Toll-like receptor cascade and gene polymorphism in host–pathogen interaction in Lyme disease
Bibliographic record
Abstract
Shusmita Rahman,1 Maria Shering,2 Nicholas H Ogden,3 Robbin Lindsay,4 Alaa Badawi1 1National Microbiology Laboratory, Public Health Agency of Canada, Toronto, 2Faculty of Arts and Science, University of Toronto, Toronto, ON, 3National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, QC, 4National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada Abstract: Lyme disease (LD) risk occurs in North America and Europe where the tick vectors of the causal agent Borrelia burgdorferi sensu lato are found. It is associated with local and systemic manifestations, and has persistent posttreatment health complications in some individuals. The innate immune system likely plays a critical role in both host defense against B. burgdorferi and disease severity. Recognition of B. burgdorferi, activation of the innate immune system, production of proinflammatory cytokines, and modulation of the host adaptive responses are all initiated by Toll-like receptors (TLRs). A number of Borrelia outer-surface proteins (eg, OspA and OspB) are recognized by TLRs. Specifically, TLR1 and TLR2 were identified as the receptors most relevant to LD. Several functional single-nucleotide polymorphisms have been identified in TLR genes, and are associated with varying cytokines types and synthesis levels, altered pathogen recognition, and disruption of the downstream signaling cascade. These single-nucleotide polymorphism-related functional alterations are postulated to be linked to disease development and posttreatment persistent illness. Elucidating the role of TLRs in LD may facilitate a better understanding of disease pathogenesis and can provide an insight into novel therapeutic targets during active disease or postinfection and posttreatment stages. Keywords: Lyme disease, Toll-like receptors, Borrelia lipoproteins, genetic polymorphisms, host–pathogen interaction
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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 | 0.001 |
| 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) | 0.004 | 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".