Improvement in edema and cognitive recovery after moderate traumatic brain injury with the neurosteroid prodrug NTS-104
Bibliographic record
Abstract
Neuroactive steroids reduce mortality, decrease edema, and improve functional outcomes in preclinical and clinical traumatic brain injury (TBI) studies. In this study, we tested the efficacy of two related novel neuroactive steroids, NTS-104 and NTS-105, in a rat model of TBI. NTS-104 is a water-soluble prodrug of NTS-105, a partial progesterone receptor agonist. To investigate the effects of NTS-104 on TBI recovery, adult male Sprague Dawley rats received moderate parasagittal fluid-percussion injury or sham surgery and were treated with vehicle or NTS-104 (10 mg/kg, intramuscularly) at 4, 10, 24, and 48 h post-TBI. The therapeutic time window was also assessed using the neuroactive steroid NTS-105 (3 mg/kg, intramuscularly). Edema in the parietal cortex and hippocampus, measured at 3 days post-injury (DPI), was reduced by NTS-104 and NTS-105. NTS-105 was effective in reducing edema when given at 4, 10, or 24 h post-injury. Sensorimotor deficits in the cylinder test at 3 DPI were ameliorated by NTS-104 and NTS-105 treatment. Cognitive recovery, assessed with cue and contextual fear conditioning and retention of the water maze task assessed subacutely 1-3 weeks post-injury, also improved with NTS-104 treatment. Cortical and hippocampal atrophy at 22 DPI did not improve, indicating that NTS-104/NTS-105 may promote post-TBI cognitive recovery by controlling edema and other processes. These results demonstrate that NTS-104/NTS-105 is a promising therapeutic approach to improve motor and cognitive recovery after moderate 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".