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Record W3133488458 · doi:10.1172/jci.insight.146883

Highly multiplexed 2-dimensional imaging mass cytometry analysis of HBV-infected liver

2021· article· en· W3133488458 on OpenAlexaff
Daniel Traum, Yue J. Wang, Kathleen B. Schwarz, Jonathan Schug, David Wong, Harry L.A. Janssen, Norah A. Terrault, Mandana Khalili, Abdus S. Wahed, Karen F. Murray, Phillip Rosenthal, Simon C. Ling, Norberto Rodriguez‐Baez, Richard K. Sterling, Daryl Lau, Timothy M. Block, Michael D. Feldman, Elizabeth E. Furth, William M. Lee, David E. Kleiner, Anna S. Lok, Klaus H. Kaestner, Kyong‐Mi Chang

Bibliographic record

VenueJCI Insight · 2021
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsHospital for Sick ChildrenToronto Liver CentreUniversity of Toronto
FundersNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of Pennsylvania
KeywordsMass cytometryImmune systemPathogenesisHBeAgInnate immune systemImmunologyHepatitis B virusHepatitis BFlow cytometryAcquired immune systemBiologyPhenotypeVirologyMedicineVirusHBsAg

Abstract

fetched live from OpenAlex

Studies of human hepatitis B virus (HBV) immune pathogenesis are hampered by limited access to liver tissues and technologies for detailed analyses. Here, utilizing imaging mass cytometry (IMC) to simultaneously detect 30 immune, viral, and structural markers in liver biopsies from patients with hepatitis B e antigen+ (HBeAg+) chronic hepatitis B, we provide potentially novel comprehensive visualization, quantitation, and phenotypic characterizations of hepatic adaptive and innate immune subsets that correlated with hepatocellular injury, histological fibrosis, and age. We further show marked correlations between adaptive and innate immune cell frequencies and phenotype, highlighting complex immune interactions within the hepatic microenvironment with relevance to HBV pathogenesis.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations36
Published2021
Admission routes1
Has abstractyes

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