Contaminant biomonitoring augmented with a qPCR array indicates hepatic mRNA gene expression effects in wild-collected seabird embryos
Bibliographic record
Abstract
Birds can bioaccumulate persistent contaminants, and maternal transfer to eggs may expose embryos to concentrations sufficient to cause adverse effects during sensitive early-life stages. However, using tissue residue concentrations alone to infer whether contaminant effects are occurring suffers from uncertainty, and efficient, sensitive biomarkers remain limited in wildlife. We studied relationships between whole embryo contaminant concentrations (total mercury, organochlorine pesticides, perfluoroalkyl substances, polychlorinated biphenyls, and halogenated flame retardants) together with mRNA expression in embryonic liver tissue from a Pacific Ocean seabird, the rhinoceros auklet (Cerorhinca monocerata). Fresh eggs were collected, incubated under controlled conditions, and from the pre-hatch embryo, hepatic RNA was extracted for qPCR array analysis to measure gene expression (2−∆Cq), while the remaining embryo was analyzed for contaminant residues. Contaminant and gene expression data were assessed with a combination of multivariate approaches and linear models. Results indicated correlations between embryonic total mercury and several genes such as sepp1, which encodes selenoprotein P. Correlation between the biotransformation gene cyp1a4 and the C7 perfluoroalkyl carboxylic acid PFHpA was also evident. This study demonstrates that egg collection from free-living populations for contaminant biomonitoring programs can relate chemical residues to in ovo mRNA gene expression effects in embryo hepatic tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".