Cutting-edge technologies for HLA typing in Côte d’Ivoire: the role of next-generation sequencing 3970
Bibliographic record
Abstract
Abstract Description Background In recent years, there has been a significant increase in the frequency of chronic diseases leading to organ failure and requiring organ transplants for patient survival. In Côte d’Ivoire, although transplants have been performed since 2012, there is no Histocompatibility platform available for testing. Objectives: Our project aims to develop a molecular HLA platform for patients in need of a transplant and to describe HLA polymorphism in Côte d’Ivoire. Methods Five ml of venous blood (EDTA) were collected. Genomic DNA was extracted and then frozen at -20 °C until typing. We performed a unique multiplex PCR amplification of 11 HLA loci. The PCR amplicons were sequenced using high-throughput sequencing with MinIon and analyzed with Nanotyper software. The immuno-polymorphism database was used for HLA analyses. Results A functional histocompatibility platform was established with the support of the French Blood Center. Approximately 400 donors were tested using next-generation sequencing (NGS) techniques and Nanotype kits from Omixon laboratories. The results are currently being interpreted, and the analysis involves biostatistical tools and the population databases of the HLA-net.eu platform, developed and maintained at the University of Geneva. Conclusion The establishment of a local Histocompatibility platform and the expertise in HLA typing techniques in Côte d’Ivoire will contribute to better management of transplants. Funding Sources Supported by FONSTI Topic Categories Transplantation Immunology (TRAN)
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".