PSI-3 An assessment of the barrier function of canine skin after repeated decontamination
Bibliographic record
Abstract
Abstract Working canines are often deployed to environments with unknown chemical and biological contaminants. Deployed canines may operate in highly contaminated disaster sites for lengthy periods of time requiring daily decontamination efforts. The skin provides a barrier by retaining moisture and preventing entry by contaminants and pathogens. However, few data exist on the impact of repeated decontamination to the canine skin. The objective of this study was to identify changes in dermal health during and after a 14-day serial decontamination program. Labrador retrievers (n = 8) were decontaminated daily using a dish detergent solution (1:8, detergent:water). Skin measurements were collected weekly for pH, trans-epidermal water loss (TEWL), sebum, and moisture. Additionally, visual assessments were recorded for skin health, coat condition, and dander scores (back and body). Statistical tests were conducted with SAS (version 9.4) with measurements analyzed using a PROC GLM Two Way ANOVA and visual assessments analyzed using PROC FREQ Chi Square test. Significance was set at 5% for all tests. Repeated decontamination significantly increased TEWL (P < 0.0001) through day 16. Sebum content was also impacted by repeated daily decontamination efforts (P = 0.0387). Sebum decreased initially before steadily rising. In contrast, moisture content (P = 0.3842) and pH (P = 0.7462), were unaffected by repeated decontamination. Interestingly, dander scores assessed on the back were worsened by repeated decontamination (P = 0.0222) but dander scores assessed across the whole body were unaffected (P = 0.1804). Coat shine was unaffected by decontamination (P = 0.1156) similar to coat softness (P = 0.3418). Overall coat condition remained unchanged as a result of repeated decontamination efforts (P = 0.9466). These data reveal that daily decontamination impacts dermal function, potentially risks for canines working in contaminated areas. Future work should include investigations into methods for decontamination to mitigate these risks.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".