Doxycycline Reduces Cleaved Caspase-3 and Microglial Activation in An Animal Model of Neonatal Hypoxia-Ischemia
Bibliographic record
Abstract
Neonatal hypoxia-ischemia (HI) is a major contributor to many perinatal neurologic disorders and, thus, the search for therapies and effective treatments for the associated brain damage has become increasingly important. The tetracycline derivative, doxycycline (DOXY), has been reported to be neuroprotective in adult animal models of cerebral ischemia. To investigate the putative neuroprotective effects of DOXY in an animal model of neonatal HI, a time-course study was run such that pups received either DOXY (10 mg/kg) or VEH immediately before hypoxia, 1, 2, or 3 hours after HI (n=6). At 7 days after injury, the pups were euthanized, and the brains were removed and processed for immunohistochemical and Western blot analyses using antibodies against specific markers for neurons, apoptotic markers, microglia, oligodendrocytes, and astrocytes. Results showed that in vulnerable brain regions including the hippocampal formation, thalamus, striatum, cerebral cortex and white matter tracts, DOXY significantly decreased caspase-3 immunoreactivity (a marker of apoptosis), promoted neuronal survival, inhibited microglial activation and reduced reactive astrocytosis compared with VEH-treated HI pups. These effects were found to occur in a time-dependent manner. Taken together, these results strongly suggest that doxycycline has potential as a pharmacological treatment for mild HI in neonates.
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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 | 0.001 |
| 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".