Predicting the aggravation of coronavirus disease-19 pneumonia using chest computed tomography scans.

Presently, coronavirus disease-19 (COVID-19) is spreading worldwide without an effective treatment method. For COVID-19, which is often asymptomatic, it is essential to adopt a method that does not cause aggravation, as well as a method to prevent infection. Whether aggravation can be predicted by analyzing the extent of lung damage on chest computed tomography (CT) scans was examined. The extent of lung damage on pre-intubation chest CT scans of 277 patients with COVID-19 was assessed. It was observed that aggravation occurred when the CT scan showed extensive damage associated with ground-glass opacification and/or consolidation (p < 0.0001). The extent of lung damage was similar across the upper, middle, and lower fields. Furthermore, upon comparing the extent of lung damage based on the number of days after onset, a significant difference was found between the severe pneumonia group (SPG) with intubation or those who died and non-severe pneumonia group (NSPG) ≥3 days after onset, with aggravation observed when ≥14.5% of the lungs exhibited damage at 3-5 days (sensitivity: 88.2%, specificity: 72.4%) and when ≥20.1% of the lungs exhibited damage at 6-8 days (sensitivity: 88.2%, specificity: 69.4%). Patients with aggravation suddenly developed hypoxemia after 7 days from the onset; however, chest CT scans obtained in the paucisymptomatic phase without hypoxemia indicated that subsequent aggravation could be predicted based on the degree of lung damage. Furthermore, in subjects aged ≥65 years, a significant difference between the SPG and NSPG was observed in the extent of lung damage early beginning from 3 days after onset, and it was found that the degree of lung damage could serve as a predictor of aggravation. Therefore, to predict and improve prognosis through rapid and appropriate management, evaluating patients with factors indicating poor prognosis using chest CT is essential.

Serum polyethylene glycol-specific IgE and IgG in patients with hypersensitivity to COVID-19 mRNA vaccines.

BACKGROUND: The mechanism of allergic reactions to COVID-19 mRNA vaccines has not been clarified. Polyethylene glycol (PEG) is a potential antigen in the components of vaccines. However, there is little evidence that allergy after COVID-19 mRNA vaccination is related to PEG. Furthermore, the role of polysorbate (PS) as an antigen has also not been clarified. The objective of this study was to investigate whether PEG and PS allergies are reasonable causes of allergic symptoms after vaccination by detecting PEG-specific and PS-specific antibodies. METHODS: Fourteen patients who developed immediate allergic reactions to BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccines and nineteen healthy controls who did not present allergic symptoms were recruited. Serum PEG-specific immunoglobulin E (IgE) and immunoglobulin G (IgG) and PS-specific IgE and IgG were measured by enzyme-linked immunosorbent assay. Skin tests using PEG-2000 and PS-80 were applied to five patients and three controls. RESULTS: Serum levels of PEG-specific IgE and IgG in patients with immediate allergic reactions to the COVID-19 mRNA vaccine were higher than those in the control group. Serum levels of PS-specific IgE in patients with allergy to the vaccine were higher than those in patients of the control group. Intradermal tests using PEG verified the results for PEG-specific IgE and IgG. CONCLUSIONS: The results suggest that PEG is one of the antigens in the allergy to COVID-19 mRNA vaccines. Cross-reactivity between PEG and PS might be crucial for allergy to the vaccines. PEG-specific IgE and IgG may be useful in diagnosing allergy to COVID-19 mRNA vaccines.

Serum gasdermin D levels are associated with the chest computed tomography findings and severity of COVID-19.

BACKGROUND: The role of programmed cell death, especially pyroptosis and apoptosis, in unfavorable immune responses in COVID-19 remains to be elucidated. METHODS: We conducted a cross-sectional analysis to investigate the association between the serum gasdermin D (GSDMD) levels, a pyroptotic marker, and caspase-cleaved cytokeratin 18 fragment (M30), an apoptotic marker, and the clinical status and abnormal chest computed tomography (CT) findings in patients with COVID-19. RESULTS: In this study, 46 patients diagnosed with COVID-19 were divided into the following three groups according to the disease severity: mild to moderate group (n = 10), severe group (n = 14), and critical group (n = 22). The serum GSDMD levels were higher in the critical group than in the mild to moderate group (P = 0.016). In contrast, serum M30 levels were lower in the critical group than in the severe group (P = 0.048). Patients who required mechanical ventilation or died had higher serum GSDMD levels than those who did not (P = 0.007). Area of consolidation only and of ground glass opacity plus consolidation positively correlated with serum GSDMD levels (r = 0.56, P < 0.001 and r = 0.53, P < 0.001, respectively). CONCLUSION: Higher serum GSDMD levels are associated with critical respiratory status and the consolidation area on chest CT in patients with COVID-19, suggesting that excessive activation of pyroptosis may affect the clinical manifestations in patients with COVID-19.

Low SARS-CoV-2 antibody titers may be associated with poor clinical outcomes for patients with severe COVID-19.

Recently, immune response to coronavirus disease (COVID-19) has attracted attention where an association between higher antibody titer and worsening disease severity has been reported. However, our experiences with severe COVID-19 patients with low antibody titers led to hypothesizing that suppressed humoral immune response may be associated with poorer prognosis in severe COVID19. In this study, antibody titers in severe COVID19 patients were measured at 7, 10, 12, and 14 days after onset. Patients were divided into survivors and non-survivors. SARS-CoV-2 IgM in survivors and non-survivors were 0.06 AU and 0.02 AU (P = 0.048) at 10 days, 0.1 AU and 0.03 AU (P = 0.02) at 12 days, and 0.17 AU and 0.06 AU (P = 0.02) at 14 days. IgG in survivors and non-survivors were 0.01 AU and 0.01 AU (P = 0.04) at 7 days, 0.42 AU and 0.01 AU (P = 0.04) at 12 days, and 0.42 AU and 0.01 AU (P = 0.02) at 14 days. Multivariate analysis showed better survival among patients with IgM positivity at 12 days (P = 0.04), IgG positivity at 12 days (P = 0.04), IgM positivity at 14 days (P = 0.008), and IgG positivity at 14 days (P = 0.005). In severe COVID-19, low antibody titers on days 12 and 14 after onset were associated with poorer prognosis.

Back to normal; serological testing for COVID-19 diagnosis unveils missed infections.

BACKGROUND: The gold standard for coronavirus disease (COVID-19) diagnosis has been the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA by nucleic acid amplification testing (NAAT). On the other hand, serological testing for COVID-19 may offer advantages in detecting possibly overlooked infections by NAAT. METHODS: To evaluate seroconversion of NAAT-negative pneumonia patients, immunoglobulin M (IgM) and IgG targeting the spike protein of SARS-CoV-2 were semiquantified by an immunofluorescence assay. Seroconversion was confirmed by another serological method, targeting the nucleocapsid protein. RESULTS: Eight suspected but unconfirmed COVID-19 pneumonia patients (median age, 39 years; range, 21-55) were included. The median period between symptom onset and NAAT sample collection was 6 days (2-27 days). None of them had tested positive for SARS-CoV-2 by NAAT. In contrast, all eight patients revealed seropositivity with the two serological methods, indicating actual seroconversion against SARS-CoV-2. The median period between onset and blood sampling was 26.5 days (7-51 days). CONCLUSION: Eight patients with COVID-19 pneumonia, initially tested negative for SARS-CoV-2 by NAAT, were finally confirmed of the diagnosis by serological testing. To cover the whole spectrum of this heterogenous infectious disease, serology testing should be implemented to the multitiered diagnostic algorithm for COVID-19.