Inducing Mechanical Stimuli to Tissues Grown on a Magnetic Gel Allows Deconvoluting the Forces Leading to Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury (TBI), which is characterized by damage to the brain resulting from a sudden traumatic event, is a major cause of death and disability worldwide. It has short- and long-term effects, including neuroinflammation, cognitive deficits, and depression. TBI consists of multiple steps that may sometimes have opposing effects or mechanisms, making it challenging to investigate and translate new knowledge into effective therapies. In order to better understand and address the underlying mechanisms of TBI, we have developed an in vitro platform that allows dynamic simulation of TBI conditions by applying external magnetic forces to induce acceleration and deceleration injury, which is often observed in human TBI. Endothelial and neuron-like cells were successfully grown on magnetic gels and applied to the platform. Both cell types showed an instant response to the TBI model, but the endothelial cells were able to recover quickly—in contrast to the neuron-like cells. In conclusion, the presented in vitro model mimics the mechanical processes of acceleration/deceleration injury involved in TBI and will be a valuable resource for further research on brain injury.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".