Efficacy of a probiotic bacterium to treat bats affected by the disease white‐nose syndrome
Bibliographic record
Abstract
Summary The management of infectious diseases is an important conservation concern for a growing number of wildlife species. However, effective disease control in wildlife is challenging because feasible management options are often lacking. White‐nose syndrome (WNS) is an infectious disease of hibernating bats that currently threatens several North American species with extinction. Currently, no effective treatments exist for WNS. We conducted a laboratory experiment to test the efficacy of probiotic treatment with Pseudomonas fluorescens, a bacterium that naturally occurs on bats, to reduce disease severity and improve survival of little brown bats Myotis lucifugus exposed to Pseudogymnoascus destructans, the fungal pathogen that causes WNS. We found that application of the probiotic bacteria at the time of fungal infection reduced several measures of disease severity and increased survival, whereas bacterial treatment prior to pathogen exposure had no effect on survival and worsened disease severity. Synthesis and applications. Our results suggest that probiotic treatment with Ps. fluorescens has potential for white‐nose syndrome disease management, but the timing of application is critical and should coincide with natural exposure of bats to P. destructans. These results add to the growing knowledge of how natural host microbiota can be implemented as a biocontrol treatment to influence disease outcomes.
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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 | 0.000 | 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) | 0.001 | 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".