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Record W2903922090 · doi:10.3390/ani8120237

Influence of Liver Condition and Copper on Selective Parameters of Post-Mortem Dog Tissue Samples

2018· article· en· W2903922090 on OpenAlexaboutno aff
Isabella Corsato Alvarenga, Charles G. Aldrich, Dennis E. Jewell

Bibliographic record

VenueAnimals · 2018
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
FundersColgate-Palmolive Company
KeywordsHistopathologyMetabolismCopper metabolismCopperCholestasisChemistryLipid metabolismInternal medicineLiver cellPathologyBiochemistryEndocrinologyMedicine

Abstract

fetched live from OpenAlex

One of the liver functions is copper storage, which can be toxic when in excess. The objective of this retrospective study was to determine the relationship between hepatic copper and pathology conditions in stored samples from 55 post-mortem dogs (37 Beagles, 12 Labrador Retrievers, and 6 Labrador Mixes). The analyses evaluated data from blood chemistry and complete blood count (CBC) that were measured immediately before euthanasia, and liver biopsies which were harvested at necropsy and frozen at −80 °C. Slides for microscopic evaluation were prepared, and liver copper and plasma metabolites were measured. Hepatic copper was correlated (p ≤ 0.001) with monoacylglycerols, 13-HODE + 9-HODE (13-hydroxy-9,11-octadecadienoic acid + 9-hydroxy-10,12-octadecadienoic acid), and stearoyl-arachidonoyl-glycerophosphocholine. This indicates lipid metabolism modification and cell membrane oxidation. However, hepatic copper was not related to liver histopathology severity or altered liver biomarkers. The severity of liver pathology was positively correlated (p ≤ 0.05) with liver enzymes, bile salts, and glycerophosphocholines, suggesting cholestasis and altered lipid and amino acid metabolism. Liver neoplasia had increased (p ≤ 0.05) metabolites derived from nucleotides, along with an increase (p ≤ 0.05) in α-ketoglutarate from the energy and amino acid metabolism (p ≤ 0.05), suggesting rapid cell division. This study offers further insight regarding changes in metabolism due to hepatic tissue damage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.339
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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