Guinea pig models for translation of the developmental origins of health and disease hypothesis into the clinic
Bibliographic record
Abstract
Over 30 years ago Professor David Barker first proposed the theory that events in early life could explain an individual's risk of non-communicable disease in later life: the developmental origins of health and disease (DOHaD) hypothesis. During the 1990s the validity of the DOHaD hypothesis was extensively tested in a number of human populations and the mechanisms underpinning it characterised in a range of experimental animal models. Over the past decade, researchers have sought to use this mechanistic understanding of DOHaD to develop therapeutic interventions during pregnancy and early life to improve adult health. A variety of animal models have been used to develop and evaluate interventions, each with strengths and limitations. It is becoming apparent that effective translational research requires that the animal paradigm selected mirrors the tempo of human fetal growth and development as closely as possible so that the effect of a perinatal insult and/or therapeutic intervention can be fully assessed. The guinea pig is one such animal model that over the past two decades has demonstrated itself to be a very useful platform for these important reproductive studies. This review highlights similarities in the in utero development between humans and guinea pigs, the strengths and limitations of the guinea pig as an experimental model of DOHaD and the guinea pig's potential to enhance clinical therapeutic innovation to improve human 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".