Generation of integration-free iPS cell lines from three sickle cell disease patients from the state of Bahia, Brazil
Bibliographic record
Abstract
Sickle cell disease (SCD) is one of the most prevalent and severe monogenetic disorders, affecting several million people around the world. Clinical manifestations and complications of the disease include sickle cell pain crisis, silent cerebral infarct, stroke, nephropathy and early death. In this study, we generated induced pluripotent stem cell (iPSC) lines from three homozygous SCD patients from the state of Bahia, Brazil, where SCD is highly prevalent. Peripheral blood mononuclear cells were collected and erythroblasts were expanded for cell reprogramming with the use of non-integrative episomal vectors. The generated iPSC lines expressed high levels of pluripotency markers, presented a normal karyotype and were able to differentiate into the three germ layers in embryoid body spontaneous differentiation assays. Moreover, the expression of the episomal vectors was lost in all iPSC lines after 15 passages. These iPSC lines may help increasing the knowledge about SCD pathogenesis and can be a useful tool for drug testing and gene editing studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".