Cell Death in Crustacean Immune Defense
Bibliographic record
Abstract
ABSTRACT Cell death mechanisms in crustaceans are a complex interplay of processes essential for maintaining cellular homeostasis and immune defense. Modes of cell death like apoptosis, necroptosis, and necrosis are well‐documented in crustaceans, serving crucial roles in removing damaged or infected cells. Unlike in other organisms, crustaceans likely lack pyroptosis, a type of programmed cell death associated with innate immunity and inflammation, because they do not possess the gasdermin genes essential for this process. Recently, NETosis and ferroptosis have emerged as significant mechanisms in pathogen defense. NETosis, involving the release of DNA fibers and antimicrobial proteins, helps trap and neutralize pathogens, while ferroptosis, an iron‐dependent form of cell death, contributes to lipid peroxidation and immune responses. Cuproptosis, although not yet studied in the context of crustacean immunity, shows potential crosstalk with ferroptosis, particularly in the regulation of metal ion homeostasis, oxidative stress, and cellular metabolism. Understanding these mechanisms offers promising applications in aquaculture, such as developing targeted immune modulators and enhancing disease resistance in economically important crustacean species.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".