COVID-19 inflammation results in urine cytokine elevation and causes COVID-19 associated cystitis (CAC)

Authrs: Laura E Lamb 1Nivedita Dhar 2Ryan Timar 3Melissa Wills 3Sorabh Dhar 4 PMID: 33213997PMCID: PMC7644255DOI: 10.1016/j.mehy.2020.110375


Coronavirus disease 2019 (COVID-19) causes a wide range of symptoms, including several unexpected symptoms such as loss of taste, skin changes, and eye problems. We recently observed patients with documented COVID-19 develop de novo severe genitourinary symptoms, most notably urinary frequency of ≥ 13 episodes/24 h and nocturia ≥ 4 episodes/night. We call these associated urinary symptoms COVID-19 associate cystitis (CAC). COVID-19 severity is associated with inflammation. We collected urine samples from COVID-19 patients, including patients with CAC, and found elevation of proinflammatory cytokines also in the urine. It has been previously shown that patients with urinary incontinence and ulcerative interstitial cystitis/bladder pain syndrome have elevated urinary inflammatory cytokines compared to normal controls. We therefore hypothesize that CAC, with presentation of de novo severe urinary symptoms, can occur in COVID-19 and is caused by increased inflammatory cytokines that are released into the urine and/or expressed in the bladder. The most important implications of our hypothesis are: 1) Physician caring for COVID-19 patients should be aware of COVID-19 associate cystitis (CAC); 2) De novo urinary symptoms should be included in the symptom complex associated with COVID-19; and 3) COVID-19 inflammation may result in bladder dysfunction.

COVID-19 inflammation results in urine cytokine elevation and causes COVID-19 associated cystitis (CAC)

Authors: Laura E Lamb 1Nivedita Dhar 2Ryan Timar 3Melissa Wills 3Sorabh Dhar 4Michael B Chancellor 5 PMID: 33213997 PMCID: PMC7644255 DOI: 10.1016/j.mehy.2020.110375


Coronavirus disease 2019 (COVID-19) causes a wide range of symptoms, including several unexpected symptoms such as loss of taste, skin changes, and eye problems. We recently observed patients with documented COVID-19 develop de novo severe genitourinary symptoms, most notably urinary frequency of ≥ 13 episodes/24 h and nocturia ≥ 4 episodes/night. We call these associated urinary symptoms COVID-19 associate cystitis (CAC). COVID-19 severity is associated with inflammation. We collected urine samples from COVID-19 patients, including patients with CAC, and found elevation of proinflammatory cytokines also in the urine. It has been previously shown that patients with urinary incontinence and ulcerative interstitial cystitis/bladder pain syndrome have elevated urinary inflammatory cytokines compared to normal controls. We therefore hypothesize that CAC, with presentation of de novo severe urinary symptoms, can occur in COVID-19 and is caused by increased inflammatory cytokines that are released into the urine and/or expressed in the bladder. The most important implications of our hypothesis are: 1) Physician caring for COVID-19 patients should be aware of COVID-19 associate cystitis (CAC); 2) De novo urinary symptoms should be included in the symptom complex associated with COVID-19; and 3) COVID-19 inflammation may result in bladder dysfunction.

How COVID-19 causes neurological damage found

Particularly in the group with the most serious neurological symptoms, the researchers identified a link with an excessive immune response.

Authors: PTI 15th November 2022 The Indian Express

COVID-19 may cause damage to the nervous system, even though it does not affect nerve cells, according to a small study.

While it was not uncommon for some people to lose their sense of taste and smell due to a Covid-19 infection, in others, the disease had had an even stronger impact on the nervous system, with effects ranging from lasting concentration problems to strokes, the study said.

Researchers, from the University of Basel and University Hospital Basel, Switzerland, have studied the mechanisms responsible for and reported new insights into the development of “neuro-Covid”, including identifying starting points for its prevention, the study said.

The team investigated how different severities of neuro-COVID can be detected and predicted by analyzing the cerebrospinal fluid and blood plasma of affected individuals.

Their findings, published in the journal Nature Communications, also indicate how to prevent neurological damage due to Covid-19.

The study included 40 Covid-19 patients with differing degrees of neurological symptoms.

n order to identify typical changes associated with neuro-Covid, the team of researchers compared cerebrospinal fluid and blood plasma of these individuals with samples from a control group.

They also measured the brain structures of test subjects and surveyed participants 13 months after their illness in order to identify any lasting symptoms.

Particularly in the group with the most serious neurological symptoms, the researchers identified a link with an excessive immune response.

On the one hand, the study said, affected individuals showed indications of impairment of the blood-brain barrier, which the study’s authors speculate was probably triggered by a “cytokine storm” — a massive release of pro-inflammatory factors in response to the virus.

On the other hand, the researchers also found antibodies that targeted parts of the body’s own cells — in other words, signs of an autoimmune reaction — as a result of the excessive immune response, the study said.

ALSO READ | Study: Blood tests might help to detect long covid in patients

“We suspect that these antibodies cross the porous blood-brain barrier into the brain, where they cause damage,” explained lead researcher Gregor Hutter.

Researchers also identified excessive activation of the immune cells specifically responsible for the brain — the microglia.

In a further step, Hutter and his team investigated whether the severity of neurological symptoms is also perceptible in brain structures.

Indeed, they found that people with serious neuro-Covid symptoms had a lower brain volume than healthy participants at specific locations in the brain and particularly at the olfactory cortex — that is, the area of the brain responsible for smell.

“We were able to link the signature of certain molecules in the blood and cerebrospinal fluid to an overwhelming immune response in the brain and reduced brain volume in certain areas, as well as neurological symptoms,” says Hutter, adding that it is now important to examine these biomarkers in a greater number of participants.

ALSO READ | Covid virus ‘more likely than not, result of a research-related incident’: US report

The aim, the study said, would be to develop a blood test that can already predict serious cases, including neuro-Covid and long Covid, at the start of an infection.

These same biomarkers point to potential targets for drugs aimed at preventing consequential damage due to a Covid-19 infection.

One of the biomarkers identified in blood, the factor MCP-3, plays a key role in the excessive immune response, and Hutter believes there is the potential to inhibit this factor medicinally, the study said.

“In our study, we show how coronavirus can affect the brain,” he says.

“The virus triggers such a strong inflammatory response in the body that it spills over to the central nervous system. This can disrupt the cellular integrity of the brain.”

Accordingly, Hutter said that the primary objective must be to identify and halt the excessive immune response at an early stage, the study said.ors

Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection

Authors: Chansavath PhetsouphanhDavid R. DarleyDaniel B. WilsonAnnett HoweC. Mee Ling MunierSheila K. PatelJennifer A. JunoLouise M. BurrellStephen J. KentGregory J. DoreAnthony D. Kelleher & Gail V. Matthews  Nature Immunology volume 23, pages210–216 (2022) June 30, 2022 Nature Immunology volume 23, pages210–216 (2022)


A proportion of patients surviving acute coronavirus disease 2019 (COVID-19) infection develop post-acute COVID syndrome (long COVID (LC)) lasting longer than 12 weeks. Here, we studied individuals with LC compared to age- and gender-matched recovered individuals without LC, unexposed donors and individuals infected with other coronaviruses. Patients with LC had highly activated innate immune cells, lacked naive T and B cells and showed elevated expression of type I IFN (IFN-β) and type III IFN (IFN-λ1) that remained persistently high at 8 months after infection. Using a log-linear classification model, we defined an optimal set of analytes that had the strongest association with LC among the 28 analytes measured. Combinations of the inflammatory mediators IFN-β, PTX3, IFN-γ, IFN-λ2/3 and IL-6 associated with LC with 78.5–81.6% accuracy. This work defines immunological parameters associated with LC and suggests future opportunities for prevention and treatment.


Acute COVID-19, caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is characterized by a broad spectrum of clinical severity, from asymptomatic to fatal1,2. The immune response during acute illness contributes to both host defense and pathogenesis of severe COVID-19 (ref. 3). Pronounced immune dysregulation with lymphopenia and increased expression of inflammatory mediators3,4 have been described in the acute phase. Following acute COVID-19 infection, a proportion of patients develop physical and neuropsychiatric symptoms lasting longer than 12 weeks (known as Long COVID, chronic COVID syndrome or post-acute sequelae of COVID-19 (ref. 5)), henceforth denoted as LC. Although similar syndromes have been described following infection with SARS-CoV-1 (ref. 6) and Middle East respiratory syndrome–related coronavirus7, LC often develops after mild-to-moderate COVID-19 (refs. 8,9). Symptoms persisting 6 months were observed in 76% of hospitalized patients, with muscle weakness and fatigue being most frequently reported10,11. LC affects between 10% and 30% of community-managed COVID-19 cases 2 to 3 months after infection12,13 and can persist >8 months after infection14. LC symptoms include severe relapsing fatigue, dyspnea, chest tightness, cough, brain fog and headache15. The underlying pathophysiology of LC is poorly understood.

Here, we analyzed a cohort of individuals followed systematically for 8 months after COVID-19 infection according to a predefined schedule, comparing them to healthy donors unexposed to SARS-CoV-2 (unexposed healthy controls (UHCs)) before December 2019, and individuals who had been infected with prevalent human coronaviruses (HCoVs; HCoV-NL63, O229E, OC43 or HKU1), but not SARS-CoV-2. The ADAPT study9 enrolled adults with SARS-CoV-2 infections confirmed by PCR at St Vincent’s Hospital community-based testing clinics in Sydney (Australia). For the majority of participants, their first visit occurred between months 2 and 3 after infection (median of 79 days after the date of initial diagnosis)9,14, with 93.6% and 84.5% of participants completing subsequent month 4 (median, 128 days) and month 8 (median, 232 days) visits (Table 1). Of the 147 patients recruited (70.5% through ADAPT sites and 29.5% externally), 31 participants (21.08%) were designated as LC based on the occurrence of one of three major symptoms (fatigue, dyspnea or chest pain) at month 4 (Supplementary Table 1). These participants were age and gender matched with 31 asymptomatic matched controls (MCs) from the same cohort who did not report symptoms at month 4 after infection but were symptomatic during the acute phase of the infection (Supplementary Table 2). There was a 10% trend toward some improvement of symptoms over time in LC, but this trend was not statistically significant (Fisher’s exact P = 0.44).Table 1 Patient characteristics

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To examine biomarkers associated with LC, we assessed 28 analytes in the serum of patients from the LC, MC, HCoV and UHC groups at month 4 after infection using a bead-based assay. Six proinflammatory cytokines (interferon β (IFN-β), IFN-λ1, IFN-γ, CXCL9, CXCL10, interleukin-8 (IL-8) and soluble T cell immunoglobulin mucin domain 3 (sTIM-3)) were elevated in the LC and MC groups compared to the HCoV and UHC groups (Fig. 1), with no difference observed in the 22 other analytes, including IL-6 and IL-33 (Extended Data Fig. 1). There was no difference between LC and MCs for any individual analyte at this time point (Extended Data Fig. 1a, b). IFN-β was 7.92-fold and 7.39-fold higher in the LC and MC groups compared to the HCoV group and 7.32- and 6.83-fold higher compared to UHCs (Fig. 1a). IFN-λ1 was increased 2.44-fold and 3.24-fold in the LC and MC groups compared to the HCoV group and 2.42- and 3.21-fold compared to UHCs. IL-8 was higher in the LC (3.43-fold) and MC (3.56-fold) groups compared to the HCoV and UHC groups (Fig. 1a). CXCL10 was elevated in the LC group compared to the HCoV (2.15-fold) and UHC (3.2-fold) groups and in the MC group compared to the HCoV (1.7-fold) and UHC (3.06-fold) groups. CXCL9 was 1.69-fold higher in the LC group than in the UHC group, and sTIM-3 was elevated in the LC group, but not the MC group, when compared to the HCoV group (1.46-fold) (Fig. 1a and Extended Data Fig. 1c).

figure 1
Fig. 1: Elevated levels of proinflammatory cytokines that persist more than 8 months following convalescence.

IFN-β and IFN-λ1 decreased 4.4-fold and 1.8-fold, respectively, in the MC group at month 8 compared to month 4 (Fig. 1b). In the LC group, IFN-β decreased by 1.5-fold, and IFN-λ1 increased by 1.05-fold at month 8 compared to month 4, which was not statistically significant (Fig. 1b). At month 8, IFN-β and IFN-λ1 remained significantly elevated in the LC group compared to the MC, HCoV and UHC groups (Extended Data Fig. 2a). Reductions in CXCL9, CXCL10, IL-8 and sTIM-3 were observed in the LC and MC groups at month 8 compared to month 4 (Fig. 1b). At month 8, there was also decreased expression of some of the 22 analytes that were not significantly different among the four groups at month 4 (Extended Data Fig. 2b,c).

Because plasma ACE2 activity has been reported to be elevated 114 days after SARS-CoV-2 infection16, we investigated whether this occurred in our cohort at months 3, 4 and 8 after infection. Median plasma ACE2 activity was significantly higher in both LC and MC groups compared to the HCoV group at month 3 (LC, 1.92-fold; MC, 2.47-fold) and month 4 (LC, 1.75-fold; MC, 2.62-fold) after infection (Fig. 1c). At month 8, plasma ACE2 activity in the LC and MC groups decreased to levels observed in the HCoV and UHC groups (Fig. 1c). No difference was observed within LC and MC groups at months 3, 4 or 8, but both groups had higher activity compared to the HCoV group, suggesting that this parameter is specific to SARS-CoV-2 infection and is not a common feature of other coronaviruses.

Next, we used a classification model to determine an optimal set of analytes most strongly associated with LC. This linear classifier was trained on log-transformed analyte data to reduce the bias observed in each of the analytes and improve model accuracy. This log-linear classification model was used to develop a metric for feature importance17. To identify analytes that were associated with LC and not MC, we used the analyte data at month 8, the time point with the greatest difference between the LC and MC groups. The performance of each of the log-linear models was quantified by an accuracy estimate and an F1 score evaluated by taking averages after bootstrapping, which randomly sampled from the original population to create a new population. By considering every possible pair of the 28 serum analytes and plasma ACE2 activity, a classification model including two analytes (IFN-β and pentraxin 3 (PTX3)) had an LC prognostic accuracy of 78.54% and an F1 score of 0.77. Three analytes (IFN-β, PTX3 and IFN-γ) achieved an accuracy of 79.68%, with an F1 score of 0.79. Four analytes (IFN-β, PTX3, IFN-λ2/3 and IL-6) achieved an accuracy of 81.59% and an F1 score of 0.81. When all 29 analytes were featured, the calculated accuracy was 77.4%, with an F1 score of 0.76 (Table 2).Table 2 Accuracy and F1 score (with confidence intervals) for the top two, three and four features and all features identified by machine learning utilizing a log-linear classification model

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After generating 1,000 randomly sampled populations, we counted the number of times each feature appeared in the best performing set of features, combining sets if several sets achieved the same accuracy. This revealed that IFN-β was the most important feature, appearing in 89%, 93% and 94% of the best sets of two, three and four features, respectively (Fig. 2a). Linear classifiers defined a decision boundary. Each patient analyte concentration at month 8 lied on either side of the boundary, and its positioning relative to the boundary determined whether the patient was predicted to experience LC or asymptomatic COVID (Fig. 2b). Although the decision boundary of the four featured analytes at month 8 is four dimensional, the boundary can be visualized with two-dimensional projections of IFN-β against the other highly associated analytes (PTX3, IFN-γ, IFN-λ2/3 and IL-6 (Fig. 2b). Longitudinal levels of these key feature cytokines indicate the advantage of log-linear models in differentiating LC from MCs (Fig. 2c).

figure 2
Fig. 2: Minimal set of analytes highly associated with LC.

To investigate differences in immune cell profiles between LC and MCs, we developed a 19-parameter flow cytometry panel and phenotyped peripheral blood mononuclear cells (PBMCs) from LC and MC donors at months 3 and 8 after infection. Dimensional reduction via TriMap coupled with Phenograph clustering (n = 14; LC = 7, MC = 7) identified 24 distinct cell clusters at month 3 and 21 clusters at month 8 (Extended Data Fig. 3a) including T, B, NK and myeloid cell clusters (Extended Data Fig. 3b,c). Concatenated phenotype data from each of the 7 LC or MC and 7 UHC contributed to every population cluster (Extended Data Fig. 4a–d). Of the 24 subsets identified at month 3, five were absent in LC donors: naive CD127lowGzmBCCR7+CD45RA+CD27+CD8+ T cells, CD57+GPR56+GzmB+CD8+ T cells, naive CD127loTIM-3CCR7+CD45RA+CD27+CD4+ T cells, innate-like CD3+CD4CD8 T cells (may comprise natural killer T cells and γδ-T cells), and naive CD127loTIM-3CD38lowCD27IgD+ B cells (Fig. 3a). Three clusters remained absent at month 8 in LC donors (naive CD127lowGzmBCCR7+CD45RA+CD27+CD8+ T cells, naive CD127lowTIM-3CCR7+CD45 RA+CD27+CD4+ T cells, and naive CD127lowTIM-3CD38lowCD27IgD+ B cells) (Fig. 3b), indicating perturbations at month 8 in LC donors. Naive T and B cells expressing low levels of CD127 and TIM-3 were detected in the MC and UHC groups but were absent in the LC group at months 3 and 8 (Extended Data Fig. 4e,f).

figure 3
Fig. 3: Distinct activation phenotype in nonlymphoid cells and absence of unactivated naive T and B cells found in LC.

The frequency of highly activated CD38+HLA-DR+ myeloid cells was elevated at month 8 in the LC group compared to MCs (Fig. 3c). Frequencies of activated CD14+CD16+ monocytes were higher in the LC group compared to MCs at months 3 and 8. The percentages of plasmacytoid dendritic cells (pDCs) expressing the activation markers CD86 and CD38 were also higher in the LC group at both time points compared to MCs (Fig. 3c). There was no difference in the frequencies of activated CD11c+ myeloid dendritic cells between month 3 and month 8 (Extended Data Fig. 5a). The T cell activation and exhaustion markers PD-1 and TIM-3 were more highly expressed on CD8+ T cells in the LC group compared to MCs at month 3 (PD-1, 3.04-fold; TIM-3, 1.6-fold) and month 8 (PD-1 2.86-fold) (Fig. 3d). However, PD-1 and TIM-3 coexpression was similar on CD4+ and CD8+ T cells in the LC and MC groups (Extended Data Fig. 5b).

Here, we show that convalescent immune profiles after COVID-19 are different from those following infection with other coronaviruses. Several cytokines (mostly type I and III IFN, but also chemokines downstream of IFN-γ) were highly elevated in individuals following the resolution of active SARS-CoV-2 infection compared to HCoVs and UHCs at month 4 after infection. IFN-β and IFN-λ1 remained elevated in the LC group at month 8 after initial infection, while their levels began to resolve in MCs. Elevated plasma ACE2 activity was noted in the LC and MC groups at month 4 but trended toward normal by month 8 after infection. We identified a set of analytes (IFN-β, PTX3, IFN-γ, IFN-λ2/3 and IL-6) that highly associated with LC at month 8, indicating that components of the acute inflammatory response and activation of fibroblast or epithelial cells, T cells and myeloid cells are associated with LC. Immune cell phenotyping indicated chronic activation of a subset of CD8+ T cells, with expansion of PD-1+ and TIM-3+ subsets and pDCs and monocytes persisting from month 3 to month 8 in the LC group. These changes were accompanied by an absence of naive T and B cell subsets expressing low levels of CD127 and TIM-3 in peripheral blood of patients with LC. These findings suggest that SARS-CoV-2 infection exerts unique prolonged residual effects on the innate and adaptive immune systems and that this may be driving the symptomology known as LC.

IFN-β and IFN-λ1 were highly elevated in convalescent COVID-19 samples compared to HCoV and UHC samples. Although these levels decreased over time in patients who recovered, they remained high in patients with LC. The morbidity of acute COVID-19 infection appears to correlate with high expression of type I and III IFN in the lungs of patients18. IFN-λ produced by murine lung dendritic cells in response to synthetic viral RNA is associated with damage to lung epithelium19, and IFN-λ signaling hampers lung repair during influenza infection in mice20. Severe acute COVID-19 has been associated with diminished type I IFN and enhanced IL-6 and tumor necrosis factor (TNF) responses19. Although our cohort of individuals with LC consisted mostly of patients with mild or moderate initial illness, elevated type I and III IFN levels were maintained to month 8 after infection and are consistent with the observed prolonged activation of pDCs, indicating a chronic inflammatory response.

Patients with COVID-19 who are admitted to the intensive care unit have high plasma levels of sTIM-3 (ref. 21). We found elevated levels of sTIM-3 in the LC group, but not in the MC or HCoV groups, which is consistent with the expanded subsets of memory CD8+ T cells expressing TIM-3 and PD-1 and indicates chronic T cell activation and potentially exhaustion. Similarly, shedding of membrane-bound protein ACE-2 during acute infection22 resulting in increased activity in plasma16 continues into convalescence, regardless of symptom severity at month 4, and normalizes at month 8 in most patients.

We employed a log-linear classification model to assess all combinations of analytes to determine the subset of analytes most strongly associated with LC. IFN-β, together with PTX3, IFN-λ2/3, IFN-γ and IL-6, differentiated LC from MCs with high accuracy at month 8. IFN-λ2/3 are secreted by pDCs following viral RNA sensing by TLR7, TLR9 and RIG-123,24. PTX3 increased in lung epithelia and plasma of patients with severe COVID-19 and can serve as an independent strong prognostic indicator of short-term mortality25,26,27. IL-6 is a pleiotropic mediator that drives inflammation and immune activation28. A high IL-6/IFN-γ ratio is associated with severe acute COVID-19 infection29. The observation that the best correlate for LC is an eclectic combination of biomarkers reinforces the breadth of host response pathways that are activated during LC.

T cell activation (indicated by CD38 and HLA-DR), T cell exhaustion and increases in B cell plasmablasts occur during severe COVID-19 (refs. 30,31,32). These markers identified highly activated monocytes and pDCs, the frequencies of which decreased over time in MCs, but not in patients with LC. Type I and type III IFN upregulate major histocompatibility complex expression, including HLA-DR33. An unbiased large-scale dimensional reduction approach identified the depletion of three clusters of naive B and T cell subsets present in the LC group at month 8 after infection. Altogether, these observations suggest persistent conversion of naive T cells into activated states, potentially due to bystander activation secondary to underlying inflammation and/or antigen presentation by activated pDCs or monocytes. The ultimate result of this chronic stimulation may be expansion of PD-1+ or TIM-3+ CD8+ memory T cells. Bystander activation of unactivated naive subsets into more activated phenotypes is consistent with observations in acute severe COVID-19 (refs. 34,35).

Although individuals with LC and MCs were matched for age and gender, it is possible that the differences observed reflect differences in unrecognized factors between these groups. Although more LC donors had severe acute disease (eight LC donors and two MCs), sensitivity analyses excluding these patients did not alter the statistical significance of the major associations described here. Because of the timing of ethics approval and cohort setup, samples were not collected during acute infection. We were therefore unable to determine whether elevations in biomarkers during convalescence correlate with levels during acute infection. Although some perturbations observed here are potentially consistent with a hypothesis that the major drivers of the expression of biomarkers in convalescence are those in the acute infection, others are not. Our results require validation in other LC cohorts. Finally, our definition of LC was set internally given the lack of international consensus. Nevertheless, the inclusion of three of the most common persisting symptoms and blinding of cases and controls helped ensure the validity of our findings.

In summary, our data indicate an ongoing, sustained inflammatory response following even mild-to-moderate acute COVID-19, which is not found following prevalent coronavirus infection. The drivers of this activation require further investigation, but possibilities include persistence of antigen, autoimmunity driven by antigenic cross-reactivity or a reflection of damage repair. These observations describe an abnormal immune profile in patients with COVID-19 at extended time points after infection and provide clear support for the existence of a syndrome of LC. Our observations provide an important foundation for understanding the pathophysiology of this syndrome and potential therapeutic avenues for intervention.


Cohort characteristics

The ADAPT study is a prospective cohort study of post–COVID-19 recovery established in April 2020 (ref. 14). A total of 147 participants with confirmed SARS-CoV-2 infection were enrolled, the majority following testing in community-based clinics run by St Vincent’s Hospital Sydney, with some patients also enrolled with confirmed infection at external sites. Initial study follow-up was planned for 12 months after COVID-19 and subsequently extended to 2 years. Extensive clinical data and a biorepository was systematically collected prospectively. The aims of ADAPT are to evaluate a number of outcomes after COVID-19 relating to pathophysiology, immunology and clinical sequalae. Laboratory testing for SARS-CoV-2 was performed using nucleic acid detection from respiratory specimens with the EasyScreen Respiratory Detection kit (Genetic Signatures) and the EasyScreen SARS-CoV-2 detection kit. Two ADAPT cohort subpopulations were defined based on initial severity of COVID-19 illness: (1) patients managed in the community and (2) patients admitted to the hospital for acute infection (including those requiring intensive care support for acute respiratory distress syndrome). Patients were defined as having LC at 4 months based on the presence of one or more of the following symptoms: fatigue, dyspnea or chest pain14. These patients were gender and age (±10 years) matched with ADAPT participants without LC (matched ADAPT controls) (Table 1). Samples for these analyses were collected at the 3-, 4- and 8-month assessments. Our cohort consisted of 62 participants (31 with LC and 31 MCs); enrollment visits were performed at a median of 76 (IQR, 64–93) days after initial infection. Their 4-month assessments were performed at a median of 128 (IQR, 115–142) days after initial infection (4.2 months). Their 8-month assessments were performed at a median of 232 (IQR, 226–253) days after initial infection (7.7 months). The dropout rate has been very low to date (approximately 9.4% at 12 months). Four participants did not complete the 8-month assessment after the 4-month assessment. The reasons for this include ‘did not attend’ (n = 2) and ‘lost to follow-up’ (n = 2). A further population of patients presenting to St Vincent’s Hospital clinics for COVID-19 testing on the multiplex respiratory panel who were PCR positive for any of the four human common cold coronaviruses (HCoV-NL63, O229E, OC43 or HKU1) and PCR negative for SARS-CoV-2 were recruited into the ADAPT-C substudy and used as a comparator group.


The ADAPT study was approved by the St Vincent’s Hospital Research Ethics Committee (2020/ETH00964) and is a registered trial (ACTRN12620000554965). The ADAPT-C substudy was approved by the same committee (2020/ETH01429). All data were stored using REDCap (v11.0.3) electronic data capture tools. Unexposed healthy donors were recruited through St Vincent’s Hospital and approved by St Vincent’s Hospital Research Ethics Committee (HREC/13/SVH/145). The University of Melbourne unexposed donors were approved by Medicine and Dentistry HESC study ID 2056689. All participants gave written informed consent, and patients were not compensated.

Sample processing and flow cytometry

Blood was collected for biomarker analysis (serum separating tube (SST) 8.5 ml x1 (serum) and EDTA 10 ml x1 (plasma)), and 36 ml was collected for PBMCs (ACD (citric acid, trisodium citrate and dextrose) 9 ml x4). Phenotyping of PBMCs was performed as described previously36. Briefly, cryopreserved PBMCs were thawed using RPMI (+L-glutamine) medium (ThermoFisher Scientific) supplemented with penicillin/streptomycin (Sigma-Aldrich) and subsequently stained with antibodies binding to extracellular markers for 20 min. Extracellular panel included Live/Dead dye Near InfraRed, CXCR5 (MU5UBEE) and CD38 (HIT2) (ThermoFisher Scientific); CD3 (UCHT1), CD8 (HIL-72021), PD-1 (EH12.1), TIM-3 (TD3), CD27 (L128), CD45RA (HI100), CD86 (BU63), CD14 (HCD14), CD16 (GB11), IgD (IA6-2), CD25 (2A3) and CD19 (HIB19) (BioLegend); and CD4 (OKT4), CD127 (A019D5), HLA-DR (L234), GRP56 (191B8), CCR7 (G043H7) and CD57 (QA17A04) (BD Biosciences). Perm Buffer II (BD Pharmingen) was used for intracellular staining of granzyme B (GB11, BD Biosciences). Samples were acquired on an Cytek Aurora (BioLegend) using Spectroflo software. Before each run, all samples were fixed in 0.5% paraformaldehyde.

Serum analytes

The LEGENDplex Human Anti-Virus Response Panel (IL-1β, IL-6, IL-8, IL-10, IL-12p70, IFN-α2, IFN-β, IFN-λ1, IFN-λ2/3, IFN-γ, TNF-α, IP-10 and GM-CSF) and a custom-made panel (IL-5, IL-9, IL-13, IL-33, PD-1, sTIM-3, sCD25, CCL2 (MCP-1), PTX3, transforming growth factor β1, CXCL9 (MIG-1), myeloperoxidase, PECAM-1, ICAM-1 and VCAM-1) were purchased from BioLegend, and assays were performed as per the manufacturer’s instructions. Beads were acquired and analyzed on a BD Fortessa X20 SORP (BD Biosciences). Samples were run in duplicate, and 4,000 beads were acquired per sample. Data analysis was performed using Qognit LEGENDplex software (BioLegend). Lower limit of detection values were used for all analytes at the lower limit.

Catalytic ACE2 detection in plasma

Plasma ACE2 activity was measured using a validated, sensitive quenched fluorescent substrate-based assay as previously described37. Briefly, plasma (0.25 ml) was diluted into low-ionic-strength buffer (20 mmol l−1 Tris-HCl, pH 6.5) and added to 200 ml ANXSepharose 4 Fast-Flow resin (Amersham Biosciences, GE Healthcare) that removed a previously characterized endogenous inhibitor of ACE2 activity. After binding and washing, the resulting eluate was assayed for ACE2 catalytic activity. Duplicate samples were incubated with the ACE2-specific quenched fluorescent substrate, with or without 100 mM ethylenediaminetetraacetic acid. The rate of substrate cleavage was determined by comparison to a standard curve of the free fluorophore 4-amino-methoxycoumarin (Sigma-Aldrich) and expressed as picomoles of substrate cleaved per milliliter of plasma per minute. The intra- and interassay coefficients of variation were 5.6% and 11.8%, respectively. Samples below the limit of detection were designated 0.02 (half the lower limit of detection; i.e., 50% × 0.04).

Linear model

The analytes most associated with LC were identified via log-linear classification. For an arbitrary set of four analytes, let the concentration of the ith analyte at 8 months be denoted wi. Log-linear classification assigns a weight ai to the logarithm of each analyte concentration. A linear function of these logged concentrations and weights takes the form f(a⃗ )f(a→) is a threshold parameter. The weights wi and the intercept w0 are selected to maximize the predictive power of the linear classifier by training on the analyte data, where f(a⃗ )>0f(a→)>0 results in the classifier predicting that the participant with analyte concentration a⃗ a→ has LC and does not have LC otherwise.

f(a⃗ )=w0+∑i=1Nwilog10(ai)f(a→)=w0+∑i=1N⁡wilog10(ai)

Because of the modest small sample size of 58 participants at month 8, we performed bootstrapping to randomly sample new populations of size 58 from our population with replacement. The sampled population was then split 29:29 into test and train datasets. The training dataset was used to train a log-linear classifier using Python3 v3.8.10 and the Scikit-learn machine learning package v0.24.1. From the test set, the number of true positives (TPs; both the classifier and data indicate the participant had LC), true negatives (TNs; both the classifier and data indicate the participant had asymptomatic COVID), false positives (FPs; classifier predicts the participant will have LC, but the data disagree) and false negatives (FNs; classifier predicts the participant will have asymptomatic COVID, but the data disagree) were identified. Then, two subsequent scores were calculated. The accuracy is defined as (TP + TN)/(TP + TN + FP + FN) and measures the proportion of test participants that had their COVID status correctly predicted. The second measure is the F1 score and is defined as TP/(TP + 0.5 × (FP + FN)), which is a measure that combines recall, how many LC cases were correctly predicted and precision (of all the participants predicted to have LC, how many were correct). This process is repeated for 1,000 different bootstrapped sample populations. The average accuracy of a model of N analytes is then calculated and used to assess which combination of N analytes performs the best.

Dimensional reduction and clustering analysis

FCS 3.0 files were compensated manually using acquisition-defined matrix as a guide, and the gating strategy was based on unstained or endogenous controls. Live singlets were gated from patients with LC and asymptomatic MCs using FlowJo v.10.7.2, samples were decoded and statistical analysis between groups and unsupervised analysis was performed, with matched asymptomatic controls as the primary comparator group. For unsupervised analysis, the following FlowJo plugins were used: DownSample (v.3), TriMap (v.0.2), Phenograph (v.3.0) and ClusterExplorer (v.1.5.9) (all FlowJo LLC). First, 100,000 events per sample were downsampled from the total live singlet gate (Extended Data Fig. 6). The newly generated FCS files were labeled according to control or patient group (LC or MCs) and concatenated per group. Subsequently, 20,000 events were taken from each grouped sample by downsampling. The two new FCS files corresponding to LC and MCs were then concatenated for dimensionality reduction analysis using TriMap (40,000 events in total). TriMap was conducted using the following parameters to include the markers CD25, CD38, CCR7, CD19, IgD, CD45RA, PD-1, TIM-3, CD4, CD57, CD127, CD27, HLA-DR, CD8, CXCR5, GPR56 and granzyme B and using the following conditions: metric = Euclidean, nearest neighbors = 15 and minimum distance = 0.5. The phenograph plugin was then used to determine clusters of phenotypically related cells. The same markers as TriMap and parameters k = 152 and Run ID = auto were used for analysis. Finally, the ClusterExplorer plugin was used to identify the phenotype of the clusters generated by phenograph.

Statistical analysis

All column graphs are presented as medians with IQRs. One-way analysis of variance with Kruskal–Wallis and Dunn’s correction for multiple comparisons was used for serum analyte analysis. A Wilcoxon paired t test was used to analyze statistical data with Prism v9.0 (GraphPad) software. For unpaired samples, a Mann–Whitney U test was used. Two-tailed P values less than 0.05 were considered significant (*P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001).

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

To protect patient privacy, underlying electronic health records may be accessed via a remote server pending a material transfer agreement and approval from study steering committee. As data within this manuscript are from an ongoing clinical trial, further data will be provide by the corresponding author upon request and will require approval from study steering committee.


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New Clues To Long Covid: Prolonged Inflammatory Response

Authors: William A. Haseltine January 2922 Posted July 2022 Forbes

For many Covid-19 patients, the end of the acute stage of infection is only the beginning of another difficult experience: Long Covid. Defined by the persistence of physical and neuropsychiatric symptoms over a period of 12 weeks or longer, the exact causes of long Covid remain largely elusive. A recent analysis by researchers at the University of New South Wales’ Kirby institute and St Vincent’s Hospital Sydney sheds some light on the topic. In long Covid patients they have uncovered evidence of sustained inflammation and activation of the immune response for at least 8 months after initial infection. These findings provide a framework through which to define more accurately and diagnose long Covid.

Phetsouphanh et al. were given a chance to look for “biomarkers” underlying long Covid with help from data gathered as part of St Vincent’s Hospital’s ADAPT study. The study collected blood samples from unvaccinated Australians during the height of the country’s first pandemic wave.

Immune biomarkers are measurable indicators that act as a kind of map key, letting researchers know what processes and responses characterize a certain disease. This study represents the first laboratory analysis of long Covid’s impact on the immune system.

To pin down exactly what’s happening to Covid “long-haulers,” as they’ve come to be known, Phetsouphanh et al. compared the blood samples from the ADAPT study with those derived from healthy donors unexposed to SARS-CoV-2. The ADAPT cohort was made up of individuals with PCR-confirmed Covid-19 infections, tracked over a period of eight months. Blood samples were drawn two months, four months, and eight months after the initial infection. After four months, 31 of a total 147 participants were classified as having long Covid based on the persistence of one of three major symptoms: fatigue, labored breathing, or chest pain. Those exhibiting long Covid symptoms were matched with 31 symptom-free participants of the same trial, used as an additional control cohort.

The team of researchers also compared the blood samples with those of individuals infected with other, non-SARS-CoV-2 human coronaviruses.

“As immunologists we’re almost like detectives at a crime scene. We have thousands of potential biomarkers – or leads – to investigate, but only a handful of them will reveal something useful. We can use some of our knowledge of what’s been measured in acute COVID and other post-viral fatigue syndromes to narrow the investigation down a little bit, but because long COVID is still a new syndrome, we have to take a broad examination of the evidence and look almost everywhere,” says Dr. Phetsouphanh.

Of the 28 potential markers the researchers analyzed, six were noticeably elevated in both the long Covid cohort and the asymptomatic control cohort four months after initial infection. All six were proinflammatory cytokines, signaling proteins that help boost inflammation as part of the innate immune response. Two proinflammatory cytokines stood out as particularly elevated in the Covid cohorts vis-à-vis the other two cohorts: interferon β (IFN-β), and interferon λ1 (IFN-λ1).

The remaining 22 analytes were the same across all four cohorts.

Inflammation is a critical part of recovery, helping the body get rid of the source of damage and helping it repair injured tissue, but too much of it can have unwanted effects. Especially when the inflammation persists beyond any actual outside threat.

Professor Gail Matthews, the study’s senior researcher, mentioned: “But what we’re seeing with long COVID is that even when the virus has completely left the body, the immune system remains switched on. If you measure the same thing after a standard cough or cold, which we did in this study through one of our control groups, this signal is not there. It’s unique to sufferers of long COVID.”

The long Covid cohort and the asymptomatic matched control cohort may have had the same readings four months in, but at eight months the two began to come apart. The levels of proinflammatory cytokines in the asymptomatic cohort dropped off, whereas those in the long Covid cohort remained more or less steady, with only a statistically insignificant decrease.

Four of the markers, analyzed via a data model, proved to be especially accurate in predicting long Covid: IFN-β, PTX3, IFN-λ2/3 and IL-6. Of these, IFN-β was the single most important indicator of long Covid, present 94% of the time when modeled in a set of four markers.

As exciting as this data is, the researchers are already looking ahead: how does the rate of long Covid incidence and distribution of biomarkers change depending on vaccination status, the variant with which one was infected, and the severity of one’s infection?

The plight of long haulers was dismissed early on in the pandemic, often leaving sufferers to deal with life-altering symptoms on their own, without clinical or institutional support. This analysis by Phetsouphanh et al. helps firmly ground their experiences in biology. Long Covid is a medical condition, often debilitating, and has to be treated as such.

Coronavirus: Kidney Damage Caused by COVID-19

Authors: C. John Sperati, M.D., M.H.S. Posted May 28, 2022 John’s Hopkins Health

COVID-19 Kidney Damage: A Known Complication

Some people suffering with severe cases of COVID-19 will show signs of kidney damage, even those who had no underlying kidney problems before they were infected with the coronavirus. Signs of kidney problems in patients with COVID-19 include high levels of protein or blood in the urine and abnormal blood work.

Studies indicate more than 30% of patients hospitalized with COVID-19 develop kidney injury, and more than 50% of patients in the intensive care unit with kidney injury may require dialysis. Sperati says early in the pandemic, some hospitals were running short on machines and sterile fluids needed to perform dialysis.

“As general treatments for patients with COVID-19 have improved, the rates of dialysis have decreased. This has helped to alleviate shortages, although intermittent supply chain disruptions remain a concern.

“Many patients with severe COVID-19 are those with co-existing, chronic conditions, including high blood pressure and diabetes. Both of these increase the risk of kidney disease,” he says.

But Sperati and other doctors are also seeing kidney damage in people who did not have kidney problems before they got infected with the virus.

How does COVID-19 damage the kidneys?

The impact of COVID-19 on the kidneys is complex. Here are some possibilities doctors and researchers are exploring:

Coronavirus might target kidney cells

The virus itself infects the cells of the kidney. Kidney cells have receptors that enable the new coronavirus to attach to them, invade, and make copies of itself, potentially damaging those tissues. Similar receptors are found on cells of the lungs and heart, where the new coronavirus has been shown to cause injury.

Too little oxygen can cause kidneys to malfunction

Another possibility is that kidney problems in patients with the coronavirus are due to abnormally low levels of oxygen in the blood, a result of the pneumonia commonly seen in severe cases of the disease.

Cytokine storms can destroy kidney tissue

The body’s reaction to the infection may be responsible as well. The immune response to the new coronavirus can be extreme in some people, leading to what is called a cytokine storm.

When that happens, the immune system sends a rush of cytokines into the body. Cytokines are small proteins that help the cells communicate as the immune system fights an infection. But this sudden, large influx of cytokines can cause severe inflammation. In trying to kill the invading virus, this inflammatory reaction can destroy healthy tissue, including that of the kidneys.

COVID-19 causes blood clots that might clog the kidneys

The kidneys are like filters that screen out toxins, extra water and waste products from the body. COVID-19 can cause tiny clots to form in the bloodstream, which can clog the smallest blood vessels in the kidney and impair its function.

Life-threatening inflammation is turning COVID-19 into a chronic disease

Authors: Chris Melore MAY 13, 2022 Study Finds

Long COVID continues to be a lingering problem for more and more coronavirus patients in the months following their infection. Now, a new study contends that the life-threatening inflammation many patients experience — causing long-term damage to their health — is turning COVID-19 into a chronic condition.

“When someone has a cold or even pneumonia, we usually think of the illness being over once the patient recovers. This is different from a chronic disease, like congestive heart failure or diabetes, which continue to affect patients after an acute episode. We may similarly need to start thinking of COVID-19 as having ongoing effects in many parts of the body after patients have recovered from the initial episode,” says first author Professor Arch G. Mainous III, vice chair for research in the Department of Community Health and Family Medicine at the University of Florida Gainesville, in a media release.

“Once we recognize the importance of ‘long COVID’ after seeming ‘recovery’, we need to focus on treatments to prevent later problems, such as strokes, brain dysfunction, and especially premature death.”

COVID inflammation increases risk of death one year later

The study finds COVID patients experiencing severe inflammation while in the hospital saw their risk of death skyrocket by 61 percent over the next year post-recovery.

Inflammation raising the risk of death after an illness is a seemingly confusing concept. Typically, inflammation is a natural part of the body’s immune response and healing process. However, some illnesses including COVID-19 cause this infection-fighting response to overshoot. Previous studies call this the “cytokine storm,” an event where the immune system starts attacking healthy tissue.

“COVID-19 is known to create inflammation, particularly during the first, acute episode. Our study is the first to examine the relationship between inflammation during hospitalization for COVID-19 and mortality after the patient has ‘recovered’,” Prof. Mainous says.

“Here we show that the stronger the inflammation during the initial hospitalization, the greater the probability that the patient will die within 12 months after seemingly ‘recovering’ from COVID-19.”

There is a way to stop harmful inflammation

The study examined the health records of 1,207 adults hospitalized for COVID-19 in the University of Florida health system between 2020 and 2021. Researchers followed them for at least one year after discharge — keeping track of their C-reactive protein (CRP) levels. This protein is secreted by the liver and is a common measure of systemic inflammation.

Results show patients with a more severe case of the virus and those needing oxygen or ventilation had higher CRP levels during their hospitalization. The patients with the highest CRP concentrations had a 61-percent increased risk of death over the next year after their release from the hospital.

However, the team did find that prescribing anti-inflammatory steroids after hospitalization lowered the risk of death by 51 percent. Study authors say their findings show that the current recommendations for care after a coronavirus infection need to change. Researchers recommend more widespread use of orally taken steroids following a severe case of COVID.


Here’s how to detox from the COVID spike protein – from the jab or the virus

Spike proteins can circulate in your body after infection or injection, causing damage to cells, tissues and organs, but the World Council for Health has compiled a list of medications to prevent this.

Thu Dec 23, 2021 – 10:38 am EST

Note: This article is an opinion and the treatments that are recommended in it have not been proven as an effective means to eliminate the spike protein from COVID or mRNA vaccines. Damage to endothelial linings of vessels and organs by the COVID-19 spike protein and how to reverse it requires new research and randomized clinical trials to determine if any treatment can detox the body of the spike protein that causes Long-haul diseases.


  • If you had COVID-19 or received a COVID-19 injection, you may have dangerous spike proteins circulating in your body
  • Spike proteins can circulate in your body after infection or injection, causing damage to cells, tissues and organs
  • The World Council for Health has released a spike protein detox guide, which provides straightforward steps you can take to potentially lessen the effects of toxic spike protein in your body
  • Spike protein inhibitors and neutralizers include pine needles, ivermectin, neem, N-acetylcysteine (NAC) and glutathione
  • The top 10 spike protein detox essentials include vitamin D, vitamin C, nigella seed, quercetin, zinc, curcumin, milk thistle extract, NAC, ivermectin and magnesium

(Mercola) – Have you had COVID-19 or received a COVID-19 injection? Then you likely have dangerous spike proteins circulating in your body. While a spike protein is naturally found in SARS-CoV-2, no matter the variant, it’s also produced in your body when you receive a COVID-19 shot. In its native form in SARS-CoV-2, the spike protein is responsible for the pathologies of the viral infection.

In its wild form it’s known to open the blood-brain barrier, cause cell damage (cytotoxicity) and, as Dr. Robert Malone – the inventor of the mRNA and DNA vaccine core platform technology – said in a commentary on News Voice, the protein “is active in manipulating the biology of the cells that coat the inside of your blood vessels — vascular endothelial cells, in part through its interaction with ACE2, which controls contraction in the blood vessels, blood pressure and other things.”

It’s also been revealed that the spike protein on its own is enough to cause inflammation and damage to the vascular system, even independent of a virus.

Now, the World Council for Health (WCH) – a worldwide coalition of health-focused organizations and civil society groups that seek to broaden public health knowledge – has released a spike protein detox guide, which provides straightforward steps you can take to potentially lessen the effects of toxic spike protein. You can view their full guide of natural remedies, including dosages, at the end of this article.

Why should you consider a spike protein detox?

Spike proteins can circulate in your body after infection or injection, causing damage to cells, tissues and organs. “Spike protein is a deadly protein,” Dr. Peter McCullough, an internist, cardiologist and trained epidemiologist, says in a video. It may cause inflammation and clotting in any tissue in which it accumulates.

For instance, Pfizer’s biodistribution study, which was used to determine where the injected substances end up in the body, showed the COVID spike protein from the shots accumulated in “quite high concentrations” in the ovaries.

Further, a Japanese biodistribution study for Pfizer’s jab found that vaccine particles move from the injection site to the blood, after which circulating spike proteins are free to travel throughout the body, including to the ovaries, liver, neurological tissues and other organs. WCH noted:

“The virus spike protein has been linked to adverse effects, such as: blood clots, brain fog, organizing pneumonia, and myocarditis. It is probably responsible for many of the Covid-19 [injection] side effects … Even if you have not had any symptoms, tested positive for Covid-19, or experienced adverse side effects after a jab, there may still be lingering spike proteins inside your body.

In order to clear these after the jab or an infection, doctors and holistic practitioners are suggesting a few simple actions. It is thought that cleansing the body of spike protein … as soon as possible after an infection or jab may protect against damage from remaining or circulating spike proteins.”

Spike protein inhibitors and neutralizers

A group of international doctors and holistic practitioners who have experience helping people recover from COVID-19 and post-injection illness compiled natural options for helping to reduce your body’s spike protein load. The following are spike protein inhibitors, which means they inhibit the binding of the spike protein to human cells:

Prunella vulgarisPine needles
Dandelion leaf extractIvermectin

Ivermectin, for example, docks to the SARS-CoV-2 spike receptor-bending domain attached to ACE2, which may interfere with its ability to attach to the human cell membrane. They also compiled a list of spike protein neutralizers, which render it unable to cause further damage to cells. This includes:

N-acetylcysteine (NAC)Glutathione
Fennel teaStar anise tea
Pine needle teaSt. John’s wort
Comfrey leafVitamin C

The plant compounds in the table above contain shikimic acid, which may counteract blood clot formation and reduce some of the spike protein’s toxic effects. Nattokinase, a form of fermented soy, may also help to reduce the occurrence of blood clots.

How to protect your ACE2 receptors and detox IL-6

Spike protein attaches to your cells’ ACE2 receptors, impairing the receptors’ normal functioning. This blockage may alter tissue functioning and could be responsible for triggering autoimmune disease or causing abnormal bleeding or clotting, including vaccine-induced thrombotic thrombocytopenia.


Ivermectin, hydroxychloroquine (with zinc), quercetin (with zinc) and fisetin (a flavonoid) are examples of substances that may naturally protect your ACE2 receptors. Ivermectin works in this regard by binding to ACE2 receptors, preventing the spike protein from doing so.

Interleukin 6 (IL-6) is a proinflammatory cytokine that is expressed post-injection, and its levels increase in people with COVID-19. It’s for this reason that the World Health Organization recommends IL-6 inhibitors for people who are severely ill with COVID-19. Many natural IL-6 inhibitors, or anti-inflammatories, exist and may be useful for those seeking to detox from COVID-19 or COVID-19 injections:

Boswellia serrata (frankincense)Dandelion leaf extract
Black cumin (Nigella sativa)Curcumin
Krill oil and other fatty acidsCinnamon
LuteolinVitamin D3 (with vitamin K)
Jasmine teaSpices
Bay leavesBlack pepper

How to detox from Furin and Serine Protease

To gain entry into your cells, SARS-CoV-2 must first bind to an ACE2 or CD147 receptor on the cell. Next, the spike protein subunit must be proteolytically cleaved (cut). Without this protein cleavage, the virus would simply attach to the receptor and not get any further.

“The furin site is why the virus is so transmissible, and why it invades the heart, the brain and the blood vessels,” Dr. Steven Quay, a physician and scientist, explained at a GOP House Oversight and Reform Subcommittee on Select Coronavirus Crisis hearing.

The existence of a novel furin cleavage site on SARS-CoV-2, while other coronaviruses do not contain a single example of a furin cleavage site, is a significant reason why many believe SARS-CoV-2 was created through gain-of-function (GOF) research in a laboratory. Natural furin inhibitors, which prevent cleavage of the spike protein, can help you detox from furin and include:

  • Rutin
  • Limonene
  • Baicalein
  • Hesperidin

Serine protease is another enzyme that’s “responsible for the proteolytic cleavage of the SARS-CoV-2 spike protein, enabling host cell fusion of the virus.” Inhibiting serine protease may therefore prevent spike protein activation and viral entry into cells. WCH compiled several natural serine protease inhibitors, which include:

Green teaPotato tubers
Blue green algaeSoybeans
N-acetyl cysteine (NAC)Boswellia

Time-restricted eating and healthy diet for all

In addition to the targeted substances mentioned above, WCH was wise to note that a healthy diet is the first step to a healthy immune system. Reducing your consumption of processed foods and other proinflammatory foods, including vegetable (seed) oils, is essential for an optimal immune response.

Time-restricted eating, which means condensing your meals into a six- to eight-hour window, is also beneficial. This will improve your health in a variety of ways, primarily by improving your mitochondrial health and metabolic flexibility. It can also increase autophagy, which helps your body clear out damaged cells. As noted by WCH:

“This method … is used to induce autophagy, which is essentially a recycling process that takes place in human cells, where cells degrade and recycle components. Autophagy is used by the body to eliminate damaged cell proteins and can destroy harmful viruses and bacteria post-infection.”

Another strategy to boost your health and longevity, and possibly to help detox spike protein, is regular sauna usage. As your body is subjected to reasonable amounts of heat stress, it gradually becomes acclimated to the heat, prompting a number of beneficial changes to occur in your body.

These adaptations include increased plasma volume and blood flow to your heart and muscles (which increase athletic endurance) along with increased muscle mass due to greater levels of heat-shock proteins and growth hormone. It’s a powerful detoxification method due to the sweating it promotes.

Top 10 spike protein detox essentials and the full guide

Below you can find WCH’s full guide of useful substances to detox from toxic spike proteins, including recommended doses, which you can confirm with your holistic health care practitioner. If you’re not sure where to start, the following 10 compounds are the “essentials” when it comes to spike protein detox. This is a good place to begin as you work out a more comprehensive health strategy:

Vitamin DVitamin C
Nigella seedQuercetin
CurcuminMilk thistle extract

World Council for Health’s spike protein detox guide

SubstanceNatural Source(s)Where to GetRecommended Dose
IvermectinSoil bacteria (avermectin)On prescription0.4 mg/kg weekly for 4 weeks, then monthly
*Check package instructions to determine if there are contraindications prior to use
HydroxychloroquineOn prescription200 mg weekly for 4 weeks
*Check package instructions to determine if there are contraindications prior to use
Vitamin CCitrus fruits (e.g. oranges) and vegetables (broccoli, cauliflower, brussels sprouts)Supplement: health food stores, pharmacies, dietary supplement stores, online6-12 g daily (divided evenly between sodium ascorbate (several grams), liposomal vitamin C (3-6 g) & ascorbyl palmitate (1–3 g)
Prunella Vulgaris (commonly known as self-heal)Self-heal plantSupplement: health food stores, pharmacies, dietary supplement stores, online7 ounces (207 ml) daily
Pine NeedlesPine treeSupplement: health food stores, pharmacies, dietary supplement stores, onlineConsume tea 3 x daily (consume oil/resin that accumulates in the tea also)
NeemNeem treeSupplement: health food stores, pharmacies, dietary supplement stores, onlineAs per your practitioner’s or preparation instructions
Dandelion Leaf ExtractDandelion plantSupplement (dandelion tea, dandelion coffee, leaf tincture): natural food stores, pharmacies, dietary supplement stores, onlineTincture as per your practitioner’s or preparation instructions
N-Acetyl Cysteine (NAC)High-protein foods (beans, lentils, spinach, bananas, salmon, tuna)Supplement: health food stores, pharmacies, dietary supplement stores, onlineUp to 1,200 mg daily (in divided doses)
Fennel TeaFennel plantSupplement: health food stores, pharmacies, dietary supplement stores, onlineNo upper limit. Start with 1 cup and monitor body’s reaction
Star Anise TeaChinese evergreen tree (Illicium verum)Supplement: health food stores, pharmacies, dietary supplement stores, onlineNo upper limit. Start with 1 cup and monitor body’s reaction
St John’s WortSt John’s wort plantSupplement: health food stores, pharmacies, dietary supplement stores, onlineAs directed on supplement
Comfrey LeafSymphytum plant genusSupplement: health food stores, pharmacies, dietary supplement stores, onlineAs directed on supplement
Or Nattokinase
Natto (Japanese soybean dish)Supplement: health food stores, pharmacies, dietary supplement stores, online2-6 capsules 3-4 times a day on empty stomach one hour before or two hours after a meal
Boswellia serrataBoswellia serrata treeSupplement: health food stores, pharmacies, dietary supplement stores, onlineAs directed on supplement
Black Cumin (Nigella Sativa)Buttercup plant familyGrocery stores, health food stores
CurcuminTurmericGrocery stores, health food stores
Fish OilFatty/oily fishGrocery stores, health food storesUp to 2,000 mg daily
CinnamonCinnamomum tree genusGrocery store
Fisetin (Flavonoid)Fruits: strawberries, apples, mangoes Vegetables: onions, nuts, wineSupplement: health food stores, pharmacies, dietary supplement stores, onlineUp to 100 mg daily Consume with fats
ApigeninFruits, veg & herbs parsley, chamomile, vine-spinach, celery, artichokes, oreganoSupplement: health food stores, pharmacies, dietary supplement stores, online50 mg daily
Quercetin (Flavonoid)Citrus fruits, onions, parsley, red wineSupplement: health food stores, pharmacies, dietary supplement stores, onlineUp to 500 mg twice daily, Consume with zinc
ResveratrolPeanuts, grapes, wine, blueberries, cocoaSupplement: health food stores, pharmacies, dietary supplement stores, onlineUp to 1,500 mg daily for up to 3 months
LuteolinVegetables: celery, parsley, onion leaves
Fruits: apple skins, chrysanthemum flowers
Supplement: health food stores, pharmacies, dietary supplement stores, online100-300 mg daily (Typical manufacturer recommendations)
Vitamin D3Fatty fish, fish liver oilsSupplement: health food stores, pharmacies, dietary supplement stores, online5,000–10,000 IU daily or whatever it takes to get to 60-80 ng/ml as tested in your blood
Vitamin KGreen leafy vegetablesSupplement: health food stores, pharmacies, dietary supplement stores, online90-120 mg daily (90 for women, 120 for men)
ZincRed meat, poultry, oysters, whole grains, milk productsSupplement: health food stores, pharmacies, dietary supplement stores, online11-40 mg daily
MagnesiumGreens, whole grains, nutsSupplement: health food stores, pharmacies, dietary supplement stores, onlineUp to 350 mg daily
Jasmine TeaLeaves of common jasmine or Sampaguita plantsGrocery store, health food storesUp to 8 cups per day
SpicesGrocery store
Bay LeavesBay leaf plantsGrocery store
Black PepperPiper nigrum plantGrocery store
NutmegMyristica fragrans tree seedGrocery store
SageSage plantGrocery store
RutinBuckwheat, asparagus, apricots, cherries, black tea, green tea, elderflower teaSupplement: health food stores, pharmacies, dietary supplement stores, online500-4,000 mg daily (consult health care provider before taking higher-end doses)
LimoneneRind of citrus fruits such as lemons, oranges, and limesSupplement: health food stores, pharmacies, dietary supplement stores, onlineUp to 2,000 mg daily
BaicaleinScutellaria plant genusSupplement: health food stores, pharmacies, dietary supplement stores, online100-2,800 mg
HesperidinCitrus fruitSupplement: health food stores, pharmacies, dietary supplement stores, onlineUp to 150 mg twice daily
Green TeaCamellia sinensis plant leavesGrocery storeUp to 8 cups of tea a day or as directed on supplement
Potatoes tubersPotatoesGrocery store
Blue Green AlgaeCyanobacteriaSupplement: health food stores, pharmacies, dietary supplement stores, online1-10 grams daily
Andrographis PaniculataGreen chiretta plantSupplement: health food stores, pharmacies, dietary supplement stores, online400 mg x 2 daily
*Check for contraindications
Milk Thistle ExtractSilymarinSupplement; Health food stores, pharmacies, dietary supplement stores, online200 mg x 3 daily
Soybeans (organic)SoybeansGrocery store, health food stores

Reprinted with permission from Mercola

Elevated Expression of Serum Endothelial Cell Adhesion Molecules in COVID-19 Patients

Authos: Ming Tong,1Yu Jiang,2Da Xia,3Ying Xiong,3Qing Zheng,4Fang Chen,2Lianhong Zou,2Wen Xiao,2 and Yimin Zhu2

J Infect Dis. 2020 Sep 15; 222(6): 894–898.Published online 2020 Jun 24. doi: 10.1093/infdis/jiaa349 PMCID: PMC7337874PMID: 32582936


In a retrospective study of 39 COVID-19 patients and 32 control participants in China, we collected clinical data and examined the expression of endothelial cell adhesion molecules by enzyme-linked immunosorbent assays. Serum levels of fractalkine, vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1), and vascular adhesion protein-1 (VAP-1) were elevated in patients with mild disease, dramatically elevated in severe cases, and decreased in the convalescence phase. We conclude the increased expression of endothelial cell adhesion molecules is related to COVID-19 disease severity and may contribute to coagulation dysfunction.Keywords: COVID-19, fractalkine, endothelial cell adhesion molecules, D-dimer, coagulopathy

In December 2019, a severe public health event, manifested mainly with fever and respiratory tract symptoms, broke out in Wuhan, China, and quickly spread throughout the country and the world [1], which was named coronavirus disease 2019 (COVID-19) by the World Health Organization. As of 1 May 2020, more than 3 million cases have been confirmed, while more than 200 000 patients have died, and the number is continuing to increase.

COVID-19 causes a systemic inflammatory response, involving dysregulation and misexpression of many inflammatory cytokines [1]. The recruitment and activation of inflammatory cells depend on the expression of many classes of inflammatory mediators, such as cytokines (interleukin [IL]-1, IL-6, and IL-18), chemokines (fractalkine [FKN]), and adhesion molecules (intercellular adhesion molecule 1 [ICAM-1)] and vascular cell adhesion molecule-1 [VCAM-1]) [2]. Pathological evidence of venous thromboembolism, direct viral infection of the endothelial cells, and diffuse endothelial inflammations have been reported in recent studies [23]. Therefore, it is of significance to investigate the expression of endothelial cell adhesion molecules in COVID-19.

Here, we collected clinical data and blood samples from confirmed COVID-19 patients in the Fourth People’s Hospital of Yiyang in Hunan, China, and performed enzyme-linked immunosorbent assays (ELISAs) to study the expression of inflammatory mediators and endothelial cell adhesion molecules in COVID-19 patients.Go to:


Study Participants

A retrospective study was conducted. From 1 February to 10 March 2020, 39 COVID-19 patients were recruited at the Infectious Disease Ward in the Fourth People’s Hospital of Yiyang, Hunan, China, and 32 uninfected participants were recruited from the physical examination center of Hunan Provincial People’s Hospital. All patients tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and were hospitalized. Nine patients were diagnosed with severe pneumonia, while 30 had mild disease. Mild pneumonia was defined as positivity in quantitative reverse transcription polymerase chain reaction (qRT-PCR) tests, with typical chest tomography imaging features of viral pneumonia [4], while severe pneumonia was defined as mild pneumonia plus 1 of the following criteria: (1) respiratory distress with a respiratory rate ≥ 30 times per minute;

(2) oxygen saturation ≤ 93% at rest; (3) oxygenation index ≤ 300 mmHg (1 mmHg = 0.133 kPa); (4) respiratory failure requiring ventilation; (5) refractory shock; and

(6) admission to the intensive care unit for other organ failure. All patients were given interferon-α2b (5 million units twice daily, atomization inhalation) and lopinavir plus ritonavir (500 mg twice daily, orally) as antiviral therapy. All patients with severe disease received preventive anticoagulant treatment with low-molecular-weight heparin (LMWH) 5000 IU/day by subcutaneous injection for 7 days. No patients died during the observation period.

The criteria for discharge were: (1) absence of fever for at least 3 days; (2) significant improvement in both lungs on chest computed tomography (CT); (3) clinical remission of respiratory symptoms; and (4) repeated negativity in RT-PCR tests of throat swab samples at least 24 hours apart.

Clinical data were measured at enrolment. The study was approved by the Medical Ethics Review Board of Hunan Provincial People’s Hospital (No. 2020-10). All study participants provided written informed consent.

Sample Collection

Blood samples were collected at admission from each patient in a fasting state and repeated during the convalescence period for severe cases. Serum lipids, glucose, C-reaction protein (CRP), and D-dimer were determined by conventional laboratory methods. Blood samples of control subjects were also collected and tested. The obtained blood samples were placed in tubes containing EDTA and immediately centrifuged at 1500g and stored at −80°C.

Enzyme-Linked Immunosorbent Assay

Quantitative determination of IL-18, tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), FKN, VCAM-1, ICAM-1, and vascular adhesion protein-1 (VAP-1) was performed using commercially available ELISA kits (BOSTER).

Statistical Analysis

Categorical variables were reported as number and percentages, and significance was detected by χ2 or Fisher exact test. The continuous variables were compared using independent group t tests and described using mean and standard deviation if normally distributed, or compared using the Mann-Whitney U test and Kruskal-Wallis H test and described using median and interquartile range (IQR) value if not. Paired comparisons of the severe group were analyzed with the Nemenyi test. Statistical analysis was performed by SPSS version 19.0. Two-sided P values < .05 were considered statistically significant.

Patient and Public Involvement

In this retrospective study, no patients were directly involved in the study design, question proposal, or the outcome measurements. No patients were asked for input concerning interpretation or recording of the results.


Patient Characteristics

Demographic information is shown in Table 1. Briefly, 20 patients were male, 19 patients were female, 16 controls were male, and 16 were female, and the median ages in the control, mild, and severe groups were 52, 49, and 54 years, respectively. Nine patients had severe disease, while 30 cases had mild disease. No significant differences were found between patients with mild disease and control participants in age, smoking, cardiovascular disease (CVD), autoimmune disease, low-density lipoprotein cholesterol (LDL-C), triglycerides, total cholesterol (CHO), glucose, and D-dimer. Significant differences in D-dimer were observed between the severe disease and control participants (median, 4.49 vs 0.34, respectively; P < .05), while no significant differences in age, smoking, CVD, autoimmune disease, and the levels of triglycerides, LDL-C, CHO, and glucose were observed. Significant differences in age (median, 54 vs 49; P < .05), triglycerides (median, 0.93 vs 1.29; P < .05), D-dimer (median, 4.49 vs 0.35; P < .05), and length of stay (mean, 16.6 vs 10.6; P < .05) were observed between patients with severe and mild disease, respectively, while no significant differences in smoking, CVD, autoimmune disease, and the levels of LDL-C, CHO, and glucose were observed.

Table 1.

Characteristics of Study Participants

CharacteristicOverallControlMild DiseaseSevere Disease
Sex, male/female, n/n36/3516/1616/144/5
Age, y, median (25, 75 percentile)a50 (42, 57)52 (44, 60)49 (25, 55)54 (47, 75)*
Current smoker, n (%)10 (14)6 (192 (7)2 (22)
Cardiovascular disease, n (%)8 (11)5 (16)1 (3)2 (22)
Autoimmune disease, n (%)0 (0)0 (0)0 (0)0 (0)
LDL-C, mmol/L, median (25, 75 percentile)a1.81 (1.53, 2.17)1.81 (1.45, 2.03)1.81 (1.52, 2.34)2.11 (1.58, 2.41)
Triglycerides, mmol/L, median (25, 75 percentile)a1.17 (0.93, 1.54)1.24 (0.93, 1.54)1.29 (0.96, 1.64)0.93 (0.71, 1.03)*
Total cholesterol, mmol/L, mean ± SD3.91 ± 1.143.96 ± 1.483.83 ± 0.914.00 ± 0.98
Glucose, mmol/L, median (25, 75 percentile)a5.5 (4.3, 6.7)5.2 (4.3, 6.7)5.5 (4.4, 6.7)6.2 (4.7, 7.8)
D-dimer, mg/L, median (25, 75 percentile)a0.37 (0.25, 0.58)0.34 (0.25, 0.46)0.35 (0.15, 0.52)4.49 (1.29, 7.00)b,,*
Length of stay, d, mean ± SD12.0 ± 4.310.6 ± 3.516.6 ± 3.5*

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Abbreviation: LDL-C, low-density lipoprotein cholesterol.

aCompared with control group.

b P value <.05 compared with mild disease group.

*< .05.

Expression of Inflammatory Mediators and Endothelial Cell Adhesion Molecules in COVID-19 Patients and Uninfected Participants

The serum levels of the following were higher in patients with mild disease than in control participants: FKN (median, 880.1 vs 684.6 pg/mL; P < .01); VCAM-1 (median, 3742.3 vs 891.4 pg/mL; P < .01); ICAM-1 (median, 2866.1 vs 1287.4 pg/mL; P < .01); VAP-1 (median, 16.81 vs 16.68 pg/mL; P = .41) (Figure 1A–D); CRP (median, 10.75 vs 1.59 mg/L; P < .01); IL-18 (median, 415.4 vs 276.5 pg/mL; P = .09); TNF-α (median, 257.1 vs 242.9 pg/mL; P < .01); and IFN-γ (median, 46.00 vs 42.51 pg/mL; P = .50) (Supplementary Figure 1A–D). Of these, CRP, TNF-α, FKN, VCAM-1, and ICAM-1 were significantly elevated.

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Figure 1.

Expression of endothelial cell adhesion molecules in COVID-19 patients and uninfected participants, the horizontal lines represent median with interquartile range: (A) fractalkine; (B) vascular cell adhesion molecule-1 (VCAM-1); (C) intercellular adhesion molecule 1 (ICAM-1); and (D) vascular adhesion protein-1 (VAP-1). * P < .05; ** P < .01.

The serum levels of the following were significantly higher in patients with severe disease than in control participants: FKN (median, 1457.5 vs 684.6 pg/mL; P < .01); VCAM-1 (median, 4991.3 vs 891.4 pg/mL; P < .01); ICAM-1 (median, 4498.2 vs 1287.4 pg/mL; P < .01); VAP-1 (median, 28.80 vs 16.68 pg/mL; P < .01) (Figure 1A–D); CRP (median, 43.64 vs 1.59 mg/L; P < .01); IL-18 (median, 670.7 vs 276.5 pg/mL; P < .01); TNF-α (median, 274.2 vs 242.9 pg/mL; P < .01); and IFN-γ (median, 76.50 vs 42.51 pg/mL; P < .01) (Supplementary Figure 1A–D).

The serum levels of the following were significantly higher in patients with severe disease than in patients with mild disease: FKN (median, 1457.5 vs 880.1 pg/mL; P < .01); VCAM-1 (median, 4991.3 vs 3742.3 pg/mL; P < .05); ICAM-1 (median, 4498.2 vs 2866.1 pg/mL; P < .05); VAP-1 (median, 28.80 vs 16.81 pg/mL; P < .01) (Figure 1A–D); CRP (median, 43.64 vs 10.75 mg/L; P < .01); IL-18 (median, 670.7 vs 415.4 pg/mL; P < .01); TNF-α (median, 274.2 vs 257.1 pg/mL; P < .05); and IFN-γ (median, 76.50 vs 46.00 pg/mL; P < .01) (Supplementary Figure 1A–D).

For severe cases, the serum levels of the following were lower in the convalescence phase than during the acute phase: FKN (median, 1028.2 vs 1457.5 pg/mL; P < .05); VCAM-1 (median, 3420.9 vs 4991.3 pg/mL; P < .01); ICAM-1 (median, 3046.9 vs 4498.2 pg/mL; P < .01); VAP-1 (median, 23.90 vs 28.80 pg/mL; P = .17) (Figure 1A–D); CRP (median, 10.20 vs 43.64 mg/L; P < .01); IL-18 (median, 514.6 vs 670.7 pg/mL; P < .01); TNF-α (median, 265.1 vs 274.2 pg/mL; P < .01); IFN-γ (median, 66.30 vs 76.50 pg/mL; P = .05) (Supplementary Figure 1A–D); and D-dimer (median, 0.45 vs 4.49; P < .01) (Supplementary Figure 2). Of these, IL-18, TNF-α, FKN, VCAM-1, ICAM-1, and D-dimer were significantly lower.Go to:


Three novel findings were identified in our study. First, the endothelial cell adhesion markers FKN, VCAM-1, and ICAM-1 were elevated in COVID-19 patients. Second, the severity of COVID-19 was associated with the serum levels of CRP, IL-18, TNF-α, IFN-γ, FKN, VCAM-1, ICAM-1, and VAP-1. Third, recovery from severe COVID-19 was associated with reductions in serum CRP, IL-18, TNF-α, FKN, VCAM-1, ICAM-1, and D-dimer levels.

Endothelial activation is related to severe COVID-19, and antiphospholipid antibodies, von Willebrand factor, and factor VIII may play a role in coagulopathy [5]. Endothelial cells express angiotensin-converting enzyme 2 (ACE2), the receptor for SARS-CoV-2 [6], and the interaction of SARS-CoV-2 and ACE2 possibly mediates endothelial activation. Endothelial cells are an essential component of the coagulation system and their integrity and functionality are critical to maintaining hemostasis, whereas endothelial cell activation or injury may result in platelet activation, thrombosis, and inflammation [7]. Dysfunctional endothelial cells activated by proinflammatory cytokines may contribute to the pathogenesis of thrombosis by altering the expression of pro- and antithrombotic factors [89].

In this cohort of COVID-19 patients, although apparent thrombosis formation was excluded by Doppler ultrasound in deep veins in the lower extremities and repeated chest CT scans, we found an interesting phenomenon in patients with severe disease, that is serum D-dimer levels were elevated during the acute phase and decreased significantly during the convalescence phase. As an indirect marker of coagulation activation, elevated D-dimer has been reported in several studies and confirmed to correlate with an increased likelihood of death in COVID-19 patients [10]. We consider that the relationship between prethrombosis levels of D-dimer and thrombotic disease is likely partly attributable to subclinical clot formation.

Severe COVID-19 is commonly complicated by coagulopathy, while disseminated intravascular coagulation may contribute to most deaths [11]. Anticoagulant treatment may decrease mortality due to coagulopathy [12]. In patients with severe disease, serum FKN, ICAM-1, VCAM-1, and D-dimer levels declined significantly after antiviral and anticoagulant treatment. In addition to stimulating the immune system to suppress viral replication and clear pathogens, interferon-α also inhibits the inflammatory immune response that leads to histological damage [13]. Hence, we speculate that the dynamic changes in these molecules resulted from the alleviation of endothelial cell injuries, the anti-inflammatory effect of medications, or recovery from COVID-19.

Limitations should be noted when interpreting the results of this study. First, the number of patients with severe disease was low, which may lead to statistical deviation. Second, due to tissue sample inaccessibility, the expression of endothelial activation molecules was not measured in tissues. Third, because we did not measure the direct biomarkers in the coagulation system, the specific disturbed pathways and mechanisms are still unknown. Fourth, due to the anti-inflammatory effect of interferon-α, the relationship between the anticoagulant effect of LMWH and the decreased expression of endothelial cell adhesion molecules in COVID-19 is still uncertain, and requires further study.

In conclusion, based on the results of this study, increased expression of endothelial cell adhesion molecules is related to COVID-19 and disease severity, and may contribute to coagulation dysfunction.

Supplementary Data

Supplementary materials are available at The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.


Click here for additional data file.(328K, png)


Click here for additional data file.(70K, png)


Acknowledgments. We sincerely thank clinicians at the Forth People’s Hospital of Yiyang, Hunan, China.

Financial support. This work was supported by the Key Research and Development Program of Hunan Province (grant number 2020SK3011).

Potential conflicts of interest. All authors: No reported conflicts of interests. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.Go to:


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What Role does Endothelial Infection Play in SARS-CoV-2 Infection?

Authors: By Dr. Liji Thomas, MD Reviewed by Emily Henderson, B.Sc.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause inflammatory lung disease, including clot formation and hyper-permeability of the lung vessels, resulting in edema and bleeding into the lung. Inflammation also affects other organs, mediated by the cytokine storm.

This inflammation is characterized by endothelial cell dysfunction in multiple organs. The cause of this endothelialopathy is unknown. It could be due to the direct infection of endothelial cells or an indirect effect of the cytokines.

SARS-CoV-2 Virus

Image Credit: Kateryna Kon/

Integrin binding by SARS-CoV-2

Unlike earlier coronaviruses pathogenic to humans, SARS-CoV-2 has a spike protein that is linked to host recognition and viral attachment via the angiotensin-converting enzyme 2 (ACE2) receptor. A unique three-residue RGD motif outside the ACE2 recognition site may allow the spike protein to bind to endothelial proteins called integrins, that bind the RGD group.

In fact, the major integrin on endothelial cells, called αVβ3, is able to bind to multiple RGD-binding ligands. It also engages multiple extracellular matrix proteins, such as fibrinogen, fibronectin, and vitronectin, via its binding pocket. These matrix proteins regulate cell adhesion, migration, and proliferation, as well as angiogenesis.

This mutation could thus enhance SARS-CoV-2 binding to the host cell and may be responsible for the high transmissibility of this virus compared to the earlier ones, while also allowing for multiple routes of entry for the virus and promoting its dissemination within the host by two receptors.

SARS-CoV-2 thus produces marked dysregulation of the endothelial barrier, causing it to lose its integrity and producing a hyper-permeable state. This leads to shock and the rapid spread of the virus to major organs.

Endothelial infection in COVID-19

Endothelial cells are key to several physiological processes including activation of immune cells, platelet aggregation and adhesion, leukocyte adhesion, and transmigration. They are also the target of many viruses, leading to multi-organ dysfunction.

Some studies have failed to show the growth of the virus within endothelial cells, which has been attributed to the lack of expression of the angiotensin-converting enzyme 2 (ACE2) receptor on these cells.

However, it may be argued that this is due to the intrinsic differences between the endothelial monolayer grown in vitro, vs the endothelial lining of the blood vessels that handle blood flowing under shear stress; the activation of the endothelial cells by the high volume of cytokines; and the tight contact with the epithelial cells of the lung capillaries.What is a Cytokine Storm?

Other researchers have reported that SARS-CoV-2 is found in association with the endothelial cell marker CD31 within the lungs, in infected mice and non-human primates (NHPs). Even more significantly, this finding has been identified in the lung tissue of people who died of severe COVID-19.

The viral proteins were also found in endothelial cells. Moreover, infected mice showed upregulated KRAS signaling pathways in lung tissue, known to mediate cellular activation and dysfunction. Experimental evidence shows that mouse endothelial cells are infected by SARS-CoV-2.

Though all endothelial cells express ACE2, all are not the targets of the virus. Instead, it requires the co-expression of other host proteases such as the transmembrane serine protease TMPRSS2, or cathepsins, that cleave the spike protein to its fusion conformation, allowing viral entry into the host cell via endocytosis.

Endothelial cell injury

Following the viral entry into the endothelial cell, it begins to translate its proteins, replicate itself, and may directly induce cell injury and apoptosis. Along with this, endothelial cells activate T cells, though less than other antigen-presenting cells do. In fact, endothelial cells activate only antigen-specific memory or effector T cells, not naïve lymphocytes.

In so doing, endothelial cells may promote the destruction of infected cells by presenting viral proteins to CD8 T cells. Moreover, endothelial cells in the microvasculature may cause memory or effector CD4 T cells to migrate through the endothelium. Antiviral cytokines including gamma-interferon (IFN-γ) may induce class I or II major histocompatibility complex (MHC) molecules, costimulatory molecules that are typically required for T cell activation to occur.

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This means that the endothelial dysfunction caused by COVID-19 blocks lymphocyte activation via endothelial cells, causing an imbalance in the adaptive immune response.

Cytokine storm

The cytokine storm leads to a kind of overreach, causing further endothelial dysfunction. These cytokines include interleukin-6 (IL-6) that stimulates endothelial cell secretion of pro-inflammatory mediators and complement activation, thus further enhancing endothelial barrier breakdown.

Lymphocyte depletion often seen in COVID-19 could also be the result of the excessive inflammation induced by the endothelial cell injury. The reduced number of CD4 lymphocytes may cause an impaired response to the infection while also stimulating further inflammation. Thus, the hyper-inflammatory response in severe and critical COVID-19 could be due to endothelial cell infection and dysfunction.

Loss of endothelial barrier integrity

SARS-CoV-2 infection causes immune dysfunction as well as extensive endothelial injury, in addition to clotting defects and systemic microangiopathy. The poor disease outcome is mediated largely through the increased vascular permeability secondary to infection-related inflammation.

This hyper-permeability is associated with the leakage of both cellular and non-cellular components of the blood in the small blood vessels of the lung, causing the alveoli to become congested with liquid. The patient drowns in the fluid from the leaky blood vessels, which can endanger life by causing asphyxiation.


Simultaneously, the clotting cascades are dysregulated, causing microthrombi to form throughout the circulation, along with leukocyte infiltration. The endothelial cell dysfunction may cause further inflammation and leukocyte recruitment and adhesion.

Since endothelial cells express glycosaminoglycans and thrombomodulin on their cell surface, they inhibit the clotting cascade component, thrombin, as well as a protein inhibitor of tissue factor. Many relaxing factors such as nitric oxide (NO) and prostacyclin (PGI2) are also produced by these cells, thus blocking leukocyte and platelet adhesion and migration, smooth muscle proliferation, and exerting an anti-inflammatory, anti-apoptotic effect.

When the endothelial cells are injured by the viral invasion, they cease to exert their anticoagulant effect, leading to a thrombotic tendency that manifests as extensive microthrombi, hyaline membrane formation in the small arterioles of the lung, and diffuse alveolar injury.

Elevated D-dimer levels occur with this hypercoagulable state, causing poor outcomes and higher mortality with COVID-19. Multiple procoagulant mechanisms are at work, from the exposure of the tissue factor to clotting factors in the blood to the loss of endothelial integrity and thus activation of the intrinsic clotting pathway by the exposed matrix under the endothelial cell layer, to the devastating release of van Willebrand factor (vWF), due to endothelial dysfunction. This molecule acts to bridge platelets for aggregation and clot formation.

Infection of the endothelial cells could be associated with viral invasion of the adjacent tissues, that is, of the smooth muscle cells of the arteries and the cardiac myocytes.

Therapeutic implications

Thus, SARS-CoV-2 infection of endothelial cells could be an underlying cause for the cardiovascular complications of COVID-19, including the end-stage multi-organ dysfunction. It is plausible that the endothelial cell apoptosis was seen in patients who have died of COVID-19, as well as the microthrombi scattered throughout the lung vascular bed along with right ventricular dysfunction, are associated with direct infection of the endothelial cells.

The binding of the spike protein to αVβ3 can be inhibited by the specific αVβ3-antagonist Cilengitide, an RGD tripeptide, that has a high affinity to this integrin and suppresses virus-endothelium binding at very low doses.

Other therapeutic strategies include serine protease inhibitors, renin-angiotensin-aldosterone system inhibitors, statins, heparin, corticosteroids, and IL-6 inhibitors, all of which act at least in part via stabilization and protection of endothelial integrity.


Further Reading