Increase in serum brain-derived neurotrophic factor levels during early withdrawal in severe alcohol users
Bibliographic record
Abstract
INTRODUCTION: Changes in brain-derived neurotrophic factor (BDNF) have been linked to the neuroadaptative consequences of chronic alcohol use and associated with disease severity and prognosis. Few studies have evaluated the influence of drug withdrawal and clinical and sociodemographic data on BDNF levels in severe alcohol users. OBJECTIVES: Our goals were (1) to evaluate variation in BDNF levels during alcohol withdrawal and, (2) to assess the influence of putative confounding factors on BDNF levels. METHODS: Our sample consists of 62 men with alcohol use disorder undergoing a detoxification process. Serum BDNF levels were measured using a commercial sandwich-ELISA kit, at two points: before and after the detoxification period. RESULTS: We found an increase in BDNF levels during alcohol withdrawal (25.4±9.6 at admission vs. 29.8±10.2 ng/ml at discharge; p < 0.001), even after controlling for potential confounders (positive family history, number of days between blood sample collections, and age) (Generalized Estimating Equation: coefficient = -4.37, 95% confidence interval [95%CI] -6.3; -2.4; p < 0.001). Moreover, individuals who had first-degree relative with alcohol dependence had smaller increases in BDNF levels than individuals with no family history (14.8 [95%CI -5.3; 35.6] vs. 35.3 [95%CI 15.4; 74.8]; p = 0.005). CONCLUSIONS: In summary, variation in BDNF levels seems to be influenced by withdrawal in severe alcohol users. A positive family history of alcohol dependence could also be a factor that influences variation in this biomarker.
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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".