Maternal expression of functional lipoprotein lipase and effects on body fat mass and body condition scores of mature cats with lipoprotein lipase deficiency
Bibliographic record
Abstract
OBJECTIVE: To assess effects of deficiency of lipoprotein lipase (LPL) on body condition scores and lean and fat body masses of adult cats. ANIMALS: 12 cats without LPL mutations and 23 cats that were heterozygous or homozygous carriers of the Gly412Arg LPL mutation. PROCEDURE: Lean and fat body masses were estimated by use of body condition scores and change in enrichment of serum after IV administration of deuterium oxide. Mass spectroscopy and infrared absorbance methods were used to determine deuterium enrichment. RESULTS: Fat body mass (mean +/- SD; 0.2 +/- 0.1 kg) and percentage body fat (6.2 +/- 1.4%) of homozygotes were significantly less than those of clinically normal cats and heterozygotes (0.7 +/- 0.1 kg, 18.2 +/- 1.6% and 0.5 +/- 0.1 kg, 15.6 +/- 1.7%, respectively). Homozygous offspring of homozygous dams had significantly less fat body mass (0.1 +/- 0.1 kg) and percentage body fat (2.1 +/- 1.0%) than homozygous offspring of heterozygous dams (0.3 +/- 0.1 kg and 9.2 +/- 1.7%, respectively). Lean body mass did not differ significantly among groups. For all groups, percentage body fat was significantly correlated with body condition score (r= 0.65), and body condition scores supported findings for fat body mass. CONCLUSIONS AND CLINICAL RELEVANCE: Deficiency of LPL activity in cats diminishes stores of body fat. This is consistent with a low rate of de novo synthesis of fat. The effect of dam on body masses in mature LPL-deficient cats indicates nutrient programming of adipose formation during gestation or lactation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".