Carbohydrate Tumor Antigen Vaccines using Unique Strategies (131.27)
Bibliographic record
Abstract
Abstract The Thomsen-Friedenreich antigen (TF-Ag), a carbohydrate tumor associated antigen, is differentially expressed on carcinomas including those of the breast, colon, and prostate. It is involved in adhesion and metastasis and even low levels of naturally formed antibody to TF-Ag is related to better prognosis. We hypothesize that vaccination to create antibody to TF-Ag may create a survival advantage for patients with TF-Ag+ tumors. The innovation of this work is that both peptide mimics and other novel constructs have been utilized as immunogens. Peptide mimics of TF-Ag, with and without C3d N- terminal peptide constructs and synthetic TF-Ag-Muc 4 constructs with the C3d N- terminal peptide, were prepared to target T cells, and CD21+ splenic B cells and follicular dendritic cells for an increased response and memory B cell production. CD21 is the C3d receptor and MUC-4 is a tumor associated peptide. The immune response was measured using TF-Ag -BSA in enzyme immunoassays. The significance of antibody production to TF-Ag can be seen in the fact that passive immunotherapy with a monoclonal Ab to this Ag decreases lung metastasis and extends survival time of mice bearing breast cancer. The improved survival time is of particular interest because the McAb was not cytotoxic, and the enhanced survival was due to blocking metastasis. A successful vaccine would replicate this inhibition of metastasis and in addition provide cytotoxic effects to improve prognosis.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".