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Record W3172759790 · doi:10.1186/s12929-021-00742-6

Inflammatory signaling mechanisms in bipolar disorder

2021· review· en· W3172759790 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Biomedical Science · 2021
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsBipolar disorderInflammationNeuroscienceDiseasePopulationMedicineBioinformaticsBiologyImmunologyInternal medicineCognition

Abstract

fetched live from OpenAlex

Bipolar disorder is a decidedly heterogeneous and multifactorial disease, with a high individual and societal burden. While not all patients display overt markers of elevated inflammation, significant evidence suggests that aberrant immune signaling contributes to all stages of the disease, and likely explains the elevated rates of comorbid inflammatory illnesses seen in this population. While individual systems have been intensely studied and targeted, a relative paucity of attention has been given to the interconnecting role of inflammatory signals therein. This review presents an updated overview of some of the most prominent pathophysiologic mechanisms in bipolar disorder, from mitochondrial, endoplasmic reticular, and calcium homeostasis, to purinergic, kynurenic, and hormonal/neurotransmitter signaling, showing inflammation to act as a powerful nexus between these systems. Several areas with a high degree of mechanistic convergence within this paradigm are highlighted to present promising future targets for therapeutic development and screening.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.337
Teacher spread0.311 · 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