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Record W2967427846 · doi:10.3390/jcm8081212

Anti-Tumor Effects of Low Dose Zoledronate on Lung Cancer-Induced Spine Metastasis

2019· article· en· W2967427846 on OpenAlexaff
Elie Akoury, Ana Sofia Ramirez Garcia Luna, Pouyan Ahangar, Xiaoya Gao, Pylyp Zolotarov, Michael H. Weber, Derek H. Rosenzweig

Bibliographic record

VenueJournal of Clinical Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineLung cancerBone metastasisMetastasisCancerBisphosphonateApoptosisTreatment of lung cancerRegimenCancer researchLungOncologySystemic administrationInternal medicineCancer cellOsteoporosisIn vivo

Abstract

fetched live from OpenAlex

Zoledronate (Zol) is an anti-resorptive/tumoral agent used for the treatment of many cancers including spinal bone metastasis. High systemic administration of a single dose is now the standard clinical care, yet it has been associated with several side effects. Here, we aimed to evaluate the effects of lower doses Zol on lung cancer and lung cancer-induced bone metastasis cells over a longer time period. Human lung cancer (HCC827) and three bone metastases secondary to lung cancer (BML1, BML3 and BML4) cells were treated with Zol at 1, 3 and 10 µM for 7 days and then assessed for cell proliferation, migration, invasion and apoptosis. Low Zol treatment significantly decreased cell proliferation (1, 3 and 10 µM), migration (3 and 10 µM) and invasion (10 µM) while increasing apoptosis (10 µM) in lung cancer and metastatic cells. Our data exploits the potential of using low doses Zol for longer treatment periods and reinforces this approach as a new therapeutic regimen to impede the development of metastatic bone cancer while limiting severe side effects following high doses of systemic drug treatment.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.048
GPT teacher head0.456
Teacher spread0.408 · 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 designObservational
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

Citations13
Published2019
Admission routes1
Has abstractyes

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