Severe-combined immunodeficient rats can be used to generate a model of perinatal hypoxic-ischemic brain injury to facilitate studies of engrafted human neural stem cells
Bibliographic record
Abstract
Cerebral palsy (CP) encompasses a group of non-progressive brain disorders that are often acquired through perinatal hypoxic-ischemic (HI) brain injury. Injury leads to a cascade of cell death events, resulting in lifetime motor and cognitive deficits. There are currently no treatments that can repair the resulting brain damage and improve functional outcomes. To date, preclinical research using neural precursor cell (NPC) transplantation as a therapy for HI brain injury has shown promise. To translate this treatment to the clinic, it is essential that human-derived NPCs also be tested in animal models, however, a major limitation is the high risk of xenograft rejection. A solution is to transplant the cells into immune-deficient rodents, but there are currently no models of HI brain injury established in such a cohort of animals. Here, we demonstrate that a model of HI brain injury can be generated in immune-deficient Prkdc knockout (KO) rats. Long-term deficits in sensorimotor function were similar between KO and wildtype (WT) rats. Interestingly, some aspects of the injury were more severe in KO rats. Additionally, human induced pluripotent stem cell derived (hiPSC)-NPCs had higher survival at 10 weeks post-transplant in KO rats when compared to their WT counterparts. This work establishes a reliable model of neonatal HI brain injury in Prkdc KO rats that will allow for future transplantation, survival, and long-term evaluation of the safety and efficacy of hiPSC-NPCs for neonatal brain damage. This model will enable critical preclinical translational research using human NPCs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".