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Record W4401974790 · doi:10.1002/cmdc.202481601

Front Cover: Lysophosphatidic Acid Receptor 1 (LPA<sub>1</sub>) Antagonists as Potential Migrastatics for Triple Negative Breast Cancer (ChemMedChem 16/2024)

2024· paratext· en· W4401974790 on OpenAlexaff
Wenjie Liu, Amr A. K. Mousa, Austin M. Hopkins, Yin Fang Wu, Kelsie L. Thu, Michael J. Campbell, Simon J. Lees, Rithwik Ramachandran, Jinqiang Hou

Bibliographic record

VenueChemMedChem · 2024
Typeparatext
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsNOSM UniversitySt. Michael's HospitalWestern UniversityLakehead UniversityUniversity of TorontoThunder Bay Regional Research Institute
Fundersnot available
KeywordsLysophosphatidic acidFront coverTriple-negative breast cancerCover (algebra)Cancer researchBreast cancerChemistryReceptorCancerInternal medicineMedicineBiochemistryEngineering

Abstract

fetched live from OpenAlex

The Front Cover shows a migrastatic candidate (LPA1 antagonist) that effectively suppresses triple-negative breast cancer (TNBC) migration and invasion, crucial processes leading to secondary tumors. Metastasis is responsible for about 90% of cancer mortality, while migrastatics, devoid of cytotoxicity, present a promising avenue to combat metastasis without inducing drug resistance. The findings offer hope for therapeutic interventions in the formidable realm of triple-negative breast cancer—a highly aggressive subtype. More details can be found in article 10.1002/cmdc.202400013 by Jinqiang Hou and co-workers. Cover design by Prof. Jinqiang Hou.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.244
Threshold uncertainty score0.816

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2440.132

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 head0.008
GPT teacher head0.259
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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