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Record W3202053570 · doi:10.1002/path.5809

Untangling the complexities of micropapillary cancer<sup>†</sup>

2021· letter· en· W3202053570 on OpenAlexfundno aff
F C Campbell

Bibliographic record

VenueThe Journal of Pathology · 2021
Typeletter
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsnot available
FundersMedical Research Council CanadaCancer Research UKMedical Research CouncilDepartment of Education and Learning, Northern IrelandAstraZeneca
KeywordsMulticellular organismColorectal cancerCancerBiologyPolarity (international relations)Morphology (biology)PhenotypePathologyCancer researchCell biologyMedicineCellGeneGenetics

Abstract

fetched live from OpenAlex

Distinct morphological subtypes of colorectal cancer (CRC) confer a bleak clinical outlook. In a recent issue of The Journal of Pathology, Onuma et al investigated morphological evolution of a highly fatal CRC subtype known as micropapillary cancer (MPC). This study enhances understanding of MPC biology including essential regulatory signals, cellular and multicellular phenotypes, as well as cancer behaviour. Iterative modelling in three-dimensional (3D) patient-derived CRC tissue-originated spheroids (CTOSs) revealed spatiotemporal oscillations of Rho-ROCK hyperactivity underlying reversal of membrane polarity and suppression of lumen formation during development of multicellular MPC morphology. Corroborative studies in CTOSs, xenografts, and archival human CRCs confirm human disease relevance. Although cancer morphology has previously been considered irreversible, targeted inhibition of Rho-ROCK activity restored membrane polarity, lumenized multicellular assembly, and suppressed MPC morphology in 3D CTOS cultures and xenografts. Collectively, the study identifies molecular, biophysical, and multicellular mechanisms implicated in morphological evolution of micropapillary CRC. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.140
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.047
GPT teacher head0.294
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2021
Admission routes1
Has abstractyes

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