Unveiling Humoral and Cellular Immune Responses to SARS-CoV-2 in Head and Neck Cancer: A Comparative Study of Vaccination and Natural Infection in Romania
Bibliographic record
Abstract
Background: To fill the knowledge gap regarding the antiviral immunity in oncologic patients, we performed a comparative study on natural/vaccine-induced SARS-CoV-2 immunity in head and neck cancer (HNC) in Romania. Methods: Blood was collected from HNC (n=49) and controls (n=14), stratified as vaccinated (RNA/adenovirus-based vaccines), convalescent, and hybrid immunity. Plasma IgG/IgA antibodies (Abs) against Spike (S1/S2), receptor binding domain (RBD), and nucleocapsid (NC), and cytokines were quantified using the MILLIPLEX; technology. The frequency/phenotype/isotype of RBD-specific B-cells were studied by flow cytometry using tetramers (Tet++). Cell proliferation in response to Spike/NC peptides was monitored by carboxyfluorescein succinimidyl ester (CFSE) assay. A longitudinal follow-up was performed on n=25 HNC. Findings: Levels of S1/S2/RBD-specific IgG/IgA Abs were similarly high in HNC and controls, but significantly increased in convalescent/hybrid versus vaccinated HNC. NC-specific IgG/IgA Abs were only detected in convalescent/hybrid immunity groups. The frequency of Tet++ B-cells in HNC was similar to controls, irrespective of the immunization status, and correlated positively with RBD IgG/IgA Abs and negatively with the time since immunization (TSI). Compared to total B-cells, Tet++ were enriched in CD27+ memory phenotype and IgG/IgA isotype. A linear regression model identified Spike S2 IgG and NC IgA Abs as strong positive predictors of Tet++ frequencies, while IL-6 was a marginally significant negative predictor. Tet++ frequency remained stable at median TSI of 341 versus 117 days, despite a decline in memory phenotype. Interpretation: HNC participants mount efficient and durable SARS-CoV-2 humoral immunity, with RBD-specific IgG/IgA Abs and Tet++ B-cells representing the major immunization outcomes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".