Analysis of Macrophages and Peptidergic Fibers in the Skin of Patients With Painful Diabetic Polyneuropathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The mechanisms of pain in patients with diabetic polyneuropathy are unknown. Studies have suggested a role of inflammation and increased neuropeptides peripherally in pain generation. This study examined the possible skin markers of painful diabetic polyneuropathy (P-DPN): macrophages, substance P (SP), and calcitonin gene-related peptide (CGRP). METHODS: The participants were included from a large Danish cross-sectional clinical study of type 2 diabetes. We diagnosed definite diabetic polyneuropathy using the Toronto criteria and used the Neuropathic Pain Special Interest Group classification for defining P-DPN. We included 60 skin biopsies from patients with diabetic polyneuropathy-30 with P-DPN and 30 with nonpainful diabetic polyneuropathy (NP-DPN)-and 30 biopsies from healthy controls of similar age and sex. The biopsies were stained using PGP 9.5, IbA1, and SP and CGRP primary markers. RESULTS: = 0.64 and 0.49, respectively). The difference between P-DPN and NP-DPN for SP and CGRP was significant only in female patients, although a trend was seen in male patients. DISCUSSION: The findings point to a possible involvement of the innate immune system in the pathogenesis of neuropathic pain in patients with DPN, although markers of activated macrophages were not measured in this study.
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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) | 0.000 | 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) | 0.001 | 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".