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Record W4403109180 · doi:10.1016/j.healun.2024.09.023

Detailed cellular and spatial characterization of chronic lung allograft dysfunction using imaging mass cytometry

2024· article· en· W4403109180 on OpenAlexafffund
Benjamin Renaud‐Picard, Sajad Moshkelgosha, Grégory Berra, May Cheung, David H. Hwang, David W. Hedley, Stephen Juvet, Tereza Martinu

Bibliographic record

VenueThe Journal of Heart and Lung Transplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsToronto General HospitalSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity Health NetworkToronto Rehabilitation Institute
FundersCanadian Institutes of Health ResearchCystic Fibrosis CanadaFondation du SouffleCystic Fibrosis Foundation
KeywordsMass cytometryCharacterization (materials science)Flow cytometryCytometryLungMedicinePathologyInternal medicineChemistryMaterials scienceImmunologyNanotechnologyBiochemistryPhenotype

Abstract

fetched live from OpenAlex

Long-term survival after lung transplantation remains limited by chronic lung allograft dysfunction (CLAD), with 2 main phenotypes: bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS). We aimed to assess CLAD lung allografts using imaging mass cytometry (IMC), a high dimensional tissue imaging system allowing a multiparametric in situ exploration at a single cell level. Four BOS, 4 RAS, and 4 control lung samples were stained with 35 heavy metal-tagged antibodies selected to assess structural and immune proteins of interest. We identified 50 immune and non-immune cell clusters. CLAD lungs had significantly reduced club cells. A Ki67-high basal cell population was mostly present in RAS and in proximity to memory T cells. Memory CD8 + T cells were more frequent in CLAD lungs, regulatory T cells more prominent in RAS. IMC is a powerful technology for detailed cellular analysis within intact organ structures that may shed further light on CLAD mechanisms.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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 designObservational
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

Citations5
Published2024
Admission routes2
Has abstractno

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