The mitochondrial division inhibitor Mdivi-1 rescues mammalian neurons from anesthetic-induced cytotoxicity
Bibliographic record
Abstract
BACKGROUND: Concerns have risen regarding the potential side effects of clinical exposure of the pediatric population to inhalational anesthetics, and how they might impact cognitive, learning, and memory functions. However, neither the mechanisms of anesthetic cytotoxicity, nor potential protective strategies, have yet been fully explored. In this study, we examined whether two of the most commonly used inhalational anesthetics, sevoflurane and desflurane, affect neuronal viability and synaptic network assembly between cultured rat cortical neurons. RESULTS: Primary rat cortical neuron cultures were exposed to equipotent sevoflurane or desflurane for 1 hour. Neuron viability, synaptic protein expression, mitochondrial morphology, and neurite growth were assayed with immunostaining and confocal microscopy techniques. The effects of anesthetics on the functional development of neural networks were evaluated with whole-cell patch clamp recordings of spontaneous synaptic currents. Our results demonstrate that an acute exposure to sevoflurane and desflurane inhibits the development of neurite processes, impacts the mitochondria, and compromises synaptic proteins - concomitant with a reduction in synaptic function in mature networks. Interestingly, pretreatment of neurons with a mitochondrial division inhibitor (Mdivi-1) not only protected mitochondria integrity but also played a protective role against anesthetic-induced structural and functional neurotoxicity. CONCLUSIONS: We show that Mdivi-1 likely plays a protective role against certain harmful effects of general anesthetics on primary rat neuronal cultures. In addition, Mdivi-1 alone plays a direct role in enhancing growth and modulating synaptic activity. This study highlights the importance of further study into possible protective agents against anesthetic neurotoxicity.
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) | 0.000 | 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".