Remote ischemic conditioning improves outcome independent of anesthetic effects following shockwave-induced traumatic brain injury
Bibliographic record
Abstract
Traumatic brain injury due to primary blast exposure is a major cause of ongoing neurological and psychological impairment in soldiers and civilians. Animal and human evidence suggests that low-level blast exposure is capable of inducing white matter injury and behavioural deficits. There are currently no effective therapies to treat the underlying suspected pathophysiology of low-level primary blast or concussion. Remote ischemic conditioning (RIC) has been shown to have cardiac, renal and neuro-protective effects in response to brief cycles of ischemia. Here we examined the effects of RIC in two models of blast injury. We used a model of low-level primary blast in rats to evaluate the effects of RIC neurofilament expression. We subsequently used a model of traumatic brain injury in adult zebrafish using pulsed high intensity focused ultrasound (pHIFU) to evaluate the effects of RIC on behavioural outcome and apoptosis in a post-traumatic setting. In blast exposed rats, RIC pretreatment modulated NF200 expression suggesting an innate biological buffering effect. In zebrafish, behavioural deficits and apoptosis due to pHIFU-induced brain injury were reduced following administration of serum derived from RIC rats. The results in the zebrafish model demonstrate the humoral effects of RIC independent of anesthetic effects that were observed in the rat model of injury. Our results indicate that RIC is effective in improving outcome following modeled brain trauma in pre- and post-injury paradigms. The results suggest a potential role for innate biological systems in the protection against pathophysiological processes associated with impairment following shockwave induced trauma.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".