A Novel Linker Methodology for the Synthesis of Tailored Conjugate Vaccines Composed of Complex Carbohydrate Antigens and Specific T<sub>H</sub>‐Cell Peptide Epitopes
Bibliographic record
Abstract
Pathogenic organisms or oncogenically transformed cells often express complex carbohydrate structures at their cell surface, which are viable targets for active immunotherapy. We describe here a novel, immunologically neutral, linker methodology for the efficient preparation of highly defined vaccine conjugates that combine complex saccharide antigens with specific TH-cell peptide epitopes. This novel heterobifunctional approach was employed for the conjugation of a (1-->2)-beta-mannan trisaccharide from the pathogenic fungus Candida albicans as well as the carbohydrate portion of tumor-associated ganglioside GM2 to a TH-cell peptide epitope derived from the murine 60 kDa self heat-shock protein (hsp60). Moreover, the linkage chemistry has proven well suited for the synthesis of more complex target structures such as a biotinylated glycopeptide, a three component vaccine containing an immunostimulatory peptide epitope from interleukin-1 beta (IL-1 beta), and for the conjugation of complex carbohydrates to carrier proteins such as bovine serum albumin.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".