Synthesis of selected fragments of the Lewis B Lewis A Tumor-Associated Carbohydrate Antigen
Bibliographic record
Abstract
Carbohydrates are the most abundant class of natural products found in living organisms. They are present on the surface of cells and play a vital role in cell recognition. It has been observed that tumor cells overexpress some oligosaccharides on their surface. These oligosaccharides have been named Tumor-Associated Carbohydrate Antigens (TACA). One TACA of interest to our research group is the LebLea heptaasaccharide, which is displayed on the surface of liver, pancreas and endometrium carcinomas. Carbohydrates are immunogenic, therefore, it is possible to design a carbohydrate-based vaccine against these tumor cells. However, an immune response against LebLea would likely result in an auto-immune response as Leb is found on healthy cells. If a fragment of LebLea is used instead, it is quite possible that an immune response is raised against LebLea while being harmless to normal healthy cells. This thesis describes the synthesis of tri- tetra- and pentasaccharide fragments of LebLea. The synthesized fragments can then be subjected to immunochemical studies to test for cross-reactivity. Successful results may lead to a potential carbohydrate based anticancer vaccine for the proposed TACA LebLea.
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) | 0.000 | 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) | 0.004 | 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".