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Record W3196474089 · doi:10.3389/fimmu.2021.725204

Targeting Indoleamine 2,3-Dioxygenase 1: Fighting Cancers via Dormancy Regulation

2021· review· en· W3196474089 on OpenAlexafffund
Chao Yang, C. E. Ng, Dan Li, Lei Zhang

Bibliographic record

VenueFrontiers in Immunology · 2021
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of Waterloo
FundersChengdu UniversityChengdu University of Traditional Chinese MedicineGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceJiangxi University of Traditional Chinese MedicineMitacsChina Postdoctoral Science Foundation
KeywordsDormancyIndoleamine 2,3-dioxygenaseKynurenineAryl hydrocarbon receptorCancer researchMetastasisKynurenine pathwayBiologyCancerMedicineInternal medicineTryptophanBiochemistryBotanyGeneTranscription factorGermination

Abstract

fetched live from OpenAlex

The connection between indoleamine 2,3-dioxygenase 1 (IDO1) and tumour dormancy – a quiescent state of tumour cells which has been consistently linked to metastasis and cancer recurrence – is rarely discussed despite the pivotal role of IDO1 in cancer development and progression. Whilst the underlying mechanisms of IDO1-mediated dormancy are elusive, we summarize the IDO1 pathways which potentially contribute to dormancy in this review. Critically, distinct IDO1 activities are involved in dormancy initiation and maintenance; factors outside the well-studied IDO1/kynurenine/aryl hydrocarbon receptor axis, including the mammalian target of rapamycin and general control nonderepressible 2, appear to be implicated in dormancy. We also discuss various strategies for cancer treatment via regulating IDO1-dependent dormancy and suggest the application of nanotechnology to deliver effective treatment.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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Same venueFrontiers in ImmunologySame topicTryptophan and brain disordersFrench-language works237,207