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Record W2608978488 · doi:10.1016/j.apsb.2017.04.001

Establishment and characterization of arsenic trioxide resistant KB/ATO cells

2017· article· en· W2608978488 on OpenAlexaff
Yun-Kai Zhang, Chun‐Ling Dai, Chun‐Gang Yuan, Hsiang-Chun Wu, Zhijie Xiao, Zi‐Ning Lei, Dong‐Hua Yang, X. Chris Le, Liwu Fu, Zhe‐Sheng Chen

Bibliographic record

VenueActa Pharmaceutica Sinica B · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsUniversity of Alberta
FundersNational Cancer InstituteNational Institutes of HealthSt. John's UniversityKagoshima University
KeywordsArsenic trioxideAbcg2Acute promyelocytic leukemiaChemistryMultiple drug resistanceCell cultureDrug resistanceCancer researchPharmacologyCisplatinMolecular biologyBiochemistryGeneATP-binding cassette transporterMedicineApoptosisBiologyChemotherapyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Arsenic trioxide (ATO) is used as a chemotherapeutic agent for the treatment of acute promyelocytic leukemia. However, increasing drug resistance is reducing its efficacy. Therefore, a better understanding of ATO resistance mechanism is required. In this study, we established an ATO-resistant human epidermoid carcinoma cell line, KB/ATO, from its parental KB-3-1 cells. In addition to ATO, KB/ATO cells also exhibited cross-resistance to other anticancer drugs such as cisplatin, antimony potassium tartrate, and 6-mercaptopurine. The arsenic accumulation in KB/ATO cells was significantly lower than that in KB-3-1 cells. Further analysis indicated that neither application of P-glycoprotein inhibitor, breast cancer resistant protein (BCRP) inhibitor, or multidrug resistance protein 1 (MRP1) inhibitor could eliminate ATO resistance. We found that the expression level of ABCB6 was increased in KB/ATO cells. In conclusion, ABCB6 could be an important factor for ATO resistance in KB/ATO cells. The ABCB6 level may serve as a predictive biomarker for the effectiveness of ATO therapy.

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 score0.135
Threshold uncertainty score0.595

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 head0.020
GPT teacher head0.302
Teacher spread0.282 · 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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