Neurosurgery (Neuro Vascular)
Bibliographic record
Abstract
Background: Blood breakdown products such as bilirubin and bilirubin oxidation products damage cortex and white matter after intracerebral hemorrhage(ICH). Here, we tested whether albumin can antagonize axonal damage caused by bilirubin. Methods: The effect of albumin on white matter injury was investigated using brain slices in vitro. After CD-1 mice brain slices were cut using a vibratome, they were incubated in one of five solutions: artificial cerebral spinal fluid (ACSF), bilirubin ACSF, bilirubin and albumin ACSF, bilirubin ACSF that had albumin added 1 hour(h) later, and bilirubin and denatured albumin ACSF. All solutions were continuously aerated with 95% O2 and 5% CO2. Subsequently, electrophysiological recordings of axonal response to electrical stimulation were performed 8h after incubation of brain slices. Results: Bilirubin treatment profoundly damaged both myelinated and unmeylinated axons in brain slices, but had a greater effect on myelinated axons. Unmyelinated axons were found to be more susceptible to damage from denatured albumin. Albumin treatment at 0 h and 1 h significantly diminished bilirubin toxicity for both myelinated and unmyelinated axons, with 1 h delayed albumin treatment conferring greater neuroprotection. Conclusions: These results implicate the role of albumin in preventing bilirubin-induced axonal damage following ICH and its potential therapeutic value for hemorrhagic stroke.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.021 |
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".