The Potential of Omics Technologies in Lyme Disease Biomarker Discovery and Early Detection
Bibliographic record
Abstract
Lyme borreliosis (LB) is the most prevalent arthropod-borne infectious disease in North America and many countries of the temperate Northern Hemisphere. It is associated with local and systemic manifestations and has persistent post-treatment health complications in some individuals. Innate and acquired immunity-related inflammation is likely to play a critical role in both host defense against Borrelia burgdorferi and disease severity. Large-scale analytical approaches to quantify gene expression (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in LB have recently emerged with a potential to advance the development of disease biomarkers in early, disseminated and posttreatment disease stages. These technologies may permit defining the disease stage and facilitate its early detection to improve diagnosis. They will also likely allow elucidating the underlying molecular pathways to aid in identifying molecular targets for therapy. This article reviews the findings within the field of omics relevant to LB and its prospective utility in developing an array of biomarkers that can be employed in LB diagnosis and detection particularly at the early disease stages.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".