N-Acetylcysteine Suppresses Microglial Inflammation and Induces Mortality Dose-Dependently via Tumor Necrosis Factor-α Signaling
Bibliographic record
Abstract
N-acetylcysteine (NAC) is an antioxidant that prevents tumor necrosis factor (TNF)-α-induced cell death, but it also acts as a pro-oxidant, promoting reactive oxygen species independent apoptosis. Although there is plausible preclinical evidence for the use of NAC in the treatment of psychiatric disorders, deleterious side effects are still of concern. Microglia, key innate immune cells in the brain, play an important role in inflammation in psychiatric disorders. This study aimed to investigate the beneficial and deleterious effects of NAC on microglia and stress-induced behavior abnormalities in mice, and its association with microglial TNF-α and nitric oxide (NO) production. The microglial cell line MG6 was stimulated by Escherichia coli lipopolysaccharide (LPS) using NAC at varying concentrations for 24 h. NAC inhibited LPS-induced TNF-α and NO synthesis, whereas high concentrations (≥30 mM) caused MG6 mortality. Intraperitoneal injections of NAC did not ameliorate stress-induced behavioral abnormalities in mice, but high-doses induced microglial mortality. Furthermore, NAC-induced mortality was alleviated in microglial TNF-α-deficient mice and human primary M2 microglia. Our findings provide ample evidence for the use of NAC as a modulating agent of inflammation in the brain. The risk of side effects from NAC on TNF-α remains unclear and merits further mechanistic investigations.
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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".