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Record W4388019629 · doi:10.3390/cells12212541

Maladaptive T-Cell Metabolic Fitness in Autoimmune Diseases

2023· review· en· W4388019629 on OpenAlexaff
Irene Rose Antony, Brandon Han Siang Wong, Dermot Kelleher, Navin Kumar Verma

Bibliographic record

VenueCells · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilMinistry of Education, IndiaNanyang Technological UniversityNational Research FoundationMinistry of Education - SingaporeNational Medical Research CouncilNational Research Foundation Singapore
KeywordsImmune systemAcquired immune systemImmunologyBiologyT cell

Abstract

fetched live from OpenAlex

Immune surveillance and adaptive immune responses, involving continuously circulating and tissue-resident T-lymphocytes, provide host defense against infectious agents and possible malignant transformation while avoiding autoimmune tissue damage. Activation, migration, and deployment of T-cells to affected tissue sites are crucial for mounting an adaptive immune response. An effective adaptive immune defense depends on the ability of T-cells to dynamically reprogram their metabolic requirements in response to environmental cues. Inability of the T-cells to adapt to specific metabolic demands may skew cells to become either hyporesponsive (creating immunocompromised conditions) or hyperactive (causing autoimmune tissue destruction). Here, we review maladaptive T-cell metabolic fitness that can cause autoimmune diseases and discuss how T-cell metabolic programs can potentially be modulated to achieve therapeutic benefits.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.032

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 head0.036
GPT teacher head0.290
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2023
Admission routes1
Has abstractyes

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