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Record W4280532627 · doi:10.3389/fncel.2022.839396

Microglia as a Hub for Suicide Neuropathology: Future Investigation and Prevention Targets

2022· review· en· W4280532627 on OpenAlexafffund
Elisa Gonçalves de Andrade, Fernando Gonzàlez Ibáñez, Marie‐Ève Tremblay

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMcGill UniversityUniversity of British ColumbiaUniversité LavalUniversity of Victoria
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaUniversity of Victoria
KeywordsNeuropathologyMicrogliaContext (archaeology)NeuroscienceMedicinePsychologyPsychiatryBiologyImmunologyInflammationPathologyDisease

Abstract

fetched live from OpenAlex

Suicide is a complex public health challenge associated worldwide with one death every 40 s. Research advances in the neuropathology of suicidal behaviors (SB) have defined discrete brain changes which may hold the key to suicide prevention. Physiological differences in microglia, the resident immune cells of the brain, are present in post-mortem tissue samples of individuals who died by suicide. Furthermore, microglia are mechanistically implicated in the outcomes of important risk factors for SB, including early-life adversity, stressful life events, and psychiatric disorders. SB risk factors result in inflammatory and oxidative stress activities which could converge to microglial synaptic remodeling affecting susceptibility or resistance to SB. To push further this perspective, in this Review we summarize current areas of opportunity that could untangle the functional participation of microglia in the context of suicide. Our discussion centers around microglial state diversity in respect to morphology, gene and protein expression, as well as function, depending on various factors, namely brain region, age, and sex.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.075
GPT teacher head0.314
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2022
Admission routes2
Has abstractyes

Explore more

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