Effects of an Environmentally Relevant Organochlorine Mixture and a Metabolized Extract of This Mixture on Porcine Sperm Parameters In Vitro
Bibliographic record
Abstract
Organochlorine chemicals are present in the environment worldwide; however, populations living in the Far North are particularly at risk because their traditional diets are mainly composed of contaminated animals (fish, seals, whales, and polar bears). It has been suggested that male fertility is globally declining, possibly because of chronic, low-level exposure to environmental contaminants. This study was designed to assess the effects on fresh sperm fertility parameters using the porcine model of 1) an environmentally relevant mixture of 15 organochlorines and 2) the metabolized extract of this mixture. In the first experiment, the organochlorine mixture (at relative concentrations of 10.5, 14.7, and 21 microg/mL polychlorinated biphenyls [PCBs]) reduced sperm total motility, progressive motility, and viability, and increased capacitation, spontaneous acrosome reaction rates, and cytosolic calcium levels, suggesting that the mixture alters the sperm membrane and is detrimental to sperm function. In the second experiment, the metabolized extract of this organochlorine mixture (at relative concentrations of 0.9, 1.8, 2.7, 3.6, and 4.5 microg/L OH-PCBs) tended to decrease only sperm total motility. In an in vitro porcine model, the mixture of organochlorines, as found in the Arctic food chain, was rapidly detrimental to sperm function at concentrations above environmental levels. In contrast, short and physiologically relevant exposure to the metabolized extract of this mixture induced only limited adverse effects on sperm motility.
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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) | 0.001 | 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".