Concise Review: Human Induced Pluripotent Stem Cell Models of Retinitis Pigmentosa
Bibliographic record
Abstract
Hereditary retinal dystrophies, specifically retinitis pigmentosa (RP) are clinically and genetically heterogeneous diseases affecting primarily retinal cells and retinal pigment epithelial cells with blindness as a final outcome. Understanding the pathogenicity behind these diseases has been largely precluded by the unavailability of affected tissue from patients, large genetic heterogeneity and animal models that do not faithfully represent some human diseases. A landmark discovery of human induced pluripotent stem cells (hiPSCs) permitted the derivation of patient-specific cells. These cells have unlimited self-renewing capacity and the ability to differentiate into RP-affected cell types, allowing the studies of disease mechanism, drug discovery, and cell replacement therapies, both as individual cell types and organoid cultures. Together with precise genome editing, the patient specific hiPSC technology offers novel strategies for targeting the pathogenic mutations and design therapies toward retinal dystrophies. This study summarizes current hiPSC-based RP models and highlights key achievements and challenges of these cellular models, as well as questions that still remain unanswered. Stem Cells 2018;36:474-481.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.003 |
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".