Cutaneous Antigen Priming via Gene Gun Leads to Skin-Selective Th2 Immune-Inflammatory Responses
Bibliographic record
Abstract
It is becoming increasingly evident that the compartmentalization of immune responses is governed, in part, by tissue-selective homing instructions imprinted during T cell differentiation. In the context of allergic diseases, the fact that "disease" primarily manifests in particular tissue sites, despite pervasive allergen exposure, supports this notion. However, whether the original site of Ag exposure distinctly privileges memory Th2 immune-inflammatory responses to the same site, while sparing remote tissue compartments, remains to be fully investigated. We examined whether skin-targeted delivery of plasmid DNA encoding OVA via gene-gun technology in mice could generate allergic sensitization and give rise to Th2 effector responses in the skin as well as in the lung upon subsequent Ag encounter. Our data show that cutaneous Ag priming induced OVA-specific serum IgE and IgG1, robust Th2-cytokine production, and late-phase cutaneous responses and systemic anaphylactic shock upon skin and systemic Ag recall, respectively. However, repeated respiratory exposure to aerosolized OVA failed to instigate airway inflammatory responses in cutaneous Ag-primed mice, but not in mice initially sensitized to OVA via the respiratory mucosa. Importantly, these contrasting airway memory responses correlated with the occurrence of Th2 differentiation events at anatomically separate sites: indeed cutaneous Ag priming resulted in Ag-specific proliferative responses and Th2 differentiation in skin-, but not thoracic-, draining lymph nodes. These data indicate that Ag exposure to the skin leads to Th2 differentiation within skin-draining lymph nodes and subsequent Th2 immunity that is selectively manifested in the skin.
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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) | 0.001 | 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".