Brain‐derived neurotrophic factor and inflammatory markers in school‐aged children with early trauma
Bibliographic record
Abstract
OBJECTIVE: The impact of childhood trauma (CT) on brain-derived neurotrophic factor (BDNF) and cytokines levels remains unclear. We investigated the association between CT and changes in BDNF and cytokines plasma levels in children. METHOD: We recruited 36 children with trauma (CT+) and 26 children without trauma (CT-). The presence of CT was based on a clinical interview and by Criteria A of DSM-IV criteria for PTSD. Blood samples were drawn from all children to assess BDNF and cytokines. ancova was performed with psychiatric symptoms and BMI as covariates to evaluate group differences in plasma levels. RESULTS: CT+ showed increased levels of BDNF and TNF-α after excluding children with history of inflammatory disease (P<0.05) when compared with those CT-. IL-12p70, IL-6, IL-8, IL-10, and IL-1β levels were not statistically different between groups. CONCLUSION: CT+ showed increased BDNF and proinflammatory cytokines levels. The increase in BDNF levels may be an attempt to neutralize the negative effects of CT, while an increase in TNF-a levels be associated with a proinflammatory state after CT. How these changes associated with trauma relate to other biological changes and illness trajectory later in life remain to be further studied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".