Ischemia-Induced Brain Damage Depends on Specific Gap-Junctional Coupling
Bibliographic record
Abstract
Ischemic brain injury results in neuronal loss and associated neurologic deficits. Although there is some evidence that intercellular communication via gap junctions can spread oxidative cell injury, the possible role of gap-junctional communication in ischemia-induced cell death is the object of debate. Because gap junctions directly connect the cytoplasms of coupled cells, they offer a way to propagate stress signals from cell to cell. The authors investigated the contribution of gap-junctional communication to cell death using an in vitro ischemia model, which was reproduced by submersion of organotypic hippocampal slices into glucose-free deoxygenated medium. The gap-junctional blocker carbenoxolone significantly decreased the spread of cell death, as measured by propidium iodide staining, over a 48-hour period after the ischemic episode. Carbenoxolone ameliorated the hypoxia-induced impairment of the intrinsic neuronal electrophysiologic characteristics, as measured by whole-cell patch clamp recordings. To determine whether specific connexins were involved in the spread of postischemic cell death, the authors partially reduced the synthesis of specific connexins using antisense oligodeoxynucleotides. Simultaneous knockdown of two connexins localized mostly in neurons, connexins 32 and 26, resulted in significant neuroprotection 48 hours after the hypoxic-hypoglycemic episode. Similarly, partial reduction of the predominant glial connexin 43 significantly decreased cell death. These results indicate that gap-junctional communication contributes to the propagation of hypoxic injury and that specific gap junctions could be a novel target to reduce brain damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".