Plasma amino acid profiles of dogs with the hepatocutaneous syndrome and dogs with other chronic liver diseases
Bibliographic record
Abstract
BACKGROUND: Dogs with hepatocutaneous syndrome (HCS) have marked plasma hypoaminoacidemia, but its occurrence in dogs with chronic liver diseases not associated with HCS (non-HCS CLD) is unknown. OBJECTIVES: To determine if plasma hypoaminoacidemia occurs in dogs with non-HCS CLD, compare plasma amino acid (PAA) profiles between dogs with non-HCS CLD and HCS, and define a sensitive and specific PAA pattern for diagnosing HCS. ANIMALS: Data were collected from client-owned dogs, a prospective cohort of 32 with CLD and 1 with HCS, and a retrospective cohort of 7 with HCS. METHODS: Prospective study. Dogs with chronic serum liver enzyme increases were recruited after hepatic biopsy. Plasma amino acid profiles were measured using high-performance liquid chromatography. Plasma amino acid concentrations were compared between dogs with non-HCS CLD and HCS. Regression analysis was performed to identify a unique PAA pattern for HCS diagnosis. RESULTS: Twelve dogs each with vacuolar hepatopathy or chronic hepatitis and 8 dogs with congenital disorders (primary hypoplasia of the portal vein or ductal plate malformations) were enrolled. Compared to non-HCS CLD dogs, HCS dogs had significantly lower plasma concentrations of several amino acids. Regression analysis revealed that glutamine, glycine, citrulline, arginine, and proline concentrations less than 30% of the mean reference value had 100% sensitivity, specificity for diagnosing HCS. CONCLUSIONS AND CLINICAL IMPORTANCE: Generalized plasma hypoaminoacidemia does not accompany non-HCS CLD. Concentrations of 5 specific amino acids less than 30% of the mean reference value can serve as a noninvasive biomarker for diagnosing HCS.
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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 | 0.001 | 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".