Single-Walled Carbon Nanotubes Deliver Peptide Antigen into Dendritic Cells and Enhance IgG Responses to Tumor-Associated Antigens
Bibliographic record
Abstract
We studied the feasibility of using single-wall carbon nanotubes (SWNTs) as antigen carriers to improve immune responses to peptides that are weak immunogens, a characteristic typical of human tumor antigens. Binding and presentation of peptide antigens by the MHC molecules of antigen presenting cells (APCs) is essential to mounting an effective immune response. The Wilm's tumor protein (WT1) is upregulated in many human leukemias and cancers and several vaccines directed at this protein are in human clinical trials. WT1 peptide 427 induces human CD4 T cell responses in the context of multiple human HLA-DR.B1 molecules, but the peptide has a poor binding affinity to BALB/c mouse MHC class II molecules. We used novel, spectrally quantifiable chemical approaches to covalently append large numbers of peptide ligands (0.4 mmol/g) onto solubilized SWNT scaffolds. Peptide-SWNT constructs were rapidly internalized into professional APCs (dendritic cells and macrophages) within minutes in vitro, in a dose dependent manner. Immunization of BALB/c mice with the SWNT-peptide constructs mixed with immunological adjuvant induced specific IgG responses against the peptide, while the peptide alone or peptide mixed with the adjuvant did not induce such a response. The conjugation of the peptide to SWNT did not enhance the peptide-specific CD4 T cell response in human and mouse cells, in vitro. The solubilized SWNTs alone were nontoxic in vitro, and we did not detect antibody responses to SWNT in vivo. These results demonstrated that SWNTs are able to serve as antigen carriers for delivery into APCs to induce humoral immune responses against weak tumor antigens.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".