Neuronal Injuries Induced by Perinatal Hypoxic-Ischemic Insults Are Potentiated by Prenatal Exposure to Lipopolysaccharide: Animal Model for Perinatally Acquired Encephalopathy
Bibliographic record
Abstract
We developed an original rat model for neonatal brain lesions whereby we explored the sequential effects of infectious and hypoxic-ischemic aggressions. We investigated the influence of combined exposure to prenatal infection with neonatal hypoxic-ischemic insult. Infectious effect was produced by administrating lipopolysaccharide (LPS) intraperitoneally to pregnant rats starting on embryonic day 17. Hypoxia-ischemia (H/I) was induced in the pups at postnatal day 1 (P1) by ligature of the right common carotid artery followed by exposure to hypoxia (8% O(2)) for 3.5 h. Animals were randomized into four groups: (1) control group: pups born to mothers subjected to intraperitoneal saline injection; (2) LPS group: pups exposed in utero to LPS; (3) H/I group: pups exposed to postnatal hypoxia after ligation of the right carotid artery, and (4) H/I plus LPS group: in utero exposure to LPS followed by postnatal hypoxia after ligation of the right carotid artery. Neuropathological findings in pups examined at P3 and P8 showed that groups 2, 3, and 4 presented a pattern of neuronal injury similar to those characterized as 'selective neuronal necrosis' within the context of human perinatal encephalopathy. Neuronal cellular injuries were particularly seen in the neocortex, mainly in parasagittal areas. The extent of neuronal cell injury in the brain of rats exposed to postnatal H/I was significantly increased by antenatal exposure to LPS. This animal model provides an experimental means to explore the respective roles of anoxic and infectious components in the pathogenesis of perinatal brain lesions and consequent cerebral palsy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".