Innate and adaptive nasal mucosal immune responses following experimental human pneumococcal colonization
Bibliographic record
Abstract
Streptococcus pneumoniae (Spn) is a common cause of respiratory infection, but also frequently colonizes the nasopharynx in the absence of disease. We used mass cytometry to study immune cells from nasal biopsy samples collected following experimental human pneumococcal challenge in order to identify immunological mechanisms of control of Spn colonization. Using 37 markers, we characterized 293 nasal immune cell clusters, of which 7 were associated with Spn colonization. B cell and CD8+CD161+ T cell clusters were significantly lower in colonized than in non-colonized subjects. By following a second cohort before and after pneumococcal challenge we observed that B cells were depleted from the nasal mucosa upon Spn colonization. This associated with an expansion of Spn polysaccharide-specific and total plasmablasts in blood. Moreover, increased responses of blood mucosal associated invariant T (MAIT) cells against in vitro stimulation with pneumococcus prior to challenge associated with protection against establishment of Spn colonization and with increased mucosal MAIT cell populations. These results implicate MAIT cells in the protection against pneumococcal colonization and demonstrate that colonization affects mucosal and circulating B cell populations.
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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 | 0.001 |
| 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".