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Combinatorial Therapeutic Approaches to DNA/RNA and Benzylpenicillin (Penicillin G), Fluoxetine Hydrochloride (Prozac and Sarafem), Propofol (Diprivan), Acetylsalicylic Acid (ASA) (Aspirin), Naproxen Sodium (Aleve and Naprosyn) and Dextromethamphetamine Nanocapsules with Surface Conjugated DNA/RNA to Targeted Nano Drugs for Enhanced Anti-Cancer Efficacy and Targeted Cancer Therapy Using Nano Drugs Delivery Systems

Published on: 17th October, 2017

OCLC Number/Unique Identifier: 7286356766

In the current study, combinatorial therapeutic approaches to DNA/RNA of human cancer cells and Benzylpenicillin (Penicillin G), Fluoxetine Hydrochloride (Prozac and Sarafem), Propofol (Diprivan), Acetylsalicylic Acid (ASA) (Aspirin), Naproxen Sodium (Aleve and Naprosyn) and Dextromethamphetamine nanocapsules with surface conjugated DNA/RNA of human cancer cells to targeted Nano drugs for enhanced anti-cancer efficacy and targeted cancer therapy using Nano drugs delivery systems were investigated.
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Pros and Cons Controversy on Molecular Imaging and Dynamics of Double-Standard DNA/RNA of Human Preserving Stem Cells-Binding Nano Molecules with Androgens/Anabolic Steroids (AAS) or Testosterone Derivatives through Tracking of Helium-4 Nucleus (Alpha Particle) Using Synchrotron Radiation

Published on: 15th November, 2017

OCLC Number/Unique Identifier: 7317627574

In the current study, we have investigated pros and cons controversy on molecular imaging and dynamics of double-standard DNA/RNA of human preserving stem cells-binding Nano molecules with Androgens/Anabolic Steroids (AAS) or Testosterone derivatives through tracking of Helium-4 nucleus (Alpha particle) using synchrotron radiation. In this regard, the enzymatic oxidation of double-standard DNA/RNA of human preserving stem cells-binding Nano molecules by haem peroxidases (or heme peroxidases) such as Horseradish Peroxidase (HPR), Chloroperoxidase (CPO), Lactoperoxidase (LPO) and Lignin Peroxidase (LiP) is an important process from both the synthetic and mechanistic point of view
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat

Computational Simulation of Phase-Molecular Separation-DNA/RNA-Related Function Based on Gene Ontology using Combination of Computational Fluid Dynamics, Machine Learning and Membrane Systems

Published on: 21st May, 2025

Our evaluation and its outcomes/outcomes/hints spotlight that gaining a (having to do with measuring matters with numbers) knowledge of the proteome company in living cells, and its outcomes/consequences/tips for the (introduction and production/ organization of objects) of condensates and MLOs, is a critical assignment that the section separation field wishes to face/address. Our findings that dosage-sensitive (tiny chemical meeting commands interior of living things), insufficient (tiny chemical meeting commands internal of living things) and homologs especially, are overrepresented amongst human LLPS drivers, spotlight furthermore the needed component of preserving the mobile (oversupply/huge quantity) of the (bearing on everyone or issue) DNA/RNA merchandise at a great degree well suited with tightly managed LLPS conduct, to keep away from extreme (diseases/the have a look at of diseases) that unexpected errors in any direction may also cause. In-depth close interest of the records on DNA/RNA concentrations used in the LLPS experiments assisting our excessive self-belief dataset of human driver DNA/RNA s laid the uncertainties related with defining the frame-shape-related meaningful ranges of this essential restriction/guiding principle that leads and controls condensate (introduction and production/ organization of items), and recommended how those uncertainties can be lessened (something awful) and (ultimately) shortened.Graphical abstract: Computational Simulation of Phase-Molecular Separation-DNA/RNA-Related Function Based on Gene Ontology Using Combination of Computational Fluid Dynamics, Machine Learning and Membrane Systems.
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat
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