Serum level of nerve growth factor is a potential biomarker of conversion to bipolar disorder in women with major depressive disorder
Bibliographic record
Abstract
AIM: The aim of this study was to identify biomarkers associated with major depressive disorder (MDD) and conversion from MDD to bipolar disorder (BD) in an outpatient sample of women. METHODS: This was a longitudinal study including women diagnosed with MDD and aged 18 to 60 years. The follow-up was 3 years. The diagnosis was performed using the Mini International Neuropsychiatric Interview Plus. Blood collection was just performed in the first phase. Serum interleukin-6, tumor necrosis factor-α, brain-derived neurotrophic factor, glial cell-derived neurotrophic factor, and nerve growth factor (NGF) levels were measured using a commercial immunoassay kit. RESULTS: We included 156 women. The conversion rate from MDD to BD was 15.4% (n = 24). NGF serum levels were increased in patients who converted to BD compared to the remitted MDD group and current MDD group (P = 0.013). The Bonferroni post-hoc test for multiple comparisons revealed significant differences for higher NGF levels in patients who converted to BD compared to patients with current MDD (P = 0.037). Interleukin-6, tumor necrosis factor-α, brain-derived neurotrophic factor, and glial cell-derived neurotrophic factor serum levels did not differ among the groups. CONCLUSION: Our results suggest that NGF might be a useful biomarker associated with early detection of conversion to BD, helping clinicians in the clinical diagnosis.
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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 | 0.000 |
| 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".