Aberrant glycosylation patterns on cancer cells: Therapeutic opportunities for glycodendrimers/metallodendrimers oncology
Bibliographic record
Abstract
Despite exciting discoveries and progresses in drug design against cancer, its cure is still rather elusive and remains one of the humanities major challenges in health care. The safety profiles of common small molecule anti-cancer therapeutics are less than at acceptable levels and limiting deleterious side-effects have to be urgently addressed. This is mainly caused by their incapacity to differentiate healthy cells from cancer cells; hence, the use of high dosage becomes necessary. One possible solution to improve the therapeutic windows of anti-cancer agents undoubtedly resides in modern nanotechnology. This review presents a discussion concerning multivalent carbohydrate-protein interactions as this topic pertains to the fundamental aspects that lead glycoscientists to tackle glyconanoparticles. The second section describes the detailed properties of cancer cells and how their aberrant glycan surfaces differ from those of healthy cells. The third section briefly describes the immune systems, both innate and adaptative, because the numerous displays of cell surface protein receptors necessitate to be addressed from the multivalent angles, a strength full characteristic of nanoparticles. The next chapter presents recent advances in glyconanotechnologies, including glycodendrimers in particular, as they apply to glycobiology and carbohydrate-based cancer vaccines. This was followed by an overview of metallodendrimers and how this rapidly evolving field may contribute to our arsenal of therapeutic tools to fight cancer. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".