Neural stimulation does not mediate attenuated vascular response in ACL‐deficient knees: Potential role of local inflammatory mediators
Bibliographic record
Abstract
Chronic inflammation associated with osteoarthritis (OA) alters normal responses and modifies the functionality of the articular vasculature. Altered responsiveness of the vasculature may be due to excessive neural activity associated with chronic pain and inflammation, or from the production of inflammatory mediators which induce vasodilation. Using laser speckle perfusion imaging (LSPI), blood flow to the medial collateral ligament (MCL) of adult rabbits was measured in denervated ACL transected knees (n = 6) and compared to unoperated control (n = 6) and 6-week anterial cruciate ligament (ACL)-transected knees (n = 6). Phenylephrine and neuropeptide Y were applied to the MCL vasculature in topical boluses of 100 microL (dose range 10(-14) to 10(-8) mol and 10(-14) to 10(-9) mol, respectively). Denervation diminished vasoconstrictive responsiveness to phenylephrine compared to both control and ACL-transected knees. Denervation minimally enhanced vascular responses to neuropeptide Y (NPY) compared to ACL deficiency alone, which nevertheless remained significantly diminished from control responses. To evaluate the potential role of inflammatory dilators in the diminished contractile responses, phenylephrine was coadministered with histamine, substance P, and prostaglandin E(2). High-dose histamine, and low-dose substance P and PGE(2) were able to inhibit contractile responses in the MCL of control knees. Excessive neural input does not mediate diminished vasoconstrictive responses in the ACL transected knee; inflammatory mediators may play a role in the deficient vascular responsiveness of the ACL transected knee.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".