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Record W1988623558 · doi:10.2174/138161206777947704

Adhesion Dependent Signalling in the Tumour Microenvironment: The Future of Drug Targetting

2006· review· en· W1988623558 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Pharmaceutical Design · 2006
Typereview
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsSudbury Regional Hospital
Fundersnot available
KeywordsCell adhesion moleculeCell adhesionAdhesionEpigeneticsCancer researchCell biologyDrug resistanceCarcinogenesisCell signalingCancerMetastasisBiologyCancer cellSignal transductionChemistryBiochemistryGenetics

Abstract

fetched live from OpenAlex

Cellular adhesion molecules are critical components during carcinogenesis and cancer metastasis and contribute to the mechanisms underlying resistance to chemotherapeutic drugs. Since drug resistance is associated with a very poor prognosis for patients with cancer, a better understanding of the role of adhesion molecules could improve patient outcome by identifying novel mechanisms that promote drug resistance. Epigenetic factors, such as cellular adhesion, are shown to promote the resistance of cancers to various chemotherapeutic drugs by altering cellular signalling pathways that activate cellular growth and inhibit apoptosis. In addition, cellular adhesion molecules can provide a means to specifically target more conventional chemotherapy to the unique tumour microenvironment. However, the expression and function of cellular adhesion molecules, and the signals activated by adhesion, are highly interrelated making the development of rational therapies more difficult.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.123
GPT teacher head0.415
Teacher spread0.292 · 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