Inhibition of Ethanol Neurotoxicity by Treatment with Growth Factors and Estrogen
Bibliographic record
Abstract
Ethanol ingestion by pregnant women is the primary cause of fetal alcohol syndrome, which is characterized by brain abnormalities and decreased mental capacity. In the present study,cultured neurons from embryonic rat cortices were used to study the effects of ethanol on cell survival and the potential for neuroprotection by certain growth factors and estrogen. Neurons were grown in the presence of a glial plane and in the absence of serum. Survival was assessed following chronic treatment with ethanol (45 mM) in the presence and absence of either nerve growth factor (NGF, 100ng/ml), basic fibroblast growth factor (bFGF, 5ng/ml), insulin-like growth factor I or II (IGF-I, IGF-II, both 10ng/ml), or estrogen (Es, 10nM) added on days one and four in vitro. On day in vitro 4 (DIV4) ethanol effects on neuronal viability were significantly prevented by NGF, bFGF, IGF-I, and Es. DIV6 survival of ethanol-treated neurons was increased significantly by treatment with NGF, bFGF, IGF-I, IGF-II, and Es. Nerve growth factor, bFGF, and IGF-I effects were shown to be dose-dependent. Administration of 1-100 ng/ml NGF, 0.05-5 ng/ml bFGF and 0.1-10ng/ml IGF-I led to statistically significant effects at 10, 5, and 1 ng/ml, respectively. Thus, ethanol’s effect on neuronal survival may be inhibited by simultaneous treatment with physiological doses of these factors.
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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".