Aberrant cortical neurogenesis in a pediatric neuroAIDS model: neurotrophic effects of growth hormone
Bibliographic record
Abstract
OBJECTIVE: To study the effects of HIV-1 and feline immunodeficiency virus (FIV) on neural stem cell viability, together with the neurotrophic properties of growth hormone (GH) in models of pediatric neuroAIDS. DESIGN AND METHODS: Mouse neural stem cells were infected in vitro with a Sindbis virus vector (SIN-HIVenv) expressing the envelope protein from the brain-derived HIV-1 strain JR-FL using a vector expressing enhanced green fluorescent protein (SIN-EGFP) as control. Cell survival and alterations in expression of neural stem cell markers upon GH treatment was assessed. Neonatal cats were infected with a neurovirulent FIV strain and 6 weeks after infection treated with GH for 6 weeks. Twelve weeks post-infection, neural progenitor cell marker expression, neuronal loss and neuroinflammation in brain were examined using real time reverse transcription-PCR and immunohistochemical analyses. RESULTS: HIV-1 envelope expression in neural stem cells reduced nestin expression (P < 0.05) and induced cell death (P < 0.001), which was blocked by GH. In the frontal cortex of FIV-infected cats neuroinflammation, loss of differentiated neurons (P < 0.01) and aberrant neuronal progenitor cell gene expression (P < 0.05) were observed. FIV envelope expression was detected in neural progenitor and monocytoid cells. GH treatment of FIV-infected animals induced insulin-like growth factor-1 expression in neurons (P < 0.01), enhanced neuronal survival (P < 0.01) and increased nestin expression (P < 0.05). Moreover, improved neurobehavioral performance (P < 0.01) and immunological status (P < 0.001) were observed, among GH-treated animals infected with FIV. CONCLUSION: GH protects neural stem cells that are susceptible to lentivirus-mediated injury. Thus, GH may be a potential treatment for pediatric neuroAIDS because of its neurotrophic actions.
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) | 0.000 | 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 | 0.001 |
| 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".