Role of the locus coeruleus-noradrenergic system in stress-related psychopathology and resilience: Clinical and pre-clinical evidences
Bibliographic record
Abstract
Stressful events, from daily stressors to traumatic experiences, are common and occur at any age. Despite the high prevalence of trauma, not everyone develops stress-related disorders like major depressive disorder (MDD) and post-traumatic stress disorder (PTSD), a variation attributed to resilience, the ability to adapt and avoid negative consequences of significant stress. This review examines the locus coeruleus-norepinephrine (LC-NE) system, a critical component in the brain's stress response. It discusses the LC-NE system's anatomical and functional complexity and its role in individual variability in stress responses. How different etiological factors and stress modalities affect the LC-NE system, influencing both adaptive stress responses and psychopathologies, are discussed and supported by evidence from human and animal studies. It also explores molecular and cellular adaptations in the LC that contribute to resilience, including roles of neuropeptide, inflammatory cytokines, and genetic modulation, and addresses developmental and sex differences in stress vulnerability. The need for a multifaceted approach to understand stress-induced psychopathologies is emphasized and pave the way for more personalized interventions for stress-related disorders.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".