Unlocking the potential of lipidomic analysis in canine atopic dermatitis research: Insights from a pilot study
Bibliographic record
Abstract
BACKGROUND: Canine atopic dermatitis (cAD) is a complex skin disease characterised by barrier dysfunction. Studies regarding the role of skin surface lipids (SSL) in cAD are needed. OBJECTIVES/HYPOTHESIS: Evaluate the feasibility of using D-squame tape-stripping for SSL collection and ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) for untargeted lipidomic analysis. A secondary objective was to identify significant differences in SSL between atopic and healthy dogs, and between different body sites. ANIMALS: Sixteen client-owned Labrador retrievers, eight atopic and eight healthy were recruited through vaccination or dermatology appointments. MATERIALS AND METHODS: Skin samples were collected from three body sites (thigh, interdigital and inguinal) using D-Squame tapes. Untargeted lipidomic analysis was conducted using UHPLC-HRMS, and data were processed with MS-DIAL and LipidSearch software. RESULTS: This study identified 114 SSLs, predominantly ceramides (66.2%) and diacylglycerols (30.5%). The percentage of lipid classes and ceramide subclasses did not significantly differ between healthy and atopic dogs. Two ceramide and two triacylglycerol species were significantly higher in atopic dogs, while another two ceramide species were significantly lower. Additionally, notable regional differences in lipid profiles were observed. CONCLUSIONS AND CLINICAL RELEVANCE: Our findings suggest that D-squame tape-stripping combined with UHPLC-HRMS is a feasible method for SSL research in cAD. Lipid species-specific differences and significant regional variations were found, emphasising the importance of considering body sites in future studies. This study underscores the need for further research to understand the role of SSL in cAD and the insights that untargeted lipidomic analysis can provide.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".