Hyperkalaemia, not apoptosis, accurately predicts insect chilling injury
Bibliographic record
Abstract
There is a growing appreciation that insect distribution and abundance are associated with the limits of thermal tolerance, but the physiology underlying thermal tolerance remains poorly understood. Many insects, like the migratory locust ( Locusta migratoria ), suffer a loss of ion and water balance leading to hyperkalaemia (high extracellular [K + ]) in the cold that indirectly causes cell death. Cells can die in several ways under stress, and how they die is of critical importance to identifying and understanding the nature of thermal adaptation. Whether apoptotic or necrotic cell death pathways are responsible for low-temperature injury is unclear. Here, we use a caspase-3 specific assay to indirectly quantify apoptotic cell death in three locust tissues (muscle, nerves and midgut) following prolonged chilling and recovery from an injury-inducing cold exposure. Furthermore, we obtain matching measurements of injury, extracellular [K + ] and muscle caspase-3 activity in individual locusts to gain further insight into the mechanistic nature of chilling injury. We found a significant increase in muscle caspase-3 activity, but no such increase was observed in either nervous or gut tissue from the same animals, suggesting that chill injury primarily relates to muscle cell death. Levels of chilling injury measured at the whole animal level, however, were strongly correlated with the degree of haemolymph hyperkalaemia, and not apoptosis. These results support the notion that cold-induced ion balance disruption triggers cell death but also that apoptosis is not the main form of cell damage driving low-temperature injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".