Innate Immunity Provides Biomarkers of Health for Teleosts Exposed to Nanoparticles
Bibliographic record
Abstract
In recent years, the unique properties of nanoparticles have fostered novel applications in various fields such as biology, pharmaceuticals, agriculture, and others. Unfortunately, their rapid integration into daily life has also led to environmental concerns due to uncontrolled release of nanoparticles into the aquatic environment. Despite increasing awareness of nanoparticle bioaccumulation in the aquatic environment, much remains to be learned about their impact on aquatic organisms and how to best monitor these effects. Herein, we provide the first review of innate immunity as an emerging tool to assess the health of fish following nanoparticle exposure. Fish are widely used as sentinels for aquatic ecosystem pollution and innate immune parameters offer sensitive and reliable tools that can be harnessed for evaluation of contamination events. The most frequent biomarkers highlighted in literature to date include, but are not limited to, parameters associated with leukocyte dynamics, oxidative stress, and cytokine production. Taken together, innate immunity offers finite and sensitive biomarkers for assessment of the impact of nanoparticles on fish health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".