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Record W4408805755 · doi:10.1101/2025.03.22.644719

A post-mortem investigation of the locus coeruleus-noradrenergic system in resilience to childhood abuse

2025· preprint· en· W4408805755 on OpenAlexaff
Déa Slavova, Maria Antonietta Davoli, Céline Keime, Érika Vigneault, Corina Nagy, Gustavo Turecki, Bruno Giros, Naguib Mechawar, Elsa Isingrini

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsLocus coeruleusPsychologyResilience (materials science)NeuroscienceMaterials scienceCentral nervous systemComposite material

Abstract

fetched live from OpenAlex

Abstract Childhood abuse (CA) is one of the strongest lifetime predictors of major depressive disorder (MDD) and suicide. However, some individuals exposed to CA are resilient, avoiding the development of psychopathology. Recently, the locus coeruleus-noradrenergic (LC-NE) system has been involved in resilience following stressful events at adulthood. We investigated how a history of CA affects the integrity of the LC-NE system at the molecular and cellular level in human post-mortem brain samples of depressed suicides, and whether differential neurobiological mechanisms can be revealed in resilient individuals. Anatomical analysis revealed that CA-induced MDD and suicide is associated with decrease in LC-NE neurons density. RNA sequencing of laser captured LC-NE neurons highlighted differentially expressed genes, principally in the RES-CA group. Resilience to CA involves specific neurobiological adaptations in the LC-NE system that potentially protect against the loss of LC-NE neurons and the negative long-term outcome of CA-induced depression and suicide. Our results provide insights into potential therapeutic targets for preventing or treating CA-induced MDD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.234
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicAnesthesia and Neurotoxicity ResearchFrench-language works237,207