In vivo imaging in experimental spinal cord injury – Techniques and trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it