AB013. Sildenafil treatment following term neonatal hypoxia-ischemia may modulate inflammation by regulating gliosis
Bibliographic record
Abstract
Background: Hypoxic-ischemic injuries following birth asphyxia often lead to long-term neurological sequelae, including visual impairments, due to cortical and retinal injuries in affected neonates. Although treatment with sildenafil has been shown to improve retinal function following term neonatal asphyxia, the underlying mode of action explaining this improvement remains yet to be elucidated. Our goal is to determine the impact of sildenafil on retinal neurons and glial cells following hypoxic-ischemic injury. Methods: Neonatal hypoxia-ischemia (HI) was induced in male Long-Evans rat pups at postnatal day 10 (P10) by left common carotid ligation followed by 2-hour exposure to 8% oxygen. 12 hours following HI, animals were randomly administered 0 (vehicle), 2, 10 or 50 mg/kg of sildenafil for 7 consecutive days. At P30, rats were sacrificed and their eyes were extracted. Immunohistochemistry was performed to examine retinal ganglion cells (Brn3a), bipolar cells (Chx10), astrocytes (GFAP) and microglia (Iba1) in order to assess the neuronal count and the inflammatory response in the retina following HI and the impact of the sildenafil treatment. In addition, the ratio of activated to non-activated Muller cells was assessed by co-staining Nestin and GS respectively. Results: In the retina, HI caused a decrease in the number of retinal ganglion cells and bipolar cells, as well as an increase in inflammation marked by an increase in the number of astrocytes and an increase in the ratio of activated to non-activated Muller cells. Sildenafil treatment restored the number of retinal ganglion cells and bipolar cells, as well as reduced neuroinflammation by decreasing the number of astrocytes and the number of activated Muller cells. Conclusions: Sildenafil seems to improve retinal injuries by reestablishing retinal neuron numbers back to sham levels and modulating inflammation following HI.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".