Synthesis of Dimeric Lewis A and Lewis B–Lewis A Tumor-Associated Carbohydrate Antigen Oligosaccharide Fragments
Bibliographic record
Abstract
to develop anticancer immunotherapies, little research has been conducted on these antigens. In our quest to discover fragments of these TACAs that could be targeted for the development of anticancer therapeutics, we report the synthesis of eight tri- to pentasaccharide fragments of these oligosaccharides. Unforeseen synthetic challenges are reported such as the incompatibility of a bromoalkyl glycoside in the reduction conditions needed to reduce a trichloroacetamide, the mismatched reactivities in a 2 + 1 synthetic strategy, and the surprising greater reactivity of a C-4 GlcNAc hydroxyl group versus that of the galactosyl OH-3 in the selective glycosylation of a trisaccharide diol. The desired final compounds were eventually obtained following a stepwise approach as nonyl or 9-aminononyl glycosides after one-step deprotection reactions in dissolving metal conditions. The 9-aminononyl glycosides will be conjugated to carrier proteins and the nonyl pentasaccharide glycoside will be used as a soluble inhibitor in binding experiments. In contrast, the nonyl tetrasaccharide glycosides are poorly soluble in water and their use in biochemical experiments will be limited.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".