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Record W2284228521 · doi:10.21010/ajtcam.v13i1.4

ANTI-TUMOR EFFECTS OF EPHEDRINE AND ANISODAMINE ON SKBR3 HUMAN BREAST CANCER CELL LINE

2015· article· en· W2284228521 on OpenAlexaff
Dan Chen, Feng Ma, Xiaohui Liu, Rui Cao, Xiong‐Zhi Wu

Bibliographic record

VenueAfrican Journal of Traditional Complementary and Alternative Medicines · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsNorthern College
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsAnisodamineEphedrineSKBR3ApoptosisIntracellularCell growthChemistryPharmacologyWestern blotCell cultureCancer cellBiochemistryMedicineBiologyCancerInternal medicineHuman breast

Abstract

fetched live from OpenAlex

Background: To investigate the effects of ephedrine and anisodamine on the proliferation of human breast cancer. Materials and Methods: SKBR3 cell was treated with or without ephedrine and / or anisodamine, respectively. The trypan blue exclusion assay was used to determine cell numbers. Flow cytometry was used to assess cell cycle distribution and apoptosis. The concentration of cAMP and cyclin D1 was analyzed by enzyme-linked immunosorbent assay. Western blot was used to measure PKA. Results: Ephedrine and anisodamine inhibited cell proliferation and arrested SKBR3 cells at G0/G1 phases. Ephedrine and anisodamine increased the level of CD1 in SKBR3 cells. Furthermore, significant change in intracellular cAMP concentration was found in SKBR3 cells treated with ephedrine and anisodamine. The phosphorylation of PKA substrate was not activated after 48 hours of treatment with ephedrine and anisodamine. Conclusion: Ephedrine and anisodamine inhibit the proliferation of SKBR3 cells via a significantly change of intracellular cAMP concentration.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 integrity0.0000.000
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 head0.063
GPT teacher head0.338
Teacher spread0.275 · 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 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
Published2015
Admission routes1
Has abstractyes

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Same venueAfrican Journal of Traditional Complementary and Alternative MedicinesSame topicPharmacology and Obesity TreatmentFrench-language works237,207