Regenerative Effect of Growth Hormone (GH) in the Retina after Kainic Acid Excitotoxic Damage
Bibliographic record
Abstract
In addition to its role as an endocrine messenger, growth hormone (GH) also acts as a neurotrophic factor in the central nervous system (CNS), whose effects are involved in neuroprotection, axonal growth, and synaptogenic modulation. An increasing amount of clinical evidence shows a beneficial effect of GH treatment in patients with brain trauma, stroke, spinal cord injury, impaired cognitive function, and neurodegenerative processes. In response to injury, Müller cells transdifferentiate into neural progenitors and proliferate, which constitutes an early regenerative process in the chicken retina. In this work, we studied the long-term protective effect of GH after causing severe excitotoxic damage in the retina. Thus, an acute neural injury was induced via the intravitreal injection of kainic acid (KA, 20 µg), which was followed by chronic administration of GH (10 injections [300 ng] over 21 days). Damage provoked a severe disruption of several retinal layers. However, in KA-damaged retinas treated with GH, we observed a significant restoration of the inner plexiform layer (IPL, 2.4-fold) and inner nuclear layer (INL, 1.5-fold) thickness and a general improvement of the retinal structure. In addition, we also observed an increase in the expression of several genes involved in important regenerative pathways, including: synaptogenic markers (DLG1, NRXN1, GAP43); glutamate receptor subunits (NR1 and GRIK4); pro-survival factors (BDNF, Bcl-2 and TNF-R2); and Notch signaling proteins (Notch1 and Hes5). Interestingly, Müller cell transdifferentiation markers (Sox2 and FGF2) were upregulated by this long-term chronic GH treatment. These results are consistent with a significant increase in the number of BrdU-positive cells observed in the KA-damaged retina, which was induced by GH administration. Our data suggest that GH is able to facilitate the early proliferative response of the injured retina and enhance the regeneration of neurite interconnections.
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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".