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Record W4387866315 · doi:10.1002/advs.202304886

Perfusion Window Chambers Enable Interventional Analyses of Tumor Microenvironments

2023· article· en· W4387866315 on OpenAlexfundno aff
Anastasia Korolj, Rainer H. Köhler, Ella Scott, Elias A. Halabi, Kilean Lucas, Jonathan Carlson, Ralph Weissleder

Bibliographic record

VenueAdvanced Science · 2023
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIn vivoIntravital microscopyTumor microenvironmentPreclinical imagingPerfusionPathologyMedicineBiomedical engineeringBiologyCancer researchTumor cellsRadiology

Abstract

fetched live from OpenAlex

Intravital microscopy (IVM) allows spatial and temporal imaging of different cell types in intact live tissue microenvironments. IVM has played a critical role in understanding cancer biology, invasion, metastases, and drug development. One considerable impediment to the field is the inability to interrogate the tumor microenvironment and its communication cascades during disease progression and therapeutic interventions. Here, a new implantable perfusion window chamber (PWC) is described that allows high-fidelity in vivo microscopy, local administration of stains and drugs, and longitudinal sampling of tumor interstitial fluid. This study shows that the new PWC design allows cyclic multiplexed imaging in vivo, imaging of drug action, and sampling of tumor-shed materials. The PWC will be broadly useful as a novel perturbable in vivo system for deciphering biology in complex microenvironments.

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 score0.120
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.349
Teacher spread0.313 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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