A new pathogenic paradigm: Netosis, microclots and circulating DNA in inflammatory diseases
Bibliographic record
Abstract
Progress is often limited by fragmented efforts, disease-specific silos, and inconsistent methodologies.To address these challenges, we established the cfDNA Research Network at the Research Institute of the McGill University Health Centre (RI-MUHC), uniting diverse expertise to accelerate biomarker discovery and clinical translation.Methods/Approach: The network integrates cfDNA research across multiple disciplines to comprehensively map the cfDNA secretome in health and disease.Core activities include: (i) development and dissemination of harmonized standard operating procedures for biospecimen collection, processing, and cfDNA analysis; (ii) access to specialized instrumentation and technical expertise in cfDNA extraction, quantification, and digital PCR; and (iii) creation of shared resources for study design, data analysis, and protocol optimization.Results: The network currently includes >50 members (32 clinicians, 17 scientists, 4 patient partners, 8 staff).Our biobank contains >5,000 liquid biopsy samples (blood, urine, saliva, etc.) with full clinical annotation (up to 2,237 data points per patient) linked to the ATiM biobanking system.Samples include patients with cancer (head and neck, cervical, breast, colorectal, thyroid, esophageal, lung, uveal melanoma), benign lesions, tuberculosis, and healthy volunteers.To expand collection across the lifespan and diseases, we implemented a uniform liquid biopsy REB framework covering precision oncology, infectious disease, transplant health, cardiovascular health, prenatal/maternal health, autoimmunity, and lifestyle studies.The network engages scientists, clinicians, and people with lived experience to co-develop research priorities and ensure real-world impact.Embedded within RI-MUHC, the network leverages a rich ecosystem of multidisciplinary investigators and clinical programs to foster translational research and support early-career investigators.Conclusions/Impact: This initiative provides a unique platform to advance cfDNA research beyond disease-specific boundaries.By integrating biospecimen resources, harmonized methodologies, and multidisciplinary expertise, the RI-MUHC network aims to accelerate discovery of cfDNA signatures predictive of disease onset, progression, and treatment response.This collaborative framework is designed to enhance scientific reproducibility, foster innovation, and translate cfDNA-based tools into improved patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".