Decoding the multifaceted roles of galectins in self-defense
Bibliographic record
Abstract
In this review, we aim to explore the multifaceted roles of galectins in host defense from a broader perspective, particularly regarding their functions when host integrity is compromised. Numerous comprehensive reviews on galectin functions in immunity have already been published. For researchers new to the field, this wealth of information may create an impression of galectins as proteins involved in a wide array of biological processes. Furthermore, due to the heterogeneity of galectin ligands, glycans, there is a risk of perceiving galectin-specific functions as ambiguous, potentially obscuring their core biological significance. To address this, we revisit foundational aspects, focusing on the significance of the recognition of galactose, a "late-comer" monosaccharide in evolutionary terms, provide an overview of galectin glycan binding specificity, with emphasis on the potential biological importance of each carbohydrate-recognition domain. We also discuss the biological implications of the galectin location paradox wherein these cytosolic lectins function in host defense despite their glycan ligands being synthesized in the secretory pathway. Additionally, we examine the role of galectins in liquid-liquid phase separation on membranes, which may facilitate their diverse functions in cellular responses. Through this approach, we aim to re-evaluate the complex and diverse biological roles of galectins in host defense.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".