Contaminants and Where to Find Them: Microbiological Quality Control in Axenic Animal Facilities
Bibliographic record
Abstract
The use of axenic animal models in experimental research has exponentially grown in the past few years and the most reliable way for confirming their axenic status remains unclear. It is especially the case when using individual ventilated positive-pressure cages such as the Isocage. This type of cage are at a greater risk of contamination and expose animals to a longer handling process leading to more potential stress when opened compared to isolators. The aim of this study was to propose simple ways to detect microbial contaminants with Isocages type isolator resulting by developing, validating and optimizing three different methods (culture, microscopy, and molecular). These three approaches were also testedin situby spiking 21 axenic mice with different microorganisms. Our results suggest that the culture method can be used for feces and surface station (IBS) swabs exclusively (in Brain Heart Infusion for 7 days at 25°C and 37°C in aerobic conditions, and at 30°C in anaerobic conditions), while microscopy (wet mounts) and molecular method (quantitative PCR) were only suitable for fecal matter analyses.In situresults suggests that the culture and molecular methods can detect up to 100% of bacterial contamination events while the microscopy approach generates many erroneous results when not performed by a skilled microscopist.In situresults also suggest that when an axenic mouse is contaminated by a microbial agent, the microorganism will colonize the mouse to such an extent that detection is obvious in 4 days, in average. This report validates simple but complimentary tests that can be used for optimal detection of contaminants in axenic animal facilities using Isocage type isolators.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".