Abstract 3874: Galectin-3 Enhances Post Ischemic Angiogenesis Via Modulation Of Integrin And Akt Pathways.
Bibliographic record
Abstract
Introduction: As restoration of blood flow is critical for improved functional recovery following ischemic stroke, enhancing cerebral angiogenesis might be immensely helpful. Galectin-3 (gal-3) is a β-galactoside-binding lectin that plays key roles in inflammation, survival, and angiogenesis. Methods: We used transient middle cerebral artery occlusion (MCAO), oxygen-glucose deprivation (OGD) model, western blot analysis, RT-PCR, Immunofluorescence staining. Results: We currently report that intra-cerebroventricular infusion of recombinant gal-3 in rats subjected to transient MCAO increases the post-ischemic functional recovery. We further show that gal-3 mRNA and protein expression is up-regulated in vitro when activated microglial BV2 cells were subjected to either oxygen-glucose deprivation (OGD) or LPS (lipo-polysaccharide) stimulation. Immunofluorescence staining showed increased cytoplasmic gal-3 that is known to have pro-survival function. This increase in gal-3 is associated with increased number of pro-angiogenic structures in a 3D human vein umbilical cord endothelial (HUVEC) and microglia co-culture model. The pro-angiogenic effect of gal-3 was suppressed by treatment with gal-3 siRNA indicating the specific role of gal-3 in promoting angiogenesis. Exogenous gal-3 treatment further augmented the angiogenic potential of BV2 microglia cells. In addition, gal-3 increased survival of microglial BV2 cells, HUVEC, and neuro-progenitor cells. Gal-3 levels were also directly correlated with increased levels of Integrin-linked kinase 1(ILK1), pro-survival factor AKT and their down stream effector pro-angiogenic factors bFGF and MMP2. Conclusion: Taken together, our studies emphasize the importance of gal-3 in enhancing angiogenesis that is critical for neuron survival and neurogenesis in the post-stroke brain.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".