Disease etiology and diagnosis by TCR repertoire analysis goes viral
Bibliographic record
Abstract
The importance of T‐cell receptor (TCR) repertoire diversity is highlighted in murine models of immunodeficiency and in many human pathologies. However, the true extent of TCR diversity and how this diversity varies in health and disease is poorly understood. In a previous issue of the European Journal of Immunology, Lossius et al. [Eur. J. Immunol. 2014. 44: 3439–3452] dissected the composition of the TCR repertoire in the context of multiple sclerosis (MS) using high‐throughput sequencing of TCR‐β chains in cerebrospinal fluid samples and blood. The authors demonstrated that the TCR repertoire of the CSF was largely distinct from the blood and enriched in EBV‐reactive CD8+ T cells in MS patients. Studies of this kind have long been hindered by technical limitations and remain scarce in the literature. However, TCR sequencing methodologies are progressing apace and will undoubtedly shed light on the genetic basis of T‐cell responses and the ontogeny of T‐cell‐mediated diseases, such as MS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".