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Record W3202743825 · doi:10.30683/1929-2279.2020.09.11

A Marine Natural Products as Modulators of Multidrug Resistance

2020· article· en· W3202743825 on OpenAlexvenueno aff
Tatjana Stanojković, Sanja Milović

Bibliographic record

VenueJournal of cancer research updates · 2020
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMultiple drug resistanceEffluxIntracellularChemistryTransmembrane proteinChemotherapeutic drugsOrganelleDrug resistancePharmacologyBiologyBiochemistryMicrobiologyReceptor

Abstract

fetched live from OpenAlex

Multidrug resistance (MDR) which enable the tumor cells to possess intrinsic or acquired cross resistance to multiple chemotherapeutic agents simultaneously is considered to be a major challenge in cancer chemotherapy during the 21st century. numerous efflux pumps and transport proteins have been found to play important roles in MDR either the phenomenon of lowering the total intracellular retention of chemotherapeutic drugs or the redistribution of intracellular accumulation of drugs away from target organelles are two of the basic mechanisms involved in this process of MDR by transmembrane proteins which are expressed in varying concentrations in different neoplasms. Multiple compounds that have the potential to inhibit these pumps or proteins can be a future prospective for adjuvant treatment of neoplastic conditions. In this regard, compounds derived from natural products bear the advantages of low-cost and relative nontoxicity thus providing a great pool of lead structures for chemical derivatizations. This review gives an overview on chemical substances isolated from natural products of marine origin which possess the MDR modulating properties

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.376
Teacher spread0.335 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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