MétaCan
Menu
Back to cohort
Record W3082950070 · doi:10.1016/j.isci.2020.101513

Expression of Inhibitory Receptors on T and NK Cells Defines Immunological Phenotypes of HCV Patients with Advanced Liver Fibrosis

2020· article· en· W3082950070 on OpenAlexafffund
Chisom Ifeoma Adaeze Okwor, Jun Oh, Angela M. Crawley, Curtis Cooper, Seung‐Hwan Lee

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsCarleton UniversityOttawa HospitalInstitute of Infection and ImmunityUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsTIGITCirrhosisHepatocellular carcinomaFibrosisImmune systemImmunologyReceptorPhenotypeMedicineChronic liver diseaseCancer researchLymphocyteBiologyCD8Internal medicineGene

Abstract

fetched live from OpenAlex

Chronic HCV can result in advanced liver disease, including cirrhosis. Patients with advanced fibrosis experience poor clinical outcomes and increased risk for hepatocellular carcinoma (HCC). These outcomes are, in part, a consequence of immune dysfunction. Increased inhibitory receptor and Galectin-9 (GAL-9) expression is a possible mechanism promoting lymphocyte dysfunction. In this study, we measured the expression of inhibitory receptors and GAL-9 on T/NK cells of patients with chronic HCV with no to moderate fibrosis (F0-F2) and advanced fibrosis (F3-F4). To analyze their co-expression, we employed t-SNE analysis. Notably, we found that F3-F4 patients had higher frequencies of >3 inhibitory receptor co-expression on NK cells. Moreover, F3-F4 patients manifest a higher frequency of NK cells co-expressing TIGIT and TIM-3, and CD4/NK cells co-expressing LAG-3 and GAL-9. In conclusion, we identified phenotypes of immune dysregulation that could explain the increased susceptibility to infection and HCC in patients with chronic HCV with advanced fibrosis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.193
Teacher spread0.184 · 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.

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

Citations16
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicImmune Cell Function and InteractionFrench-language works237,207