Seroprevalence in Bats and Detection of Borrelia burgdorferi in Bat Ectoparasites
Bibliographic record
Abstract
The role of bats in the enzootic cycle of Lyme disease and relapsing fever-causing bacteria is a matter of speculation. In Canada, Borrelia burgdorferi sensu stricto (ss) is the genospecies that is responsible for most cases of Lyme disease in humans. In this study, we determined if big brown bats, Eptesicus fuscus, have been exposed to spirochetes from the genus Borrelia. We collected serum from 31 bats and tested them for the presence of anti-Borrelia burgdorferi antibodies using a commercial enzyme-linked immunosorbent assay (ELISA). We detected cross-reactive antibodies to Borrelia spp. in 14 of 31 bats. We confirmed the ELISA data using a commercial immunoblot assay. Pooled sera from ELISA-positive bats also cross-reacted with Borrelia antigens coated on the immunoblot strips, whereas pooled sera from ELISA-negative bats did not bind to Borrelia spp. antigens. Furthermore, to identify if bat ectoparasites, such as mites, can carry Borrelia spp., we analyzed DNA from 142 bat ectoparasites that were collected between 2003 and 2019. We detected DNA for the Borrelia burgdorferi flaB gene in one bat mite, Spinturnix americanus. The low detection rate of Borrelia burgdorferi DNA in bat ectoparasites suggests that bats are not reservoirs of this bacterium. Data from this study also raises intriguing questions about Borrelia infections in bats, including the role of humoral immunity and the ability of bats to be infected with Borrelia burgdorferi. This study can lead to more sampling efforts and controlled laboratory studies to identify if bats can be infected with Borrelia burgdorferi and the role of bat ectoparasites, such as S. americanus, in the transmission of this spirochete. Furthermore, we outlined reagents that can be used to adapt ELISA kits and immunoblot strips for use with bat sera.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".