Recent Advances In Developing Synthetic Carbohydrate-Based Vaccines For Cancer Immunotherapies
Bibliographic record
Abstract
Cancer cells can often be distinguished from healthy cells by the expression of unique carbohydrate sequences decorating the cell surface as a result of aberrant glycosyltransferase activity occurring within the cell; these unusual carbohydrates can be used as valuable immunological targets in modern vaccine designs to raise carbohydrate-specific antibodies. Many tumor antigens (e.g., GM2, Le(y), globo H, sialyl Tn and TF) have been identified to date in a variety of cancers. Unfortunately, carbohydrates alone evoke poor immunogenicity, owing to their lack of ability in inducing T-cell-dependent immune responses. In order to enhance their immunogenicity and promote long-lasting immune responses, carbohydrates are often chemically modified to link to an immunogenic protein or peptide fragment for eliciting T-cell-dependent responses. This review will present a summary of efforts and advancements made to date on creating carbohydrate-based anticancer vaccines, and will include novel approaches to overcoming the poor immunogenicity of carbohydrate-based vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".