories using large and automated analysers, increasing waiting time and costs. Smaller, faster, and cheaper devices could potentially replace these time-consuming laboratory analyses and make analytical results available as point-of-care diagnostics. Innovative biosensor-based strategies could allow biomarkers to be tested reliably in a decentralised setting. Early diagnosis of COVID-19 patients has a key role in order to use quarantine and treatment strategies in a timely manner. Raised levels of several biomarkers in COVID-19 patients are associated with respiratory infections or dysfunction of various organs. Through clinical studies of COVID-19 patient biomarkers such as ferritin, Interleukins, albumin and …are found to reveals significant differences in their excretion ranges from healthy patients and patients with SARS-CoV-2, in addition to the development of biomarkers based biosensor such as stated biomarkers can be used and to investigate more specific biomarkers further proteomic analysis can be performed. This review presents several biomarker alterations in COVID-19 patients such as salivary, circulatory, coagulation, cardiovascular, renal, liver, C-reactive protein (CRP), immunological and inflammatory biomarkers. Also, biomarker sensors based on electrochemical, optical, and lateral flow characteristics which have potential applications for SARS-COV-2 in the recent COVID-19 pandemic, will be discussed.


Subject(s)
Biosensing Techniques , COVID-19 , Biomarkers/analysis , Biosensing Techniques/methods , COVID-19/diagnosis , COVID-19 Testing , Humans , Pandemics , Proteomics , SARS-CoV-2
11.
Environ Pollut ; 305: 119308, 2022 Jul 15.
Article in English | MEDLINE | ID: covidwho-1796874

ABSTRACT

Numerous epidemiological studies have shown a close relationship between outdoor air pollution and increased risks for cancer, infection, and cardiopulmonary diseases. However, very few studies have investigated the potential health effects of coexposure to airborne particulate matter (PM) and bioaerosols through the transmission of infectious agents, particularly under the current circumstances of the coronavirus disease 2019 pandemic. In this study, we aimed to identify urinary metabolite biomarkers that might serve as clinically predictive or diagnostic standards for relevant diseases in a real-time manner. We performed an unbiased gas/liquid chromatography-mass spectroscopy (GC/LC-MS) approach to detect urinary metabolites in 92 samples from young healthy individuals collected at three different time points after exposure to clean air, polluted ambient, or purified air, as well as two additional time points after air repollution or repurification. Subsequently, we compared the metabolomic profiles between the two time points using an integrated analysis, along with Kyoto Encyclopedia of Genes and Genomes-enriched pathway and time-series analysis. We identified 33 and 155 differential metabolites (DMs) associated with PM and bioaerosol exposure using GC/LC-MS and follow-up analyses, respectively. Our findings suggest that 16-dehydroprogesterone and 4-hydroxyphenylethanol in urine samples may serve as potential biomarkers to predict or diagnose PM- or bioaerosol-related diseases, respectively. The results indicated apparent differences between PM- and bioaerosol-associated DMs at five different time points and revealed dynamic alterations in the urinary metabolic profiles of young healthy humans with cyclic exposure to clean and polluted air environments. Our findings will help in investigating the detrimental health effects of short-term coexposure to airborne PM and bioaerosols in a real-time manner and improve clinically predictive or diagnostic strategies for preventing air pollution-related diseases.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Biomarkers/analysis , Humans , Particulate Matter/analysis , Young Adult
12.
PLoS Genet ; 18(3): e1010042, 2022 03.
Article in English | MEDLINE | ID: covidwho-1793655

ABSTRACT

In November 2021, the COVID-19 pandemic death toll surpassed five million individuals. We applied Mendelian randomization including >3,000 blood proteins as exposures to identify potential biomarkers that may indicate risk for hospitalization or need for respiratory support or death due to COVID-19, respectively. After multiple testing correction, using genetic instruments and under the assumptions of Mendelian Randomization, our results were consistent with higher blood levels of five proteins GCNT4, CD207, RAB14, C1GALT1C1, and ABO being causally associated with an increased risk of hospitalization or respiratory support/death due to COVID-19 (ORs = 1.12-1.35). Higher levels of FAAH2 were solely associated with an increased risk of hospitalization (OR = 1.19). On the contrary, higher levels of SELL, SELE, and PECAM-1 decrease risk of hospitalization or need for respiratory support/death (ORs = 0.80-0.91). Higher levels of LCTL, SFTPD, KEL, and ATP2A3 were solely associated with a decreased risk of hospitalization (ORs = 0.86-0.93), whilst higher levels of ICAM-1 were solely associated with a decreased risk of respiratory support/death of COVID-19 (OR = 0.84). Our findings implicate blood group markers and binding proteins in both hospitalization and need for respiratory support/death. They, additionally, suggest that higher levels of endocannabinoid enzymes may increase the risk of hospitalization. Our research replicates findings of blood markers previously associated with COVID-19 and prioritises additional blood markers for risk prediction of severe forms of COVID-19. Furthermore, we pinpoint druggable targets potentially implicated in disease pathology.


Subject(s)
Blood Proteins/metabolism , COVID-19/blood , COVID-19/pathology , Biomarkers/analysis , Biomarkers/blood , Blood Proteins/analysis , Blood Proteins/genetics , COVID-19/diagnosis , COVID-19/mortality , Causality , Genome-Wide Association Study , Hospitalization , Humans , Mendelian Randomization Analysis , Mortality , Pandemics , Polymorphism, Single Nucleotide , Prognosis , Proteome/analysis , Proteome/genetics , Proteome/metabolism , Respiratory Insufficiency/blood , Respiratory Insufficiency/diagnosis , Respiratory Insufficiency/mortality , Respiratory Insufficiency/pathology , Risk Factors , SARS-CoV-2/physiology , Severity of Illness Index
13.
J Ethnopharmacol ; 291: 115038, 2022 Jun 12.
Article in English | MEDLINE | ID: covidwho-1739924

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Red sage (Lantana camara L.) (Verbenaceae) is a widely spread plant that was traditionally used in Brazil, India, Kenya, Thailand, Mexico, Nigeria, Australia and Southeast Asia for treating several ailments including rheumatism and leprosy. Despite its historical role in relieving respiratory diseases, limited studies progressed to the plant's probable inhibition to respiratory viruses especially after the striking spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. AIM OF THE STUDY: This study aimed to investigate the inhibitory activity of different L. camara cultivars to SARS-CoV-2, that was not previously inspected, and clarify their mechanisms of action in the metabolomics viewpoint, and to determine the biomarkers that are related to such activity using UPLC-MS/MS coupled to in vitro-studies and chemometric analysis. MATERIALS AND METHODS: Chemical profiling of different cultivars was accomplished via UPLC-MS/MS. Principle component analysis (PCA) and orthogonal projection to latent structures (OPLS) models were built using SIMCA® (multivariate data analysis software). Cytotoxicity and COVID-19 inhibitory activity testing were done followed by TaqMan Real-time RT-PCR (Reverse transcription polymerase chain reaction) assay that aimed to study extracts' effects on RNA-dependent RNA polymerase (RdRp) and E-genes expression levels. Detected biomarkers from OPLS analysis were docked into potential targets pockets to investigate their possible interaction patterns using Schrodinger® suite. RESULTS: UPLC-MS/MS analysis of different cultivars yielded 47 metabolites, most of them are triterpenoids and flavonoids. PCA plots revealed that inter-cultivar factor has no pronounced effect on the chemical profiles of extracts except for L. camara, cultivar Drap d'or flowers and leaves extracts as well as for L. camara cv Chelsea gem leaves extract. Among the tested extracts, flowers and leaves extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or showed the highest selectivity indices scoring 12.3, 10.1, 8.6 and 7.8, respectively, indicating their relative high safety and efficacy. Leaves and flowers extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or were the most promising inhibitors to viral plaques exhibiting IC50 values of 3.18, 3.67, 4.18 and 5.01 µg/mL, respectively. This was incremented by OPLS analysis that related their promising COVID-19 inhibitory activities to the presence of twelve biomarkers. Inhibiting the expression of RdRp gene is the major mechanism behind the antiviral activity of most extracts at almost all concentration levels. Molecular docking of the active biomarkers against RdRp revealed that isoverbascoside, luteolin-7,4'-O-diglucoside, camarolic acid and lantoic acid exhibited higher docking scores of -11.378, -10.64, -6.72 and -6.07 kcal/mol, respectively, when compared to remdesivir (-5.75 kcal/mol), thus these four compounds can serve as promising anti-COVID-19 candidates. CONCLUSION: Flowers and leaves extracts of four L. camara cultivars were recognized as rich sources of phytoconstituents possessing anti-COVID-19 activity. Combination of UPLC-MS/MS and chemometrics is a promising approach to detect chemical composition differences among the cultivars and correlate them to COVID-19 inhibitory activities allowing to pinpoint possible biomarkers. Further in-vitro and in-vivo studies are required to verify their activity.


Subject(s)
COVID-19 , Lantana , Biomarkers/analysis , COVID-19/drug therapy , Chromatography, High Pressure Liquid , Chromatography, Liquid , Lantana/chemistry , Molecular Docking Simulation , Plant Extracts/analysis , Plant Extracts/pharmacology , Plant Leaves/chemistry , SARS-CoV-2 , Tandem Mass Spectrometry
14.
Biomark Med ; 16(4): 291-301, 2022 03.
Article in English | MEDLINE | ID: covidwho-1706742

ABSTRACT

Aim: Pulmonary disease burden and biomarkers are possible predictors of outcomes in patients with COVID-19 and provide complementary information. In this study, the prognostic value of adding quantitative chest computed tomography (CT) to a multiple biomarker approach was evaluated among 148 hospitalized patients with confirmed COVID-19. Materials & methods: Patients admitted between March and July 2020 who were submitted to chest CT and biomarker measurement (troponin I, D-dimer and C-reactive protein) were retrospectively analyzed. Biomarker and tomographic data were compared and associated with death and intensive care unit admission. Results: The number of elevated biomarkers was significantly associated with greater opacification percentages, lower lung volumes and higher death and intensive care unit admission rates. Total lung volume <3.0 l provided further stratification for mortality when combined with biomarker evaluation. Conclusion: Adding automated CT data to a multiple biomarker approach may provide a simple strategy for enhancing risk stratification of patients with COVID-19.


Subject(s)
Biomarkers/analysis , COVID-19/diagnosis , Thorax/diagnostic imaging , Aged , Aged, 80 and over , Biomarkers/blood , C-Reactive Protein/analysis , COVID-19/mortality , COVID-19/virology , Female , Fibrin Fibrinogen Degradation Products/analysis , Hospital Mortality , Humans , Intensive Care Units , Length of Stay , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Tomography, X-Ray Computed , Troponin I/blood
15.
Ren Fail ; 44(1): 233-240, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-1692442

ABSTRACT

BACKGROUND: Literature with regard to coronavirus disease 2019 (COVID-19) associated morbidities and the risk factors for death are still emerging. In this study, we investigated the presence of kidney damage markers and their predictive value for survival among hospitalized subjects with COVID-19. METHODS: Forty-seven participants was included and grouped as: 'COVID-19 patients before treatment', 'COVID-19 patients after treatment', 'COVID-19 patients under treatment in intensive care unit (ICU)', and 'controls'. Kidney function tests and several kidney injury biomarkers were compared between the groups. Cumulative rates of death from COVID-19 were determined using the Kaplan-Meier method. The associations between covariates including kidney injury markers and death from COVID-19 were examined, as well. RESULTS: Serum creatinine and cystatin C levels, urine Kidney Injury Molecule-1 (KIM-1)/creatinine ratio, and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), CKD-EPI cystatin C, and CKD-EPI creatinine-cystatin C levels demonstrated significant difference among the groups. The most significant difference was noted between the groups 'COVID-19 patients before treatment' and 'COVID-19 patients under treatment in ICU'. Advancing age, proteinuria, elevated serum cystatin C, and urine KIM-1/creatinine ratio were all significant univariate correlates of death (p < 0.05, for all). However, only elevated urine KIM-1/creatinine ratio retained significance in an age, sex, and comorbidities adjusted multivariable Cox regression (OR 6.11; 95% CI: 1.22-30.53; p = 0.02), whereas serum cystatin C showing only a statistically non-significant trend (OR 1.42; 95% CI: 0.00-2.52; p = 0.09). CONCLUSIONS: Our findings clearly demonstrated the acute kidney injury related to COVID-19. Moreover, urine KIM-1/creatinine ratio was associated with COVID-19 specific death.


Subject(s)
Acute Kidney Injury/etiology , Biomarkers/analysis , COVID-19/complications , Proteinuria/etiology , Acute Kidney Injury/diagnosis , Adult , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/therapy , Creatinine/urine , Cystatin C/blood , Female , Hepatitis A Virus Cellular Receptor 1/metabolism , Humans , Male , Middle Aged , Pilot Projects , Prospective Studies , Proteinuria/diagnosis , Risk Factors , SARS-CoV-2/metabolism , Survival Analysis , Urinalysis
16.
PLoS One ; 16(12): e0261656, 2021.
Article in English | MEDLINE | ID: covidwho-1623659

ABSTRACT

SARS-CoV-2 infection elicits a robust B cell response, resulting in the generation of long-lived plasma cells and memory B cells. Here, we aimed to determine the effect of COVID-19 severity on the memory B cell response and characterize changes in the memory B cell compartment between recovery and five months post-symptom onset. Using high-parameter spectral flow cytometry, we analyzed the phenotype of memory B cells with reactivity against the SARS-CoV-2 spike protein or the spike receptor binding domain (RBD) in recovered individuals who had been hospitalized with non-severe (n = 8) or severe (n = 5) COVID-19. One month after symptom onset, a substantial proportion of spike-specific IgG+ B cells showed an activated phenotype. In individuals who experienced non-severe disease, spike-specific IgG+ B cells showed increased expression of markers associated with durable B cell memory, including T-bet and FcRL5, as compared to individuals who experienced severe disease. While the frequency of T-bet+ spike-specific IgG+ B cells differed between the two groups, these cells predominantly showed an activated switched memory B cell phenotype in both groups. Five months post-symptom onset, the majority of spike-specific memory B cells had a resting phenotype and the percentage of spike-specific T-bet+ IgG+ memory B cells decreased to baseline levels. Collectively, our results highlight subtle differences in the B cells response after non-severe and severe COVID-19 and suggest that the memory B cell response elicited during non-severe COVID-19 may be of higher quality than the response after severe disease.


Subject(s)
COVID-19/immunology , Receptors, Fc/metabolism , T-Box Domain Proteins/metabolism , Adult , Aged , Antibodies, Viral/blood , B-Lymphocytes/metabolism , Biomarkers/analysis , COVID-19/metabolism , Female , Flow Cytometry/methods , Hospitalization/trends , Humans , Immunoglobulin G/blood , Immunologic Memory , Male , /metabolism , Middle Aged , Receptors, Fc/blood , Receptors, Fc/genetics , SARS-CoV-2/immunology , SARS-CoV-2/pathogenicity , Severity of Illness Index , Spike Glycoprotein, Coronavirus/immunology , T-Box Domain Proteins/blood
17.
J. coloproctol. (Rio J., Impr.) ; 41(4): 361-366, Out.-Dec. 2021. tab, ilus
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1590942

ABSTRACT

Coronavirus disease 2019 (COVID-19) is highly transmittable through contact with respiratory droplets. The virus is also shed in fecal matter. Some patients may present with effects in more than one system; however, there are no defined biomarkers that can accurately predict the course or progression of the disease. The present study aimed to estimate the severity of the disease, to correlate the severity of the disease with biochemical predictors, to identify valuable biomarkers indicative of gastrointestinal disease, and to determine the cutoff values. A cross-sectional study was conducted on COVID-19 patients admitted to the Kafrelsheikh University Hospital (isolation unit) between July 10, 2020, and October 30, 2020. The diagnosis of COVID- 19 was confirmed via reverse transcription-polymerase chain reaction (RT-PCR), which was employed for the detection of the viral RNA. We conclude that lymphopenia, elevated C-reactive protein (CRP) level, and liver enzymes were among the most important laboratory findings in COVID-19 patients. Statistically significant differences in platelet count, neutrophil count, D-dimer level, and fecal calprotectin levels were observed among patients presenting with chest symptoms only and patients with both chest and gastrointestinal symptoms (p=0.004;<0.001; 0.010; 0.003; and<0.001, respectively). C-reactive protein, D-dimer, and fecal calprotectin levels positively correlated with disease severity. The cutoff value for fecal calprotectin that can predict gastrointestinal involvement in COVID-19 was 165.0, with a sensitivity of 88.1% and a specificity of 76.5%. (AU)


Subject(s)
Humans , Male , Female , Adult , Middle Aged , Biomarkers/analysis , Leukocyte L1 Antigen Complex , COVID-19 , Blood Chemical Analysis
18.
Open Heart ; 8(2)2021 12.
Article in English | MEDLINE | ID: covidwho-1582998

ABSTRACT

OBJECTIVE: Soluble ST2 (sST2) reflects inflammation, endothelial dysfunction and myocardial fibrosis, is produced in the lungs and is an established biomarker in heart failure. We sought to determine the role of sST2 in COVID-19 by assessing pathophysiological correlates and its association to in-hospital outcomes. METHODS: We enrolled 123 consecutive, hospitalised patients with COVID-19 in the prospective, observational COVID-19 MECH study. Biobank samples were collected at baseline, day 3 and day 9. The key exposure variable was sST2, and the outcome was ICU treatment with mechanical ventilation or in-hospital death. RESULTS: Concentrations of sST2 at baseline was median 48 (IQR 37-67) ng/mL, and 74% had elevated concentrations (>37.9 ng/mL). Higher baseline sST2 concentrations were associated with older age, male sex, white race, smoking, diabetes, hypertension and chronic kidney disease. Baseline sST2 also associated with the presence of SARS-CoV-2 viraemia, lower oxygen saturation, higher respiratory rate and increasing concentrations of biomarkers reflecting inflammation, thrombosis and cardiovascular disease. During the hospitalisation, 8 (7%) patients died and 27 (22%) survivors received intensive care unit (ICU) treatment. Baseline sST2 concentrations demonstrated a graded association with disease severity (median, IQR): medical ward 43 (36-59) ng/mL; ICU 67 (39-104) ng/mL and non-survivors 107 (72-116) ng/mL (p<0.001 for all comparisons). These associations persisted at day 3 and day 9 . CONCLUSIONS: sST2 concentrations associate with SARS-CoV-2 viraemia, hypoxaemia and concentrations of inflammatory and cardiovascular biomarkers. There was a robust association between baseline sST2 and disease severity that was independent of, and superior to, established risk factors. sST2 reflects key pathophysiology and may be a promising biomarker in COVID-19. TRIAL REGISTRATION NUMBER: NCT04314232.


Subject(s)
COVID-19 , Hypoxia , Interleukin-1 Receptor-Like 1 Protein/analysis , SARS-CoV-2/isolation & purification , Viremia , Aged , Biomarkers/analysis , COVID-19/blood , COVID-19/mortality , COVID-19/physiopathology , Comorbidity , Correlation of Data , Female , Hospital Mortality , Humans , Hypoxia/diagnosis , Hypoxia/etiology , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Norway/epidemiology , Prognosis , Risk Factors , Severity of Illness Index , Smoking/epidemiology , Viremia/diagnosis , Viremia/etiology
20.
J Clin Lab Anal ; 36(2): e24177, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1589070

ABSTRACT

BACKGROUND: Limited research has been conducted on early laboratory biomarkers to identify patients with severe coronavirus disease (COVID-19). This study fills this gap to ensure appropriate treatment delivery and optimal resource utilization. METHODS: In this retrospective, multicentre, cohort study, 52 and 64 participants with severe and mild cases of COVID-19, respectively, were enrolled during January-March 2020. Least absolute shrinkage and selection operator and binary forward stepwise logistic regression were used to construct a predictive risk score. A prediction model was then developed and verified using data from four hospitals. RESULTS: Of the 50 variables assessed, eight were independent predictors of COVID-19 and used to calculate risk scores for severe COVID-19: age (odds ratio (OR = 14.01, 95% confidence interval (CI) 2.1-22.7), number of comorbidities (OR = 7.8, 95% CI 1.4-15.5), abnormal bilateral chest computed tomography images (OR = 8.5, 95% CI 4.5-10), neutrophil count (OR = 10.1, 95% CI 1.88-21.1), lactate dehydrogenase (OR = 4.6, 95% CI 1.2-19.2), C-reactive protein OR = 16.7, 95% CI 2.9-18.9), haemoglobin (OR = 16.8, 95% CI 2.4-19.1) and D-dimer levels (OR = 5.2, 95% CI 1.2-23.1). The model was effective, with an area under the receiver-operating characteristic curve of 0.944 (95% CI 0.89-0.99, p < 0.001) in the derived cohort and 0.8152 (95% CI 0.803-0.97; p < 0.001) in the validation cohort. CONCLUSION: Predictors based on the characteristics of patients with COVID-19 at hospital admission may help predict the risk of subsequent critical illness.


Subject(s)
COVID-19/epidemiology , Adult , Aged , Aged, 80 and over , Biomarkers/analysis , COVID-19/blood , COVID-19/diagnosis , Critical Illness , Female , Hospitalization , Humans , Male , Middle Aged , ROC Curve , Retrospective Studies , Risk Factors , Young Adult
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