Toll-Like Receptor 4/MyD88–Mediated Signaling of Hepcidin Expression Causing Brain Iron Accumulation, Oxidative Injury, and Cognitive Impairment After Intracerebral Hemorrhage
Bibliographic record
Abstract
Background: Disturbance of brain iron metabolism after intracerebral hemorrhage (ICH) results in oxidative brain injury and cognition impairment. Hepcidin plays an important role in regulating iron metabolism, and we have reported that serum hepcidin is positively correlated with poor outcomes in patients with ICH. However, the roles of hepcidin in brain iron metabolism after ICH remain largely unknown. Methods: Parabiosis and ICH models combined with in vivo and in vitro experiments were used to investigate the roles of hepcidin in brain iron metabolism after ICH. Results: Increased hepcidin-25 was found in serum and primarily in astrocytes after ICH. The brain iron efflux, oxidative brain injury, and cognition impairment were improved in Hepc −/− ICH mice but aggravated by the human hepcidin-25 peptide in C57BL/6 ICH mice. Data obtained in in vitro studies showed that increased hepcidin inhibited the intracellular iron efflux of brain microvascular endothelial cells but was rescued by a hepcidin antagonist, fursultiamine. Using parabiosis ICH models also shows that increased serum hepcidin prevents brain iron efflux. In addition, Toll-like receptor 4 (TLR4)/MyD88 signaling pathway increased hepcidin expression by promoting interleukin-6 expression and signal transducer and activator of transcription 3 phosphorylation. TLR4 −/− and MyD88 −/− mice exhibited improvement in brain iron efflux at 7, 14, and 28 days after ICH, and the TLR4 antagonist (6R)-6-[ N -(2-chloro-4-fluorophenyl) sulfamoyl] cyclohex-1-ene-1–carboxylate significantly decreased brain iron levels at days 14 and 28 after ICH and improved cognition impairment at day 28. Conclusions: The results presented here show that increased hepcidin expression caused by inflammation prevents brain iron efflux via inhibition of the intracellular iron efflux of brain microvascular endothelial cells entering into circulation and aggravating oxidative brain injury and cognition impairment, which identifies a mechanistic target for muting inflammation to promote brain iron efflux and to attenuate oxidative brain injury after ICH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".