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Record W2872661560 · doi:10.1016/j.cyto.2018.06.034

Interleukin-6 as a “metabolic hormone”

2018· review· en· W2872661560 on OpenAlexaff
Abdelaziz Ghanemi, Jonny St‐Amand

Bibliographic record

VenueCytokine · 2018
Typereview
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsImmune systemHormoneEndocrine systemCytokineEnergy homeostasisInterleukinBioinformaticsBiologyMedicineImmunologyObesityEndocrinology

Abstract

fetched live from OpenAlex

• Beyond its immunological roles, Interleukin-6 has been shown to play important metabolic roles. • Such metabolic implications covers lipids , glucose, proteins as well as energy balance. • The applications of this metabolic implications include therapy, research and diagnosis. • The highlighted physiological functions of IL-6 point IL-6 as a “metabolic hormone”. Interleukin 6 (IL-6) is a cytokine that is involved in divers immune responses and implicated in a number of diseases. However, this cytokine has other non-immune functions. Within this review, we highlight selected effects on metabolic pathways, which are mediated, controlled or modified by the IL-6. Importantly, putting spotlight on such concepts could allow us to classify IL-6 among the metabolic hormones and further study it to both deepen our knowledge on disorders involving metabolic or energy imbalances such as obesity and develop novel therapeutic strategies. Furthermore, potential explanations related to IL-6 roles in both physiology and pathology as well as relevant implications and applications on both research and therapeutic fields are also pointed as consequences of the involvement of IL-6 in the energy and metabolic homeostasis via its “endocrine” roles.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.067
GPT teacher head0.384
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations57
Published2018
Admission routes1
Has abstractyes

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