Managing the bacterial contamination risk in an axenic mice animal facility
Bibliographic record
Abstract
A gap exists between good laboratory practices with axenic animals and the procedures applied. This work examined the efficacy of sodium dichloroisocyanurate (MB-10) and potassium peroxymonosulfate (Virkon™) disinfectants, as well as the appropriate soaking time for materials used with the ISOcage Biosafety Station™. We also compared the microbial load in cage systems hosting mice over 2 weeks in axenic rooms (ARs) and in typical specific-pathogen-free (SPF) non-axenic rooms (NARs) to identify resistant microorganisms, targeted for longer soaking disinfection, and evaluated the necessary procedures for reducing the microbial load in AR. Staphylococcus was the most frequently isolated genus (in both ARs and NARs). An average of three spore-forming microorganisms per cage were counted from AR. The disinfection time to reach 1 log reduction for Bacillus atrophaeus spores varied from 138 s (100 ppm MB-10) to 290 (Virkon™) to <20 s for S. epidermidis (100 ppm MB-10). AR management protocols lead to a microbial load that is 1000 times lower than that found in NARs. Data comparing the microbial load in SPF and axenic facilities can be used to improve the effectiveness of their microbial control procedures.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".