MétaCan
Menu
Back to cohort
Record W2548467431 · doi:10.1038/srep36641

Biodistribution of cisplatin revealed by imaging mass cytometry identifies extensive collagen binding in tumor and normal tissues

2016· article· en· W2548467431 on OpenAlexafffund
Qing Chang, Olga Ornatsky, Iram Siddiqui, Rita Straus, Vladimir Baranov, David W. Hedley

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenFluidigm (Canada)
FundersCanadian Cancer Society Research Institute
KeywordsMass cytometryCisplatinBiodistributionFlow cytometryIn vivoCytometryCancer researchPathologyChemistryStromaCancerCell biologyBiologyMedicineMolecular biologyBiochemistryInternal medicineImmunohistochemistryPhenotypeChemotherapyGene

Abstract

fetched live from OpenAlex

Imaging mass cytometry was used for direct visualization of platinum localization in tissue sections from tumor and normal tissues of cisplatin-treated mice bearing pancreas cancer patient-derived xenografts. This recently-developed technology enabled simultaneous detection of multiple markers to define cell lineage, DNA damage response, cell proliferation and functional state, providing a highly detailed view of drug incorporation in tumor and normal tissues at the cellular level. A striking and unanticipated finding was the extensive binding of platinum to collagen fibers in both tumor and normal mouse tissues. Time course experiments indicated the slow release of stroma-bound platinum, although it is currently unclear if released platinum retains biological activity. Imaging mass cytometry offers a unique window into the in vivo effects of platinum compounds, and it is likely that this can be extended into the clinic in order to optimize the use of this important class of agent.

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)0.001
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 score0.079
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.237
Teacher spread0.232 · 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

Citations118
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicCancer Genomics and DiagnosticsFrench-language works237,207