Serum neutrophil gelatinase-associated lipocalin as a potential biomarker for cognitive decline in spinal cord injury
Bibliographic record
Abstract
Objective Neutrophil gelatinase-associated lipoprotein (NGAL), a protein encoded by the lipocalcin-2 (LCN2) gene, has been reported to be involved in multiple processes of innate immunity, but its relationship with spinal cord injury (SCI) remains unclear. This study set out to determine whether NGAL played a role in the development of cognitive impairment following SCI. Methods At the Neck-Shoulder and Lumbocrural Pain Hospital, a total of 100 SCI patients and 72 controls were enrolled in the study through recruitment. Through questionnaires, baseline data on the participants' age, gender, education level, lifestyle choices (drinking and smoking) and underlying illnesses (hypertension, diabetes, coronary heart disease, and hyperlipidemia) were gathered. The individuals' cognitive performance was evaluated using the Montreal Cognitive Scale (MoCA), and their serum NGAL levels were discovered using ELISA. Results The investigation included 72 controls and 100 SCI patients. The baseline data did not differ substantially between the two groups, however the SCI group's serum NGAL level was higher than the control group's (p < 0.05), and this elevated level was adversely connected with the MoCA score (p < 0.05). According to the results of the ROC analysis, NGAL had a sensitivity of 58.24% and a specificity of 86.72% for predicting cognitive impairment following SCI. Conclusions The changes in serum NGAL level could serve as a biomarker for cognitive impairment in SCI patients, and this holds true even after taking in account several confounding variables.
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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) | 0.001 | 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".