HLA-Based Banking of Human Induced Pluripotent Stem Cells in Saudi Arabia
Bibliographic record
Abstract
Abstract Human iPSCs’ derivation and use in clinical studies are transforming medicine. Yet, there is a high cost and long waiting time for autologous iPS-based cellular therapy, and the genetic engineering of hypo-immunogenic iPS cell lines is hampered with numerous hurdles. Therefore, it is increasingly interesting to create cell stocks based on HLA haplotype distribution in a given population. In this study, we assessed the potential of HLA-based iPS banking for the Saudi population. First, we analyzed the HLA database of the Saudi Stem Cell Donor Registry (SSCDR), which contains high-resolution HLA genotype data of 64,315 registered Saudi donors at the time of analysis. We found that only 13 iPS lines would be required to cover 30% of the Saudi population, 39 iPS lines would offer 50% coverage and 596 for more than 90% coverage. Next, As a proof-of-concept, we launched the first HLA-based banking of iPSCs in Saudi Arabia. Using clinically relevant methods, we generated the first iPSC line from a homozygous donor for the most common HLA haplotype in Saudi. The two generated clones expressed pluripotency markers, could be differentiated into all three germ layers, beating cardiomyocytes and neuronal progenitors. To ensure that our reprogramming method generates genetically stable iPSCs, we assessed the mutational burden in the generated clones and the original blood sample from which the iPSCs were derived using whole-genome sequencing. All detected variants were found in the original donor sample and were classified as benign according to current guidelines of the American College of Medical Genetics and Genomics (ACMG). This study sets a road map for introducing iPS-based cell therapy in the Kingdom of Saudi Arabia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".