Potential predictive biomarker for diabetic peripheral neuropathy: serum neuron-specific enolase
Bibliographic record
Abstract
Abstract The early stages of diabetic peripheral neuropathy (DPN) are symptomless. A reliable dependable and sensitive biomarker is needed for the purpose of early identification of diabetic peripheral neuropathy. The main objective of the study was to evaluate the accuracy of serum neuron-specific enolase (NSE) as a biomarker for early identification of diabetic peripheral neuropathy. Patient samples were collected from the National Diabetes Center, Mustansiriyah University; a case control study was done from April 2022 to November 2022, in Baghdad, Iraq. One hundred sixty individuals between 30 to 60 years-old were included. Participants were divided into three groups: group one included 40 type 2 diabetic patients with peripheral neuropathy, group two consisted of 40 type 2 diabetic patients without peripheral neuropathy and group three included 80 apparently in good health as the control. Toronto Clinical Neuropathy Scoring System (TCSS) was used for clinical evaluation of peripheral neuropathy. Glycated hemoglobin (HbA1c) was measured by the CLOVER A1c system. In addition, serum NSE levels were measured by Enzyme Linked Immunosorbent Assay (ELISA) technique. Age, sex, and other standard variables were used as a basis for comparisons between groups. Statistically, diabetic patients with peripheral neuropathy demonstrated higher level of NSE (28.42±6.93 ng/ml) than did either diabetic patients without peripheral neuropathy (21.07±2.0 ng/ml) or controls (12.54±2.34 ng/ml) with a high degree of significance (p <0.001). In the context of Discrimination between DPN patients and diabetic patients without neuropathy, the area under curve for neuron-specific enolase was 0.812, 95% confidence interval [CI] = 0.716-0.909, p <0.001. Cut-off value of serum neuron-specific enolase was 22.53 ng/ml, sensitivity and specificity were 70% and 77%, respectively. In the context of discrimination between DPN and controls, the area under curve for neuron-specific enolase was 1.00, 95% confidence interval was 1.0-1.0, p <0.001. At a cut-off value of serum neuron-specific enolase = 18.3 ng/ml, both the sensitivity and specificity were 100%. Neuron-specific enolase could potentially be used as a biomarker to detect early diabetic peripheral neuropathy and prevent it from developing to an advanced state.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".