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Short Communication: Studying the Role of Smart Flare Gold Nano Particles in Studying Micro RNA and Oncogene Differential Expression in Prostate Cancer Cell Lines

2017· article· en· W2625496589 on OpenAlexvenueno aff
Jamel Joyner, Monet Stevenson, William Kahan, Christopher Krauss, Sasha Hodges, Eduardo Santos, Myla Worthington, J. M. Rousch, Gloria Payne, Vinod Manglik, Narendra Banerjee, Brianna Morris, Dayton Bell, Santosh K. Mandal

Bibliographic record

VenueJournal of cancer research updates · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsOncogeneCancerProstate cancerRNAGene expressionNanotechnologyFluorescence microscopeBiologyCancer cellComputational biologyNucleolusGeneCancer researchMolecular biologyMaterials scienceCell biologyFluorescenceCell cycleGeneticsPhysics

Abstract

fetched live from OpenAlex

Nano technology is a cutting edge science which is now effectively used in the field of cancer biology. Smart Flare gold nanoparticles are now used often for differential gene expression analysis. In this manuscript we are reporting the use of micro RNA miR 146a and onco gene EZH2 Smart Flare probes to study their expression in different prostate cancer cell lines and the effect of novel Rhenium compounds on these genes using a flow cytometer and a Fluorescence microscope. Our results showed this novel nanotechnology can be effectively used in cancer biology to successfully detect the effect of novel drugs on oncogenes and could be a very useful tool for next generation of cancer researchers.

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 score0.006
Threshold uncertainty score0.020

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.041
GPT teacher head0.370
Teacher spread0.329 · 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
Published2017
Admission routes1
Has abstractyes

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