Pattern recognition receptors in Crustacea: immunological roles under environmental stress
Bibliographic record
Abstract
Innate immunity is the first line of defense against infections and the only known available strategy for invertebrates. Crustaceans, being mostly aquatic invertebrates, are constantly exposed to potential pathogens in the surrounding water. Their immune system abolishes most microbes that enter and are recognized as a threat. However, the stress produced by high population densities and abiotic changes, in aquaculture, disrupts the host-pathogen balance, leading to severe economic losses in this industry. Consequently, crustacean immunology has become a prime area of research where significant progress has been made. This review provides our current understanding of the key pattern recognition receptors in crustaceans, with special focus on Decapoda, and their roles in triggering an immune response. We discuss recent developments in the field of signal transduction pathways such as Toll-like receptors (TLRs) and the immune deficiency (IMD) pathway, and examine the role of antimicrobial peptides (AMPs) in pathogen defense. Additionally, we analyze how environmental stressors-such as temperature fluctuations, ammonia levels, and pollution-impact immune responses and increase susceptibility to diseases. Finally, we highlight future research directions, emphasizing the need to explore the interactions between environmental stressors and immune signaling pathways and to develop strategies to enhance immune responses in crustaceans within aquaculture settings. Altogether, these advancements deepen our understanding of pathogen recognition in invertebrates and the specific defense mechanisms employed by crustaceans, particularly in response to infections triggered by pathogens under abiotic stressors.
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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 | 0.001 | 0.001 |
| 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".