Molecular impacts of childhood abuse on the human brain
Bibliographic record
Abstract
Childhood abuse (CA) is a prevalent global health concern, increasing the risk of negative mental health outcomes later in life. In the literature, CA is commonly defined as physical, sexual, and emotional abuse, as well as neglect. Several mental disorders have been associated with CA, including depression, bipolar disorder, schizophrenia, and post-traumatic stress disorder, along with an increased risk of suicide. It is thought that traumatic life events occurring during childhood and adolescence may have a significant impact on essential brain functions, which may persist throughout adulthood. The interaction between the brain and the external environment can be mediated by epigenetic alterations in gene expression, and there is a growing body of evidence to show that such changes occur as a function of CA. Disruptions in the HPA axis, myelination, plasticity, and signaling have been identified in individuals with a history of CA. Understanding the molecular impact of CA on the brain is essential for the development of treatment and prevention measures. In this review, we will summarize studies that highlight the molecular changes associated with CA in the human brain, along with supporting evidence from peripheral studies and animal models. We will also discuss some of the limitations surrounding the study of CA and propose extracellular vesicles as a promising future approach in the field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".