114 Effects of Dietary Cameline oil Supplementation on Inflammatory and Oxidative Markers, Trans-Epidermal Water Loss, and Skin and Coat Health Parameters in Healthy Adult Dogs
Bibliographic record
Abstract
Abstract Camelina oil has a desirable ratio of the essential fatty acids (EFAs) omega-3 alpha-linolenic acid (C18:3n-3; ALA), and omega-6 linoleic acid (C18:2n-6; LA). Alpha-linolenic acid supports canine skin and coat health, and inflammation. Therefore, an investigation of the effects of camelina oil on skin and coat health, and inflammation in comparison with other plant-derived EFA oil sources is needed. The objective of this study was to compare the effects of camelina oil to those of flaxseed and canola oil on skin and coat health, skin barrier function, and pro- and anti-inflammatory biomarkers. Thirty privately-owned, adult dogs of various breeds (17 females; 13 males), with an average age of 7.2±3.1 years and body weight (BW) of 27.4±14.0 were used. After a 4-week wash-in period using sunflower oil and a commercial kibble, dogs were blocked by age, breed, and size, and randomly allocated to one of three treatment oils: camelina, canola, or flaxseed. Trans-epidermal water loss (TEWL) was measured using a VapoMeter on the pinna, paw pad, and inner leg. Fasted blood samples were collected to determine serum pro- and anti-inflammatory biomarker concentrations. Prostaglandin E2 (PGE2) and Plakoglobin (JUP) concentrations were measured via enzyme-linked immunosorbent assay (ELISA) kits, while nitric oxide (NO) and glycosaminoglycan (GAG) concentrations were determined using spectrophotometric assays. A 5-point-Likert scale was used to assess skin and coat characteristics. All data were collected on weeks 0, 2, 4, 10, and 16 and assessed using PROC GLIMMIX in SAS. Follicle density, fur color, shine, and softness increased and skin moisture and dander decreased from baseline in all treatment groups (P >0.05). Outcomes did not differ (P >0.05) among treatment groups over 16 weeks, indicating that camelina oil can be considered comparable with existing plant-based canine oil supplements, flaxseed and canola, to support skin and coat health, and inflammation in dogs.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".