The excitability of dorsal horn neurons is affected by cerebrospinal fluid from humans with osteoarthritis
Bibliographic record
Abstract
Changes in central neural processing are thought to contribute to the development of chronic osteoarthritis pain. This may be reflected as the presence of inflammatory mediators in the cerebral spinal fluid (CSF). We therefore exposed organotypically cultured slices of rat spinal cord to CSF from human subjects with osteoarthritis (OACSF) at a ratio of 1 part CSF in 9 parts culture medium for 5-6 days, and measured changes in neuronal electrophysiological properties by means of whole-cell recording. Although OACSF had no effect on the membrane properties and excitability of neurons in the substantia gelatinosa, synaptic transmission was clearly altered. The frequency of spontaneous excitatory postsynaptic currents (sEPSC) in delay-firing putative excitatory neurons was increased, as was sEPSC amplitude and frequency in tonic-firing inhibitory neurons. These changes could affect sensory processing in the dorsal horn, and may affect the transfer of nociceptive information. Although OACSF also affected inhibitory synaptic transmission (frequency of spontaneous inhibitory synaptic currents; sIPSC), this may have little bearing on sensory processing by substantia gelatinosa neurons, as sEPSC frequency is >3× greater than sIPSC frequency in this predominantly excitatory network. These results support the clinical notion that changes in nociceptive processing at the spinal level contribute to the generation of chronic osteoarthritis pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".