Microglia‐derived galectin‐3 in neuroinflammation; a bittersweet ligand?
Bibliographic record
Abstract
Galectins are soluble β-galactoside-binding proteins found in all multicellular organisms. Galectins may act as danger-associated molecular patterns in innate immunity and/or as pattern-recognition receptors that bind to pathogen-associated molecular patterns. Among different galectin family members, galectin-3 has been the focus of studies in neurodegenerative diseases in recent years. This lectin modulates brain innate immune responses, microglia activation patterns in physiological and pathophysiological settings in a context-dependent manner. Galectin-3 is considered as a pivotal tuner of macrophage and microglial activity. Indeed galectin-3 acts as a double edged sword in neuroinflammatory context and this multimodal lectin has diverse roles in physiological and pathophysiological conditions. Better understanding of galectin-3 physiology (its extracellular and intracellular actions) and structure (its C terminus vs. N terminus) is instrumental to design molecules that selectively modulate galectin-3 function toward neuroprotective phenotypes. Several experimental studies using different approaches and methods have demonstrated both protective and deleterious effects of galectin-3 in neuroinflammatory diseases. According to the crucial role of galectin-3 in modulation of innate immune response in brain, it is an attractive target in drug discovery of neurodegenerative diseases. The current insight attempts to provide an updated and balanced discussion on the role of galectin-3 as a complex endogenous immune modulator. This helps to have a better insight into the development of galectin-3 modulators with translational value in different neurological disorders including stroke and neurodegenerative diseases, such as Alzheimer's disease, Huntington's disease and Parkinson's disease.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".