Reassuring humoral and cellular immune responses to SARS-CoV-2 vaccination in participants with systemic sclerosis
Bibliographic record
Abstract
• Compared with controls, participants with SSc do not display weaker humoral responses following SARS-CoV-2 vaccination. • Participants with SSc and controls have comparable spike-specific CD4+ and CD8+ T cell responses following SARS-CoV-2 vaccination. • Spike-specific CD4+ T cell polarization (Th1, Th2, Th17, Treg) does not largely differ between participants with SSc and controls. Individuals with systemic sclerosis (SSc) are particularly susceptible to SARS-CoV-2 infections, yet it remains to be determined if they generate humoral and cellular responses comparable to controls following SARS-CoV-2 vaccinations. Herein, we collected blood and serum after second, third, and fourth SARS-CoV-2 vaccinations in patients with SSc and controls. Following each dose, participants with SSc mounted comparable serum anti-RBD IgG, anti-RBD IgA, and spike-specific CD4 + and CD8 + T cell responses to those found in controls. At 3 months post dose 2, the frequencies of Th1, Th2, Th17, and Treg spike-specific CD4 + T cells in participants with SSc did not differ from controls. At 2–6 weeks post dose 3, participants with SSc displayed reduced frequencies, but not numbers, of Th17-polarized spike-specific CD4 + T cells. Thus, participants with SSc did not display significantly weaker humoral or cellular responses to SARS-CoV-2 vaccination than controls, enabling reassurance of vaccine immunogenicity in participants with SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".