Microglial AGE-Albumin Is Critical in Promoting Alcohol-Induced Neurodegeneration in Rats and Humans
Bibliographic record
Abstract
Alcohol is a neurotoxic agent, since long-term heavy ingestion of alcohol can cause various neural diseases including fetal alcohol syndrome, cerebellar degeneracy and alcoholic dementia. However, the molecular mechanisms of alcohol-induced neurotoxicity are still poorly understood despite numerous studies. Thus, we hypothesized that activated microglial cells with elevated AGE-albumin levels play an important role in promoting alcohol-induced neurodegeneration. Our results revealed that microglial activation and neuronal damage were found in the hippocampus and entorhinal cortex following alcohol treatment in a rat model. Increased AGE-albumin synthesis and secretion were also observed in activated microglial cells after alcohol exposure. The expressed levels of receptor for AGE (RAGE)-positive neurons and RAGE-dependent neuronal death were markedly elevated by AGE-albumin through the mitogen activated protein kinase pathway. Treatment with soluble RAGE or AGE inhibitors significantly diminished neuronal damage in the animal model. Furthermore, the levels of activated microglial cells, AGE-albumin and neuronal loss were significantly elevated in human brains from alcoholic indivisuals compared to normal controls. Taken together, our data suggest that increased AGE-albumin from activated microglial cells induces neuronal death, and that efficient regulation of its synthesis and secretion is a therapeutic target for preventing alcohol-induced neurodegeneration.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".