Antibiotic drug piperacillin induces neuron cell death through mitochondrial dysfunction and oxidative damage
Bibliographic record
Abstract
Although nerve damage/toxicity has been shown to be one of the side effects in patients given prolonged antibiotic treatment, the mechanisms of the action of antibiotics on neuron cells are not clear. In this work, we investigated the toxicity of piperacillin (an antibiotic that can penetrate the blood-brain barrier) on neuron cells and its underlying mechanisms. We show that clinically relevant doses of piperacillin induce apoptosis in SH-SY5Y and human primary neuron cells through activating caspase-3 activity and decreasing Mcl-1 and Bcl-2 levels. In addition, piperacillin causes mitochondrial dysfunction in neuron cells as shown by the reduction of mitochondrial respiration, membrane potential, and ATP production. We further demonstrate that piperacillin increases accumulation of mitochondrial superoxide and reactive oxygen species, suggesting the oxidative stress in neuron cells. Consistently, oxidative damage to DNA, proteins, and membrane lipids are observed in neuron cells exposed to piperacillin. The deleterious effects of piperacillin are abolished in neuron cells by antioxidant N-acetyl-l-cysteine, further confirming that piperacillin causes neuron cell death through inducing mitochondrial dysfunction and oxidative damage. Our work demonstrates the role of piperacillin in inducing oxidative damage in neuron cells and also provides a therapeutic strategy to prevent the side effects of antibiotic treatment.
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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 | 0.000 |
| 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".