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Record W1826495180 · doi:10.4137/ijtr.s26862

Tryptophan Catabolism in Chronic Viral Infections: Handling Uninvited Guests

2015· review· en· W1826495180 on OpenAlexaff
Vikram Mehraj, Jean‐Pierre Routy

Bibliographic record

VenueInternational Journal of Tryptophan Research · 2015
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsImmune systemKynurenineIndoleamine 2,3-dioxygenaseBiologyAryl hydrocarbon receptorVirusHepatitis C virusImmunologyImmunosuppressionVirologyTryptophanAmino acidBiochemistry

Abstract

fetched live from OpenAlex

l-Tryptophan (l-Trp) is an essential amino acid that possesses diverse metabolic, neurological, and immunological roles spanning from the synthesis of proteins, neurotransmitter serotonin, and neurohormone melatonin, to its degradation into immunosuppressive catabolites by indoleamine-2, 3-dioxygenase (IDO) in the kynurenine pathway (KP). Trp catabolites, by activating aryl hydrocarbon receptor (AhR), play an important role in antimicrobial defense and immune regulation. IDO/AhR acts as a double-edged sword by both depleting l-Trp to starve the invaders and by contributing to the state of immunosuppression with microorganisms that were not cleared during acute infection. Pathogens experiencing Trp deprivation by IDO-mediated degradation include certain bacteria, parasites, and less likely viruses. However, chronic viral infections highjack the host immune response to create a state of disease tolerance via kynurenine catabolites. This review covers the latest data involving chronic viral infections such as human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV), herpes, and cytomegalovirus (CMV) and their cellular interplay with Trp catabolites. Strategies developed by viruses to escape immune control also represent new avenues for therapeutic interventions based on Trp metabolism.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.003
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 head0.211
GPT teacher head0.488
Teacher spread0.277 · 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 designOther design
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

Citations128
Published2015
Admission routes1
Has abstractyes

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