Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

Association of TLR8 Variants in Sex-Based Clinical Differences in Patients with COVID-19

The host immune response might confer differential vulnerability to SARS-CoV-2 infection. The Toll-like receptor 8 (TLR8), could participated for severe COVID-19 outcomes. To investigated the relationship of TLR8 rs3764879-C/G, rs3764880-A/G, and rs3761624-A/G with COVID-19 outcomes and with biochemical parameters. A cross-sectional study of 830 laboratory-confirmed COVID-19 patients was performed, and classified into mild, severe, critical, and deceased outcomes. The TLR8 rs3764879-C/G, rs3764880-A/G, and rs3761624-A/G polymorphisms were genotyped. A logistic regression analysis was performed to determinate the association with COVID-19. A stratified analysis was by alleles was done with clinical and metabolic markets. In all outcomes, men presented the highest ferritin levels compared to women (P < 0.001). LDH levels were significantly different between sex in mild (P = 0.003), severe (P < 0.001) and deceased (P = 0.01) COVID-19 outcomes. The GGG haplotype showed an Odds Ratio of 1.55 (Interval Confidence 95% 1.05-2.32; P = 0.03) in men. Among patients with severe outcome, we observed that the carriers of the GGG haplotype had lower Ferritin, C-reactive protein and LDH levels than the CAA carriers (P < 0.01). After further stratified by sex, these associations were also seen in the male patients, except for D-dimer. Interestingly, among men patients, we could observe associations between TLR8 haplotypes and Ferritin (P < 0.001), D-dimer (P = 0.04), C-reactive protein, and Lactate dehydrogenase in mild (P = 0.04) group. Our results suggest that even though TLR8 haplotypes show a significant association with COVID-19 outcomes, they are associated with clinical markers in COVID-19 severity.

Proteomics of serum-derived extracellular vesicles are associated with the severity and different clinical profiles of patients with COVID-19: An exploratory secondary analysis

BACKGROUND AIMS

Coronavirus disease 2019 (COVID-19) is characterized by a broad spectrum of clinical manifestations with the potential to progress to multiple organ dysfunction in severe cases. Extracellular vesicles (EVs) carry a range of biological cargoes, which may be used as biomarkers of disease state.

METHODS

An exploratory secondary analysis of the SARITA-2 and SARITA-1 datasets (randomized clinical trials on patients with mild and moderate/severe COVID-19) was performed. Serum-derived EVs were used for proteomic analysis to identify enriched biological processes and key proteins, thus providing insights into differences in disease severity. Serum-derived EVs were separated from patients with COVID-19 by size exclusion chromatography and nanoparticle tracking analysis was used to determine particle concentration and diameter. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was performed to identify and quantify protein signatures. Bioinformatics and multivariate statistical analysis were applied to distinguish candidate proteins associated with disease severity (mild versus moderate/severe COVID-19).

RESULTS

No differences were observed in terms of the concentration and diameter of enriched EVs between mild (n = 14) and moderate/severe (n = 30) COVID-19. A total of 414 proteins were found to be present in EVs, of which 360 were shared while 48 were uniquely present in severe/moderate compared to mild COVID-19. The main biological signatures in moderate/severe COVID-19 were associated with platelet degranulation, exocytosis, complement activation, immune effector activation, and humoral immune response. Von Willebrand factor, serum amyloid A-2 protein, histone H4 and H2A type 2-C, and fibrinogen β-chain were the most differentially expressed proteins between severity groups.

CONCLUSION

Exploratory proteomic analysis of serum-derived EVs from patients with COVID-19 detected key proteins related to immune response and activation of coagulation and complement pathways, which are associated with disease severity. Our data suggest that EV proteins may be relevant biomarkers of disease state and prognosis.

Prediction of acute lung injury assessed by chest computed tomography, oxygen saturation/fraction of inspired oxygen ratio, and serum lactate dehydrogenase in patients with COVID-19

INTRODUCTION

In treating acute hypoxemic respiratory failure (AHRF) caused by coronavirus disease 2019 (COVID-19), clinicians choose respiratory therapies such as low-flow nasal cannula oxygenation, high-flow nasal cannula oxygenation, or mechanical ventilation after assessment of the patient's condition. Chest computed tomography (CT) imaging contributes significantly to diagnosing COVID-19 pneumonia. However, the costs and potential harm to patients from radiation exposure need to be considered. This study was performed to predict the quantitative extent of COVID-19 acute lung injury using clinical indicators such as an oxygenation index and blood test results.

METHODS

We analyzed data from 192 patients with COVID-19 AHRF. Multiple logistic regression was used to determine correlations between the lung infiltration volume (LIV) and other pathophysiological or biochemical laboratory parameters.

RESULTS

Among 13 clinical parameters, we identified the oxygen saturation/fraction of inspired oxygen ratio (SF ratio) and serum lactate dehydrogenase (LD) concentration as factors associated with the LIV. In the binary classification of an LIV of ≥20 % or not and with the borderline LD = 2.2 × [SF ratio]-182.4, the accuracy, precision, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.828, 0.818, 23.400, and 0.870, respectively.

CONCLUSIONS

These data suggest that acute lung injury due to COVID-19 pneumonia can be estimated using the SF ratio and LD concentration without a CT scan. These findings may provide significant clinical benefit by allowing clinicians to predict acute lung injury levels using simple, minimally invasive assessment of oxygenation capacity and biochemical blood tests.

Blood Count and Renal Functionality Assessments in the Emergency Section Disclose Morbidity and Mortality in Omicron COVID-19 Patients: A Retrospective Study

Background: SARS-CoV-2 is the coronavirus responsible for the COVID-19 pandemic. Even though we are no longer in a pandemic situation, people are still getting infected, some of them need hospitalization and a few of them die. Methods: We conducted a retrospective study including 445 patients who accessed the Emergency Section of Policlinico Umberto I, Rome, Italy, where they had routine blood exams. In this study, we focused on the complete blood count, serum creatinine and azotemia. The data were analyzed using ANOVA, Spearman correlation and ROC analyses. They were divided into four groups based on their clinical outcomes: (1) the emergency group (patients who had mild forms and were quickly discharged); (2) the hospital ward group (patients who were admitted to the emergency section and were then hospitalized in a COVID-19 ward); (3) the intensive care unit (ICU) group (patients who required intensive assistance after the admission in the emergency section); (4) the deceased group (patients who had a fatal outcome after admission to the emergency section). Results: We found significant changes for creatinine, azotemia, hematocrit, mean corpuscular hemoglobin concentration, basophils, monocytes, red blood cell distribution width, hemoglobin, hematocrit and red blood cell numbers using ANOVA according to their clinical outcomes, particularly for the deceased group. Also, we found linear correlations of clinical outcomes with eosinophils, hemoglobin, hematocrit, mean corpuscular hemoglobin concentration, lymphocyte, neutrophil, platelet and red blood cell number and red blood cell distribution width. Conclusions: This study discloses an early association between "classical" routine blood biomarkers and the severity of clinical outcomes in Omicron patients.

Meta-analysis of evaluating neuron specific enolase as a serum biomarker for sepsis-associated encephalopathy

INTRODUCTION

Brain dysfunction in sepsis is known as Sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Neuron specific enolase (NSE) may serve as an important neurocritical biomarker for detection and longitudinal monitoring in SAE patients. Our Meta-analysis aimed to explore the diagnostic and prognostic value of serum NSE in SAE patients. Currently, no systematic Review and Meta-analysis have been assessed that NSE as a biomarker of SAE.

METHODS

The study protocol was registered in the PROSPERO database (CRD42023398736) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We conducted a systematic review and Meta-analysis to evaluate the serum NSE's diagnostic accuracy for SAE and prognostic strength for probability of death of septic patients. We systematic searched electronic bibliographic databases from PubMed, MEDLINE, Web of Science, Embase, Cochrane databases, CNKI, CQVIP, and WFSD. QUADAS-2 assessment tool was used to evaluate quality and risk of bias of the selected studies. Subgroup analyses, funnel plots, sensitivity analyses were also carried out. Review Manager version 5.4 and Stata16.0. was used for statistical analysis.

RESULTS

This Meta-analysis included 22 studies with 1361 serum samples from SAE patients and 1580 serum samples from no-encephalopathy septic (NE) patients. The Meta-analysis showed that individuals with SAE had higher serum NSE level than NE controls (SMD 1.93 (95 % CI 1.51-2.35), P < 0.00001). In addition, there are 948 serum samples from survival septic patients and 446 serum samples from non-survival septic patients, septic patients with survival outcomes had lower serum NSE levels than those with death outcomes (SMD -1.87 (95 % CI -2.43 to -1.32), P < 0.00001).

CONCLUSION

Our Meta-analysis reveals a significant association between elevated NSE concentrations and the increased likelihood of concomitant SAE and mortality during septic patients. This comprehensive analysis will equip ICU physicians with up-to-date insights to accurately identify patients at risk of SAE and implement appropriate intervention strategies to mitigate morbidity and improve neurological outcomes. However, it is important to note that the presence of substantial heterogeneity among studies poses challenges in determining the most effective discrimination cutoff values and optimal sampling collection time.

Characteristics and biomarkers associated with mortality in COVID-19 patients presenting to the emergency department

This study aimed to investigate the diverse clinical manifestations and simple early biomarkers predicting mortality of COVID-19 patients admitted to the emergency department (ED). A total of 710 patients with COVID-19 were enrolled from 6,896 patients presenting to the ED between January 2022 and March 2022. During the study period, a total of 478 patients tested positive for COVID-19, among whom 222 (46.4%) presented with extrapulmonary manifestations of COVID-19; 49 (10.3%) patients displayed gastrointestinal manifestations, followed by neurological (n = 41; 8.6%) and cardiac manifestations (n = 31; 6.5%). In total, 54 (11.3%) patients died. A Cox proportional hazards model revealed that old age, acute kidney injury at presentation, increased total leukocyte counts, low platelet counts, decreased albumin levels, and increased LDH levels were the independent predictors of mortality. The albumin levels exhibited the highest area under the curve in receiver operating characteristic analysis, with a value of 0.860 (95% confidence interval, 0.796-0.875). The study showed the diverse clinical presentations and simple-to-measure prognostic markers in COVID-19 patients presenting to the ED. Serum albumin levels can serve as a novel and simple early biomarker to identify COVID-19 patients at high risk of death.

Development and validation of a predicting nomogram for in-hospital mortality of COVID-19 Omicron variant: A cohort study of 1324 cases in Beijing Anzhen Hospital

Coronavirus disease 2019 (COVID-19) is continuously posing high global public health concerns due to its high morbidity and mortality. This study aimed to construct a convenient risk model for predicting in-hospital mortality of COVID-19 Omicron variant. A total of 1324 hospitalized patients with Omicron variant were enrolled from Beijing Anzhen Hospital. During hospitalization, the Omicron variant mortality rate was found to be 24.4%. Using the datasets of clinical demographics and laboratory tests, three machine learning algorithms, including best subset selection, stepwise selection, and least absolute shrinkage and selection operator regression analyses were employed to identify the potential predictors of in-hospital mortality. The results found that a panel of twenty-four clinical variables (including age, hyperlipemia, stroke, tumor, and several cardiovascular markers) identified by stepwise selection model exhibited significant performances in predicting the in-hospital mortality of COVID-19. The resultant nomogram showed good discrimination, highlighted by the areas under the curve values of 0.88 for 10 days, 0.81 for 20 days, and 0.82 for 30 days, respectively. Furthermore, decision curve analysis showed a significant reliability and precision for the established stepwise selection model. Collectively, this study developed an accurate and convenience risk model for predicting the in-hospital mortality of COVID-19 Omicron.

Prognostic significance of the chemerin level in coronavirus disease 2019 patients

Increased serum chemerin levels have been reported in several inflammatory diseases. Few studies have investigated the relationship between chemerin and clinical features of COVID-19. Thus, chemerin may modulate the development and progression of COVID-19. We compared the serum chemerin concentration between patients with and without SARS-CoV-2 infection and its association with the severity and prognosis of COVID-19 pneumonia. This is a prospective, single-center, cross-sectional study. We enrolled COVID-19 patients who presented to our tertiary hospital and healthy controls. The COVID-19 patients were conducted and the dates of symptom onset were recorded. After admission to the hospital and stabilization, blood samples were obtained for routine hemogram, biochemistry, and chemerin. The chemerin level was 37.93 ± 17.3 ng/mL in patients followed in the ICU, 29.41 ± 12.79 ng/mL in inpatients, 30.48 ± 10.86 ng/mL in outpatients, and 25.12 ± 9.82 ng/mL in healthy controls. The difference between patients treated in the ICU and healthy controls was significant (P < .001). The high-sensitivity C-reactive protein (hs-CRP), ferritin, procalcitonin (PCT), and D-dimer levels were significantly higher in the intensive care unit (ICU) group (P < .001). Moreover, the chemerin level of patients who died was significantly higher than that of those who survived (P < .001). The chemerin level was increased in COVID-19 patients and also increased with increasing disease severity. The chemerin level was higher in the COVID-19 patients than healthy controls and was significantly higher in patients who died compared to those who did not.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians.

Methods

Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity.

Results

Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively.

Conclusion

Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

The laboratory parameters in predicting the severity and death of COVID-19 patients: Future pandemic readiness strategies

The range of clinical manifestations associated with the infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encompasses a broad spectrum, ranging from flu-like symptoms to the occurrence of multiple organ failure and death. The severity of the coronavirus disease 2019 (COVID-19) is categorized based on clinical presentation and is divided into three distinct levels of severity identified as non-severe, severe, and critical. Although individuals of all age groups are susceptible to SARS-CoV-2 infection, middle-aged and older adults are more frequently impacted, with the latter being more likely to develop severe illness. Various laboratory characteristics observed in hospitalized COVID-19 patients have been correlated with adverse outcomes. These include elevated levels of D-dimer, liver enzymes, lactate dehydrogenase, C-reactive protein, ferritin, prothrombin time, and troponin, as well as decreased lymphocyte and platelets counts. This review investigated the relationship between baseline clinical characteristics, initial laboratory parameters upon hospital admission, and the severity of illness and mortality rates among COVID-19 patients. Although the COVID-19 pandemic has concluded, understanding the laboratory predictors of virus severity and mortality remains crucial, and examining these predictors can have long-term effects. Such insights can help healthcare systems manage resources more effectively and deliver timely and appropriate care by identifying and targeting high-risk individuals. This knowledge can also help us better prepare for future pandemics. By examining these predictors, we can take steps to protect public health and mitigate the impact of future pandemics.

Integrated Bioinformatics Exploration and Preliminary Clinical Verification for the Identification of Crucial Biomarkers in Severe Cases of COVID-19

Background

Coronavirus disease 2019 (COVID-19) is a respiratory infectious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The objective of this study is to identify reliable and accurate biomarkers for the early stratification of disease severity, a crucial aspect that is currently lacking for the impending phases of the next COVID-19 pandemic.

Methods

In this study, we identified important module and hub genes related to clinical severe COVID-19 using differentially expressed genes (DEGs) screening combing weighted gene co-expression network analysis (WGCNA) in dataset GSE213313. We further screened and confirmed these hub genes in another two new independent datasets (GSE172114 and GSE157103). In order to evaluate these key genes' stability and robustness for diagnosing or predicting the progression of illness, we used RT-PCR validation of selected genes in blood samples obtained from hospitalized COVID-19 patients.

Results

A total of 968 and 52 DEGs were identified between COVID-19 patients and normal people, critical and non-critical patients, respectively. Then, using WGCNA, 10 modules were constructed. Among them, the blue module positively associated with clinic disease severity of COVID-19. From overlapped section between DEGs and blue module, 12 intersected common differential genes were obtained. Subsequently, these hub genes were validated in another two new independent datasets as well and 9 genes that overlapped showed a highly correlation with disease severity. Finally, the mRNA expression levels of these hub genes were tested in blood samples from COVID-19 patients. In severe cases, there was increased expression of MCEMP1, ANXA3, CD177, and SCN9A. In particular, MCEMP1 increased with disease severity, which suggested an unfavorable development and a frustrating prognosis.

Conclusion

Using comprehensive bioinformatical analysis and the validation of clinical samples, we identified four major candidate genes, MCEMP1, ANXA3, CD177, and SCN9A, which are essential for diagnosis or development of COVID-19.

The association between coronary artery calcification and vectorcardiography in mechanically ventilated COVID-19 patients: the Maastricht Intensive Care COVID cohort

BACKGROUND

Coronary artery calcification (CAC) is associated with poor outcome in critically ill patients. A deterioration in cardiac conduction and loss of myocardial tissue could be an underlying cause. Vectorcardiography (VCG) and cardiac biomarkers provide insight into these underlying causes. The aim of this study was to investigate whether a high degree of CAC is associated with VCG-derived variables and biomarkers, including high-sensitivity troponin-T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP).

METHODS

Mechanically ventilated coronavirus-19 (COVID-19) patients with an available chest computed tomography (CT) and 12-lead electrocardiogram (ECG) were studied. CAC scores were determined using chest CT scans. Patients were categorized into 3 sex-specific tertiles: low, intermediate, and high CAC. Daily 12 leads-ECGs were converted to VCGs. Daily hs-cTnT and NT-proBNP levels were determined. Linear mixed-effects regression models examined the associations between CAC tertiles and VCG variables, and between CAC tertiles and hs-cTnT or NT-proBNP levels.

RESULTS

In this study, 205 patients (73.2% men, median age 65 years [IQR 57.0; 71.0]) were included. Compared to the lowest CAC tertile, the highest CAC tertile had a larger QRS area at baseline (6.65 µVs larger [1.50; 11.81], p = 0.012), which decreased during admission (- 0.27 µVs per day [- 0.43; - 0.11], p = 0.001). Patients with the highest CAC tertile also had a longer QRS duration (12.02 ms longer [4.74; 19.30], p = 0.001), higher levels of log hs-cTnT (0.79 ng/L higher [0.40; 1.19], p < 0.001) and log NT-proBNP (0.83 pmol/L higher [0.30; 1.37], p = 0.002).

CONCLUSION

Patients with a high degree of CAC had the largest QRS area and higher QRS amplitude, which decreased more over time when compared to patients with a low degree of CAC. These results suggest that CAC might contribute to loss of myocardial tissue during critical illness. These insights could improve risk stratification and prognostication of patients with critical illness.

Stratifying Disease Severity in Pediatric COVID-19: A Correlative Study of Serum Biomarkers and Lung Ultrasound-A Retrospective Observational Dual-Center Study

The COVID-19 pandemic, caused by SARS-CoV-2, has manifested distinct impacts on infants and children. This study delves into the intricate connection between lung ultrasound (LUS) findings and serum biomarkers in neonates and infants with COVID-19. Exploring factors contributing to the mild symptoms in this demographic, including immune responses and pre-existing immunity, the study spans 3 years and 9 months, involving 42 patients. Respiratory and gastrointestinal symptoms predominate, and LUS emerges as a vital, non-irradiating tool for evaluating pulmonary abnormalities. Serum biomarkers like CRP, procalcitonin, and cytokines provide key insights into the pathophysiology. Correlations reveal nuanced links between LUS score and clinical parameters, unveiling associations with hospitalization duration (rho = 0.49), oxygen saturation (rho = -0.88), and inflammatory markers, like ferritin (rho = 0.62), LDH (rho = 0.73), and D-dimer (rho = 0.73) with significance level (p < 0.05). The absence of large consolidations in LUS suggests unique pulmonary characteristics. The novelty of these findings lies in the comprehensive integration of LUS with serum biomarkers to assess and monitor the severity of lung involvement in neonates and infants affected by SARS-CoV-2. This approach offers valuable insights into disease severity, biomarker levels, the duration of hospitalization, and oxygen saturation, providing a multifaceted understanding of COVID-19's impact on this vulnerable population.

Nucleated red blood cells are a late biomarker in predicting intensive care unit mortality in patients with COVID-19 acute respiratory distress syndrome: an observational cohort study

Background

Nucleated red blood cells (nRBC) are precursor cells of the erythropoiesis that are absent from the peripheral blood under physiological conditions. Their presence is associated with adverse outcomes in critically ill patients. This study aimed to evaluate the predictive value of nRBC on mortality in intensive care unit (ICU) patients with COVID-19 acute respiratory distress syndrome (ARDS).

Material and methods

This retrospective, observational cohort study analyzed data on 206 ICU patients diagnosed with COVID-19 ARDS between March 2020 and March 2022. The primary endpoint was ICU mortality, and secondary endpoints included ICU and hospital stay lengths, ventilation hours, and the time courses of disease severity scores and clinical and laboratory parameters.

Results

Among the included patients, 68.9% tested positive for nRBC at least once during their ICU stay. A maximum nRBC of 105 µl-1 had the highest accuracy in predicting ICU mortality (area under the curve of the receiver operating characteristic [AUCROC] 0.780, p < 0.001, sensitivity 69.0%, specificity 75.5%). Mortality was significantly higher among patients with nRBC >105 µl-1 than ≤105 µl-1 (86.5% vs. 51.3%, p = 0.008). Compared to patients negative for nRBC in their peripheral blood, those positive for nRBC required longer mechanical ventilation (127 [44 - 289] h vs. 517 [255 - 950] h, p < 0.001), ICU stays (12 [8 - 19] vs. 27 [13 - 51] d, p < 0.001), and hospital stays (19 [12 - 29] d vs. 31 [16 - 58] d, p < 0.001). Peak Sepsis-related Organ Failure Assessment (SOFA), Simplified Acute Physiology Score, PaO2/FiO2, interleukin-6, and procalcitonin values were reached before the peak nRBC level. However, the predictive performance of the SOFA (AUCROC 0.842, p < 0.001) was considerably improved when a maximum SOFA score >8 and nRBC >105 µl-1 were combined.

Discussion

nRBC predict ICU mortality and indicate disease severity among patients with COVID-19 ARDS, and they should be considered a clinical alarm signal for a worse outcome. nRBC are a late predictor of ICU mortality compared to other established clinical scoring systems and laboratory parameters but improve the prediction accuracy when combined with the SOFA score.

Meta-analysis of the systemic immune-inflammatory index and in-hospital mortality of COVID-19 patients

Background

The potential significance of immunoinflammatory factors in the prognosis of individuals afflicted with coronavirus disease 2019 (COVID-19) is worthy of examination. The systemic immune-inflammatory index (SII), a recently developed immunoinflammatory metric based on the enumeration of neutrophils, platelets, and lymphocytes in blood samples, holds promise for elucidating this relationship. Consequently, in order to explore any possible correlation between the SII levels at admission and the in-hospital mortality of patients with COVID-19, we undertook a thorough systematic review and meta-analysis.

Methods

In pursuit of accomplishing the aim of this meta-analysis, an extensive search was conducted to seek out pertinent observational studies featuring longitudinal follow-up across PubMed, Cochrane Library, Embase and the Web of Science databases. The I2 statistic was utilized to estimate the extent of heterogeneity and the Cochrane Q test was employed to evaluate heterogeneity between studies. The synthesis of outcomes involved the use of random-effects models, accounting for the possible influence of heterogeneity.

Results

Our analysis included sixteen studies, encompassing 10,007 hospitalized COVID-19 patients. Among them, 1801 patients (18.0 %) succumbed during hospitalization. The pooled results indicated that a high SII at admission was substantially linked to a higher risk of all-cause mortality (risk ratio [RR]: 2.41, 95 % confidence interval: 1.78 to 3.24, p < 0.001; I2 = 86 %). Meta-regression analysis demonstrated a negative correlation between mean SII at baseline and patient mortality in individual studies (coefficients = -0.00023 and -0.030, p < 0.05), effectively explaining the observed heterogeneity. Furthermore, in patients with lower baseline SII (<1300) and a lower risk of mortality (<20 %), we observed a more pronounced association between high SII levels and the risk of all-cause mortality.

Conclusion

The results of our study indicate that a high SII upon admission could potentially function as a prognostic indicator for mortality during hospitalization in patients diagnosed with COVID-19, particularly in individuals categorized as low risk.

Clinical characteristics of patients hospitalized for COVID-19: comparison between different age groups

BACKGROUND

To test whether known prognosticators of COVID-19 maintained their stratification ability across age groups.

METHODS

We performed a retrospective study. We included all patients (n = 2225), who presented to the Emergency Department of the Careggi University Hospital for COVID-19 in the period February 2020-May 2021, and were admitted to the hospital. The following parameters were analyzed as dichotomized: 1) SpO2/FiO2 ≤ or > 214; 2) creatinine < or ≥ 1.1 mg/dL; 3) Lactic dehydrogenase (LDH) < or ≥ 250 U/mL; 4) C Reactive Protein (CRP) < or ≥ 60 mg/100 mL. We divided the study population in four subgroups, based on the quartiles of distribution of age (G1 18-57 years, G2 57-71 years, G3 72-81 years, G4 > 82). The primary end-point was in-hospital mortality.

RESULTS

By the univariate analysis, the aforementioned dichotomized variables demonstrated a significant association with in-hospital mortality in all subgroups. We introduced them in a multivariate model: in G1 SpO2/FiO2 ≤ 214 (Relative Risk, RR 15.66; 95%CI 3.98-61,74), in G2 creatinine ≥ 1.1 mg/L (RR 2.87, 95%CI 1.30-6.32) and LDH ≥ 250 UI/L (RR 8.71, 95%CI 1,15-65,70), in G3 creatinine ≥ 1.1 mg/L (RR 1.98, 95%CI 1,17-3.36) and CRP ≥ 60 ng/L (RR 2.14, 95%CI 1.23-3.71), in G4 SpO2/FiO2 ≤ 214 (RR 5.15, 95%CI 2.35-11.29), creatinine ≥ 1.1 mg/L (RR 1.75, 95%CI 1.09-2.80) and CRP ≥ 60 ng/L (RR 1.82, 95%CI 1.11-2.98) were independently associated with an increased in-hospital mortality.

CONCLUSIONS

A mild to moderate respiratory failure showed an independent association with an increased mortality rate only in youngest and oldest patients, while kidney disease maintained a prognostic role regardless of age.

Advances and Challenges in Nanomaterial-Based Electrochemical Immunosensors for Small Cell Lung Cancer Biomarker Neuron-Specific Enolase

Early and rapid detection of neuron-specific enolase (NSE) is highly significant, as it is putative biomarker for small-cell lung cancer as well as COVID-19. Electrochemical techniques have attracted substantial attention for the early detection of cancer biomarkers due to the important properties of simplicity, high sensitivity, specificity, low cost, and point-of-care detection. This work reviews the clinically relevant labeled and label-free electrochemical immunosensors developed so far for the analysis of NSE. The prevailing role of nanostructured materials as electrode matrices is thoroughly discussed. Subsequently, the key performances of various immunoassays are critically evaluated in terms of limit of detection, linear ranges, and incubation time for clinical translation. Electrochemical techniques coupled with screen-printed electrodes developing market level commercialization of NSE sensors is also discussed. Finally, the review concludes with the current challenges associated with available methods and provides a future outlook toward commercialization opportunities for easy detection of NSE.

Baseline NT-proBNP levels as a predictor of short-and long-term prognosis in COVID-19 patients: a prospective observational study

BACKGROUND

Up to 20% of COVID-19 patients can suffer COVID-19-related myocardial injury. Elevated cardiac biomarkers, such as hs-cTnT and NT-proBNP, have been related to worse short-term prognosis. However, data on NT-proBNP and long-term prognosis are scarce. We have evaluated the potential association of baseline age-adjusted NT-proBNP levels and outcomes at one-year follow-up in COVID-19 patients.

METHODS

This was a single-center prospective study of 499 COVID-19 patients in whom NT-proBNP was assessed at hospital admission. NT-proBNP levels were age-adjusted and patients were classified as high or low NT-proBNP. Clinical and demographic characteristics, comorbidities, laboratory results, and in-hospital complications and mortality were compared between the two groups. Survivors of the acute phase of COVID-19 were followed up for one year from admission to detect readmissions and mortality.

RESULTS

The 68 patients with high NT-proBNP levels at hospital admission were older, with more cardiovascular risk factors, cardiovascular disease, comorbidities, myocardial injury, and higher levels of inflammatory markers than patients with low NT-proBNP levels. They also had more in-hospital complications and a higher acute-phase mortality rate (60.3% vs. 10.2%, p < 0.001). High NT-proBNP levels were an independent marker of death during hospitalization (HR 1.95; CI 1.07-3.52). At one-year follow-up, high NT-proBNP levels were independently associated with mortality (HR 2.69; CI 1.47-4.89). Among survivors of the acute phase of COVID-19, there were no differences in hospital readmissions between those with high vs. low NT-proBNP levels, but survivors with high baseline NT-proBNP levels showed a higher 1-year mortality rate (7.4% vs. 1.3%, p = 0.018).

CONCLUSIONS

High age-adjusted NT-proBNP levels at the time of hospital admission for COVID-19 are associated with poor short and long-term prognosis. High NT-proBNP seems also to be related to worse prognosis in survivors of the acute phase of COVID-19. A closer follow-up on these patients may be crucial.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Background

Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.

Objective

This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.

Methods

Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.

Results

The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.

Conclusion

This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Systematic review and evidence gap mapping of biomarkers associated with neurological manifestations in patients with COVID-19

OBJECTIVE

This study aimed to synthesize the existing evidence on biomarkers related to coronavirus disease 2019 (COVID-19) patients who presented neurological events.

METHODS

A systematic review of observational studies (any design) following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Cochrane Collaboration recommendations was performed (PROSPERO: CRD42021266995). Searches were conducted in PubMed and Scopus (updated April 2023). The methodological quality of nonrandomized studies was assessed using the Newcastle‒Ottawa Scale (NOS). An evidence gap map was built considering the reported biomarkers and NOS results.

RESULTS

Nine specific markers of glial activation and neuronal injury were mapped from 35 studies published between 2020 and 2023. A total of 2,237 adult patients were evaluated in the included studies, especially during the acute phase of COVID-19. Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) biomarkers were the most frequently assessed (n = 27 studies, 77%, and n = 14 studies, 40%, respectively). Although these biomarkers were found to be correlated with disease severity and worse outcomes in the acute phase in several studies (p < 0.05), they were not necessarily associated with neurological events. Overall, 12 studies (34%) were judged as having low methodological quality, 9 (26%) had moderate quality, and 9 (26%) had high quality.

CONCLUSIONS

Different neurological biomarkers in neurosymptomatic COVID-19 patients were identified in observational studies. Although the evidence is still scarce and conflicting for some biomarkers, well-designed longitudinal studies should further explore the pathophysiological role of NfL, GFAP, and tau protein and their potential use for COVID-19 diagnosis and management.

Expression level of non-coding (MiR-155) gene as biomarker for severity of coronaviruses infection among vaccinated and non-vaccinated Iraqi patients

BACKGROUND

The COVID-19 pandemic caused by SARS-CoV-2 is influenced by genetic and epigenetic factors, including miR-155, which affects immune cell and virus functions and laboratory biomarkers.

OBJECTIVE

To evaluates miR-155's role as a biomarker for SARS-CoV-2 detection and monitoring, examining its significance in identifying infection in both vaccinated and unvaccinated individuals using ROC curve analysis.

METHODS

Blood samples were collected from 70 patients who attended Medical City Hospital in Baghdad from June 2022 to April 2023 and were determined to be associated with SARS-CoV-2 (35 patients were hospitalized at the Intensive Care Units due to the severity of their symptoms while the other 35 were left in the hospital upon treatment.). Additionally, 35 samples were collected as a healthy control group.

RESULTS

The expression level of miR-155 in the serum of samples showed a high level (fold change: 9.81 ± 5.50) in the severe patients' group in comparison with the moderate patients' group (fold change: 4.17 ± 2.93) and healthy group (fold change: 1.08 ± 0.01). To assess the performance of miR-155 and laboratory biomarkers, a (ROC) curve was utilized to determine the sensitivity and specificity.

CONCLUSIONS

The miR-155 gene, overexpressed in SARS-CoV-2 patients, correlates with disease activity and severity, potentially serving as a biomarker for diagnosis and a potential therapeutic target.

Relating D-Dimer, blood sugars, haemoglobin and liver function among COVID patients with T2DM

It is of interest to evaluate the correlation of D-Dimer, blood sugars, haemoglobin and liver function tests with novel coronavirus in patients with T2DM with and without symptoms. We recruited 200 patients with T2DM and COVID 19 with and without symptoms admitted in Rajarajeshwari Medical College and Hospital, Karnataka. Blood sugars, HbA1c, D-Dimer and also the incidence of T2DM and COVID 19 with and without symptoms were evaluated in all study subjects. There was a significant increased levels of biochemical parameters in T2DM and COVID 19 with symptoms when compared to T2DM and COVID 19 without symptoms (P<0.05). The D-Dimer levels was positively correlated with CT values, (r=0.518, P<0.05). Based on the study findings, the novel coronavirus enhances the insulin resistance, hyper-glycemia, abnormality in the liver and thrombolysis. Additionally, we also suggest that the subjects with T2DM and COVID 19 with and without symptoms require continuous monitoring of D-DIMER and LFT.

Association of eosinopenia with worsening prognosis in hospitalized Azvudine-treated COVID-19 patients: a retrospective cohort study

Background

Current guidelines prioritize the use of Azvudine in Coronavirus Disease 2019 (COVID-19) patients, while biomarkers for prognosis in Azvudine-treated COVID-19 patients are still lacking. Here, we aim to assess the prognostic value of eosinopenia in Azvudine-treated COVID-19 patients.

Methods

We retrospectively reviewed 290 consecutive Azvudine-treated hospitalized COVID-19 patients. Clinical characteristics and prognosis data were analyzed between patients with eosinopenia and with normal eosinophil levels.

Results

A total of 290 patients were enrolled in this study, with a median age of 69 years. Among them, 40.69% presented with eosinopenia and 59.31% had normal eosinophil levels. Common symptoms included cough (87.6%), expectoration (76.2%), fever (67.9%), poor appetite (47.2%), and polypnea (46.6%). Compared to patients with normal eosinophil levels, those with eosinopenia were older and less likely to experience fatigue (25.4% vs. 39.0%, P=0.016). Significant differences in laboratory parameters, particularly in blood routine and blood biochemical indicators, were observed between the two groups. Patients with eosinopenia were also less likely to develop severe illness subtypes, requiring more medication and oxygen support. The Cox proportional hazard model showed that eosinopenia was associated with worsening progression in Azvudine-treated COVID-19 patients (adjusted hazard ratio=2.79, 95% confidence interval: 1.04, 7.50), adjusting for potential confounders.

Conclusion

Eosinopenia is associated with worsening prognosis in Azvudine-treated COVID-19 patients.

Evaluation of cardiac diagnostic tests findings based on pro-BNP levels in COVID-19 pregnant patients

BACKGROUND

Pro-b-type natriuretic peptide (Pro-BNP) is an inflammatory marker that indicates cardiac damage and inflammation. The elevation of this marker in COVID-19 patients can be used as a predictive factor in the prognosis of these patients.

METHOD

Our cross-sectional study investigated the evaluation of cardiac diagnostic test findings based on pro-BNP levels in pregnant COVID-19 patients in Sayyad Shirazi Hospital, Gorgan, Iran, in 2020-2022. A hundred and ten pregnant patients diagnosed with COVID-19 infection were evaluated for cardiac diagnostic tests (electrocardiogram (ECG) and echocardiography (Echo)) and pro-BNP levels. Data were analyzed using SPSS 25 software. Chi-square and Student's t-test will be used to test and compare the relationship between variables and compare them. A P-value less than 0.05 is considered statistically significant. The chi-square test was used to compare the ratio of qualitative variables among the groups if the presuppositions of chi-square distribution were established. Otherwise, Fisher's exact test was used.

RESULT

The mean age of participants were 31.06 ± 5.533 years and 49.1% of patients had pro-BNP levels above the cut-off value for predicting an adverse outcome of COVID-19. The mean ± standard deviation of pro-BNP levels in the low group was 46.125 ± 17.523 pg/mL and in the high group was 878.814 ± 1038.060 pg/mL. This study revealed that patients with higher pro-BNP plasma levels had a significant relation between, myocardial infarction (MI), pericardial effusion (PE), urgent Caesarean section (C/S), and mortality. In addition, no significant relation between gravid, trimester, vaccination, arrhythmia, heart block, and valves diseases with high pro-BNP levels was found.

CONCLUSION

The current research showed that pro-BNP levels can be used as a diagnostic and valuable prognostic tool in pregnant women to diagnose cardiac complications by using ECG and Echo.

GPX3 Variant Genotype Affects the Risk of Developing Severe Forms of COVID-19

In SARS-CoV-2 infection, excessive activation of the immune system intensively increases reactive oxygen species levels, causing harmful hyperinflammatory and oxidative state cumulative effects which may contribute to COVID-19 severity. Therefore, we assumed that antioxidant genetic profile, independently and complemented with laboratory markers, modulates COVID-19 severity. The study included 265 COVID-19 patients. Polymorphism of GSTM1, GSTT1, Nrf2 rs6721961, GSTM3 rs1332018, GPX3 rs8177412, GSTP1 rs1695, GSTO1 rs4925, GSTO2 rs156697, SOD2 rs4880 and GPX1 rs1050450 genes was determined with appropriate PCR-based methods. Inflammation (interleukin-6, CRP, fibrinogen, ferritin) and organ damage (urea, creatinine, transaminases and LDH) markers, complete blood count and coagulation status (d-dimer, fibrinogen) were measured. We found significant association for COVID-19 progression for patients with lymphocytes below 1.0 × 109/L (OR = 2.97, p = 0.002). Increased IL-6 and CRP were also associated with disease progression (OR = 8.52, p = 0.001, and OR = 10.97, p < 0.001, respectively), as well as elevated plasma AST and LDH (OR = 2.25, p = 0.021, and OR = 4.76, p < 0.001, respectively). Of all the examined polymorphisms, we found significant association with the risk of developing severe forms of COVID-19 for GPX3 rs8177412 variant genotype (OR = 2.42, p = 0.032). This finding could be of particular importance in the future, complementing other diagnostic tools for prediction of COVID-19 disease course.

Serial laboratory biomarkers are associated with ICU outcomes in patients hospitalized with COVID-19

BACKGROUND

Clinical utility of routinely measured serial biomarkers in predicting escalation of inpatient care intensity and mortality among hospitalized patients with COVID-19 remains unknown.

METHODS

This retrospective cohort study included patients with COVID-19 who admitted to the Massachusetts General Hospital between March and June 2020 and January to March 2021. White blood cell (WBC) count, platelet count, C-reactive protein (CRP), and D-dimer values were measured on days 1, 3, and 7 of admission. Clinical outcomes include 30- and 60-day morality, ICU transfer, and overall survival (OS) over a follow-up period of 90 days. The association between serial biomarkers and outcomes were assessed using multivariable logistic regression and Cox proportional hazards models.

MEASUREMENTS AND MAIN RESULTS

Of the 456 patients hospitalized with COVID-19, 199 (43.6%) were ICU, 179 (39.3%) were medical floor, and 78 (17.1%) were initially admitted to the medical floor and then transferred to the ICU. In adjusted analyses, each unit increase in the slope of CRP was associated with a 42% higher odds of ICU transfer after controlling for the initial admission level (OR = 1.42, 95% CI: 1.25-1.65, P < 0.001). Including serial change in CRP levels from initial level on admission achieved the greatest predictive accuracy for ICU transfer (AUC = 0.72, 95% CI: 0.64-0.79).

CONCLUSIONS

Serial change in CRP levels from admission is associated with escalations of inpatient care intensity and mortality among hospitalized patients with COVID-19.

Aftereffects in Epigenetic Age Related to Cognitive Decline and Inflammatory Markers in Healthcare Personnel with Post-COVID-19: A Cross-Sectional Study

Purpose

Epigenetic age and inflammatory markers have been proposed as indicators of severity and mortality in patients with COVID-19. Furthermore, they have been associated with the occurrence of neurological symptoms, psychiatric manifestations, and cognitive impairment. Therefore, we aimed to explore the possible associations between epigenetic age, neuropsychiatric manifestations and inflammatory markers (neutrophil-lymphocyte ratio [NLR], platelet-lymphocyte ratio [PLR], monocyte-lymphocyte ratio [MLR], and systemic immune-inflammation index [SII]) in healthcare personnel with post-COVID condition.

Patients and Methods

We applied the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) tests to 51 Mexican healthcare workers with post-COVID-19 condition; we also estimated their epigenetic age using the PhenoAge calculator.

Results

The participants had a post-COVID condition that lasted a median of 14 months (range: 1-20). High NLR (>1.73) had association with mild cognitive impairment by MMSE (p=0.013). Likewise, high MLR (>0.24) were associated with language domain in MOCA (p=0.046). Low PLR (<103.9) was also related to delayed recall in MOCA (p=0.040). Regarding comorbidities, hypertension was associated with SII (p=0.007), overweight with PLR (p=0.047) and alcoholism was associated with MLR (p=0.043). Interestingly, we observed associations of low PLR (<103.9) and low SII (<1.35) levels with increased duration of post-COVID condition (p=0.027, p=0.031). Likewise, increases in PhenoAge were associated with high levels of SII (OR=1.11, p=0.049), PLR (OR=1.12, p=0.035) and MLR (OR=1.12, p=0.030).

Conclusion

We observed neurocognitive changes related to inflammatory markers and increases in epigenetic age in healthcare personnel with post-COVID-19 condition. Future research is required to assess mental and physical health in individuals with post-COVID-19 symptoms.

A Transcriptomic Severity Classifier IMX-SEV-3b to Predict Mortality in Intensive Care Unit Patients with COVID-19: A Prospective Observational Pilot Study

The prediction of disease outcomes in COVID-19 patients in the ICU is of critical importance, and the examination of host gene expressions is a promising tool. The 29-host mRNA Inflam-matix-Severity-3b (IMX-SEV-3b) classifier has been reported to predict mortality in emergency department COVID-19 patients and surgical ICU patients. The accuracy of the IMX-SEV-3b in predicting mortality in COVID-19 patients admitted to the ICU is yet unknown. Our aim was to investigate the accuracy of the IMX-SEV-3b in predicting the ICU mortality of COVID-19 patients. In addition, we assessed the predictive performance of routinely measured biomarkers and the Sequential Organ Failure Assessment (SOFA) score as well. This was a prospective observational study enrolling COVID-19 patients who received mechanical ventilation on the ICU of the Erasmus MC, the Netherlands. The IMX-SEV-3b scores were generated by amplifying 29 host response genes from blood collected in PAXgene® Blood RNA tubes. A severity score was provided, ranging from 0 to 1 for increasing disease severity. The primary outcome was the accuracy of the IMX-SEV-3b in predicting ICU mortality, and we calculated the AUROC of the IMX-SEV-3b score, the biomarkers C-reactive protein (CRP), D-dimer, ferritin, leukocyte count, interleukin-6 (IL-6), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), procalcitonin (PCT) and the SOFA score. A total of 53 patients were included between 1 March and 30 April 2020, with 47 of them being included within 72 h of their admission to the ICU. Of these, 18 (34%) patients died during their ICU stay, and the IMX-SEV-3b scores were significantly higher in non-survivors compared to survivors (0.65 versus 0.57, p = 0.05). The Area Under the Receiver Operating Characteristic Curve (AUROC) for prediction of ICU mortality by the IMX-SEV-3b was 0.65 (0.48-0.82). The AUROCs of the biomarkers ranged from 0.52 to 0.66, and the SOFA score had an AUROC of 0.81 (0.69-0.93). The AUROC of the pooled biomarkers CRP, D-dimer, ferritin, leukocyte count, IL-6, LDH, NLR and PCT for prediction of ICU mortality was 0.81 (IQR 0.69-0.93). Further validation in a larger interventional trial of a point-of-care version of the IMX-SEV-3b classifier is warranted to determine its value for patient management.

COVID-19 patients display changes in lymphocyte subsets with a higher frequency of dysfunctional CD8lo T cells associated with disease severity

This work examines cellular immunity against SARS-CoV-2 in patients from Córdoba, Argentina, during two major waves characterized by different circulating viral variants and different social behavior. Using flow cytometry, we evaluated the main lymphocyte populations of peripheral blood from hospitalized patients with moderate and severe COVID-19 disease. Our results show disturbances in the cellular immune compartment, as previously reported in different cohorts worldwide. We observed an increased frequency of B cells and a significant decrease in the frequency of CD3+ T cells in COVID-19 patients compared to healthy donors (HD). We also found a reduction in Tregs, which was more pronounced in severe patients. During the first wave, the frequency of GZMB, CD107a, CD39, and PD-1-expressing conventional CD4+ T (T conv) cells was significantly higher in moderate and severe patients than in HD. During the second wave, only the GZMB+ T conv cells of moderate and severe patients increased significantly. In addition, these patients showed a decreased frequency in IL-2-producing T conv cells. Interestingly, we identified two subsets of circulating CD8+ T cells with low and high CD8 surface expression in both HD and COVID-19 patients. While the percentages of CD8hi and CD8lo T cells within the CD8+ population in HD are similar, a significant increase was observed in CD8lo T cell frequency in COVID-19 patients. CD8lo T cell populations from HD as well as from SARS-CoV-2 infected patients exhibited lower frequencies of the effector cytokine-producing cells, TNF, IL-2, and IFN-γ, than CD8hi T cells. Interestingly, the frequency of CD8lo T cells increased with disease severity, suggesting that this parameter could be a potential marker for disease progression. Indeed, the CD8hi/CD8lo index helped to significantly improve the patient's clinical stratification and disease outcome prediction. Our data support the addition of, at least, a CD8hi/CD8lo index into the panel of biomarkers commonly used in clinical labs, since its determination may be a useful tool with impact on the therapeutic management of the patients.

COVID-19 and Long COVID: Disruption of the Neurovascular Unit, Blood-Brain Barrier, and Tight Junctions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), could affect brain structure and function. SARS-CoV-2 can enter the brain through different routes, including the olfactory, trigeminal, and vagus nerves, and through blood and immunocytes. SARS-CoV-2 may also enter the brain from the peripheral blood through a disrupted blood-brain barrier (BBB). The neurovascular unit in the brain, composed of neurons, astrocytes, endothelial cells, and pericytes, protects brain parenchyma by regulating the entry of substances from the blood. The endothelial cells, pericytes, and astrocytes highly express angiotensin converting enzyme 2 (ACE2), indicating that the BBB can be disturbed by SARS-CoV-2 and lead to derangements of tight junction and adherens junction proteins. This leads to increased BBB permeability, leakage of blood components, and movement of immune cells into the brain parenchyma. SARS-CoV-2 may also cross microvascular endothelial cells through an ACE2 receptor-associated pathway. The exact mechanism of BBB dysregulation in COVID-19/neuro-COVID is not clearly known, nor is the development of long COVID. Various blood biomarkers could indicate disease severity and neurologic complications in COVID-19 and help objectively diagnose those developing long COVID. This review highlights the importance of neurovascular and BBB disruption, as well as some potentially useful biomarkers in COVID-19, and long COVID/neuro-COVID.

Combination of Chest Computed Tomography Value and Clinical Laboratory Data for the Prognostic Risk Evaluation of Patients with COVID-19

Objective

This study aims to investigate the independent prognostic factors of patients with coronavirus disease 2019 (COVID-19) and thereafter construct a related prognostic model.

Methods

The subjects were screened following the COVID-19 diagnostic criteria. The independent prognostic factors were selected based on the indicators, including medical history, clinical manifestation, laboratory tests, imaging examination and clinical prognosis. Subsequently, we constructed a nomogram model to predict short-term prognosis.

Results

Clinical information was obtained from 393 COVID-19 patients admitted to Zhongshan Hospital at Xiamen University between December 2022 and January 2023. The independent risk factors determined by Cox multivariate regression analysis included gender (OR: 0.355, 95% CI: 0.16~0.745), age (OR: 3.938, 95% CI: 1.221~15.9), pectoral muscle index (PMI, OR: 4.985, 95% CI: 2.336~11.443), pneumonia severity score (PSS, OR: 6.486, 95% CI: 2.082~21.416) and lactate dehydrogenase (LDH, OR: 3.857, 95% CI: 1.571~10.266). A short-term prognostic nomogram was developed based on the five independent risk factors above. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram model was 0.857. The calibration curve confirmed the outcomes of the prognostic model, which exhibited excellent consistency with the actual results.

Conclusion

In summary, gender, age, pectoral muscle index, pneumonia severity score, and lactate dehydrogenase are all independent risk factors for COVID-19 mortality. Thus, the nomogram based on the above indicators can predict the risk of mortality in COVID-19 patients. This may have the potential of being clinical application in prognostic evaluation of COVID-19.

Serum Total Antioxidant Capacity (TAC) and TAC/Lymphocyte Ratio as Promising Predictive Markers in COVID-19

SARS-CoV-2 infection might cause a critical disease, and patients' follow-up is based on multiple parameters. Oxidative stress is one of the key factors in the pathogenesis of COVID-19 suggesting that its level could be a prognostic marker. Therefore, we elucidated the predictive value of the serum non-enzymatic total antioxidant capacity (TAC) and that of the newly introduced TAC/lymphocyte ratio in COVID-19. We included 61 COVID-19 (n = 27 ward, n = 34 intensive care unit, ICU) patients and 29 controls in our study. Serum TAC on admission was measured by an enhanced chemiluminescence (ECL) microplate assay previously validated by our research group. TAC levels were higher (p < 0.01) in ICU (median: 407.88 µmol/L) than in ward patients (315.44 µmol/L) and controls (296.60 µmol/L). Besides the classical parameters, both the TAC/lymphocyte ratio and TAC had significant predictive values regarding the severity (AUC-ROC for the TAC/lymphocyte ratio: 0.811; for TAC: 0.728) and acute kidney injury (AUC-ROC for the TAC/lymphocyte ratio: 0.747; for TAC: 0.733) in COVID-19. Moreover, the TAC/lymphocyte ratio had significant predictive value regarding mortality (AUC-ROC: 0.752). Serum TAC and the TAC/lymphocyte ratio might offer valuable information regarding the severity of COVID-19. TAC measured by our ECL microplate assay serves as a promising marker for the prediction of systemic inflammatory diseases.

A diagnostic model for COVID-19 based on proteomics analysis

BACKGROUND AND OBJECTIVE

Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease.

METHODS

The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify the severity level of the COVID-19 infection. The proposed model is evaluated using four performance measures: accuracy, sensitivity, specificity, and precision.

RESULTS

The experiment results show that the proposed model accuracy can reach 80% using RF classifier with PCA. The PCA selects 22 proteins and 10 metabolites. While ANOVA selects 9 proteins and 5 metabolites. The accuracy reaches 92% after applying RF classifier with the ANOVA. Finally, the accuracy reaches 93% using the RF classifier with only ten features. The selected features are 7 proteins and 3 metabolites. Moreover, it shows that the selected features have a relation to the immune system and respiratory systems.

CONCLUSION

The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.

Importance of Demographic and Clinical Features in Evaluating the Severity of COVID-19 in Hospitalized Patients: A Serbian Retrospective Study in the First Pandemic Year

The aim of this study is to determine the demographic and initial clinical characteristics of patients with COVID-19 and their importance in evaluating the severity of the disease. A retrospective study included patients suffering from COVID-19 who were hospitalized at The Department of Infectious Disease of the Clinical Hospital Center Pristina-Gracanica from the beginning of the pandemic until the end of 2020. We compared the symptoms of the disease, radiographic findings of pneumonia, laboratory parameters, duration of symptoms before admission, the difference in the need for certain therapies, and the presence of comorbidities between non-severe and severe groups of patients. Patients with a severe disease were statistically significantly older. Hypertension was significantly associated with severe clinical conditions. Radiographic findings of bilateral pneumonia on admission were much more frequent among the severe group, and these patients' need for oxygen support was significantly higher. Lower neutrophil and higher lymphocyte counts were statistically significant in the non-severe group. Biochemical parameters at admission also showed statistical significance between the examined groups. Based on our research, we can conclude that a complete overview of the patient, including demographic and laboratory parameters as perhaps the most significant attributes, can help doctors in the timely clinical assessment of patients and, thus, in the timely application of adequate therapeutic protocols in the treatment of COVID-19.

Effect of antiviral and immunomodulatory treatment on a cytokine profile in patients with COVID-19

Background

The severity of COVID-19 is associated with an elevated level of a variety of inflammatory mediators. Increasing evidence suggests that the Th17 response contributes to the severity of COVID-19 pneumonia, whereas Th22 response plays a regulatory role in SARS-CoV-2 infection. Two main types of available COVID-19 treatments are antivirals and immunomodulatory drugs; however, their effect on a cytokine profile is yet to be determined.

Methods

This study aim to analyse a cytokine profile in peripheral blood from patients with COVID-19 (n=44) undergoing antiviral or/and immunomodulatory treatment and healthy controls (n=20). Circulating CD4+ and CD8+ T cells and their intracellular expression of IL-17A and IL-22 were assessed by flow cytometry.

Results

Initial results showed an overexpression of IL-17F, IL-17A, CCL5/RANTES, GM-CSF, IL-4, IL-10, CXCL-10/IP-10 and IL-6 in COVID-19 patients compared to healthy controls. Treatment with remdesivir resulted in a significant decline in concentrations of IL-6, IL-10, IFN-alpha and CXCL10/IP-10. Immunomodulatory treatment contributed to a significant downregulation of IL-10, IFN-alpha, CXCL10/IP-10 and B7-H3 as well as upregulation of IL-22 and IL-1 beta. A combination of an antiviral and immunomodulatory treatment resulted in a significant decrease in IL-17F, IL-10, IFN-alpha, CXCL10/IP-10 and B7-H3 levels as well as an increase in IL-17A and IL-1 beta. We found significantly higher percentage of both CD4+ and CD8+ T cells producing IL-17A and CD4+ T cells producing IL-22 in patients with COVID-19.

Conclusion

Administration of antiviral or/and immunomodulatory treatment resulted in a significant downregulation of pro-inflammatory cytokine expression and an upregulation of T cell absolute counts in most cases, thus showing effectiveness of treatment in COVID-19. SARS-CoV-2 infection induced cytokine overexpression in hospitalized patients with COVID-19 as well as lymphopenia, particularly a decrease in CD4+ and CD8+ T cell counts. Moreover, despite the reduced counts of CD4+ and CD8+ T cells, both subsets showed overactivation and increased expression of IL-17A and IL-22, thus targeting Th17 response might alleviate inflammatory response in severe disease.

Post-acute (long) COVID-19 quality of life: validation of the German version of (PAC19QoL) instrument

Purpose

The aim of our study was to validate a German translation of the post-acute (long) COVID-19 quality of life (PAC-19QoL) instrument among German patients with long COVID-19 syndrome.

Patients and methods

The PAC-19QoL instrument was translated into the German language and administrated to patients with long COVID-19 syndrome. Cronbach's alpha coefficient was used to analyze the internal consistency of the instrument. Construction validity was evaluated by using Pearson's correlation coefficient and Spearman's rank correlation. Scores of patients and controls were compared using the Mann-Whitney U-test.

Results

A total of 45 asymptomatic and 41 symptomatic participants were included. In total, 41 patients with long COVID-19 syndrome completed the PAC-19QoL and EQ-5D-5L questionnaires. PAC-19QoL domain scores were significantly different between symptomatic and asymptomatic participants. All items achieved a Cronbach's alpha >0.7. There was a significant correlation between all domains on the test (p < 0.001), with the highest correlation between total (r = 0.994) and domain 1 (r = 0.991). Spearman's rank correlation analysis confirmed that the instrument items correlated with the objective PAC-19QoL examination findings.

Conclusion

The German version of the instrument is valid and reliable and can be a suitable tool for research and daily clinical practice among patients with long COVID-19 syndrome.

A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

BACKGROUND

The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU.

METHODS

This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group.

RESULTS

Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways.

CONCLUSIONS

A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.

Metabolic alterations upon SARS-CoV-2 infection and potential therapeutic targets against coronavirus infection

The coronavirus disease 2019 (COVID-19) caused by coronavirus SARS-CoV-2 infection has become a global pandemic due to the high viral transmissibility and pathogenesis, bringing enormous burden to our society. Most patients infected by SARS-CoV-2 are asymptomatic or have mild symptoms. Although only a small proportion of patients progressed to severe COVID-19 with symptoms including acute respiratory distress syndrome (ARDS), disseminated coagulopathy, and cardiovascular disorders, severe COVID-19 is accompanied by high mortality rates with near 7 million deaths. Nowadays, effective therapeutic patterns for severe COVID-19 are still lacking. It has been extensively reported that host metabolism plays essential roles in various physiological processes during virus infection. Many viruses manipulate host metabolism to avoid immunity, facilitate their own replication, or to initiate pathological response. Targeting the interaction between SARS-CoV-2 and host metabolism holds promise for developing therapeutic strategies. In this review, we summarize and discuss recent studies dedicated to uncovering the role of host metabolism during the life cycle of SARS-CoV-2 in aspects of entry, replication, assembly, and pathogenesis with an emphasis on glucose metabolism and lipid metabolism. Microbiota and long COVID-19 are also discussed. Ultimately, we recapitulate metabolism-modulating drugs repurposed for COVID-19 including statins, ASM inhibitors, NSAIDs, Montelukast, omega-3 fatty acids, 2-DG, and metformin.

An Updated Systematic Review and Meta-Analysis of the Association between the De Ritis Ratio and Disease Severity and Mortality in Patients with COVID-19

Patients with Coronavirus disease 2019 (COVID-19) often have elevations in markers of liver injury, particularly serum aspartate transaminase (AST) and alanine transaminase (ALT). Such alterations may affect the AST/ALT ratio (De Ritis ratio) and, potentially, clinical outcomes. We conducted an updated systematic review and meta-analysis of the association between the De Ritis ratio and COVID-19 severity and mortality in hospitalized patients. PubMed, Web of Science, and Scopus were searched between 1 December 2019 and 15 February 2023. The Joanna Briggs Institute Critical Appraisal Checklist and the Grading of Recommendations, Assessment, Development, and Evaluation were used to assess the risk of bias and the certainty of the evidence, respectively. Twenty-four studies were identified. The De Ritis ratio on admission was significantly higher in patients with severe disease and non-survivors vs. patients with non-severe disease and survivors (15 studies, weighted mean difference = 0.36, 95% CI 0.24 to 0.49, p < 0.001). The De Ritis ratio was also associated with severe disease and/or mortality using odds ratios (1.83, 95% CI 1.40 to 2.39, p ˂ 0.001; nine studies). Similar results were observed using hazard ratios (2.36, 95% CI 1.17 to 4.79, p = 0.017; five studies). In six studies, the pooled area under the receiver operating characteristic curve was 0.677 (95% CI 0.612 to 0.743). In our systematic review and meta-analysis, higher De Ritis ratios were significantly associated with severe disease and mortality in COVID-19 patients. Therefore, the De Ritis ratio can be useful for early risk stratification and management in this patient group (PROSPERO registration number: CRD42023406916).

Serum butyrylcholinesterase as a marker of COVID-19 mortality: Results of the monocentric prospective observational study

The COVID-19 pandemic represents an excessive burden on health care systems worldwide and the number of patients who require special care in the clinical setting is often hard to predict. Consequently, there is an unmet need for a reliable biomarker that could predict clinical outcomes of high-risk patients. Lower serum butyrylcholinesterase (BChE) activity was recently linked with poor outcomes of COVID-19 patients. In line with this, our monocentric observational study on hospitalized COVID-19 patients focused on changes in serum BChE activity in relation to disease progression. Blood samples from 148 adult patients of both sexes were collected during their hospital stay at the Clinics of Infectiology and Clinics of Anesthesiology and Intensive Care, Trnava University Hospital in alignment with routine blood tests. Sera were analyzed using modified Ellman's method. Patient data with information about the health status, comorbidities and other blood parameters were collected in pseudonymized form. Our results show a lower serum BChE activity together with progressive decline of BChE activity in non-survivors, while higher stable values were present in discharged or transferred patients requiring further care. Lower BChE activity was associated with higher age and lower BMI. Moreover, we observed a negative correlation of serum BChE activity with the routinely used inflammatory markers, C-reactive protein and interleukin-6. Serum BChE activity mirrored clinical outcomes of COVID-19 patients and thus serves as a novel prognostic marker in high-risk patients.

Variant-Related Differences in Laboratory Biomarkers among Patients Affected with Alpha, Delta and Omicron: A Retrospective Whole Viral Genome Sequencing and Hospital-Setting Cohort Study

During the COVID-19 pandemic, different SARS-CoV-2 variants of concern (VOC) with specific characteristics have emerged and spread worldwide. At the same time, clinicians routinely evaluate the results of certain blood tests upon patient admission as well as during hospitalization to assess disease severity and the overall patient status. In the present study, we searched for significant cell blood count and biomarker differences among patients affected with the Alpha, Delta and Omicron VOCs at admission. Data from 330 patients were retrieved regarding age, gender, VOC, cell blood count results (WBC, Neut%, Lymph%, Ig%, PLT), common biomarkers (D-dimers, urea, creatinine, SGOT, SGPT, CRP, IL-6, suPAR), ICU admission and death. Statistical analyses were performed using ANOVA, the Kruskal-Wallis test, two-way ANOVA, Chi-square, T-test, the Mann-Whitney test and logistic regression was performed where appropriate using SPSS v.28 and STATA 14. Age and VOC were significantly associated with hospitalization, whereas significant differences among VOC groups were found for WBC, PLT, Neut%, IL-6, creatinine, CRP, D-dimers and suPAR. Our analyses showed that throughout the current pandemic, not only the SARS-CoV-2 VOCs but also the laboratory parameters that are used to evaluate the patient's status at admission are subject to changes.

Biomarkers as predictors of mortality in critically ill obese patients with COVID-19 at high altitude

BACKGROUND

Obesity is a common chronic comorbidity of patients with COVID-19, that has been associated with disease severity and mortality. COVID-19 at high altitude seems to be associated with increased rate of ICU discharge and hospital survival than at sea-level, despite higher immune levels and inflammation. The primary aim of this study was to investigate the survival rate of critically ill obese patients with COVID-19 at altitude in comparison with overweight and normal patients. Secondary aims were to assess the predictive factors for mortality, characteristics of mechanical ventilation setting, extubation rates, and analytical parameters.

METHODS

This is a retrospective cohort study in critically ill patients with COVID-19 admitted to a hospital in Quito-Ecuador (2,850 m) from Apr 1, 2020, to Nov 1, 2021. Patients were cathegorized as normal weight, overweight, and obese, according to body mass index [BMI]).

RESULTS

In the final analysis 340 patients were included, of whom 154 (45%) were obese, of these 35 (22.7%) were hypertensive and 25 (16.2%) were diabetic. Mortality in obese patients (31%) was lower than in the normal weight (48%) and overweight (40%) groups, but not statistically significant (p = 0.076). At multivariable analysis, in the overall population, older age (> 50 years) was independent risk factor for mortality (B = 0.93, Wald = 14.94, OR = 2.54 95%CI = 1.58-4.07, p < 0.001). Ferritin and the neutrophil/lymphocyte ratio were independent predictors of mortality in obese patients. Overweight and obese patients required more positive and-expiratory pressure compared to normal-weight patients. In obese patients, plateau pressure and mechanical power were significantly higher, whereas extubation failure was lower as compared to overweight and normal weight.

CONCLUSIONS

This preliminary study suggests that BMI was not associated with mortality in critically ill patients at high altitude. Age was associated with an increase in mortality independent of the BMI. Biomarkers such as ferritin and neutrophils/lymphocytes ratio were independent predictors of mortality in obese patients with COVID-19 at high altitude.

Exploring Potential Biomarkers and Molecular Mechanisms of Ischemic Cardiomyopathy and COVID-19 Comorbidity Based on Bioinformatics and Systems Biology

Cardiovascular complications combined with COVID-19 (SARS-CoV-2) lead to a poor prognosis in patients. The common pathogenesis of ischemic cardiomyopathy (ICM) and COVID-19 is still unclear. Here, we explored potential molecular mechanisms and biomarkers for ICM and COVID-19. Common differentially expressed genes (DEGs) of ICM (GSE5406) and COVID-19 (GSE164805) were identified using GEO2R. We performed enrichment and protein–protein interaction analyses and screened key genes. To confirm the diagnostic performance for these hub genes, we used external datasets (GSE116250 and GSE211979) and plotted ROC curves. Transcription factor and microRNA regulatory networks were constructed for the validated hub genes. Finally, drug prediction and molecular docking validation were performed using cMAP. We identified 81 common DEGs, many of which were enriched in terms of their relation to angiogenesis. Three DEGs were identified as key hub genes (HSP90AA1, HSPA9, and SRSF1) in the protein–protein interaction analysis. These hub genes had high diagnostic performance in the four datasets (AUC > 0.7). Mir-16-5p and KLF9 transcription factor co-regulated these hub genes. The drugs vindesine and ON-01910 showed good binding performance to the hub genes. We identified HSP90AA1, HSPA9, and SRSF1 as markers for the co-pathogenesis of ICM and COVID-19, and showed that co-pathogenesis of ICM and COVID-19 may be related to angiogenesis. Vindesine and ON-01910 were predicted as potential therapeutic agents. Our findings will contribute to a deeper understanding of the comorbidity of ICM with COVID-19.

Serum Lactate Dehydrogenase Level One Week after Admission Is the Strongest Predictor of Prognosis of COVID-19: A Large Observational Study Using the COVID-19 Registry Japan

Clinical features of COVID-19 are diverse, and a useful tool for predicting clinical outcomes based on clinical characteristics of COVID-19 is needed. This study examined the laboratory values and trends that influence mortality in hospitalised COVID-19 patients. Data on hospitalised patients enrolled in a registry study in Japan (COVID-19 Registry Japan) were obtained. Patients with records on basic information, outcomes, and laboratory data on the day of admission (day 1) and day 8 were included. In-hospital mortality was set as the outcome, and associated factors were identified by multivariate analysis using the stepwise method. A total of 8860 hospitalised patients were included. The group with lactate dehydrogenase (LDH) levels >222 IU/L on day 8 had a higher mortality rate compared to the group with LDH levels ≤222 IU/L. Similar results were observed in subgroups formed by age, body mass index (BMI), underlying disease, and mutation type, except for those aged <50 years. When age, sex, BMI, underlying disease, and laboratory values on days 1 and 8 were tested for factors strongly associated with in-hospital mortality, LDH on day 8 was most strongly associated with mortality. LDH level on day 8 was the strongest predictor of in-hospital mortality in hospitalised COVID-19 patients, indicating its potential usefulness in post-treatment decision-making in severe COVID-19 cases.

Early peripheral blood MCEMP1 and HLA-DRA expression predicts COVID-19 prognosis

BACKGROUND

Mass vaccination has dramatically reduced the incidence of severe COVID-19, with most cases now presenting as self-limiting upper respiratory tract infections. However, those with co-morbidities, the elderly and immunocompromised, as well as the unvaccinated, remain disproportionately vulnerable to severe COVID-19 and its sequelae. Furthermore, as the effectiveness of vaccination wanes with time, immune escape SARS-CoV-2 variants could emerge to cause severe COVID-19. Reliable prognostic biomarkers for severe disease could be used as early indicator of re-emergence of severe COVID-19 as well as for triaging of patients for antiviral therapy.

METHODS

We performed a systematic review and re-analysis of 7 publicly available datasets, analysing a total of 140 severe and 181 mild COVID-19 patients, to determine the most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients. In addition, we included an independent cohort where blood transcriptomics of COVID-19 patients were prospectively and longitudinally monitored previously, to track the time in which these gene expression changes occur before nadir of respiratory function. Single cell RNA-sequencing of peripheral blood mononuclear cells from publicly available datasets was then used to determine the immune cell subsets involved.

FINDINGS

The most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients were MCEMP1, HLA-DRA and ETS1 across the 7 transcriptomics datasets. Moreover, we found significantly heightened MCEMP1 and reduced HLA-DRA expression as early as four days before the nadir of respiratory function, and the differential expression of MCEMP1 and HLA-DRA occurred predominantly in CD14+ cells. The online platform which we developed is publicly available at https://kuanrongchan-covid19-severity-app-t7l38g.streamlitapp.com/, for users to query gene expression differences between severe and mild COVID-19 patients in these datasets.

INTERPRETATION

Elevated MCEMP1 and reduced HLA-DRA gene expression in CD14+ cells during the early phase of disease are prognostic of severe COVID-19.

FUNDING

K.R.C is funded by the National Medical Research Council (NMRC) of Singapore under the Open Fund Individual Research Grant (MOH-000610). E.E.O. is funded by the NMRC Senior Clinician-Scientist Award (MOH-000135-00). J.G.H.L. is funded by the NMRC under the Clinician-Scientist Award (NMRC/CSAINV/013/2016-01). S.K. is funded by the NMRC under the Transition Award. This study was sponsored in part by a generous gift from The Hour Glass.

Patients with SARS-CoV-2-Induced Viral Sepsis Simultaneously Show Immune Activation, Impaired Immune Function and a Procoagulatory Disease State

BACKGROUND

It is widely accepted that SARS-CoV-2 causes a dysregulation of immune and coagulation processes. In severely affected patients, viral sepsis may result in life endangering multiple organ dysfunction. Furthermore, most therapies for COVID-19 patients target either the immune system or coagulation processes. As the exact mechanism causing SARS-CoV-2-induced morbidity and mortality was unknown, we started an in-depth analysis of immunologic and coagulation processes.

METHODS

127 COVID-19 patients were treated at the University Hospital Essen, Germany, between May 2020 and February 2022. Patients were divided according to their maximum COVID-19 WHO ordinal severity score (WHO 0-10) into hospitalized patients with a non-severe course of disease (WHO 4-5, n = 52) and those with a severe course of disease (WHO 6-10, n = 75). Non-infected individuals served as healthy controls (WHO 0, n = 42). Blood was analyzed with respect to cell numbers, clotting factors, as well as pro- and anti-inflammatory mediators in plasma. As functional parameters, phagocytosis and inflammatory responses to LPS and antigen-specific stimulation were determined in monocytes, granulocytes, and T cells using flow cytometry.

FINDINGS

In the present study, immune and coagulation systems were analyzed simultaneously. Interestingly, many severe COVID-19 patients showed an upregulation of pro-inflammatory mediators and at the same time clear signs of immunosuppression. Furthermore, severe COVID-19 patients not only exhibited a disturbed immune system, but in addition showed a pronounced pro-coagulation phenotype with impaired fibrinolysis. Therefore, our study adds another puzzle piece to the already complex picture of COVID-19 pathology implying that therapies in COVID-19 must be individualized.

CONCLUSION

Despite years of research, COVID-19 has not been understood completely and still no therapies exist, fitting all requirements and phases of COVID-19 disease. This observation is highly reminiscent to sepsis. Research in sepsis has been going on for decades, while the disease is still not completely understood and therapies fitting all patients are lacking as well. In both septic and COVID-19 patients, immune activation can be accompanied by immune paralysis, complicating therapeutic intervention. Accordingly, therapies that lower immune activation may cause detrimental effects in patients, who are immune paralyzed by viral infections or sepsis. We therefore suggest individualizing therapies and to broaden the spectrum of immunological parameters analyzed before therapy. Only if the immune status of a patient is understood, can a therapeutic intervention be successful.

An Interplay between Oxidative Stress (Lactate Dehydrogenase) and Inflammation (Anisocytosis) Mediates COVID-19 Severity Defined by Routine Clinical Markers

Viral infections activate the innate immune response and the secretion of inflammatory cytokines. They also alter oxidative stress markers, which potentially can have an involvement in the pathogenesis of the disease. The aim of this research was to study the role of the oxidative stress process assessed through lactate dehydrogenase (LDH) on the severity of COVID-19 measured by oxygen saturation (SaO₂) and the putative interaction with inflammation. The investigation enrolled 1808 patients (mean age of 68 and 60% male) with COVID-19 from the HM Hospitals database. To explore interactions, a regression model and mediation analyses were performed. The patients with lower SaO₂ presented lymphopenia and higher values of neutrophils-to-lymphocytes ratio and on the anisocytosis coefficient. The regression model showed an interaction between LDH and anisocytosis, suggesting that high levels of LDH (>544 U/L) and an anisocytosis coefficient higher than 10% can impact SaO₂ in COVID-19 patients. Moreover, analysis revealed that LDH mediated 41% (p value = 0.001) of the effect of anisocytosis on SaO₂ in this cohort. This investigation revealed that the oxidative stress marker LDH and the interaction with anisocytosis have an important role in the severity of COVID-19 infection and should be considered for the management and treatment of the oxidative phenomena concerning this within a precision medicine strategy.

Circulating Peptidome Is Strongly Altered in COVID-19 Patients

Whilst the impact of coronavirus disease 2019 (COVID-19) on the host proteome, metabolome, and lipidome has been largely investigated in different bio-fluids, to date, the circulating peptidome remains unexplored. Thus, the present study aimed to apply an untargeted peptidomic approach to provide insight into alterations of circulating peptides in the development and severity of SARS-CoV-2 infection. The circulating peptidome from COVID-19 severe and mildly symptomatic patients and negative controls was characterized using LC-MS/MS analysis for identification and quantification purposes. Database search and statistical analysis allowed a complete characterization of the plasma peptidome and the detection of the most significant modulated peptides that were impacted by the infection. Our results highlighted not only that peptide abundance inversely correlates with disease severity, but also the involvement of biomolecules belonging to inflammatory, immune-response, and coagulation proteins/processes. Moreover, our data suggested a possible involvement of changes in protein degradation patterns. In the present research, for the first time, the untargeted peptidomic approach enabled the identification of circulating peptides potentially playing a crucial role in the progression of COVID-19.

Biological Markers to Predict Outcome in Mechanically Ventilated Patients with Severe COVID-19 Living at High Altitude

BACKGROUND

There is not much evidence on the prognostic utility of different biological markers in patients with severe COVID-19 living at high altitude. The objective of this study was to determine the predictive value of inflammatory and hematological markers for the risk of mortality at 28 days in patients with severe COVID-19 under invasive mechanical ventilation, living at high altitude and in a low-resource setting.

METHODS

We performed a retrospective observational study including patients with severe COVID-19, under mechanical ventilation and admitted to the intensive care unit (ICU) located at 2850 m above sea level, between 1 April 2020 and 1 August 2021. Inflammatory (interleukin-6 (IL-6), ferritin, D-dimer, lactate dehydrogenase (LDH)) and hematologic (mean platelet volume (MPV), neutrophil/lymphocyte ratio (NLR), MPV/platelet ratio) markers were evaluated at 24 h and in subsequent controls, and when available at 48 h and 72 h after admission to the ICU. The primary outcome was the association of inflammatory and hematological markers with the risk of mortality at 28 days.

RESULTS

We analyzed 223 patients (median age (1st quartile [Q1]-3rd quartile [Q3]) 51 (26-75) years and 70.4% male). Patients with severe COVID-19 and with IL-6 values at 24 h ≥ 11, NLR values at 24 h ≥ 22, and NLR values at 72 h ≥ 14 were 8.3, 3.8, and 3.8 times more likely to die at 28 days, respectively. The SOFA and APACHE-II scores were not able to independently predict mortality.

CONCLUSIONS

In mechanically ventilated patients with severe COVID-19 and living at high altitude, low-cost and immediately available blood markers such as IL-6 and NLR may predict the severity of the disease in low-resource settings.