<p>A Cross-Sectional Study on the Correlation Between Inflammatory Cytokines, Negative Emotions, and Onset of Peripheral Neuropathy in Type 2 Diabetes</p>
Bibliographic record
Abstract
Objective: This study explored the changes in the levels of IL-6, IL-17, TNF-α, and TNF-β, whether such changes were associated with anxiety and depression in diabetic peripheral neuropathy (DPN), and what factors associated with the occurrence of DPN. Methods: Forty-four patients diagnosed with DPN comprised the DPN group, including DPN1 (mild diabetic peripheral neuropathy, 29 cases) and DPN2 groups (moderate-severe diabetic peripheral neuropathy, 15 cases). Thirty-seven individuals with type 2 diabetes mellitus constituted the diabetes mellitus with no neuropathy (NDPN) group. Electromyography was applied to confirm DPN, and the Toronto clinical scoring system (TCSS) score was used to assess the severity of DPN. All subjects’ emotions were evaluated using the self-rating anxiety scale (SAS) and self-rating depression scale (SDS). Triiodothyronine (T 3 ), tetraiodothyronine (T 4 ), and thyroid-stimulating hormone (TSH) levels were measured using chemiluminescent immunoassay. The relevant biochemical indicators were detected using an automatic biochemical analyzer. The plasma levels of cytokines were detected using quantitative sandwich enzyme-linked immunosorbent assay. Results: Patients with DPN had elevated levels of anxiety, IL-6, IL-17, and TNF-α. There were some positive associations between negative emotions and cytokines. The TCSS score positively correlated with IL-17, SAS score, and T 3 . DPN independently correlated with age, disease duration, fasting plasma glucose (FPG), and IL-17. The combination of IL-17 and TNF-α had higher diagnostic value for DPN than any single cytokine. Conclusion: Patients with DPN had elevated levels of inflammatory cytokines, which were associated with negative emotion, and IL-17 had independent correlation with DPN. Keywords: diabetic peripheral neuropathy, emotions, cytokines
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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 | 0.000 | 0.001 |
| 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".