Rapid cold hardening modifies mechanisms of ion regulation to delay anoxia-induced spreading depolarization in the CNS of <i>Locusta migratoria</i>
Bibliographic record
Abstract
Abstract Insects live in varied habitats and experience different kinds of environmental stresses. These stresses can impair neural performance, leading to spreading depolarization (SD) of nerve cells and neural shutdown underlying coma. The sensitivity of an insect’s nervous system to stress (e.g., anoxia) can be modulated by acute pre-treatment. Rapid cold hardening (RCH) is a form of preconditioning, in which a brief exposure to low temperature can enhance the stress tolerance of insects. SD is associated with a sudden loss of ion, notably K + , homeostasis. We used a pharmacological approach to investigate whether RCH affects anoxia-induced SD in the locust, L. migratoria , via one or more of the following homeostatic mechanisms: (1) Na + /K + -ATPase (NKA), (2) Na + /K + /2Cl - co-transporter (NKCC), and (3) voltage-gated K + (K v ) channels. We also assessed abundance and phosphorylation of NKCC using immunoblotting. We found that inhibition of NKA or K v channels delayed the onset of anoxia-induced SD in both control and RCH preparations. However, NKCC inhibition preferentially abrogated the effect of RCH. Additionally, we observed a higher abundance of NKCC in RCH preps but no statistical difference in its phosphorylation level, indicating the involvement of NKCC expression or degradation as part of the RCH mechanism.
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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".