Elucidating Hepatic Lipidosis in Stray Cats Through Serum Biochemistry, Liver Histopathology and Liver RNA Expression of PPAR-δ and PPAR-γ
Bibliographic record
Abstract
Early detection of feline hepatic lipidosis (FHL) with appropriate treatment can increase prognosis significantly. This study looks into the serum biochemistry and lipid composition of serum and liver samples in a group of stray cats (N=18) collected from pounds in Klang Valley, Malaysia. Alanine aminotransferase (ALT) in blood serum was used to detect for liver damage possibly due to FHL, confirmed through light microscopy, serum biochemistry (triglyceride, cholesterol, creatinine, and urea), liver triglyceride and cholesterol concentrations, and liver RNA expression of lipid droplet regulators peroxisome proliferator-activated receptors (PPARs). Differing severity of FHL in samples were divided and grouped using an adapted scoring method observing fatty change of liver (FCL) with trends between FCL groups investigated. Elevated serum ALT reflective of increasing FCL severity was observed with elevated concentrations of liver TAG and cholesterol levels. Serum TAG and cholesterol decreased with heightened FCL pointing to fatty acid oxidation and lipid restoration in the liver, supported by PPAR-γ expression which also propose macrophage activation for liver recovery alongside PPAR-δ for lipogenesis and inflammatory reactions. Elevated serum creatinine and urea levels with increasing FCL severity propose overall intact hepatic function in the stray cat samples.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".