Identification of Bacteria Infecting <i>Ixodes ricinus</i> Ticks by 16S rDNA Amplification and Denaturing Gradient Gel Electrophoresis
Bibliographic record
Abstract
Ticks harbor a complex microbial population, which they acquire while feeding on a variety of mammalians and birds. Zoonotic diseases transferred by ticks are an increasing problem and have become a burden to the community. 16S rDNA amplification and denaturing gradient gel electrophoresis (DGGE) enables detection of the broad spectrum of bacteria that settles in the ticks. Profiling the complete microbial population in ticks may provide a better understanding of the ticks' potential to harbor and disperse pathogens. Separation of pathogenic species by DGGE is based on variation in %GC content within the 16S rDNA genetic region. Sequencing of these fragments allows identification of bacterial species. Present study identified some well-known tick-infecting bacteria, such as members of genus Borrelia, Rickettsiales, and Pseudomonas, but also less described tick-infecting bacteria such as Rhodococcus erythropolis, Spiroplasma spp., and an endosymbiont of the microarthropod Folsomia candida. This is the first report of Segniliparus rugosus-infected Ixodes ricinus ticks. Also, it is the first report of several of these pathogens in the Norwegian tick population.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".