Growth hormone prevents human immunodeficiency virus–induced neuronal p53 expression
Bibliographic record
Abstract
Growth hormone (GH) is neuroprotective, presumably through its actions on GH receptor-mediated pathways. Here, we examined the effects of GH using in vitro and in vivo assays of human immunodeficiency virus (HIV)-induced neuronal injury. Neuronal cultures were in assays of neurotoxicity induced by supernatants from HIV-1 tat-transfected monocytoid cells (Tat supernatant). GH treatment reduced neuronal death compared with untreated cultures (p < 0.001), which was blocked by a GH receptor antagonist, B2036. Tat supernatant-induced p53 expression in neurons was also reduced by GH treatment. Expression of both p53 and GH receptor were increased in brain tissue from HIV-infected persons compared with controls (p < 0.05). Mice receiving intrastriatal implants of Tat supernatant and treated with GH showed less neurobehavioral abnormalities together with reduced neuroinflammation and neuronal injury compared with untreated animals (p < 0.01). Three acquired immunodeficiency syndrome-defined patients with neurocognitive impairment were serially evaluated during daily GH treatment showing a sustained improvement in neuropsychological performance (p < 0.01). GH prevents neuronal death through its actions on neurons involving a p53-mediated pathway and also improved in vivo neurological function, indicating that GH may have a role in the treatment of HIV-induced neurodegeneration.
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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".