Stereoselective Synthesis of Sialyl Lewis<sup>a</sup> Antigen and the Effective Anticancer Activity of Its Bacteriophage Qβ Conjugate as an Anticancer Vaccine
Bibliographic record
Abstract
Abstract Sialyl Lewisa (sLea), also known as cancer antigen 19‐9 (CA19‐9), is a tumor‐associated carbohydrate antigen. The overexpression of sLea on the surface of a variety of cancer cells makes it an attractive target for anticancer immunotherapy. However, sLea‐based anticancer vaccines have been under‐explored. To develop a new vaccine, efficient stereoselective synthesis of sLea with an amine‐bearing linker was achieved, which was subsequently conjugated with a powerful carrier bacteriophage, Qβ. Mouse immunization with the Qβ‐sLea conjugate generated strong and long‐lasting anti‐sLea IgG antibody responses, which were superior to those induced by the corresponding conjugate of sLea with the benchmark carrier keyhole limpet hemocyanin. Antibodies elicited by Qβ‐sLea were highly selective toward the sLea structure, could bind strongly with sLea‐expressing cancer cells and human pancreatic cancer tissues, and kill tumor cells through complement‐mediated cytotoxicity. Furthermore, vaccination with Qβ‐sLea significantly reduced tumor development in a metastatic cancer model in mice, demonstrating tumor protection for the first time by a sLea‐based vaccine, thus highlighting the significant potential of sLea as a promising cancer antigen.
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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) | 0.001 | 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".