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Maladaptive T-Cell Metabolic Fitness in Autoimmune Diseases

2023· preprint· en· W4387184543 on OpenAlexaff
Irene Rose Antony, Brandon Han Siang Wong, Dermot Kelleher, Navin Kumar Verma

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of British Columbia
FundersMinistry of Education, IndiaNational Medical Research CouncilMedical Research CouncilNational Research Foundation SingaporeNational Research Foundation
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 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.084
GPT teacher head0.314
Teacher spread0.230 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes1
Has abstractyes

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