Laser-Assisted Intradermal Delivery of Adjuvant-Free Vaccines Targeting XCR1+ Dendritic Cells Induces Potent Antitumoral Responses
Bibliographic record
Abstract
The development of vaccines inducing efficient CD8(+) T cell responses is the focus of intense research. Dendritic cells (DCs) expressing the XCR1 chemokine receptor, also known as CD103(+) or CD8α(+) DCs, excel in the presentation of extracellular Ags to CD8(+) T cells. Because of its high numbers of DCs, including XCR1(+) DCs, the skin dermis is an attractive site for vaccine administration. By creating laser-generated micropores through the epidermis, we targeted a model protein Ag fused to XCL1, the ligand of XCR1, to dermal XCR1(+) DCs and induced Ag-specific CD8(+) and CD4(+) T cell responses. Efficient immunization required the emigration of XCR1(+) dermal DCs to draining lymph nodes and occurred irrespective of TLR signaling. Moreover, a single intradermal immunization protected mice against melanoma tumor growth in prophylactic and therapeutic settings, in the absence of exogenous adjuvant. The mild inflammatory milieu created in the dermis by skin laser microporation itself most likely favored the development of potent T cell responses in the absence of exogenous adjuvants. The existence of functionally equivalent XCR1(+) dermal DCs in humans should permit the translation of laser-assisted intradermal delivery of a tumor-specific vaccine targeting XCR1(+) DCs to human cancer immunotherapy. Moreover, considering that the use of adjuvants in vaccines is often associated with safety issues, the possibility of inducing protective responses against melanoma tumor growth independently of the administration of exogenous adjuvants should facilitate the development of safer vaccines.
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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".