Concussion/Mild Traumatic Brain Injury (TBI) Induces Brain Insulin Resistance: A Positron Emission Tomography (PET) Scanning Study
Bibliographic record
Abstract
Brain injury/concussion is a growing epidemic throughout the world. Although evidence supports association between traumatic brain injury (TBI) and disturbance in brain glucose metabolism, the underlying molecular mechanisms are not well established. Previously, we reported the release of cellular prion protein (PrPc) from the brain to circulation following TBI. The PrPc level was also found to be decreased in insulin-resistant rat brains. In the present study, we investigated the molecular link between PrPc and brain insulin resistance in a single and repeated mild TBI-induced mouse model. Mild TBI was induced in mice by dropping a weight (~95 g at 1 m high) on the right side of the head. The procedure was performed once and thrice (once daily) for single (SI) and repeated induction (RI), respectively. Micro PET/CT imaging revealed that RI mice showed significant reduction in cortical, hippocampal and cerebellum glucose uptake compared to SI and control. Mice that received RI also showed significant motor and cognitive deficits. In co-immunoprecipitation, the interaction between PrPc, flotillin and Cbl-associated protein (CAP) observed in the control mice brains was disrupted by RI. Lipid raft isolation showed decreased levels of PrPc, flotillin and CAP in the RI mice brains. Based on observation, it is clear that PrPc has an interaction with CAP and the dislodgment of PrPc from cell membranes may lead to brain insulin resistance in a mild TBI mouse model. The present study generated a new insight into the pathogenesis of brain injury, which may result in the development of novel therapy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".