Hemokinin-1 as a Mediator of Arthritis-Related Pain via Direct Activation of Primary Sensory Neurons
Bibliographic record
Abstract
The tachykinin hemokinin-1 (HK-1) is involved in immune cell development and inflammation, but little is known about its function in pain. It acts through the NK1 tachykinin receptor, but several effects are mediated by a yet unidentified target. Therefore, we investigated the role and mechanism of action of HK-1 in arthritis models of distinct mechanisms with special emphasis on pain. Arthritis was induced by i.p. K/BxN serum (passive transfer of inflammatory cytokines, autoantibodies), intra-articular mast cell tryptase or Complete Freund’s Adjuvant (CFA, active immunization) in wild type, HK-1- and NK1-deficient mice. Mechanical- and heat hyperalgesia determined by dynamic plantar esthesiometry and increasing temperature hot plate, respectively, swelling measured by plethysmometry or micrometry were significantly reduced in HK-1-deleted, but not NK1-deficient mice in all models. K/BxN serum-induced histopathological changes (day 14) were also decreased, but early myeloperoxidase activity detected by luminescent in vivo imaging increased in HK-1-deleted mice similarly to the CFA model. However, vasodilation and plasma protein extravasation determined by laser Speckle and fluorescent imaging, respectively, were not altered by HK-1 deficiency in any models. HK-1 induced Ca 2+ -influx in primary sensory neurons, which was also seen in NK1-deficient cells and after pertussis toxin-pretreatment, but not in extracellular Ca 2+ -free medium. These are the first results showing that HK-1 mediates arthritic pain and cellular, but not vascular inflammatory mechanisms, independently of NK1 activation. HK-1 activates primary sensory neurons presumably via Ca 2+ channel-linked receptor. Identifying its target opens new directions to understand joint pain leading to novel therapeutic opportunities.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".