How do primary/patient-derived cell models compare to mouse models in the study of chronic disease? Do either of these models carry increased translational potential?
Bibliographic record
Abstract
How do primary/patient-derived cell models compare to mouse models in the study of chronic disease?Do either of these models carry increased translational potential?The study of chronic disease has long used animal models to elucidate mechanisms, investigate physiology, and test potential therapies.Among the various animal models, the mouse is one of the most widely used.Genetically, humans and mice share sizeable DNA sequence homology, with many of the disease-related genes being near-identical [1,2].The ability to create transgenic, knockout, and knockin mice in whole-body or tissue specific manners allows for powerful in vivo studies and research on isolated tissues providing valuable insight into complex physiological and disease processes.However, experimental interventions developed using mouse models do not always translate well into humans.A well-known example of this trend is the TGN1412 anti-CD28 monoclonal antibody developed by TeGenero for the treatment of multiple sclerosis, rheumatoid arthritis, and certain cancers [3].Toxicity studies performed on mice and non-human primates demonstrated safety at doses hundreds of times higher than what would be introduced into humans.However, the first human clinical trials of this drug at sub-clinical doses caused a cytokine storm and devastating organ failure in all the participating patients, all of whom were fortunately rescued with intervention [4].Indeed, the majority of drugs that enter clinical trials never reach the marketplace and the limitations of animal models used in drug testing are an important contributing factor [5].Moreover, mouse models that were created to recapitulate human genetic diseases have frequently had phenotypes that differ from their human counterparts [6] and models that do work use genetically identical or near-identical animals that lack the genomic diversity that is the reality of a human population.Recent progress in the stem cell field has established a variety of techniques that can be utilized to generate cultures enriched for mature cell populations or tissue-specific organoids from human pluripotent stem cells (hPSCs) and adult
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 | 0.037 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".