Novel probiotic approach to counter <i>Paenibacillus larvae</i> infection in honey bees
Bibliographic record
Abstract
American foulbrood (AFB) is a highly virulent disease afflicting honey bees (Apis mellifera). The causative organism, Paenibacillus larvae, attacks honey bee brood and renders entire hives dysfunctional during active disease states, but more commonly resides in hives asymptomatically as inactive spores that elude even vigilant beekeepers. The mechanism of this pathogenic transition is not fully understood, and no cure exists for AFB. Here, we evaluated how hive supplementation with probiotic lactobacilli (delivered through a nutrient patty; BioPatty) affected colony resistance towards a naturally occurring AFB outbreak. Results demonstrated a significantly lower pathogen load and proteolytic activity of honey bee larvae from BioPatty-treated hives. Interestingly, a distinctive shift in the microbiota composition of adult nurse bees occurred irrespective of treatment group during the monitoring period, but only vehicle-supplemented nurse bees exhibited higher P. larvae loads. In vitro experiments utilizing laboratory-reared honey bee larvae showed Lactobacillus plantarum Lp39, Lactobacillus rhamnosus GR-1, and Lactobacillus kunkeei BR-1 (contained in the BioPatty) could reduce pathogen load, upregulate expression of key immune genes, and improve survival during P. larvae infection. These findings suggest the usage of a lactobacilli-containing hive supplement, which is practical and affordable for beekeepers, may be effective for reducing enzootic pathogen-related hive losses.
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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".