Rat models of cardiometabolic diseases: baseline clinical chemistries, and rationale for their use in examining air pollution health effects
Bibliographic record
Abstract
Individuals with cardiovascular and metabolic diseases (CVD) are shown to be more susceptible to adverse health effects of pollutants. Rodent models of CVD are used for examining susceptibility variations. CVD models developed by selective inbreeding are shown to represent the etiology of human disease and metabolic dysfunction. The goal of this article was to review the origin and the pathobiological features of rat models of varying CVD with or without metabolic syndrome and healthy laboratory rat strains to allow better interpretation of the data regarding their susceptibility to air pollutant exposures. Age-matched healthy Sprague-Dawley (SD), Wistar (WIS) and Wistar Kyoto (WKY), and CVD-prone spontaneously hypertensive (SH), Fawn-Hooded hypertensive (FHH), SH stroke-prone (SHSP), SHHF/Mcc heart failure obese (SHHF) and insulin-resistant JCR:LA-cp obese (JCR) rat models were considered for this study. The genetics and the underlying pathologies differ between these models. Normalized heart weights correlated with underlying cardiac disease while wide differences exist in the number of white blood cells and platelets within healthy strains and those with CVD. High plasma fibrinogen and low angiotensin converting enzyme activity in FHH might relate to kidney disease and associated hypertension. However, other obese strains with known kidney lesions do not exhibit decreases in ACE activity. The increased activated partial thromboplastin time only in SHSP correlates with their hemorrhagic stroke susceptibility. Increases plasma lipid peroxidation in JCR might reflect their susceptibility to acquire atherosclerosis. These underlying pathologies involving CVD and metabolic dysfunction are critical in interpretation of findings related to susceptibility variations of air pollution health effects.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".