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Record W4205121672 · doi:10.4049/jimmunol.2100853

Metabolic Sex Dimorphism of the Brain at the Gene, Cell, and Tissue Level

2022· review· en· W4205121672 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Immunology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsUniversity of TorontoSunnybrook HospitalSunnybrook Health Science Centre
FundersNational Multiple Sclerosis Society
KeywordsSexual dimorphismBiologyContext (archaeology)Immune systemBrain tissueDiseaseBrain CellNeuroscienceCell typeMetabolomicsPhysiologyCellBioinformaticsPathologyMedicineImmunologyGeneticsEndocrinology

Abstract

fetched live from OpenAlex

The palpable observation in the sex bias of disease prevalence in the CNS has fascinated scientists for several generations. Brain sex dimorphism has been visualized by imaging and analytical tools at the tissue, cellular, and molecular levels. Recent work highlighted the specificity of such sex bias in the brain and its subregions, offering a unique lens through which disease pathogenesis can be investigated. The brain is the largest consumer of energy in the body and provides a unique metabolic environment for diverse lineages of cells. Immune cells are increasingly recognized as an integral part of brain physiology, and their function depends on metabolic homeostasis. This review focuses on metabolic sex dimorphism in brain tissue, resident, and infiltrating immune cells. In this context, we highlight the relevance of recent advances in metabolomics and RNA sequencing technologies at the single cell resolution and the development of novel computational approaches.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.041
GPT teacher head0.271
Teacher spread0.231 · 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