Unique spectral signatures of the nucleic acid dye acridine orange can distinguish cell death by apoptosis and necroptosis
Bibliographic record
Abstract
Cellular injury and death are ubiquitous features of disease, yet tools to detect them are limited and insensitive to subtle pathological changes. Acridine orange (AO), a nucleic acid dye with unique spectral properties, enables real-time measurement of RNA and DNA as proxies for cell viability during exposure to various noxious stimuli. This tool illuminates spectral signatures unique to various modes of cell death, such as cells undergoing apoptosis versus necrosis/necroptosis. This new approach also shows that cellular RNA decreases during necrotic, necroptotic, and apoptotic cell death caused by demyelinating, ischemic, and traumatic injuries, implying its involvement in a wide spectrum of tissue pathologies. Furthermore, cells with pathologically low levels of cytoplasmic RNA are detected earlier and in higher numbers than with standard markers including TdT-mediated dUTP biotin nick-end labeling and cleaved caspase 3 immunofluorescence. Our technique highlights AO-labeled cytoplasmic RNA as an important early marker of cellular injury and a sensitive indicator of various modes of cell death in a range of experimental models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".