WORKING CANINE DECONTAMINATION: EFFECT OF CLEANSER & SERIAL DECONTAMINATION ON DERMAL BARRIER FUNCTION
Bibliographic record
Abstract
Working canines provide a key service to society. During their work, they may be exposed to various environmental contaminants. Unfortunately, previous work has shown that current working canine decontamination protocols are lacking in efficacy (Venable et al., 2017). Furthermore, little work has been done to explore cleanser options and serial decontamination. Therefore, we designed two studies to investigate the impact of cleanser on contaminant removal, and the impact of serial cleanser application on canine dermal health and skin barrier function when applied daily for 14 days. Both studies were approved by the Southern Illinois University Institutional Animal Use and Care Committee (#15-032, #19-031) prior to initiation of the work. Treatments for analysis of cleansers for canine decontamination included Dawn, povidone surgical scrub, chlorohexidine surgical Scrub, or water. Each treatment was balanced by coat type (long; short). A fluorescent marker was used to determine efficacy in contaminant reduction. Significant decreases associated with coat type (P < 0.0082) and cleanser (P < 0.0001) were measured for dermal pH. Additionally, treatment impacted TEWL (P = 0.0049). Yet, TEWL was unaffected by coat type (P = 0.4881). Visual scoring of images revealed that all cleansers were more successful at reduction when compared to control (P < 0.0001). However, software analysis software, yielded no differences in treatment (P = 0.5567). Interestingly, we found a significant correlation between the visual scores and the Image J (P = 0.0006). Repeated decontamination on 8 (n= 8) Labrador retrievers significantly increased TEWL (P< 0.0001) through day 21 and then decreasing the remainder of the study. Similarly, sebum data was also impacted by repeated daily decontamination efforts, decreasing during bathing and then increasing (P =0.0387). Higher sebum values are seen later in the study and may be associated with recovery. 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 increasing the animal’s health risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".