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Record W1972719755 · doi:10.1038/sj.bjc.6601766

Effects of tumour acidification with glucose+MIBG on the spontaneous metastatic potential of two murine cell lines

2004· article· en· W1972719755 on OpenAlexaff
Tuula Kalliomäki, Rićhard P. Hill

Bibliographic record

VenueBritish Journal of Cancer · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
FundersNational Cancer Institute
KeywordsExtracellularFibrosarcomaHypoxia (environmental)Cell cultureMelanomaIn vivoCancer researchMetastasisHyperthermiaCellChemistryPathologyBiologyMedicineInternal medicineOxygenCancerBiochemistry

Abstract

fetched live from OpenAlex

In addition to hypoxia, acidic extracellular pH (pH(e)) is recognised as one of the microenvironmental characteristics of solid tumours. A number of studies have examined ways to increase tumour acidity in order to improve tumour-specific targeting of certain drugs and the effectiveness of hyperthermia. However, previous data have shown that exposure of murine tumour cells to acid conditions in culture can enhance their metastatic potential when injected subsequently into mice, raising the concern that deliberate tumour acidification might increase the probability of metastasis. In this study, we examined the effects of in vivo tumour acidification and hypoxia on the spontaneous metastatic potential of the murine KHT-C fibrosarcoma and B16F1 melanoma cell lines. A tumour-specific increase in extracellular acidity, demonstrated by measurements with pH electrodes, was achieved by daily intraperitoneal injections of meta-iodo-benzylguanidine (MIBG) and/or glucose. This method of tumour acidification during tumour growth did not significantly enhance the spontaneous metastatic potential of the two murine cell lines.

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)0.000
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 score0.024
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.230
Teacher spread0.225 · 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

Citations34
Published2004
Admission routes1
Has abstractyes

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