The fatal trajectory of pulmonary COVID-19 is driven by lobular ischemia and fibrotic remodelling.

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.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees.

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Saliva - a new opportunity for fluid biopsy.

Saliva is a complex biological fluid with a variety of biomolecules, such as DNA, RNA, proteins, metabolites and microbiota, which can be used for the screening and diagnosis of many diseases. In addition, saliva has the characteristics of simple collection, non-invasive and convenient storage, which gives it the potential to replace blood as a new main body of fluid biopsy, and it is an excellent biological diagnostic fluid. This review integrates recent studies and summarizes the research contents of salivaomics and the research progress of saliva in early diagnosis of oral and systemic diseases. This review aims to explore the value and prospect of saliva diagnosis in clinical application.

Intelligent COVID-19 screening platform based on breath analysis.

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.

Wastewater-based epidemiology for the assessment of population exposure to chemicals: The need for integration with human biomonitoring for global One Health actions.

WBE has now become a complimentary tool in SARS-CoV-2 surveillance. This was preceded by the established application of WBE to assess the consumption of illicit drugs in communities. It is now timely to build on this and take the opportunity to expand WBE to enable comprehensive assessment of community exposure to chemical stressors and their mixtures. The goal of WBE is to quantify community exposure, discover exposure-outcome associations, and trigger policy, technological or societal intervention strategies with the overarching aim of exposure prevention and public health promotion. To achieve WBE's full potential, the following key aspects require further action: (1) Integration of WBE-HBM (human biomonitoring) initiatives that provide comprehensive community-individual multichemical exposure assessment. (2) Global WBE monitoring campaigns to provide much needed data on exposure in low- and middle-income countries (LMICs) and fill in the gaps in knowledge especially in the underrepresented highly urbanised as well as rural settings in LMICs. (3) Combining WBE with One Health actions to enable effective interventions. (4) Advancements in new analytical tools and methodologies for WBE progression to enable biomarker selection for exposure studies, and to provide sensitive and selective multiresidue analysis for trace multi-biomarker quantification in a complex wastewater matrix. Most of all, further developments of WBE needs to be undertaken by co-design with key stakeholder groups: government organisations, health authorities and private sector.

Assessment of an e-nose performance for the detection of COVID-19 specific biomarkers.

Early, rapid and non-invasive diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is needed for the prevention and control of coronavirus disease 2019 (COVID-19). COVID-19 mainly affects the respiratory tract and lungs. Therefore, analysis of exhaled breath could be an alternative scalable method for reliable SARS-CoV-2 screening. In the current study, an experimental protocol using an electronic-nose ('e-nose') for attempting to identify a specific respiratory imprint in COVID-19 patients was optimized. Thus the analytical performances of the Cyranose®, a commercial e-nose device, were characterized under various controlled conditions. In addition, the effect of various experimental conditions on its sensor array response was assessed, including relative humidity, sampling time and flow rate, aiming to select the optimal parameters. A statistical data analysis was applied to e-nose sensor response using common statistical analysis algorithms in an attempt to demonstrate the possibility to detect the presence of low concentrations of spiked acetone and nonanal in the breath samples of a healthy volunteer. Cyranose®reveals a possible detection of low concentrations of these two compounds, in particular of 25 ppm nonanal, a possible marker of SARS-CoV-2 in the breath.

Reducing unnecessary routine laboratory testing for noncritically ill patients with COVID-19.

BACKGROUND: Reducing unnecessary routine laboratory testing is a Choosing Wisely® recommendation, and new areas of overuse were noted during the COVID-19 pandemic. OBJECTIVE: To reduce unnecessary repetitive routine laboratory testing for patients with COVID-19 during the pandemic across a large safety net health system. DESIGNS, SETTINGS AND PARTICIPANTS: This quality improvement initiative was initiated by the System High-Value Care Council at New York City Health + Hospitals (H + H), the largest public healthcare system in the United States consisting of 11 acute care hospitals. INTERVENTION: four overused laboratory tests in noncritically ill hospitalized patients with COVID-19 were identified: C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), and procalcitonin. A two-pronged electronic health record intervention was implemented consisting of (1) nonintrusive, informational nudge statements placed on selected order sets, and (2) a forcing function of one consecutive day limit on ordering. MAIN OUTCOME AND MEASURES: The average of excess tests per encounter days (ETPED) for each of four target laboratory testing only in patients with COVID-19. OBJECTIVE: Interdisciplinary System High-Value Care Council identified four overused laboratory tests (inflammatory markers) in noncritically ill hospitalized patients with COVID-19: C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), and procalcitonin. Within an 11-hospital safety net health system, a two-pronged electronic health record intervention was implemented consisting of (1) nonintrusive, informational nudge statements placed on selected order sets, and (2) a forcing function of one consecutive day limit on ordering. The preintervention period (March 16, 2020 to January 24, 2021) was compared to the postintervention period (January 25, 2021 to March 22, 2022). RESULTS: Time series linear regression showed decreases in CRP (-17.9%, p < .05), ferritin (-37.6%, p < .001), and LDH (-30.1%, p < .001). Slope differences were significant (CRP, ferritin, and LDH p < 0.001; procalcitonin p < 0.05). Decreases were observed across weekly averages: CRP (-19%, p < .01), ferritin (-37.9%, p < .001), LDH (-28.7%, p < .001), and procalcitonin (-18.4%, p < .05). CONCLUSION: This intervention was associated with reduced routine inflammatory marker testing in non-intensive care unit COVID-19 hospitalized patients across 11 hospitals. Variation was high among individual hospitals.

Advanced setup for safe breath sampling and patient monitoring under highly infectious conditions in the clinical environment.

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.

Systematic comparison of published host gene expression signatures for bacterial/viral discrimination.

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.

A glimpse in post-COVID pathophysiology: the role of exhaled breath condensate pH as an early marker of residual alveolar inflammation.

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.

Smartphone-Based Multiplexed Biosensing Tools for Health Monitoring.

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.

Machine Learning Approaches to Analyze MALDI-TOF Mass Spectrometry Protein Profiles.

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.

Procalcitonin and lung ultrasonography point-of-care testing to determine antibiotic prescription in patients with lower respiratory tract infection in primary care: pragmatic cluster randomised trial.

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.

Role of Breath Biopsy in COVID-19.

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.

Biomarker based biosensors: An opportunity for diagnosis of COVID-19.

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.

Potential urinary biomarkers in young adults with short-term exposure to particulate matter and bioaerosols identified using an unbiased metabolomic approach.

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.

Proteome-wide Mendelian randomization identifies causal links between blood proteins and severe COVID-19.

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.

Chemical profiling and unraveling of anti-COVID-19 biomarkers of red sage (Lantana camara L.) cultivars using UPLC-MS/MS coupled to chemometric analysis, in vitro study and molecular docking.

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.

Biomarker evaluation for prognostic stratification of patients with COVID-19: the added value of quantitative chest CT.

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.

Altered kidney function induced by SARS-CoV-2 infection and acute kidney damage markers predict survival outcomes of COVID-19 patients: a prospective pilot study.

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.