CRP, SAA, LDH, and DD predict poor prognosis of coronavirus disease (COVID-19): a meta-analysis from 7739 patients

Integrated Analysis of Noncoding RNAs (PVT-1 and miR-200c) and Their Correlation with STAT4/IL-6 Axis as Reliable Biomarkers for COVID-19 Severity

Inefficient control of elevated inflammatory mediators in coronavirus disease 2019 (COVID-19) has led to health complications, prompting the exploration of efficient biomarkers for monitoring this condition. We herein sought to investigate the implications of plasmacytoma variant translocation 1 (PVT-1), microRNA-200c (miR-200c), signal transducer and activator of transcription 4 (STAT-4), and interleukin-6 (IL-6), as well as how they correlated with creatinine, C-reactive protein (CRP), and lactate dehydrogenase (LDH) activity to identify biomarkers able to the early prognosis and diagnosis of COVID-19. Our study included a total of 105 infected COVID-19 patients and 35 healthy subjects as controls. Individuals with COVID-19 showed a significant increase in CRP, creatinine, and LDH activity. In addition, COVID-19 patients exhibited significantly higher levels of IL-6. These patients also demonstrated notably elevated expressions of miR-200c and PVT-1. The expression level of STAT4 decreased in the COVID-19 patients, and this decrease was negatively correlated with creatinine and LDH activity. The levels of miR-200c and PVT-1 expressions, and their connections with IL-6 and STAT4 levels, increased significantly with the severity of COVID-19 cases. In addition, receiver operating characteristic analysis showed that PVT-1 and miR-200c could be reliable biomarkers for determining the severity of COVID-19.

Outcomes of COVID-19 in 24 hospitalized liver transplant recipients: an observational study

BACKGROUND

Although liver transplant (LT) recipients are considered a population at risk of severe features of coronavirus disease 2019 (COVID-19), data in this regard are scarce and controversial. In this study, we reported the outcome of 24 cases of LT recipients who were hospitalized due to COVID-19 and investigated the role-playing factors in the severity of the disease.

METHODS

In this single-center, analytic case-series study, eligible patients were among LT recipients who were hospitalized due to the diagnosis of COVID-19 based on positive results of polymerase chain reaction. Participants were categorized as severe COVID-19 if they were admitted to the intensive care unit, experienced respiratory failure demanding mechanical ventilation, or eventually died. Demographic and clinical data, COVID-19 symptoms and specific treatments, laboratory biomarkers, and immunosuppressive regimens and their alteration during the admission were recorded. Analysis was done using SPSS software.

RESULTS

Twenty-four hospitalized LT patients were included, of which nine had severe and fifteen had non-severe COVID-19. Out of 9 patients with severe COVID-19, four sadly died. The analysis and comparison between the two groups revealed longer hospital stays (P = 0.02), lower lymphocyte counts (P = 0.002), and higher levels of C-reactive protein (CRP) (P = 0.006) in patients with severe COVID-19. Patients with non-severe COVID-19 had higher doses of tacrolimus and mycophenolate in their baseline immunosuppressive regimen (both P = 0.02).

CONCLUSION

Lymphopenia and high CRP levels are associated with more severe forms of COVID-19 in LT patients. Mycophenolate may have protective properties against severe COVID-19. The role of severity indicators in LT patients with COVID-19 needs to be systematically recognized.

Association of serum amyloid A and prognosis in people with diabetes and COVID-19: A retrospective cohort study

AIMS/INTRODUCTION

Serum amyloid A (SAA) is an acute phase reactive protein that plays a vital role in the early diagnosis, risk prediction, efficacy observation and prognosis evaluation of infectious diseases. This study aimed to assess the association between SAA levels and the prognosis of patients with coronavirus disease 2019 (COVID-19) and diabetes.

MATERIALS AND METHODS

We carried out this retrospective cohort study from March 2022 to May 2022. The population was stratified by tertiles of SAA levels: low (<8.5 mg/L), medium (8.5-36 mg/L) and high (>36 mg/L). The primary outcome was whether the patient developed severe COVID-19, and secondary outcomes included the need for invasive mechanical ventilation and length of hospital stay. Logistic regression analyses were carried out to identify risk factors affecting the prognosis of patients with COVID-19 and diabetes.

RESULTS

We analyzed 910 diabetes patients with COVID-19. The median age of the patients was 69 years, and 52.3% were men. As SAA levels increased, the proportion of severe COVID-19 (6.3% vs 7.3% vs 22.8%, P < 0.001) and the proportion of invasive mechanical ventilation also increased among the three groups. Patients with high SAA levels had a longer length of hospital stay compared with patients with medium SAA and low SAA levels. Univariate logistic regression analysis showed that SAA >36 mg/L further increased the odds ratio to 4.423 (P < 0.001) for the development of severe COVID-19 compared with low SAA. Multivariate logistic regression analysis, adjusted for age and sex, confirmed that SAA >36 mg/L remained an independent risk factor for the development of severe COVID-19 (adjusted odds ratio 3.038, P < 0.001).

CONCLUSIONS

SAA levels are strongly associated with poor prognosis in patients with COVID-19 and diabetes.

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

Low Levels of Serum and Intracellular Vitamin C in Hospitalized COVID-19 Patients

Vitamin C is a crucial micronutrient for human immune cell function and has potent antioxidant properties. It is hypothesized that vitamin C serum levels decline during infection. However, the precise mechanisms remain unknown. To gain deeper insights into the true role of vitamin C during infections, we aimed to evaluate the body's vitamin C storage during a SARS-CoV-2 infection. In this single-center study, we examined serum and intracellular vitamin C levels in peripheral blood mononuclear cells (PBMCs) of 70 hospitalized COVID-19 patients on the first and fifth days of hospitalization. Also, clinical COVID-19 severity was evaluated at these timepoints. Our findings revealed a high prevalence of hypovitaminosis C and vitamin C deficiency in hospitalized COVID-19 patients (36% and 15%). Moreover, patients with severe or critical disease exhibited a higher prevalence of low serum vitamin C levels than those with moderate illness. Serum vitamin C levels had a weak negative correlation with clinical COVID-19 severity classification on the day of hospitalization; however, there was no correlation with intracellular vitamin C. Intracellular vitamin C levels were decreased in this cohort as compared to a healthy cohort and showed further decline during hospitalization, while serum levels showed no relevant change. Based on this observation, it can be suggested that the reduction of intracellular vitamin C may be attributed to its antioxidative function, the need for replenishing serum levels, or enhanced turnover by immune cells. These data give an incentive to further investigate the role of intracellular vitamin C in a larger and more heterogeneous cohort as well as the underlying mechanisms.

Clinical and biochemical short-term effects of hyperbaric oxygen therapy on SARS-Cov-2+ hospitalized patients with hypoxemic respiratory failure

BACKGROUND

Hyperbaric oxygen therapy (HBOT) has been proposed to address COVID-19- associated respiratory failure. However, its biochemical effects are poorly known.

METHOD

50 patients with hypoxemic COVID-19 pneumonia were divided into C group (standard care) and H group (standard care plus HBOT). Blood was obtained at t = 0 and t = 5 days. Oxygen saturation (O2 Sat) was followed up. White blood cell (WC) count, lymphocytes (L) and platelets (P) and serum analysis (glucose, urea, creatinine, sodium, potassium, ferritin, D dimer, LDH and CRP) were carried out. Plasma levels of sVCAM, sICAM, sPselectin, SAA and MPO, and of cytokines (IL-1β, IL-1RA, IL-6, TNFα, IFNα, IFNγ, IL-15, VEGF, MIP1α, IL-12p70, IL-2 and IP-10) were measured by multiplex assays. Angiotensin Converting Enzyme 2 (ACE-2) levels were determined by ELISA.

RESULTS

The average basal O2 Sat was 85 ± 3%. The days needed to reach O2 Sat >90% were: H: 3 ± 1 and C: 5 ± 1 (P < 0,01). At term, H increased WC, L and P counts (all, H vs C: P < 0,01). Also, H diminished D dimer levels (H vs C, P < 0,001) and LDH concentration (H vs C, P < 0.01]. At term, H showed lower levels of sVCAM, sPselectin and SAA than C with respect to basal values (H vs C: ΔsVCAM: P < 0,01; ΔsPselectin: P < 0,05; ΔSAA: P < 0,01). Similarly, H showed diminished levels of TNFα (ΔTNFα: P < 0,05) and increased levels of IL-1RA and VEGF than C respect to basal values (H vs C: ΔIL-1RA and ΔVEGF: P < 0,05).

CONCLUSION

Patients underwent HBOT improved O2 Sat with lower levels of severity markers (WC and platelets count, D dimer, LDH, SAA). Moreover, HBOT reduced proinflammatory agents (sVCAM, sPselectin, TNFα) and increased anti-inflammatory and pro-angiogenic ones (IL-1RA and VEGF).

Dysregulated thrombospondin 1 and miRNA-29a-3p in severe COVID-19

Although nearly a fifth of symptomatic COVID-19 patients suffers from severe pulmonary inflammation, the mechanism of developing severe illness is not yet fully understood. To identify significantly altered genes in severe COVID-19, we generated messenger RNA and micro-RNA profiling data of peripheral blood mononuclear cells (PBMCs) from five COVID-19 patients (2 severe and 3 mild patients) and three healthy controls (HC). For further evaluation, two publicly available RNA-Seq datasets (GSE157103 and GSE152418) and one single-cell RNA-Seq dataset (GSE174072) were employed. Based on RNA-Seq datasets, thrombospondin 1 (THBS1) and interleukin-17 receptor A (IL17RA) were significantly upregulated in severe COVID-19 patients' blood. From single-cell RNA-sequencing data, IL17RA level is increased in monocytes and neutrophils, whereas THBS1 level is mainly increased in the platelets. Moreover, we identified three differentially expressed microRNAs in severe COVID-19 using micro-RNA sequencings. Intriguingly, hsa-miR-29a-3p significantly downregulated in severe COVID-19 was predicted to bind the 3'-untranslated regions of both IL17RA and THBS1 mRNAs. Further validation analysis of our cohort (8 HC, 7 severe and 8 mild patients) showed that THBS1, but not IL17RA, was significantly upregulated, whereas hsa-miR-29a-3p was downregulated, in PBMCs from severe patients. These findings strongly suggest that dysregulated expression of THBS1, IL17RA, and hsa-miR-29a-3p involves severe COVID-19.

SARS-CoV-2-Induced Amyloidgenesis: Not One, but Three Hypotheses for Cerebral COVID-19 Outcomes

The main neuropathological feature of Alzheimer's disease (AD) is extracellular amyloid deposition in senile plaques, resulting from an imbalance between the production and clearance of amyloid beta peptides. Amyloid deposition is also found around cerebral blood vessels, termed cerebral amyloid angiopathy (CAA), in 90% of AD cases. Although the relationship between these two amyloid disorders is obvious, this does not make CAA a characteristic of AD, as 40% of the non-demented population presents this derangement. AD is predominantly sporadic; therefore, many factors contribute to its genesis. Herein, the starting point for discussion is the COVID-19 pandemic that we are experiencing and how SARS-CoV-2 may be able to, both directly and indirectly, contribute to CAA, with consequences for the outcome and extent of the disease. We highlight the role of astrocytes and endothelial cells in the process of amyloidgenesis, as well as the role of other amyloidgenic proteins, such as fibrinogen and serum amyloid A protein, in addition to the neuronal amyloid precursor protein. We discuss three independent hypotheses that complement each other to explain the cerebrovascular amyloidgenesis that may underlie long-term COVID-19 and new cases of dementia.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

Background

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers.

Methods

We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers.

Findings

We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80.

Interpretation

COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients.

Funding

National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504).

Research in context

Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

TNF-related apoptosis-inducing ligand, interferon gamma-induced protein 10, and C-reactive protein in predicting the progression of SARS-CoV-2 infection: a prospective cohort study

BACKGROUND

Early prognostication of COVID-19 severity will potentially improve patient care. Biomarkers, such as TNF-related apoptosis-inducing ligand (TRAIL), interferon gamma-induced protein 10 (IP-10), and C-reactive protein (CRP), might represent possible tools for point-of-care testing and severity prediction.

METHODS

In this prospective cohort study, we analyzed serum levels of TRAIL, IP-10, and CRP in patients with COVID-19, compared them with control subjects, and investigated the association with disease severity.

RESULTS

A total of 899 measurements were performed in 132 patients (mean age 64 years, 40.2% females). Among patients with COVID-19, TRAIL levels were lower (49.5 vs 87 pg/ml, P = 0.0142), whereas IP-10 and CRP showed higher levels (667.5 vs 127 pg/ml, P <0.001; 75.3 vs 1.6 mg/l, P <0.001) than healthy controls. TRAIL yielded an inverse correlation with length of hospital and intensive care unit (ICU) stay, Simplified Acute Physiology Score II, and National Early Warning Score, and IP-10 showed a positive correlation with disease severity. Multivariable regression revealed that obesity (adjusted odds ratio [aOR] 5.434, 95% confidence interval [CI] 1.005-29.38), CRP (aOR 1.014, 95% CI 1.002-1.027), and peak IP-10 (aOR 1.001, 95% CI 1.00-1.002) were independent predictors of in-ICU mortality.

CONCLUSIONS

We demonstrated a correlation between COVID-19 severity and TRAIL, IP-10, and CRP. Multivariable regression showed a role for IP-10 in predicting unfavourable outcomes, such as in-ICU mortality.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT04655521.

Real-World Use of Molnupiravir in the Treatment of Outpatients with SARS-CoV-2 Infection-A Patient Profile Based on the Experience of a Tertiary Infectious Disease Center

During the current pandemic, the gap between fundamental research and clinical practice has been narrowing at a faster pace than ever before. While clinical trials play the main role of confirming the safety and efficacy of new drugs, a drug's introduction into clinical practice creates the need for further research in order to best position the use of the novel drug in terms of when, to whom, and how it would be best administered to achieve the best possible outcome under feasible clinical circumstances. We briefly present the results of a retrospective analysis of the characteristics of outpatients treated with molnupiravir in a tertiary care infectious disease hospital in Bucharest, Romania, between February and March 2022, when Romania was experiencing its fifth wave of COVID-19. A total of 46 outpatients received molnupiravir treatment and had complete clinical data available; of them, 56.5% (n = 20) were males and the median age was 48.5 years (IQR: 37.8, 67.0 years). A total of 54.2% (n = 26) of patients had at least one chronic condition. Of the 45 patients who underwent lung CT imaging evaluation, 13 (28.9%) showed changes suggestive of COVID-19 pneumonia. COVID-19 vaccination status was strongly protective for pneumonia (p = 0.002). All patients were symptomatic, and molnupiravir was initiated at a mean time from onset of symptoms of 3.5 (±1.5) days. At phone follow-up 5 days after the initial evaluation and initiation of molnupiravir treatment, all patients, except for one, confirmed a favorable course under treatment, with no worsening of COVID-19 severity and improvement in symptoms; none of them progressed to respiratory failure or required hospitalization. In conclusion, treatment was well tolerated and associated a favorable outcome of COVID-19 in routine practice in a clinical population that was slightly older and had a smaller burden of comorbidities and a higher rate of COVID-19 vaccination compared to that from the pivotal trial.

Effective Natural Killer Cell Degranulation Is an Essential Key in COVID-19 Evolution

NK degranulation plays an important role in the cytotoxic activity of innate immunity in the clearance of intracellular infections and is an important factor in the outcome of the disease. This work has studied NK degranulation and innate immunological profiles and functionalities in COVID-19 patients and its association with the severity of the disease. A prospective observational study with 99 COVID-19 patients was conducted. Patients were grouped according to hospital requirements and severity. Innate immune cell subpopulations and functionalities were analyzed. The profile and functionality of innate immune cells differ between healthy controls and severe patients; CD56dim NK cells increased and MAIT cells and NK degranulation rates decreased in the COVID-19 subjects. Higher degranulation rates were observed in the non-severe patients and in the healthy controls compared to the severe patients. Benign forms of the disease had a higher granzymeA/granzymeB ratio than complex forms. In a multivariate analysis, the degranulation capacity resulted in a protective factor against severe forms of the disease (OR: 0.86), whereas the permanent expression of NKG2D in NKT cells was an independent risk factor (OR: 3.81; AUC: 0.84). In conclusion, a prompt and efficient degranulation functionality in the early stages of infection could be used as a tool to identify patients who will have a better evolution.

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree

Background

Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters.

Methods

Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between −600 and −250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2).

Results

A total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters.

Conclusions

The decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.