Development of a Computational Pipeline and Associated Quantitative Assessment of Superficial Dorsal Horn Activity Across Pain States
Bibliographic record
Abstract
The superficial dorsal horn (SDH) is a critical site for pain processing and regulation.A large proportion of synaptic activity within the SDH is produced by subnetworks of local interneurons, which are responsible for the integration of sensory information received from peripheral afferents.Investigation of these subnetworks in rodent models is aimed to further elucidate the SDH's role across models of chronic pain at the subnetwork level.Through epifluorescent microscopy on acute SDH slices, intracellular calcium increases were recorded as a marker for action potential firing.Functional neural populations within the SDH were quantified using a series of glutamate challenges on each slice, to identify all active neurons and respective evoked response magnitudes.Responses within sham control animals were compared to an anterior cruciate ligament transection model of osteoarthritic pain, as well as to a spared nerve injury model of neuropathic pain.A developed custom image processing pipeline combined MATLAB and ImageJ-Fiji Macro Language scripts with peer-reviewed, open-source toolboxes to quantify changes in SDH subnetwork excitability across pain models.The developed pipeline quantitatively assessed image reconstruction quality following parallel application of popular image denoising techniques.Non-local means denoising was observed to improve image quality significantly more than the other methods, and was prescribed to the full dataset.The presented harmonized pipeline serves as a novel assay for specific and multidimensional evaluation of SDH circuitry.No significant differences were observed in evoked SDH network response following two levels of glutamate challenges across experimental groups.Future experiments will use these pipeline approaches to investigate whether there are differences in spontaneous SDH network activity between the experimental groups, which may underlie differential pain sensitivities in these chronic pain conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".