Density of nerve fibres and expression of substance P, NR2B‐receptors and nerve growth factor in healthy human masseter muscle: An immunohistochemical study
Bibliographic record
Abstract
BACKGROUND: In skeletal muscle, free nerve endings are mostly located within the connective tissue. However, the distribution of sensory afferent fibres in healthy human masseter muscle tissues has not been studied. OBJECTIVES: Primarily to investigate human masseter muscle nerve fibre densities as well as expression of NR2B receptors, substance P (SP) and nerve growth factor (NGF), and secondarily to compare this between a) nerve fibres associated with myocytes and within connective tissue; b) sexes; and c) ages. METHODS: Microbiopsies of the masseter muscle were obtained from 60 sex- and age-matched healthy participants. Biopsy sections were analysed using immunohistochemistry and were visualised with a Leica TCS SPE confocal microscope. The Mann-Whitney U test was used for statistical analyses. RESULTS: The density of nerve fibres within connective tissue was significantly greater than in nerve fibres associated with myocytes (P < .001). Nerve fibres within connective tissue expressed SP alone or together with NR2B significantly more often than those associated with myocytes (P < .001). The frequency of nerve fibres, which expressed SP alone or in combination with NR2B or NGF, was significantly greater in women than in men (P < .050). Moreover, the co-expression of the three markers together was inversely correlated with age in women (P < .002). CONCLUSIONS: There is a higher density and greater expression of sensory nerve fibres within the connective tissue than associated with myocytes in healthy human masseter muscle. This suggests that nerve fibres within connective tissue are more involved in nociception than nerve fibres associated with myocytes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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".