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Record W2803697596 · doi:10.1002/hed.25210

Effect of cell‐phone radiofrequency on angiogenesis and cell invasion in human head and neck cancer cells

2018· article· en· W2803697596 on OpenAlexaff
Yaman M. Alahmad, Mohammed Aljaber, Alaaeldin Saleh, Huseyin C. Yalcin, Tahar Aboulkassim, Amber Yasmeen, Gerald Batist, Ala‐Eddin Al Moustafa

Bibliographic record

VenueHead & Neck · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsMcGill UniversityJewish General Hospital
FundersQatar University
KeywordsAngiogenesisCellCancer cellChorioallantoic membraneCancer researchCell growthCancerCell cultureBiologyPathologyMedicineCell biologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Today, the cell phone is the most widespread technology globally. However, the outcome of cell-phone radiofrequency on head and neck cancer progression has not yet been explored. METHODS: The chorioallantoic membrane (CAM) and human head and neck cancer cell lines, FaDu and SCC25, were used to explore the outcome of cell-phone radiofrequency on angiogenesis, cell invasion, and colony formation of head and neck cancer cells, respectively. Western blot analysis was used to investigate the impact of the cell phone on the regulation of E-cadherin and Erk1/Erk2 genes. RESULTS: Our data revealed that cell-phone radiofrequency promotes angiogenesis of the CAM. In addition, the cell phone enhances cell invasion and colony formation of human head and neck cancer cells; this is accompanied by a downregulation of E-cadherin expression. More significantly, we found that the cell phone can activate Erk1/Erk2 in our experimental models. CONCLUSION: Our investigation reveals that cell-phone radiofrequency could enhance head and neck cancer by stimulating angiogenesis and cell invasion via Erk1/Erk2 activation.

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 score0.019
Threshold uncertainty score0.558

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 head0.007
GPT teacher head0.257
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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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