In vivo imaging in experimental spinal cord injury – Techniques and trends
Bibliographic record
Abstract
Traumatic Spinal Cord Injury (SCI) is one of the leading causes of disability in the world. Treatment is limited to supportive care and no curative therapy exists. Experimental research to understand the complex pathophysiology and potential mediators of spinal cord regeneration is essential to develop innovative translational therapies. A multitude of experimental imaging methods to monitor spinal cord regeneration in vivo have developed over the last years. However, little literature exists to deal with advanced imaging methods specifically available in SCI research. This systematic literature review examines the current standards in experimental imaging in SCI allowing for in vivo imaging of spinal cord regeneration on a neuronal, vascular, and cellular basis. Articles were included meeting the following criteria: experimental research, original studies, rodent subjects, and intravital imaging. Reviewed in detail are microstructural and functional Magnetic Resonance Imaging, Micro-Computed Tomography, Laser Speckle Imaging, Very High Resolution Ultrasound, and in vivo microscopy techniques. Following the PRISMA guidelines for systematic reviews, 689 articles were identified for review, of which 492 were sorted out after screening and an additional 104 after detailed review. For qualitative synthesis 93 articles were included in this publication. With this study we give an up-to-date overview about modern experimental imaging techniques with the potential to advance the knowledge on spinal cord regeneration following SCI. A thorough knowledge of the strengths and limitations of the reviewed techniques will help to optimally exploit our current experimental armamentarium in the field.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".