Down‐regulation of MIF by NFκB under hypoxia accelerated neuronal loss during stroke
Bibliographic record
Abstract
Neuronal apoptosis is one of the major causes of poststroke neurological deficits. Inflammation during the acute phase of stroke results in nuclear translocation of NFκB in affected cells in the infarct area. Macrophage migration inhibitory factor (MIF) promotes cardiomyocyte survival in mice following heart ischemia. However, the role of MIF during stroke remains limited. In this study, we showed that MIF expression is down-regulated by 0.75 ± 0.10-fold of the control in the infarct area in the mouse brains. Two functional cis-acing NFκB response elements were identified in the human MIF promoter. Dual activation of hypoxia and NFκB signaling resulted in significant reduction of MIF promoter activity to 0.86 ± 0.01-fold of the control. Furthermore, MIF reduced caspase-3 activation and protected neurons from oxidative stress- and in vitro ischemia/reperfusion-induced apoptosis. H2O2 significantly induced cell death with 12.81 ± 0.58-fold increase of TUNEL-positive cells, and overexpression of MIF blocked the H2O2-induced cell death. Disruption of the MIF gene in MIF-knockout mice resulted in caspase-3 activation, neuronal loss, and increased infarct development during stroke in vivo. The infarct volume was increased from 6.51 ± 0.74% in the wild-type mice to 9.07 ± 0.66% in the MIF-knockout mice. Our study demonstrates that MIF exerts a neuronal protective effect and that down-regulation of MIF by NFκB-mediated signaling under hypoxia accelerates neuronal loss during stroke. Our results suggest that MIF is an important molecule for preserving a longer time window for stroke treatment, and strategies to maintain MIF expression at physiological level could have beneficial effects for stroke patients.
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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 | 0.000 | 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) | 0.001 | 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".