Biologic Markers of Antibiotic-Refractory Lyme Arthritis in Human: A Systematic Review
Bibliographic record
Abstract
Lyme disease—also known as Lyme borreliosis (LB)—is the most common vector-borne disease in North America and Europe. It may result in substantial morbidity, primarily from persistent Lyme arthritis (LA) that—although treatable—can develop into antibiotic-refractory LA (A-RLA). The aim of this study is to systematically review and evaluate a range of biomarkers for their potential predictive value in the development of A-RLA. We conducted a systematic review of studies examining biomarkers among patients with A-RLA from MEDLINE via OVID, EMBASE and Web of Science databases and identified a total of 26 studies for qualitative analysis. All studies were of patient populations from the USA, with the exception of one from Europe. We identified an array of biomarkers that are commonly modulated in the A-RLA compared with subjects with antibiotic-responsive LA. These included a range of inflammatory markers (IL-6, IL-8, IL-10, IL-1β, IL-23, IL-17F, TNFα, IFNγ, CXCL9, CXCL10, CCL2, CCL3 and CCL4, CRP), factors along the innate and adaptive immune response pathways (e.g., CD4 + T cells, GITR receptors, OX40 receptors, IL-4 + CD4 + Th2 cells, IL-17 + CD4 + T cells) and an array of miRNA species (e.g., miR-142, miR-17, miR-20a, let-7c and miR-30fam). The evidence base of biologic markers for A-RLA is limited. However, a range of promising biomarkers have been identified. Cytokines and chemokines related to Th17 pathway together with a number of miRNAs species (miR-146a, miR-155 and let-7a) may be promising candidates in the prediction of A-RLA. A panel of multiple biomarkers may yield clinically relevant prediction of the possible resistance at the time of LA first diagnosis. Public Health Agency of Canada.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".