Traumatic Brain Injury in a Well: A Modular Three-Dimensional Printed Tool for Inducing Traumatic Brain Injury <i>In vitro</i>
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a major health problem that affects millions of persons worldwide every year among all age groups, mainly young children, and elderly persons. It is the leading cause of death for children under the age of 16 and is highly correlated with a variety of neuronal disorders, such as epilepsy, and neurodegenerative disease, such as Alzheimer's disease or amyotrophic lateral sclerosis. Over the past few decades, our comprehension of the molecular pathway of TBI has improved, yet despite being a major public health issue, there is currently no U.S. Food and Drug Administration–approved treatment for TBI, and a gap remains between these advances and their application to the clinical treatment of TBI. One of the major hurdles for pushing TBI research forward is the accessibility of TBI models and tools. Most of the TBI models require costume-made, complex, and expensive equipment, which often requires special knowledge to operate. In this study, we present a modular, three-dimensional printed TBI induction device, which induces, by the pulse of a pressure shock, a TBI-like injury on any standard cell-culture tool. Moreover, we demonstrate that our device can be used on multiple systems and cell types and can induce repetitive TBIs, which is very common in clinical TBI. Further, we demonstrate that our platform can recapitulate the hallmarks of TBI, which include cell death, decrease in neuronal functionality, axonal swelling (for neurons), and increase permeability (for endothelium). In addition, in view of the continued discussion on the need, benefits, and ethics of the use of animals in scientific research, this in vitro , high-throughput platform will make TBI research more accessible to other labs that prefer to avoid the use of animals yet are interested in this field. We believe that this will enable us to push the field forward and facilitate/accelerate the availability of novel treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".