Effect of sex steroid hormones on brain edema, intracranial pressure, and neurologic outcomes after traumatic brain injury
Bibliographic record
Abstract
Recent studies have reported that estrogen and progesterone have a neuroprotective effect after traumatic brain injury (TBI); however, the mechanism(s) for this effect have not yet been elucidated. The aim of the present study was to investigate the role of sex steroid hormones on changes in brain edema, intracranial pressure (ICP), and cerebral perfusion pressure (CPP) after TBI in ovariectomized (OVX) rats. In this study, 50 female rats were divided into 5 groups: control (intact), sham, and 3 TBI groups consisting of vehicle, estrogen (1 mg/kg), and progesterone (8 mg/kg). TBI was induced by the Marmarou method, and the hormones were injected i.p. 30 min after TBI. ICP was measured in the spinal cord, and CPP was calculated by subtracting the mean arterial pressure (MAP) from ICP. The results revealed that brain water content after TBI was lower (p < 0.001) in the estrogen and progesterone groups than in the vehicle group. After trauma, ICP was significantly higher in TBI rats (p < 0.001). The ICP in the estrogen and progesterone groups decreased at 4 and 24 h after TBI compared with vehicle (p < 0.001 and p < 0.05, respectively). The CPP in the estrogen and progesterone groups increased after 24 h compared with vehicle (p < 0.001). Also after TBI, the neurological score (veterinary coma scale) was significantly higher than vehicle at 1 h (p < 0.01) and 24 h (p < 0.001) in the group treated with estrogen. In conclusion, pharmacological doses of estrogen and progesterone improved ICP, CPP, and neurological scores after TBI in OVX rats, which implies that these hormones play a neuroprotective role in TBI.
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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) | 0.001 | 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".