Clinical applications of next-generation sequencing in histocompatibility and transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Next-generation sequencing (NGS) can overcome traditional methodological barriers to facilitate detailed studies of large genomes. Here, we summarize recent NGS-based developments in histocompatibility and transplantation, and highlight the dynamic range of clinical applications achievable on this platform. RECENT FINDINGS: Multiple NGS-based protocols have been established to achieve unambiguous human leukocyte antigen genotyping. These methods are presently engaged to serve the high-throughput demand of large bone marrow registries; however, the scalable nature of NGS makes it an equally attractive technology for select applications within solid organ transplantation. Recently, the exquisite sensitivity of NGS has been leveraged to perform noninvasive allograft monitoring by tracking the dynamics of donor-derived cell-free DNA. Further, NGS-based T-cell receptor and immunoglobulin heavy chain repertoire profiling appear to be useful in clarifying disease-specific diagnoses in certain complex allograft pathology; detecting/quantifying minimal residual disease following allogeneic stem cell transplantation; and tracking donor-reactive T cells to understand the mechanism of tolerance in kidney transplant recipients. SUMMARY: NGS is superior to classical Sanger sequencing in its throughput, sensitivity, and the ability to provide phase-defined sequence data. These unique properties allow its broad application to diverse areas in clinical transplantation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".