Research ArticleClinical Studies
Open Access

Hyperexpression of PTAFR and PF4 as Possible Platelet Risk Biomarkers in Patients With COVID-19

LÍVIA DE OLIVEIRA SALES, JEAN BRENO SILVEIRA DA SILVA, FLÁVIA MELO CUNHA DE PINHO PESSOA, BEATRIZ MARIA DIAS NOGUEIRA, LAIS LACERDA BRASIL DE OLIVEIRA, ANDRÉ SALIM KHAYAT, MANOEL ODORICO DE MORAES FILHO, MARIA ELISABETE AMARAL DE MORAES, RAQUEL CARVALHO MONTENEGRO and CAROLINE AQUINO MOREIRA-NUNES
In Vivo November 2024, 38 (6) 2853-2863; DOI: https://doi.org/10.21873/invivo.13766

Abstract

Background/Aim: SARS-CoV-2 infection presents different severity levels that suggest the influence of genetic factors on the clinical outcome of the disease. In cases of severe COVID-19, the presence of elevated coagulation markers, increased platelet activation and aggregation and the risk of thrombotic complications are described. Given the participation of these cells in several serious viral infections and their negative role when associated with a prothrombotic response, it is important to understand the mechanistic role of SARS-CoV-2 in platelet physiology. This study evaluated the hyperexpression of platelet-activating factor receptor (PTAFR) and platelet factor 4 (PF4) in unvaccinated and hospitalized patients with COVID-19. Patients and Methods: The study included 43 COVID-19 patients stratified according to WHO guidelines. Subsequently, the expression of the PTAFR and PF4 genes were evaluated using the real-time quantitative PCR and their possible correlation with the severity of the disease and clinical variables including hospitalization, outcome, sex, age and laboratory parameters (platelet count, INR and D-dimer). Results: The analysis demonstrated a significant (p<0.05) hyperexpression of these genes COVID-19 patients (n=43) compared to healthy controls. Expression of these genes in patients was not statistically significant (p>0.05) different between patients stratified according to clinical variables. Conclusion: The expression of PTAFR and PF4 suggests an important molecular pathway in the pathophysiology of the disease and may be valuable platelet biomarkers to indicate increased risk in patients with COVID-19 who require hospital care, contributing to personalized intervention strategies and improving their clinical management.

Key Words:

COVID-19 is an infectious disease caused by SARS-CoV-2, an enveloped, positive-sense, single-stranded RNA virus belonging to the Coronaviridae family. The virus was first reported in China, and quickly spread to other parts of the world, leading to a global pandemic with significant impacts on public health systems (1).

Its variable clinical presentation from mild to severe suggests the influence of genetic factors on the susceptibility and severity of COVID-19 (2-4). Thrombo-inflammatory complications are well described in critically ill patients, mediated by platelet and inflammatory dysfunctions, including changes in hemostatic function, in the production of cytokines (5, 6) and in promoting a hypercoagulable inflammatory endotheliopathy (7).

Platelets are known for their contributions to thrombosis and hemostasis (8). However, it has been described that changes in the molecular profiles of platelets, such as platelet activating factor receptor (PTAFR) and platelet factor 4 (PF4), in different viral infections – including dengue (9, 10), HIV (11, 12), H1N1 (13) and respiratory syncytial virus (RSV) (14) – result in a more severe course of these diseases, and the triggering of a hyperinflammatory response and an increase in platelet activation (15, 16).

Platelet activating factor (PAF) is an important inflammatory mediator, which binds to its receptor on the platelet surface. Within platelet cells, it stimulates platelet aggregation and clot formation (17). PF4 is one of the main cytokines released by platelet granules, contributing not only to coagulation, but also to the progression of thrombosis due to its neutralizing effect on anticoagulants such as heparin (18, 19).

In COVID-19, although theorized, changes in PTAFR and PF4 would influence the clinical context of severe SARS-CoV-2 infection, being identified in the literature as possible new biomarkers of disease risk (20, 21). In addition, evaluating the gene expression of these markers in hospitalized patients could help understand the coagulation and inflammation profile of severe COVID-19 and enable pharmacological repurposing and development of target-directed therapies.

In this study, we evaluated the expression of the platelet genes PTAFR and PF4 in sick, unvaccinated patients with COVID-19 who required hospitalization, with the aim of helping to understand thrombo-inflammation and its possible role in stratifying the disease’s risk profiles.

Patients and Methods

Patients and biological samples. Forty-three patients with confirmed COVID-19 and a control group of 15 healthy volunteers from both sexes, with median age of 30 years (18-45 years) were included. SARS-CoV-2 infection was confirmed by immunochromatographic testing and/or reverse-transcriptase polymerase chain reaction (PCR; RT-qPCR). The study was carried out at the São José Hospital for Infectious Diseases (HSJ) and at the Instituto Doctor José Frota (IJF) (Fortaleza, CE, Brazil), between March 2020 and March 2021. Blood was collected in tubes containing EDTA anticoagulant, after approval by the Ethics and Research Committee of the Federal University of Ceara (approval number: 4.029.490) and consent for inclusion in the study provided in writing by the participant or their legal representative, in accordance with the Declaration of Helsinki.

All unvaccinated patients included in our study were aged >17 years and were hospitalized for more than 24 hours in intensive care unit (ICU) or hospital bed (non-ICU). Inclusion criteria included a positive test for SARS-CoV-2, hospitalization for COVID-19, absence of pre-existing coagulopathy, and informed consent.

Study patients were classified according to WHO guidelines for moderate, severe e critical patients and stratified according to age, sex, underlying diseases, laboratory tests, type of hospitalization, outcome and symptoms for subsequent analysis of risk factors for the development of severe forms of the disease, established according to the parameters addressed in the study, presented Table I and Table II.

View this table:
Table I.

Study stratification criteria and its clinical aspects.

View this table:
Table II.

COVID-19 severity classification according to the WHO (58).

RNA extraction and cDNA synthesis. RNA from the samples was extracted with TRIzol Reagent® (Invitrogen, Waltham, MA, USA) according to the manufacturer’s instructions. From 10 ng of RNA, cDNA was synthesized using High-Capacity cDNA Reverse Transcriptase kit (Life Technologies, Carlsbad, CA, USA) to convert the extracted and purified RNA into cDNA. The conversion step was performed using Veriti® Thermal Cycler (Applied Biosystems, Foster City, CA, USA). After this step, the samples were stored in a freezer at −20°C until analysis.

RT-qPCR. The platelet genes selected for expression analysis were PTAFR (Hs00265399_s1) and PF4 (Hs00236998_m1), with ACTB (Hs01060665_g1) used as an internal control. TaqMan gene expression assays for these genes were purchased from Applied Biosystems and qPCR was performed using the QuantStudio5® Real-time PCR system (Applied Biosystems).

For each sample, the following concentrations were used: 1 μl of cDNA, 1 μl of TaqMan® Gene expression assay, 5 μl of TaqMan® Gene Expression Master Mix, 3 μl of ultrapure water, totaling 10 μl per reaction. The reactions’ amplification protocol consisted of the following cycling conditions: 50°C/2 min, 95°C/10 min, 50 cycles of 95°C/15 s and 60°C/1 min. Gene expression levels were based on absolute and relative analyses and calculated using the 2−ΔΔCT (delta-delta threshold cycle) method, where the expression level of the gene of interest is reported relative to the reference gene for each sample (22). Each sample was analyzed accordance with the Minimum Information Guidelines for Publication of Quantitative Real-Time PCR Experiments (23).

Statistical analysis. Data were expressed as mean or median±dispersion measurements, depending on the normality of the samples. The Shapiro-Wilk test was used to evaluate the sample’s distribution. The Chi-Square or Fisher tests were applied to compare the likely influence of the study’s clinical variables. For parametric analyses, the t-test or Analysis of Variance (ANOVA) were used, followed Bonferroni’s post-test, when two or more groups were analyzed, respectively. For non-parametric statistics, the Mann-Whitney test was used to compare two groups and the Kruskal-Wallis test, followed by the Dunn’s Multiple Comparison post-test when more than two groups were analyzed. All analyses were performed using GraphPad Prism version 8.0.1 for Windows (GraphPad Software, Boston, MA, USA). Significant differences were considered with a confidence interval of 95% (p≤0.05).

Results

Demographic and clinical characteristics. Among the 43 patients, the median age was 57 years (IQR=17-87 years), and 26 (60.5%) were males. There were 6 cases in the moderate group, 13 cases in the severe group and 24 cases in the critical group. There was no significant difference in the median age and sex ratio of each group. The main clinical manifestations were dyspnea, cough and myalgia in all patients, and fever and headache were more common in severe and critical patients. In terms of chronic diseases, except hypertension and smoking, diabetes, obesity, coronary heart disease, cerebrovascular disease and chronic renal disease in the severe and critical groups were higher than those of the moderate group (Table III).

View this table:
Table III.

Clinical and baseline characteristics of patients with COVID-19.

Drug treatment and clinical evolution. Antibiotic therapy and corticosteroid therapy were the main treatments adopted between the groups, with fibrinolytics, such as enoxaparin and rivaroxaban, most used in patients with severe and critical COVID-19 (100 vs. 81.8%) (Table IV). While anticoagulant therapy was similarly administered at different stages of the disease (40.0 vs. 36.4 vs. 45.5%), antiparasitics – albendazole and ivermectin – were more frequent among patients with moderate severity (60.0%). Other emergency use medications such as hydroxychloroquine (18.2%) and immunoglobulins (20.0 vs. 27.3%) were little used in our study groups. Furthermore, data information about the chosen treatment was not available for 16 (37.2%) patients.

View this table:
Table IV.

Treatments and clinical evolution of patients with COVID-19.

Regarding the evolution of the patients, of the 26 admitted to the ICU, 18 cases belonged to the critical group (75.0%), six to the severe group (46.2%) and two cases to the moderate group (33.3%). At the final date of the research, 31 patients (72.1%) had been discharged from the hospital and 12 (27.9%) died. The mean length of stay was 29 days (ranging from 7 to 85 days).

Clinical and laboratory characteristics of patients with COVID-19. Table V stratifies patients by type of hospitalization (non-ICU or ICU) and outcome (discharge or death), which are subgrouped according to sex (male or female), age (17-44, 45-64, ≥65 years) and laboratory parameters (platelet count, INR and D-dimer).

View this table:
Table V.

Correlation analysis of clinical and laboratory variables in COVID-19 patients.

Our analysis showed a significant difference in clinical outcomes according to age (p<0.05), where it was observed that patients aged 65 years or over (66.7%) had a worse clinical outcome and did not survive. There were also significant differences in clinical outcome according to comparing platelet count (p=0.0038) and INR (p=0.0050); patients who fell within the normal ranges had a better prognosis associated with hospital discharge, while 50.0% of those with platelet values above 400×103/mm3 died.

Furthermore, 92.3% of patients with D-dimer levels values greater than 1,000 ng/ml had a significantly (p=0.0158) greater need for hospitalization in ICU.

PTAFR and PF4 genes show increased expression in hospitalized patients with COVID-19. We performed platelet-activating factor receptor (PTAFR) and platelet factor 4 (PF4) gene expression analyses in 43 blood samples from unvaccinated patients with COVID-19 who required hospitalization and compared them with blood samples from 15 healthy patients (control group). In this analysis, we observed a sixfold increase in the expression of PTAFR and PF4 in patients with COVID-19 when compared to the control group (p<0.05) (Figure 1A and B).

Figure 1.
Figure 1.

Patients with COVID-19 demonstrated increased expressions of PTAFR and PF4. The data are presented as the mean and median of two gene expression comparison experiments, where each dot plot represents the expression values in a single patient. Relative expressions of genes of interest were calculated using ACTB as an endogenous normalizer. A) Comparison of PTAFR expression between samples from hospitalized COVID-19 patients and healthy individuals. B) Comparison of PF4 expression between samples from hospitalized COVID-19 patients and healthy individuals. Statistical significance between the patient and control groups was determined using the Mann-Whitney test for PTAFR and t-test for PF4. *p<0.05.

However, when we compared the PTAFR and PF4 levels of patients according to COVID-19 severity classifications, despite the higher expression of these genes in individuals in the severe and critical groups, the difference was not significant (p>0.05) (Figure 2A and B).

Figure 2.
Figure 2.

The patient cohort (n=43) was separated according to the COVID-19 severity classification and the relative expression of PTAFR and PF4. Data are presented as mean and median of gene expression comparison experiments, where each dot plot represents expression values in a single patient. A) Comparison of PTAFR expression between samples from patients hospitalized with COVID-19 with varying degrees of severity. B) Comparison of PF4 expression between samples from patients hospitalized with COVID-19 with varying degrees of severity. Statistical analyses between groups was determined using the Kruskal-Wallis test, followed by Dunn’s multiple comparison test for PTAFR and ANOVA with a subsequent Bonferroni post-test for PF4. p>0.05. ns, Not significant.

Next, we investigated whether PTAFR and PF4 expressions differed according to laboratory parameters of patients hospitalized with the disease. For this, the data were normalized and categorized according to the variables under study, assuming Log2 fold change expression levels.

For comparative analysis of gene expression and coagulation parameters, we stratified patients into three groups: platelet count, when <150×103/mm3 for patients with thrombocytopenia, between 150-400×103/mm3 for those within the reference range and >400×103/mm3 for cases of thrombocytosis; INR, when between 0.80-1.30 for normal values and >1.30 for individuals with this parameter prolonged; and D-dimer values between 501-1,000 and >1,000 ng/ml, where the latter is associated with a more severe clinical context and an unfavorable prognosis. However, a statistically significant result was only found when we analyzed the expression of PF4 among groups of patients with values between 150-400×103/mm3 and >400×103/mm3 (p<0.05) (Figure 3).

Figure 3.
Figure 3.

Analysis of patients hospitalized with COVID-19 (n=43) stratified by laboratory coagulation parameters according to the relative expression of PTAFR and PF4. Data are presented as the mean and median of the gene expression comparison experiments, where each dot plot represents the expression values in a single patient. A) and B) For platelets, a significant difference was observed only in the PF4 expression in the groups with platelet counts between 150-400×103/mm3 and >400×103/mm3. C) and D) INR and E) and F) D-dimer did not show significant differences between the groups. Statistical analysis for INR utilized the Mann-Whitney and t-test, while platelet count and D-dimer evaluations employed the Kruskal-Wallis test, supplemented by Dunn’s multiple comparison test, and ANOVA with Bonferroni correction for post-hoc testing *p<0.05. ns, Not significant.

Discussion

SARS-CoV-2 infection begins when the viral spike protein binds to its receptor, angiotensin converting enzyme 2 (ACE2), in the alveoli. Following binding, the TMPRSS2 protease cleaves and activates the spike protein, favoring the fusion of the viral envelope with the cell membrane (24, 25). As the infection progresses, defense cells are recruited and there is a consequent increase in inflammatory cytokines in the lungs, where an exacerbated immune response is observed, mediated mainly by platelet-derived signaling proteins (26). Although platelets are considered the main effector cells of hemostasis, their role in combating viral infections and increasing the inflammatory state are widely discussed (16, 27). In severe COVID-19, platelet changes such as increased aggregation, elevated coagulation markers and thromboembolic events are common and, more recently, its variable clinical presentation has been associated with the possible influence of individual genetic factors (5, 28, 29).

In the present study, we demonstrated that the platelet genes PTAFR and PF4 are significantly hyperexpressed in patients with COVID-19 who required hospital admission, compared to healthy individuals and, therefore, supporting their potential as genetic biomarkers for disease risk stratification, thereby enabling new therapeutic approaches and pharmacological reuse.

Although the mechanisms of PTAFR and PF4 in COVID-19 are still not fully understood, there is observed involvement of these markers in the exacerbation of various inflammatory and infectious processes. Physiologically, the binding of PAF to its receptor occurs mainly on the surface of platelets, acting as a mediator of inflammation during viral and bacterial infections. This interaction also stimulates platelet aggregation and clot formation. Diseases or genetic variations that disrupt the production or degradation cycle of this protein can result in abnormal and uncontrolled activity (17, 30). This may cause endothelial damage and thrombus formation, similar to pathologies observed in patients with severe COVID-19 (31). In addition, there are reports that it facilitates the entry of HIV (12) into host cells. Differential expression of this gene has also been described in children with severe infections caused by the respiratory syncytial virus (14).

In addition to SARS-CoV-2 infection, CXCL4 (PF4) is one of the main cytokines released by activated platelets in response to viral infections, including HIV infection (32), H1N1 (13) and, mainly, in dengue fever, where its relationship with increased viral propagation and replication was described (10). The release of PF4 by platelet granules contributes to clot formation and the progression of thrombosis, neutralizing the anticoagulant effects of heparin (19) and to tissue inflammation (33, 34).

A correlation analysis was also carried out between PTAFR and PF4 gene expression and the severity of COVID-19 (moderate, severe e critical stages) and platelet risk variables: platelet count, INR and D-dimer. Although the individual analysis demonstrated a higher expression of PTAFR and PF-4 in moderately and critically ill patients, no statistically significant difference was observed (p<0.05) and this fact may be associated with our limited sample size. Patients with increased relative expression of PTAFR had no significant differences when stratified according to platelet count (p>0.05). However, when we compared those with platelet counts between 150-400×103/mm3 and >400×103/mm in relation to the relative expression of PF4, they showed significant differences (p<0.05). This is considered an important finding, since patients with high levels of platelet counts, despite being associated with better survival, are more likely to develop long COVID-19 and have a significant frequency of thrombotic events (35), which can act as an additional marker of severity. The INR and D-dimer variables, demonstrated separately, did not show a significant association with the hyperexpression of these genes (p>0.05).

In the comparative analysis, a significant influence was observed between age and clinical outcome, with patients aged ≥65 years having a negative evolution. Similar findings were described in relation to advanced age as a risk factor for in-hospital death, being associated with age-dependent defects in T and B cell function, and increased production of cytokines that can lead to a failure to control viral replication and a consequent exacerbated pro-inflammatory response (36-38). While the literature describes a greater number of ICU admissions associated with elderly patients compared to non-ICU (39), no significant correlation was found in our study between age and type of hospitalization.

The intense inflammatory conditions in COVID-19 cause an interruption in hemostasis, affecting coagulation parameters and causing the development of thrombotic complications (40, 41), being identified as an efficient predictor of prognosis, assisting in the clinical management of patients (42). The majority of patients in the study had platelets within the normal range (150-400×103/mm3) and a better clinical evolution when compared to thrombocytosis cases (>400×103/mm3), which were more associated with death. In contrast, the literature showed that patients with thrombocytosis had significantly better overall survival compared to patients with normal platelet counts (35).

In addition, we did not observe a significant influence of INR values on hospitalization, despite its significance when correlated with the outcome, where a higher hospital discharge rate was observed in patients with INR within normal ranges. These results are in agreement with the literature, which shows that prolonged INR values are significantly associated with the severity and mortality of COVID-19 than with mild disease or survivor status (43). Furthermore, in our study, D-dimer values >1,000 ng/ml were more present in patients admitted to the ICU, as reported in a similar study (44). Increased D-dimer levels reflect a hypercoagulable state that is extremely useful for early identification of thrombotic changes in severe cases of COVID-19 (38, 45, 46), being strongly related to a higher risk of death when associated with advanced age, male sex and comorbidities (38, 47).

It has been reported that the symptoms of patients with COVID-19 are mainly fever, cough, myalgia and dyspnea (44, 48). The greater number of male COVID-19 patients requiring hospital care is linked to behavioral health conditions, such as smoking and alcohol consumption (49, 50). However, comorbidities are associated with greater severity and mortality in COVID-19 (51, 52). Hyperglycemia caused by diabetes predisposes individuals to more severe disease due to its changes in inflammatory processes and metabolic disorders (51). Obesity is associated with changes in the distribution and number of immune cells, and higher levels of ACE2 in adipose tissues may aid in the entry and spread of the virus (53, 54). Furthermore, the use of antihypertensives is associated with greater ACE2 activity, leaving these patients more vulnerable to viral infection (55).

In our group, the most used pharmacological classes were antibiotics, corticosteroids, anticoagulants and antifibrinolytics. However, considering the period during which the study was carried out, it is important to remember that most interventions were empirical and emergency in nature, with the aim of reducing patients’ symptoms. Our study has some limitations, such as discontinuity in filling out medical records due to the pandemic context, small sample size, the lack of standardization of the exact dates for carrying out laboratory tests and the administration of medications, all of which may have had some effect on the plasma levels of the biomarkers mentioned in the study. Therefore, non-significant statistical tests do not necessarily rule out significant differences. However, additional research is needed to validate both the relationship between PTAFR and PF4 and COVID-19, as well as its severity prediction.

Conclusion

The data presented in this study indicate that the over-expression of PTAFR and PF4 in patients with COVID-19 may be linked to greater need for specialized hospital care and be a risk factor associated with a severe disease, due to its effects on inflammatory and thrombotic signaling mechanisms. We also demonstrated that platelet count, INR and D-dimer levels are important parameters for monitoring and prognosis of the disease.

Footnotes

  • Authors’ Contributions

    Conceptualization, L.O.S, L.L.B.O, J.B.S.d.S, R.C.M, and C.A.M.-N.; Provision of data and subsequent analysis and interpretation, L.O.S, J.B.S.d.S, F.M.C.d.P.P R.M.R, B.M.D.N, A.S.K, M.O.d.M.F, M.E.A.d.M and C.A.M.-N.; Writing – original draft preparation, L.O.S, and C.A.M.-N; Writing – review and editing, L.O.S, and C.A.M.-N; Funding acquisition, M.O.d.M.F, R.C.M, and C.A.M.-N. All Authors have read and agreed to published version of the manuscript.

  • Funding

    This study was supported by Brazilian funding agencies: Cearense Foundation of Scientific and Technological Support (FUNCAP grant number 03195011/2020 to M.O.d.M.F.F) Coordination for the Improvement of Higher Education Personnel (CAPES; to J.B.S.d.S), National Council of Technological and Scientific Development (CNPq grant number 404213/2021-9 to CAM-N; and Productivity in Research PQ scholarships to M.O.d.M.F, M.E.A.d.M, R.C.M and C.A.M.-N.).

  • Conflicts of Interest

    The Authors declare no conflicts of interest in relation to this study. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

  • Received July 4, 2024.
  • Revision received July 18, 2024.
  • Accepted July 19, 2024.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

References

  1. ↵
    1. Hu B,
    2. Guo H,
    3. Zhou P,
    4. Shi ZL
    : Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol 19(3): 141-154, 2021. DOI: 10.1038/s41579-020-00459-7
    OpenUrlCrossRefPubMed
  2. ↵
    1. Anastassopoulou C,
    2. Gkizarioti Z,
    3. Patrinos GP,
    4. Tsakris A
    : Human genetic factors associated with susceptibility to SARS-CoV-2 infection and COVID-19 disease severity. Hum Genomics 14(1): 40, 2020. DOI: 10.1186/s40246-020-00290-4
    OpenUrlCrossRefPubMed
    1. Elhabyan A,
    2. Elyaacoub S,
    3. Sanad E,
    4. Abukhadra A,
    5. Elhabyan A,
    6. Dinu V
    : The role of host genetics in susceptibility to severe viral infections in humans and insights into host genetics of severe COVID-19: A systematic review. Virus Res 289: 198163, 2020. DOI: 10.1016/j.virusres.2020.198163
    OpenUrlCrossRefPubMed
  4. ↵
    1. Secolin R,
    2. de Araujo TK,
    3. Gonsales MC,
    4. Rocha CS,
    5. Naslavsky M,
    6. Marco L,
    7. Bicalho MAC,
    8. Vazquez VL,
    9. Zatz M,
    10. Silva WA,
    11. Lopes-Cendes I
    : Genetic variability in COVID-19-related genes in the Brazilian population. Hum Genome Var 8: 15, 2021. DOI: 10.1038/s41439-021-00146-w
    OpenUrlCrossRefPubMed
  5. ↵
    1. Manne BK,
    2. Denorme F,
    3. Middleton EA,
    4. Portier I,
    5. Rowley JW,
    6. Stubben C,
    7. Petrey AC,
    8. Tolley ND,
    9. Guo L,
    10. Cody M,
    11. Weyrich AS,
    12. Yost CC,
    13. Rondina MT,
    14. Campbell RA
    : Platelet gene expression and function in patients with COVID-19. Blood 136(11): 1317-1329, 2020. DOI: 10.1182/blood.2020007214
    OpenUrlCrossRefPubMed
  6. ↵
    1. Zaid Y,
    2. Puhm F,
    3. Allaeys I,
    4. Naya A,
    5. Oudghiri M,
    6. Khalki L,
    7. Limami Y,
    8. Zaid N,
    9. Sadki K,
    10. Ben El Haj R,
    11. Mahir W,
    12. Belayachi L,
    13. Belefquih B,
    14. Benouda A,
    15. Cheikh A,
    16. Langlois MA,
    17. Cherrah Y,
    18. Flamand L,
    19. Guessous F,
    20. Boilard E
    : Platelets can associate with SARS-Cov-2 RNA and are hyperactivated in COVID-19. Circ Res 127(11): 1404-1418, 2020. DOI: 10.1161/CIRCRESAHA.120.317703
    OpenUrlCrossRef
  7. ↵
    1. Barrett TJ,
    2. Bilaloglu S,
    3. Cornwell M,
    4. Burgess HM,
    5. Virginio VW,
    6. Drenkova K,
    7. Ibrahim H,
    8. Yuriditsky E,
    9. Aphinyanaphongs Y,
    10. Lifshitz M,
    11. Xia Liang F,
    12. Alejo J,
    13. Smith G,
    14. Pittaluga S,
    15. Rapkiewicz AV,
    16. Wang J,
    17. Iancu-Rubin C,
    18. Mohr I,
    19. Ruggles K,
    20. Stapleford KA,
    21. Hochman J,
    22. Berger JS
    : Platelets contribute to disease severity in COVID-19. J Thromb Haemost 19(12): 3139-3153, 2021. DOI: 10.1111/jth.15534
    OpenUrlCrossRefPubMed
  8. ↵
    1. Koupenova M,
    2. Clancy L,
    3. Corkrey HA,
    4. Freedman JE
    : Circulating platelets as mediators of immunity, inflammation, and thrombosis. Circ Res 122(2): 337-351, 2018. DOI: 10.1161/CIRCRESAHA.117.310795
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. Souza DG,
    2. Fagundes CT,
    3. Sousa LP,
    4. Amaral FA,
    5. Souza RS,
    6. Souza AL,
    7. Kroon EG,
    8. Sachs D,
    9. Cunha FQ,
    10. Bukin E,
    11. Atrasheuskaya A,
    12. Ignatyev G,
    13. Teixeira MM
    : Essential role of platelet-activating factor receptor in the pathogenesis of Dengue virus infection. Proc Natl Acad Sci U S A 106(33): 14138-14143, 2009. DOI: 10.1073/pnas.0906467106
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Ojha A,
    2. Bhasym A,
    3. Mukherjee S,
    4. Annarapu GK,
    5. Bhakuni T,
    6. Akbar I,
    7. Seth T,
    8. Vikram NK,
    9. Vrati S,
    10. Basu A,
    11. Bhattacharyya S,
    12. Guchhait P
    : Platelet factor 4 promotes rapid replication and propagation of Dengue and Japanese encephalitis viruses. EBioMedicine 39: 332-347, 2019. DOI: 10.1016/j.ebiom.2018.11.049
    OpenUrlCrossRefPubMed
  11. ↵
    1. Tsoupras AB,
    2. Chini M,
    3. Tsogas N,
    4. Fragopoulou E,
    5. Nomikos T,
    6. Lioni A,
    7. Mangafas N,
    8. Demopoulos CA,
    9. Antonopoulou S,
    10. Lazanas MC
    : Anti-platelet-activating factor effects of highly active antiretroviral therapy (HAART): a new insight in the drug therapy of HIV infection? AIDS Res Hum Retroviruses 24(8): 1079-86, 2008. DOI: 10.1089/aid.2007.0263
    OpenUrlCrossRefPubMed
  12. ↵
    1. Kelesidis T,
    2. Papakonstantinou V,
    3. Detopoulou P,
    4. Fragopoulou E,
    5. Chini M,
    6. Lazanas MC,
    7. Antonopoulou S
    : The role of platelet-activating factor in chronic inflammation, immune activation, and comorbidities associated with HIV infection. AIDS Rev 17: 191-201, 2015.
    OpenUrlPubMed
  13. ↵
    1. Guo L,
    2. Feng K,
    3. Wang YC,
    4. Mei JJ,
    5. Ning RT,
    6. Zheng HW,
    7. Wang JJ,
    8. Worthen GS,
    9. Wang X,
    10. Song J,
    11. Li QH,
    12. Liu LD
    : Critical role of CXCL4 in the lung pathogenesis of influenza (H1N1) respiratory infection. Mucosal Immunol 10(6): 1529-1541, 2017. DOI: 10.1038/mi.2017.1
    OpenUrlCrossRefPubMed
  14. ↵
    1. Dapat C,
    2. Kumaki S,
    3. Sakurai H,
    4. Nishimura H,
    5. Labayo HKM,
    6. Okamoto M,
    7. Saito M,
    8. Oshitani H
    : Gene signature of children with severe respiratory syncytial virus infection. Pediatr Res 89(7): 1664-1672, 2021. DOI: 10.1038/s41390-020-01347-9
    OpenUrlCrossRefPubMed
  15. ↵
    1. Antoniak S,
    2. Mackman N
    : Platelets and viruses. Platelets 32(3): 325-330, 2021. DOI: 10.1080/09537104.2021.1887842
    OpenUrlCrossRefPubMed
  16. ↵
    1. Koupenova M,
    2. Freedman JE
    : Platelets and immunity: going viral. Arterioscler Thromb Vasc Biol 40(7): 1605-1607, 2020. DOI: 10.1161/ATVBAHA.120.314620
    OpenUrlCrossRefPubMed
  17. ↵
    1. Zimmerman GA,
    2. McIntyre TM,
    3. Prescott SM,
    4. Stafforini DM
    : The platelet-activating factor signaling system and its regulators in syndromes of inflammation and thrombosis. Crit Care Med 30(Suppl): S294-S301, 2002. DOI: 10.1097/00003246-200205001-00020
    OpenUrlCrossRefPubMed
  18. ↵
    1. Kowalska MA,
    2. Rauova L,
    3. Poncz M
    : Role of the platelet chemokine platelet factor 4 (PF4) in hemostasis and thrombosis. Thromb Res 125(4): 292-296, 2010. DOI: 10.1016/j.thromres.2009.11.023
    OpenUrlCrossRefPubMed
  19. ↵
    1. Eslin DE,
    2. Zhang C,
    3. Samuels KJ,
    4. Rauova L,
    5. Zhai L,
    6. Niewiarowski S,
    7. Cines DB,
    8. Poncz M,
    9. Kowalska MA
    : Transgenic mice studies demonstrate a role for platelet factor 4 in thrombosis: dissociation between anticoagulant and antithrombotic effect of heparin. Blood 104(10): 3173-3180, 2004. DOI: 10.1182/blood-2003-11-3994
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Comer SP,
    2. Cullivan S,
    3. Szklanna PB,
    4. Weiss L,
    5. Cullen S,
    6. Kelliher S,
    7. Smolenski A,
    8. Murphy C,
    9. Altaie H,
    10. Curran J,
    11. O’Reilly K,
    12. Cotter AG,
    13. Marsh B,
    14. Gaine S,
    15. Mallon P,
    16. McCullagh B,
    17. Moran N,
    18. Ní Áinle F,
    19. Kevane B,
    20. Maguire PB, COCOON Study investigators
    : COVID-19 induces a hyperactive phenotype in circulating platelets. PLoS Biol 19(2): e3001109, 2021. DOI: 10.1371/journal.pbio.3001109
    OpenUrlCrossRefPubMed
  21. ↵
    1. de Oliveira Sales L,
    2. de Oliveira LLB,
    3. da Silva JBS,
    4. de Moraes Filho MO,
    5. de Moraes MEA,
    6. Montenegro RC,
    7. Moreira-Nunes CA
    : The role of platelet molecules in risk stratification of patients with COVID-19. Hemato 4(4): 364-383, 2023. DOI: 10.3390/hemato4040029
    OpenUrlCrossRef
  22. ↵
    1. Schmittgen TD,
    2. Livak KJ
    : Analyzing real-time PCR data by the comparative CT method. Nat Protoc 3(6): 1101-1108, 2008. DOI: 10.1038/nprot.2008.73
    OpenUrlCrossRefPubMed
  23. ↵
    1. Bustin SA,
    2. Benes V,
    3. Garson JA,
    4. Hellemans J,
    5. Huggett J,
    6. Kubista M,
    7. Mueller R,
    8. Nolan T,
    9. Pfaffl MW,
    10. Shipley GL,
    11. Vandesompele J,
    12. Wittwer CT
    : The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clin Chem 55(4): 611-622, 2009. DOI: 10.1373/clinchem.2008.112797
    OpenUrlAbstract/FREE Full Text
  24. ↵
    1. Hoffmann M,
    2. Kleine-Weber H,
    3. Schroeder S,
    4. Krüger N,
    5. Herrler T,
    6. Erichsen S,
    7. Schiergens TS,
    8. Herrler G,
    9. Wu NH,
    10. Nitsche A,
    11. Müller MA,
    12. Drosten C,
    13. Pöhlmann S
    : SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181(2): 271-280.e8, 2020. DOI: 10.1016/j.cell.2020.02.052
    OpenUrlCrossRefPubMed
  25. ↵
    1. Dhama K,
    2. Khan S,
    3. Tiwari R,
    4. Sircar S,
    5. Bhat S,
    6. Malik YS,
    7. Singh KP,
    8. Chaicumpa W,
    9. Bonilla-Aldana DK,
    10. Rodriguez-Morales AJ
    : Coronavirus disease 2019-COVID-19. Clin Microbiol Rev 33(4): e00028-20, 2020. DOI: 10.1128/CMR.00028-20
    OpenUrlCrossRefPubMed
  26. ↵
    1. Darif D,
    2. Hammi I,
    3. Kihel A,
    4. El Idrissi Saik I,
    5. Guessous F,
    6. Akarid K
    : The pro-inflammatory cytokines in COVID-19 pathogenesis: What goes wrong? Microb Pathog 153: 104799, 2021. DOI: 10.1016/j.micpath.2021.104799
    OpenUrlCrossRefPubMed
  27. ↵
    1. Middleton EA,
    2. Weyrich AS,
    3. Zimmerman GA
    : Platelets in pulmonary immune responses and inflammatory lung diseases. Physiol Rev 96(4): 1211-1259, 2016. DOI: 10.1152/physrev.00038.2015
    OpenUrlCrossRefPubMed
  28. ↵
    1. Hottz ED,
    2. Azevedo-Quintanilha IG,
    3. Palhinha L,
    4. Teixeira L,
    5. Barreto EA,
    6. Pão CRR,
    7. Righy C,
    8. Franco S,
    9. Souza TML,
    10. Kurtz P,
    11. Bozza FA,
    12. Bozza PT
    : Platelet activation and platelet-monocyte aggregate formation trigger tissue factor expression in patients with severe COVID-19. Blood 136(11): 1330-1341, 2020. DOI: 10.1182/blood.2020007252
    OpenUrlCrossRefPubMed
  29. ↵
    1. Biswas M,
    2. Sawajan N,
    3. Rungrotmongkol T,
    4. Sanachai K,
    5. Ershadian M,
    6. Sukasem C
    : Pharmacogenetics and precision medicine approaches for the improvement of COVID-19 therapies. Front Pharmacol 13: 835136, 2022. DOI: 10.3389/fphar.2022.835136
    OpenUrlCrossRefPubMed
  30. ↵
    1. Klein M,
    2. Dao V,
    3. Khan F
    : A review of platelet-activating factor as a potential contributor to morbidity and mortality associated with severe COVID-19. Clin Appl Thromb Hemost 27: 10760296211051764, 2021. DOI: 10.1177/10760296211051764
    OpenUrlCrossRefPubMed
  31. ↵
    1. Demopoulos C,
    2. Antonopoulou S,
    3. Theoharides TC
    : COVID-19, microthromboses, inflammation, and platelet activating factor. Biofactors 46(6): 927-933, 2020. DOI: 10.1002/biof.1696
    OpenUrlCrossRefPubMed
  32. ↵
    1. Chaipan C,
    2. Soilleux EJ,
    3. Simpson P,
    4. Hofmann H,
    5. Gramberg T,
    6. Marzi A,
    7. Geier M,
    8. Stewart EA,
    9. Eisemann J,
    10. Steinkasserer A,
    11. Suzuki-Inoue K,
    12. Fuller GL,
    13. Pearce AC,
    14. Watson SP,
    15. Hoxie JA,
    16. Baribaud F,
    17. Pöhlmann S
    : DC-SIGN and CLEC-2 mediate human immunodeficiency virus type 1 capture by platelets. J Virol 80(18): 8951-8960, 2006. DOI: 10.1128/JVI.00136-06
    OpenUrlAbstract/FREE Full Text
  33. ↵
    1. Brandt E,
    2. Ernst M,
    3. Loppnow H,
    4. Flad HD
    : Characterization of a platelet derived factor modulating phagocyte functions and cooperating with interleukin 1. Lymphokine Res 8: 281-287, 1989.
    OpenUrlPubMed
  34. ↵
    1. Deuel TF,
    2. Senior RM,
    3. Chang D,
    4. Griffin GL,
    5. Heinrikson RL,
    6. Kaiser ET
    : Platelet factor 4 is chemotactic for neutrophils and monocytes. Proc Natl Acad Sci U.S.A. 78(7): 4584-4587, 1981. DOI: 10.1073/pnas.78.7.4584
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. Lucijanic M,
    2. Krecak I,
    3. Soric E,
    4. Sedinic M,
    5. Sabljic A,
    6. Derek L,
    7. Jaksic O,
    8. Kusec R
    : Thrombocytosis in COVID-19 patients without myeloproliferative neoplasms is associated with better prognosis but higher rate of venous thromboembolism. Blood Cancer J 11(11): 189, 2021. DOI: 10.1038/s41408-021-00585-2
    OpenUrlCrossRefPubMed
  36. ↵
    1. Opal SM,
    2. Girard TD,
    3. Ely EW
    : The immunopathogenesis of sepsis in elderly patients. Clin Infect Dis 41(Suppl7): S504-S512, 2005. DOI: 10.1086/432007
    OpenUrlCrossRefPubMed
    1. Yanez ND,
    2. Weiss NS,
    3. Romand JA,
    4. Treggiari MM
    : COVID-19 mortality risk for older men and women. BMC Public Health 20(1): 1742, 2020. DOI: 10.1186/s12889-020-09826-8
    OpenUrlCrossRefPubMed
  38. ↵
    1. Zhou F,
    2. Yu T,
    3. Du R,
    4. Fan G,
    5. Liu Y,
    6. Liu Z,
    7. Xiang J,
    8. Wang Y,
    9. Song B,
    10. Gu X,
    11. Guan L,
    12. Wei Y,
    13. Li H,
    14. Wu X,
    15. Xu J,
    16. Tu S,
    17. Zhang Y,
    18. Chen H,
    19. Cao B
    : Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395(10229): 1054-1062, 2020. DOI: 10.1016/S0140-6736(20)30566-3
    OpenUrlCrossRefPubMed
  39. ↵
    1. Wang D,
    2. Hu B,
    3. Hu C,
    4. Zhu F,
    5. Liu X,
    6. Zhang J,
    7. Wang B,
    8. Xiang H,
    9. Cheng Z,
    10. Xiong Y,
    11. Zhao Y,
    12. Li Y,
    13. Wang X,
    14. Peng Z
    : Clinical characteristics of 138 hospitalized patients with 2019 novel Coronavirus-infected pneumonia in Wuhan, China. JAMA 323(11): 1061-1069, 2020. DOI: 10.1001/jama.2020.1585
    OpenUrlCrossRefPubMed
  40. ↵
    1. Fei Y,
    2. Tang N,
    3. Liu H,
    4. Cao W
    : Coagulation dysfunction. Arch Pathol Lab Med 144(10): 1223-1229, 2020. DOI: 10.5858/arpa.2020-0324-SA
    OpenUrlCrossRef
  41. ↵
    1. Panigada M,
    2. Bottino N,
    3. Tagliabue P,
    4. Grasselli G,
    5. Novembrino C,
    6. Chantarangkul V,
    7. Pesenti A,
    8. Peyvandi F,
    9. Tripodi A
    : Hypercoagulability of COVID-19 patients in intensive care unit: A report of thromboelastography findings and other parameters of hemostasis. J Thromb Haemost 18(7): 1738-1742, 2020. DOI: 10.1111/jth.14850
    OpenUrlCrossRefPubMed
  42. ↵
    1. Quintana-Díaz M,
    2. Andrés-Esteban EM,
    3. Ramírez-Cervantes KL,
    4. Olivan-Blázquez B,
    5. Juárez-Vela R,
    6. Gea-Caballero V
    : Coagulation parameters: an efficient measure for predicting the prognosis and clinical management of patients with COVID-19. J Clin Med 9(11): 3482, 2020. DOI: 10.3390/jcm9113482
    OpenUrlCrossRefPubMed
  43. ↵
    1. Zinellu A,
    2. Paliogiannis P,
    3. Carru C,
    4. Mangoni AA
    : INR and COVID-19 severity and mortality: A systematic review with meta-analysis and meta-regression. Adv Med Sci 66(2): 372-380, 2021. DOI: 10.1016/j.advms.2021.07.009
    OpenUrlCrossRefPubMed
  44. ↵
    1. Huang C,
    2. Wang Y,
    3. Li X,
    4. Ren L,
    5. Zhao J,
    6. Hu Y,
    7. Zhang L,
    8. Fan G,
    9. Xu J,
    10. Gu X,
    11. Cheng Z,
    12. Yu T,
    13. Xia J,
    14. Wei Y,
    15. Wu W,
    16. Xie X,
    17. Yin W,
    18. Li H,
    19. Liu M,
    20. Xiao Y,
    21. Gao H,
    22. Guo L,
    23. Xie J,
    24. Wang G,
    25. Jiang R,
    26. Gao Z,
    27. Jin Q,
    28. Wang J,
    29. Cao B
    : Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223): 497-506, 2020. DOI: 10.1016/S0140-6736(20)30183-5
    OpenUrlCrossRefPubMed
  45. ↵
    1. Adam SS,
    2. Key NS,
    3. Greenberg CS
    : D-dimer antigen: current concepts and future prospects. Blood 113(13): 2878-2887, 2009. DOI: 10.1182/blood-2008-06-165845
    OpenUrlAbstract/FREE Full Text
  46. ↵
    1. Li J,
    2. Liu Z,
    3. Wu G,
    4. Yi M,
    5. Chen Y,
    6. Li K,
    7. Xu X,
    8. Xiao L,
    9. Wu Q,
    10. Chen J,
    11. Wu X
    : D-dimer as a prognostic indicator in critically ill patients hospitalized with COVID-19 in Leishenshan Hospital, Wuhan, China. Front Pharmacol 11: 600592, 2020. DOI: 10.3389/fphar.2020.600592
    OpenUrlCrossRefPubMed
  47. ↵
    1. He X,
    2. Yao F,
    3. Chen J,
    4. Wang Y,
    5. Fang X,
    6. Lin X,
    7. Long H,
    8. Wang Q,
    9. Wu Q
    : The poor prognosis and influencing factors of high D-dimer levels for COVID-19 patients. Sci Rep 11(1): 1830, 2021. DOI: 10.1038/s41598-021-81300-w
    OpenUrlCrossRefPubMed
  48. ↵
    1. Chen N,
    2. Zhou M,
    3. Dong X,
    4. Qu J,
    5. Gong F,
    6. Han Y,
    7. Qiu Y,
    8. Wang J,
    9. Liu Y,
    10. Wei Y,
    11. Xia J,
    12. Yu T,
    13. Zhang X,
    14. Zhang L
    : Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223): 507-513, 2020. DOI: 10.1016/S0140-6736(20)30211-7
    OpenUrlCrossRefPubMed
  49. ↵
    1. Haitao T,
    2. Vermunt JV,
    3. Abeykoon J,
    4. Ghamrawi R,
    5. Gunaratne M,
    6. Jayachandran M,
    7. Narang K,
    8. Parashuram S,
    9. Suvakov S,
    10. Garovic VD
    : COVID-19 and sex differences: mechanisms and biomarkers. Mayo Clin Proc 95(10): 2189-2203, 2020. DOI: 10.1016/j.mayocp.2020.07.024
    OpenUrlCrossRefPubMed
  50. ↵
    1. Jaillon S,
    2. Berthenet K,
    3. Garlanda C
    : Sexual dimorphism in innate immunity. Clin Rev Allergy Immunol 56(3): 308-321, 2019. DOI: 10.1007/s12016-017-8648-x
    OpenUrlCrossRefPubMed
  51. ↵
    1. Yang J,
    2. Zheng Y,
    3. Gou X,
    4. Pu K,
    5. Chen Z,
    6. Guo Q,
    7. Ji R,
    8. Wang H,
    9. Wang Y,
    10. Zhou Y
    : Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis 94: 91-95, 2020. DOI: 10.1016/j.ijid.2020.03.017
    OpenUrlCrossRefPubMed
  52. ↵
    1. Mahamat-Saleh Y,
    2. Fiolet T,
    3. Rebeaud ME,
    4. Mulot M,
    5. Guihur A,
    6. El Fatouhi D,
    7. Laouali N,
    8. Peiffer-Smadja N,
    9. Aune D,
    10. Severi G
    : Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies. BMJ Open 11(10): e052777, 2021. DOI: 10.1136/bmjopen-2021-052777
    OpenUrlAbstract/FREE Full Text
  53. ↵
    1. Badawi A,
    2. Ryoo SG
    : Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. Int J Infect Dis 49: 129-133, 2016. DOI: 10.1016/j.ijid.2016.06.015
    OpenUrlCrossRefPubMed
  54. ↵
    1. Al-Benna S
    : Association of high level gene expression of ACE2 in adipose tissue with mortality of COVID-19 infection in obese patients. Obes Med 19: 100283, 2020. DOI: 10.1016/j.obmed.2020.100283
    OpenUrlCrossRefPubMed
  55. ↵
    1. Kuster GM,
    2. Pfister O,
    3. Burkard T,
    4. Zhou Q,
    5. Twerenbold R,
    6. Haaf P,
    7. Widmer AF,
    8. Osswald S
    : SARS-CoV2: should inhibitors of the renin-angiotensin system be withdrawn in patients with COVID-19? Eur Heart J 41(19): 1801-1803, 2020. DOI: 10.1093/eurheartj/ehaa235
    OpenUrlCrossRefPubMed
    1. Li Q,
    2. Guan X,
    3. Wu P,
    4. Wang X,
    5. Zhou L,
    6. Tong Y,
    7. Ren R,
    8. Leung KSM,
    9. Lau EHY,
    10. Wong JY,
    11. Xing X,
    12. Xiang N,
    13. Wu Y,
    14. Li C,
    15. Chen Q,
    16. Li D,
    17. Liu T,
    18. Zhao J,
    19. Liu M,
    20. Tu W,
    21. Chen C,
    22. Jin L,
    23. Yang R,
    24. Wang Q,
    25. Zhou S,
    26. Wang R,
    27. Liu H,
    28. Luo Y,
    29. Liu Y,
    30. Shao G,
    31. Li H,
    32. Tao Z,
    33. Yang Y,
    34. Deng Z,
    35. Liu B,
    36. Ma Z,
    37. Zhang Y,
    38. Shi G,
    39. Lam TTY,
    40. Wu JT,
    41. Gao GF,
    42. Cowling BJ,
    43. Yang B,
    44. Leung GM,
    45. Feng Z
    : Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 382(13): 1199-1207, 2020. DOI: 10.1056/NEJMoa2001316
    OpenUrlCrossRefPubMed
    1. Esmaeel HM,
    2. Ahmed HA,
    3. Elbadry MI,
    4. Khalaf AR,
    5. Mohammed NA,
    6. Mahmoud HA,
    7. Taha EM
    : Coagulation parameters abnormalities and their relation to clinical outcomes in hospitalized and severe COVID-19 patients: prospective study. Sci Rep 12(1): 13155, 2022. DOI: 10.1038/s41598-022-16915-8
    OpenUrlCrossRefPubMed
  58. ↵
    1. WHO
    : COVID-19 clinical management: living guidance, 25 January 2021. Available at: https://apps.who.int/iris/handle/10665/338882 [Last accessed on May 6, 2024]
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
Hyperexpression of PTAFR and PF4 as Possible Platelet Risk Biomarkers in Patients With COVID-19
LÍVIA DE OLIVEIRA SALES, JEAN BRENO SILVEIRA DA SILVA, FLÁVIA MELO CUNHA DE PINHO PESSOA, BEATRIZ MARIA DIAS NOGUEIRA, LAIS LACERDA BRASIL DE OLIVEIRA, ANDRÉ SALIM KHAYAT, MANOEL ODORICO DE MORAES FILHO, MARIA ELISABETE AMARAL DE MORAES, RAQUEL CARVALHO MONTENEGRO, CAROLINE AQUINO MOREIRA-NUNES
In Vivo Nov 2024, 38 (6) 2853-2863; DOI: 10.21873/invivo.13766
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords