Processing and sectioning of organ donor spinal cord tissue for electrophysiology on acute human spinal cord slices
Bibliographic record
Abstract
Acute spinal cord slice electrophysiology is a powerful technique used in preclinical basic science research to investigate sensory and motor neuron function and pathophysiology. A major barrier that stands between implementing these findings into effective clinical treatments is the translational gap between rodent models and human patients. To date, no methods or protocols describe how to prepare viable human spinal cord slices for acute electrophysiological recordings. To bridge this translational divide, we describe here a protocol for the extraction of spinal cord tissue from consenting human organ donors and the preparation and sectioning of this tissue for acute spinal cord slice electrophysiology. With the collaboration of a transplant service and licensed surgeon, tissue can be extracted in 30-50 min. Acute spinal cord slices can then be prepared in the laboratory by trained graduate students in 2.5-5 h, depending on the amount of tissue and scope of experiments. Using a viability stain to confirm that spinal slices are of sufficient quality to proceed, slices can then be used for either patch-clamp recordings to study the excitability of individual neurons or for high-density multielectrode array recordings to study intact sensory circuits. Slices remain viable for 4-8 h, providing ample time for investigating synaptic and circuit-level signalling dynamics, including the use of pharmacological agents to probe the roles of specific molecular targets. The approaches described here can be implemented to improve translational physiological research and as a human tissue-based preclinical drug target identification and validation assay.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".