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Record W2584139191 · doi:10.1016/j.mehy.2017.01.019

Transfer of malignant traits as opposed to migration of cells: A novel concept to explain metastatic disease

2017· article· en· W2584139191 on OpenAlexaff
Goffredo Arena, Vincenzo Arena, Manuel Arena, Mohamed Abdouh

Bibliographic record

VenueMedical Hypotheses · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMicrovesiclesMetastasisBiologyCancer cellImmune systemCancerDiseaseReceptorCancer metastasisIntegrinImmunologyCancer researchGeneNeurosciencemicroRNAMedicinePathologyGenetics

Abstract

fetched live from OpenAlex

Metastatic disease is believed to develop following dissemination of cells to target organs. Inability of this theory to effectively explain certain phenomena such as patterns of metastatic spread, late metastasis formation, different gene patterns between primary cancer and metastasis have brought forward the need for alternative models. Recent discoveries have strengthened the validity of theories supporting a humoral transfer of malignant traits as opposed to migration of malignant cells to explain metastatic disease in cancer patients. In light of this new evidence, we would like to highlight a model that offers a new perspective to explain cancer metastasis. In the system that we theorize, genetic material released by cancer cells would travel, either free or packed in exosomes, through the blood. Target cells located in organs deriving from the same embryological layer might uptake this genetic material due to expression of specific receptors. Interplay with the immune system would determine the fate of these oncofactors and would regulate their ability to circulate in the blood, integrate in the genome and be transcribed. We also hypothesize that the expression of cell membrane receptors such as integrins, to which cancer exosomes ligate might be mediated by inherited or acquired oncosuppressor mutations.

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)0.002
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 score0.017
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
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 head0.029
GPT teacher head0.298
Teacher spread0.268 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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