Emerging role of galectin 3 in neuroinflammation and neurodegeneration
Bibliographic record
Abstract
Neuroinflammation and neurodegeneration are key processes that mediate the development and progression of neurological diseases. However, the mechanisms modulating these processes in different diseases remain incompletely understood. Advances in single cell based multi-omic analyses have helped to identify distinct molecular signatures such as Lgals3 that is associated with neuroinflammation and neurodegeneration in the central nervous system (CNS). Lgals3 encodes galectin-3 (Gal3), a β-galactoside and glycan binding glycoprotein that is frequently upregulated by reactive microglia/macrophages in the CNS during various neurological diseases. While Gal3 has previously been associated with non-CNS inflammatory and fibrotic diseases, recent studies highlight Gal3 as a prominent regulator of inflammation and neuroaxonal damage in the CNS during diseases such as multiple sclerosis, Alzheimer's disease, and Parkinson's disease. In this review, we summarize the pleiotropic functions of Gal3 and discuss evidence that demonstrates its detrimental role in neuroinflammation and neurodegeneration during different neurological diseases. We also consider the challenges of translating preclinical observations into targeting Gal3 in the human CNS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".