Applying hiPSCs and Biomaterials Towards an Understanding and Treatment of Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) is the leading cause of disability and mortality in children and young adults and has a profound impact on the socio-economic wellbeing of patients and their families. Initially, brain damage is caused by mechanical stress-induced axonal injury and vascular dysfunction, which can include hemorrhage, blood-brain barrier disruption, and ischemia. Subsequent neuronal degeneration, chronic inflammation, demyelination, oxidative stress, and the spread of excitotoxicity can further aggravate disease pathology. Thus, TBI treatment requires prompt intervention to protect against neuronal and vascular degeneration. Rapid advances in the field of stem cells (SCs) have revolutionized the prospect of repairing brain function following TBI. However, more than that, SCs can contribute substantially to our knowledge of this multifaced pathology. Research, based on human induced pluripotent SCs (hiPSCs) can help decode the molecular pathways of degeneration and recovery of neuronal and glial function, which makes these cells valuable tools for drug screening. Additionally, experimental approaches that include hiPSC-derived engineered tissues (brain organoids and bio-printed constructs) and biomaterials represent a step forward for the field of regenerative medicine since they provide a more suitable microenvironment that enhances cell survival and grafting success. In this review, we highlight the important role of hiPSCs in better understanding the molecular pathways of TBI-related pathology and in developing novel therapeutic approaches, building on where we are at present. We summarize some of the most relevant findings for regenerative therapies using biomaterials and outline key challenges for TBI treatments that remain to be addressed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".