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Record W3024586327 · doi:10.1149/ma2020-01452554mtgabs

Electrochemical Detection of Drug Efflux from Living Cells - Towards the Quantification of Drug Resistance in Bacteria and Cancer

2020· article· en· W3024586327 on OpenAlexaff
Sabine Kuss, Huy Tran Le Luu, Rafiqul Islam

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEffluxDrug resistanceDrugAntibioticsCancer cellAntibiotic resistancePharmacologyCancerBiologyMedicineMicrobiologyBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

The rapid spread of drug resistance in bacteria as well as cancer has developed into a significant threat to the global public health. According to the World Health Organization (WHO), antibiotic resistance is present in every country, and various national and international health organizations, including the United Nations and the Infectious Diseases Society of America have called for the urgent development of new treatment and diagnostic strategies. The Centers for Disease Control and Prevention reports approximately 10 million deaths worldwide each year in connection with antibiotic resistance. Similarly, drug resistance in cancer is believed to be responsible for treatment failure in up to 90% of metastatic cancer patients. Cellular resistance mechanisms in both bacteria and cancer include cell membrane protein modifications, intracellular drug target alterations, and the over expression of efflux pumps. The latter are the result of an over-expression of efflux pump proteins, which enable cells to expel drugs rapidly from the cell interior, before these compounds can take effective action. Drug compounds and efflux pump proteins have recently caught the attention by the electrochemical community to develop new methodologies to understand and detect drug resistance in both bacteria and cancer by electrochemistry. Our research efforts focus specifically on drug compounds expelled from living biological cells by electrochemistry and their detection by standard electrochemical techniques, such as volatmmetry, as well as specialized instrumentation, such as scanning electrochemical microscopy (SECM). This presentation covers the characterization of common drug compounds, but also newly investigational antibiotic hybrids. Recent advances are presented towards the quantification of drug resistance in epithelial ovarian cancer (EOC), which has the highest mortality rate among gynecological cancers.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.247
Teacher spread0.236 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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