Tropomyosin receptor kinase B-mediated signaling in integration of neuropathic pain and obesity in diabetic polyneuropathy
Bibliographic record
Abstract
OBJECTIVE: To assess the quantitative serum levels of tropomyosin receptor kinase receptor B, and to estimate its association with serum concentration of brain-derived neurotrophic factor and obesity in patients with painful and painless forms of diabetic polyneuropathy. METHODS: We examined 70 patients with diabetic polyneuropathy with confirming peripheral nerve dysfunction by electroneuromyography and measuring of serum levels tropomyosin receptor kinase receptor B and brain-derived neurotrophic factor by enzyme immunoassay. Diabetic polyneuropathy was diagnosed using the modified Toronto Consensus (2011) criteria, while neuropathic pain was assessed using an 11-point Numerical Pain Rating Scale. The patients were divided into two groups according to presence or absence of neuropathic pain. Control Group consisted of 14 healthy persons. RESULTS: The serum levels of tropomyosin receptor kinase receptor B and brain-derived neurotrophic factor in patients with diabetic polyneuropathy are significantly higher than healthy controls (p=0.000). Hyperexpression of brain-derived neurotrophic factor in serum was associated with painful form of diabetic polyneuropathy (R=0.392, p=0.012) and obesity (R=0.412, p=0.001). On the contrary high concentration of tropomyosin receptor kinase receptor B in serum associated with painless diabetic polyneuropathy by Pain DETECT (R=-0.354, p=0.015), low body weight (R=-0.354, p=0.015) and severe demyelization of nerve fibers (R=-0.574, p=0.001), indicated "non-working" receptor detected in serum. CONCLUSION: Tropomyosin receptor kinase receptor B signaling is involved in the modulation of neuropathic pain and obesity in diabetic polyneuropathy.
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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 | 0.000 | 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 | 0.000 |
| 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".