Roles for Siglec-glycan interactions in regulating immune cells
Bibliographic record
Abstract
Cell surface complex carbohydrates, known as glycans, are positioned to be the first point of contact between two cells. Indeed, interactions between glycans with glycan-binding can modulate cell-cell interactions. This concept is particularly relevant for immune cells, which use an array of glycan-binding proteins to help in the process of differentiating ‘self’ from ‘non-self’. This is exemplified by the sialic acid-binding immunoglobulin-type lectins (Siglecs), which recognize sialic acid. Given that sialic acid is relatively unique to vertebrates, immune cells leverage Siglecs to recognize sialic acid as a marker of ‘self’. Siglecs serve many biological roles, with most of these functions regulated through interactions with their sialoglycan ligands. In this review, we provide a comprehensive update on the ligands of Siglecs and how Siglec-sialoglycan interactions help regulate immune cells in the adaptive and innate immune system. • Siglec-ligand and expression table. • Siglecs in innate and adaptive immunity. • The roles of Siglecs in the central nervous system. • Contributions of Siglecs in the fetal maternal interface.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".