From Local to Systemic: The Journey of Tick Bite Biomarkers in Australian Patients
Bibliographic record
Abstract
Tick bites and tick-related diseases are on the rise. Diagnostic tests that identify well-characterised tick-borne pathogens (TBPs) possess limited capacity to address the causation of symptoms associated with poorly characterised tick-related illnesses, such as debilitating symptom complexes attributed to ticks (DSCATT) in Australia. Identification of local signals in tick-bitten skin that can be detected systemically in blood would have both clinical (diagnostic or prognostic) and research (mechanistic insight) utility, as a blood sample is more readily obtainable than tissue biopsies. We hypothesised that blood samples may reveal signals which reflect relevant local (tissue) events and that the time course of these signals may align with local pathophysiology. As a first step towards testing this hypothesis, we compared molecular signatures in skin biopsies taken from the tick-bite location of human participants, as published in our previous study, together with peripheral blood signatures obtained concurrently. This approach captures differentially expressed molecules across multiple omics datasets derived from peripheral blood (including cellular and cell-free transcriptomics, proteomics, metabolomics, and DNA methylation), and skin biopsies (spatial transcriptomics). Our original data revealed that extracellular matrix organisation and platelet degranulation pathways were upregulated in the skin within 72 h of a tick bite. The same signals appeared in blood, where they then remained elevated for three months, displaying longitudinally consistent alterations of biological functions. Despite the limited sample size, these data represent proof-of-concept that molecular events in the skin following a tick bite can be detectable systemically. This underscores the potential value of blood samples, akin to a liquid biopsy, to capture biomarkers reflecting local tissue processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".