Bibliographic record
Abstract
In order to establish malignant lesions, tumors must first evade their detection by immune cells. Tumors achieve this by embellishing and tailoring their glycocalyx, a network of polysaccharides and glycosylated proteins that refracts the phagocytic efforts of myeloid cells, shrouds neoantigens and other ligands from cells of the acquired immune system, and skews immune responses. The barriers imposed by the glycocalyx are biophysical and also linked to the inhibitory receptor signaling pathways of immune cells that engage tumor sialic acids as markers of healthy "self". This would explain the pressure for cancers to upregulate the synthases, transmembrane mucins, and other heavily sialylated glycoproteins involved in establishing a repulsive glycocalyx. Accordingly, individual tumor cells that are best capable of constructing a shielding glycocalyx on their surface show higher metastatic potential in immunocompetent mice. Reciprocally, therapeutics have recently been devised to edit and dismantle the glycocalyx barrier in an effort to invigorate an immune response aimed at tumor destruction. We discuss the features of the tumor-associated glycocalyx that afford immune evasion of cancers and how strategies that target this barrier may potentiate antitumor immunity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".