Construction and investigation of breast‐cancer‐specific ceRNA network based on the mRNA and miRNA expression data

It has been proved and widely acknowledged that messenger RNAs can talk to each other by competing for a limited pool of miRNAs. The competing endogenous RNAs are called as ceRNAs. Although some researchers have recently used ceRNAs to do biological function annotations, few of them have investigated the ceRNA network on specific disease systematically. In this work, using both miRNA expression data and mRNA expression data of breast cancer patient as well as the miRNA target relations, the authors proposed a computational method to construct a breast‐cancer‐specific ceRNA network by checking whether the shared miRNA sponges between the gene pairs are significant. The ceRNA network is shown to be scale‐free, thus the topological characters such as hub nodes and communities may provide important clues for the biological mechanism. Through investigation on the communities (the dense clusters) in the network, it was found that they are related to cancer hallmarks. In addition, through function annotation of the hub genes in the network, it was found that they are related to breast cancer. Moreover, classifiers based on the discriminative hubs can significantly distinguish breast cancer patients’ risks of distant metastasis in all the three independent data sets.Inspec keywords: cancer, genetics, medical computing, molecular biophysics, RNAOther keywords: breast‐cancer specific ceRNA network construction, miRNA expression data, mRNA expression data, gene pairs, computational method, dense clusters, cancer hallmarks, biological mechanism, discriminative hub genes     

Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co‐expression network analysis

This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low‐grade glioma (LGG) and identify hub genes. A co‐expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co‐expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.     

Hilbert transform‐based time‐series analysis of the circadian gene regulatory network

In this work, the authors propose the Hilbert transform (HT)‐based numerical method to analyse the time series of the circadian rhythms. They demonstrate the application of HT by taking both deterministic and stochastic time series that they get from the simulation of the fruit fly model Drosophila melanogaster and show how to extract the period, construct phase response curves, determine period sensitivity of the parameters to perturbations and build Arnold tongues to identify the regions of entrainment. They also derive a phase model that they numerically simulate to capture whether the circadian time series entrains to the forcing period completely (phase locking) or only partially (phase slips) or neither. They validate the phase model, and numerics with the experimental time series forced under different temperature cycles. Application of HT to the circadian time series appears to be a promising tool to extract the characteristic information about circadian rhythms.Inspec keywords: time series, genetics, Hilbert transforms, stochastic processes, circadian rhythms, signal processing, medical signal processingOther keywords: phase model, experimental time series, circadian time series, circadian rhythms, circadian gene regulatory network, deterministic time series, stochastic time series, fruit fly model, phase response curves, period sensitivity, phase locking, phase slips, Hilbert transform, time‐series analysis, signal processing     

Identification of self‐regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data

Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self‐regulatory interactions. The values of gene co‐expressions could help in identifying co‐regulations among them. The proposed approach aims at computing the differences in variances of co‐expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co‐variances using principal component analysis (PCA) to predict an asymmetric and non‐diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time‐series RTX therapy, Arabidopsis thaliana, DREAM‐3, and DREAM‐8 datasets, in comparison with existing state‐of‐the‐art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self‐regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.Inspec keywords: molecular biophysics, principal component analysis, genetics, biology computing, reverse engineeringOther keywords: controlling regulatory agents, interacting genes, unlinked network components, self‐regulatory interactions, gene pair regulatory interactions, self‐regulatory network motifs, reverse engineering gene regulatory networks, microarray gene expression data, diversified computational approaches, symmetric interaction, diagonal interaction, gene pairs, gene co‐expressions, co‐expressed genes, mean expressions, gene regulatory expressions, asymmetric gene regulatory interactions     

Identification and analysis of circRNA–miRNA–mRNA regulatory network in hepatocellular carcinoma

This study was to identify important circRNA–miRNA–mRNA (ceRNAs) regulatory mechanisms in hepatocellular carcinoma (HCC). The circRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE97332","term_id":"97332"}}GSE97332 and miRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE57555","term_id":"57555"}}GSE57555 were used for analyses. Functional enrichment analysis for miRNA and target gene was conducted using cluster Profiler. Survival analysis was conducted through R package Survival. The ceRNAs and drug–gene interaction networks were constructed. The ceRNAs network contained five miRNAs including hsa‐miR‐25‐3p, hsa‐miR‐3692‐5p, hsa‐miR‐4270, hsa‐miR‐331‐3p, and hsa‐miR‐125a‐3p. Among the network, hsa‐miR‐25‐3p targeted the most genes, hsa‐miR‐3692‐5p and hsa‐miR‐4270 were targeted by more circRNAs than other miRNAs, hsa‐circ‐0034326 and hsa‐circ‐0011950 interacted with three miRNAs. Furthermore, target genes, including NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were obtained in drug–gene interaction network. Survival analysis showed NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were significantly associated with prognosis of HCC. NRAS, ITGA5, and SMAD2 were significantly enriched in proteoglycans in cancer. Moreover, hsa‐circ‐0034326 and hsa‐circ‐0011950 might function as ceRNAs to play key roles in HCC. Furthermore, miR‐25‐3p, miR‐3692‐5p, and miR‐4270 might be significant for HCC development. NRAS, ITGA5, SEC14L2, SLC12A5, and SMAD2 might be prognostic factors for HCC patients via proteoglycans in cancer pathway. Taken together, the findings will provide novel insight into pathogenesis, selection of therapeutic targets and prognostic factors for HCC.Inspec keywords: cancer, cellular biophysics, patient diagnosis, bioinformatics, tumours, biochemistry, molecular biophysics, genetics, drugs, RNAOther keywords: ITGA5, SMAD2, hsa‐circ‐0034326, SEC14L2, SLC12A5, target gene, survival analysis, drug–gene interaction network, miRNAs, hsa‐miR‐25‐3p, hsa‐miR‐3692‐5p, hsa‐miR‐4270, hsa‐miR‐331‐3p, hsa‐miR‐125a‐3p, hsa‐circ‐0011950, SLC7A1, pathogenesis, therapeutic targets, prognostic factors, circRNA‐miRNA‐mRNA regulatory network, current 125.0 A     

Enhanced stability of L‐asparaginase by its bioconjugation to poly(styrene‐co‐maleic acid) and Ecoflex nanoparticles

Acute lymphoblastic leukemia (ALL) is the white blood cell cancer in children. L‐asparaginase (L‐ASNase) is one of the first drugs used in ALL treatment. Anti‐tumor activity of L‐ASNase is not specific and indicates limited stability in different biological environments, in addition to its quick clearance from blood. The purpose of the present study was to achieve a new L‐ASNase polymer bioconjugate to improve pharmacokinetic, increase half‐life and stability of the enzyme. The conjugations were achieved by the cross‐linking agent of 1‐ethyl‐3‐(3‐ dimethylaminopropyl) carbodiimide (EDC) which activates the carboxylic acid groups of polymeric nanoparticles to create amide bond. EDC conjugated the L‐ASNase to two biodegradable polymers including; Ecoflex^® and poly (styrene‐co‐maleic acid) (PSMA) nanoparticles. To achieve optimal L‐ASNase nanoparticles the amounts of each polymer and the crosslinker were optimized and the nanoparticles were characterized according to their particle size, zeta potential and percent of conjugation of the enzyme. The results showed that conjugated enzyme had more stability against pH changes and proteolysis. It had lower Km value (indicating more affinity to the substrate) and greater half‐life in plasma and phosphate buffered saline, in comparison to native enzyme. Generally, the conjugated enzyme to PSMA nanoparticles showed greater results than Ecoflex^® nanoparticles.Inspec keywords: enzymes, polymer blends, nanomedicine, biomedical materials, blood, nanoparticles, cancer, molecular biophysics, molecular configurations, biochemistry, conducting polymers, electrokinetic effects, particle size, bonds (chemical), biodegradable materials, pHOther keywords: enhanced stability, L‐asparaginase, bioconjugation, poly(styrene‐co‐maleic acid), Ecoflex nanoparticles, acute lymphoblastic leukaemia, white blood cell cancer, children, drugs, ALL treatment, antitumour activity, biological environments, L‐ASNase polymer bioconjugate, pharmacokinetic, enzyme, crosslinking agent, amide bond, 1‐ethyl‐3‐(3‐dimethylaminopropyl) carbodiimide, carboxylic acid groups, polymeric nanoparticles, EDC conjugation, biodegradable polymers, PSMA nanoparticles, optimal L‐ASNase nanoparticles, particle size, zeta potential, pH changes, proteolysis, native enzyme, conjugated enzyme     

Construction and characterization of rectal cancer‐related lncRNA‐mRNA ceRNA network reveals prognostic biomarkers in rectal cancer

Rectal cancer is an important cause of cancer‐related deaths worldwide. In this study, the differentially expressed (DE) lncRNAs/mRNAs were first identified and the correlation level between DE lncRNAs and mRNAs were calculated. The results showed that genes of highly correlated lncRNA‐mRNA pairs presented strong prognosis effects, such as GPM6A, METTL24, SCN7A, HAND2‐AS1 and PDZRN4. Then, the rectal cancer‐related lncRNA‐mRNA network was constructed based on the ceRNA theory. Topological analysis of the network revealed that the network was maintained by hub nodes and a hub subnetwork was constructed, including the hub lncRNA MIR143HG and MBNL1‐SA1. Further analysis indicated that the hub subnetwork was highly related to cancer pathways, such as ‘Focal adhesion’ and ‘Wnt signalling pathway’. Hub subnetwork also had significant prognosis capability. A closed lncRNA‐mRNA module was identified by bilateral network clustering. Genes in modules also showed high prognosis effects. Finally, a core lncRNA‐TF crosstalk network was identified to uncover the crosstalk and regulatory mechanisms of lncRNAs and TFs by integrating ceRNA crosstalks and TF binding affinities. Some core genes, such as MEIS1, GLI3 and HAND2‐AS1 were considered as the key regulators in tumourigenesis. Based on the authors’ comprehensive analysis, all these lncRNA‐mRNA crosstalks provided promising clues for biological prognosis of rectal cancer.     

M‐matrix‐based stability conditions for genetic regulatory networks with time‐varying delays and noise perturbations

Stability is essential for designing and controlling any dynamic systems. Recently, the stability of genetic regulatory networks has been widely studied by employing linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high‐dimensional LMIs. In the previous study, the authors present several stability conditions for genetic regulatory networks with time‐varying delays, based on M ‐matrix theory and using the non‐smooth Lyapunov function, which results in determining whether a low‐dimensional matrix is a non‐singular M ‐matrix. However, the previous approach cannot be applied to analyse the stability of genetic regulatory networks with noise perturbations. Here, the authors design a smooth Lyapunov function quadratic in state variables and employ M ‐matrix theory to derive new stability conditions for genetic regulatory networks with time‐varying delays. Theoretically, these conditions are less conservative than existing ones in some genetic regulatory networks. Then the results are extended to genetic regulatory networks with time‐varying delays and noise perturbations. For genetic regulatory networks with n genes and n proteins, the derived conditions are to check if an n × n matrix is a non‐singular M ‐matrix. To further present the new theories proposed in this study, three example regulatory networks are analysed.Inspec keywords: genetics, linear matrix inequalities, Lyapunov matrix equations, molecular biophysics, noise, proteinsOther keywords: M‐matrix‐based stability condition, genetic regulatory networks, time‐varying delays, noise perturbations, linear matrix inequality approach, high‐dimensional LMI, Lyapunov function, state variables, M‐matrix theory, proteins, nonsingular M‐matrix     

Formulation and characterisation of poly(lactic‐co‐glycolic acid) encapsulated clove oil nanoparticles for dental applications

This study investigated synthesis and characterisation of Nano‐PLGA (poly(lactic‐co‐glycolic acid))/CO (clove‐oil) nanoparticles. The delivery of drug‐loaded nanoparticles to demineralised dentin substrates and their morphological association with a two‐step etch‐and‐rinse adhesive system was studied. The effect of Nano‐PLGA/CO pretreatment on micro‐tensile bond strength of resin‐dentin bonding was scrutinised. This study employed CO‐containing PLGA nanoparticles as a delivery vehicle for sustainable drug release inside dentinal‐tubules for potential dental applications. Emulsion evaporation resulted in uniformly distributed negatively‐charged Nano‐PLGA/Blank and Nano‐PLGA/CO nanoparticles. Scanning electron microscopy/ transmission electron microscopy revealed even spherical nanoparticles with smooth texture. High CO‐loading and encapsulation were achieved. Moreover, controlled CO‐release was evidenced after 15 days, in‐vitro and ex‐vivo. Nanoparticles exhibited low initial toxicity towards human mesenchymal stem cells with excellent antibacterial properties. Nanoparticles penetration inside dentinal‐tubules indicated a close correlation with resin‐tags. Nano‐PLGA/CO pretreatment indicated reduction in short‐term bond strength of resin‐dentin specimens. Nano‐PLGA/CO as model drug‐loaded nanoparticles showed excellent metric and antibacterial properties, low toxicity and sustained CO release. However, the loading of nanoparticles with CO up to ∼10 mg (Nano‐PLGA/CO:10) did not adversely affect short‐term bond strength values. This drug‐delivery strategy could be further expanded to deliver other pulp‐sedative agents and medications with other dental relevance.Inspec keywords: nanoparticles, dentistry, encapsulation, filled polymers, nanofabrication, nanocomposites, nanomedicine, biomedical materials, drug delivery systems, adhesives, tensile strength, biomechanics, resins, proteins, molecular biophysics, biochemistry, emulsions, evaporation, scanning electron microscopy, transmission electron microscopy, texture, cellular biophysics, antibacterial activity, bonds (chemical)Other keywords: poly(lactic‐co‐glycolic acid) encapsulated clove oil nanoparticles, dental applications, drug‐loaded nanoparticle delivery, demineralised dentin substrates, morphological association, two‐step etch‐and‐rinse adhesive system, simulated pulpal pressure, nanoPLGA‐CO pretreatment, microtensile bond strength, resin‐dentin bonded specimens, CO‐containing PLGA nanoparticles, delivery vehicle, sustainable drug release, dentinal‐tubules, potential dental applications, emulsion evaporation, uniformly‐distributed negatively‐charged nanoPLGA‐blank, scanning electron microscopy‐transmission electron microscopy, spherical nanoparticles, smooth texture, high CO‐loading, controlled CO‐release, human mesenchymal stem cells, antibacterial properties, antibiofilm properties, deep nanoparticle penetration, resin‐tags, short‐term bond strength, resin‐dentin specimens, metric properties, antibacterial properties, sustained CO release, pulp‐sedative agents, time 15 d     

Effects of noise and time delay on E2F's expression level in a bistable Rb‐E2F gene’s regulatory network

The bistable Rb‐E2F gene regulatory network plays a central role in regulating cellular proliferation‐quiescence transition. Based on Gillespie''s chemical Langevin method, the stochastic bistable Rb‐E2F gene’s regulatory network with time delays is proposed. It is found that under the moderate intensity of internal noise, delay in the Cyclin E synthesis rate can greatly increase the average concentration value of E2F. When the delay is considered in both E2F‐related positive feedback loops, within a specific range of delay (3‐13)hr, the average expression of E2F is significantly increased. Also, this range is in the scope with that experimentally given by Dong et al. [65]. By analysing the quasi‐potential curves at different delay times, simulation results show that delay regulates the dynamic behaviour of the system in the following way: small delay stabilises the bistable system; the medium delay is conducive to a high steady‐state, making the system fluctuate near the high steady‐state; large delay induces approximately periodic transitions between high and low steady‐state. Therefore, by regulating noise and time delay, the cell itself can control the expression level of E2F to respond to different situations. These findings may provide an explanation of some experimental result intricacies related to the cell cycle.     

RYR2 mutation in non‐small cell lung cancer prolongs survival via down‐regulation of DKK1 and up‐regulation of GS1‐115G20.1: A weighted gene Co‐expression network analysis and risk prognostic models

RYR2 mutation is clinically frequent in non‐small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into RYR2 mutant group (n = 337) and RYR2 wild group (n = 634), and established Kaplan‐Meier curves. The results showed that RYR2 mutant group lived longer than the wild group (p = 0.027). Weighted gene co‐expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis‐related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of DKK1 and GS1‐115G20.1 expression levels were 0.607 and 0.560, respectively, which predicted the 3‐year survival rate of NSCLC sufferers. GSEA identified an association of high DKK1 expression with TP53, MTOR, and VEGF expression. Several target miRNAs interacting with GS1‐115G20.1 were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with RYR2 mutation may obtain better prognosis by down‐regulating DKK1 and up‐regulating GS1‐115G20.1.     

Assessing the antiproliferative effect of biogenic silver chloride nanoparticles on glioblastoma cell lines by quantitative image‐based analysis

Glioblastoma is the most life‐threatening tumour of the central nervous system. Temozolomide (TMZ) is the first‐choice oral drug for the treatment of glioblastoma, although it shows low efficacy. Silver nanoparticles (AgNPs) have been shown to exhibit biocidal activity in a variety of microorganisms, including some pathogenic microorganisms. Herein, the antiproliferative effect of AgCl‐NPs on glioblastoma cell lines (GBM02 and GBM11) and on astrocytes was evaluated through automated quantitative image‐based analysis (HCA) of the cells. The cells were treated with 0.1‐5.0 μg/ml AgCl‐NPs or with 9.7‐48.5 μg/ml TMZ. Cells that received combined treatment were also analysed. At a maximum tested concentration of AgCl‐NPs, GBM02 and GBM11, the growth decreased by 93% and 40%, respectively, following 72 h of treatment. TMZ treatment decreased the proliferation of GBM02 and GBM11 cells by 58% and 34%, respectively. Combinations of AgCl‐NPs and TMZ showed intermediate antiproliferative effects; the lowest concentrations caused an inhibition similar to that obtained with TMZ, and the highest concentrations caused inhibition similar to that obtained with AgCl‐NPs alone. No significant changes in astrocyte proliferation were observed. The authors’ findings showed that HCA is a fast and reliable approach that can be used to evaluate the antiproliferative effect of the nanoparticles at the single‐cell level and that AgCl‐NPs are promising agents for glioblastoma treatment.     

Weighted gene co expression network analysis (WGCNA) with key pathways and hub‐genes related to micro RNAs in ischemic stroke

Ischemic stroke (IS) is one of the major causes of death and disability worldwide. However, the specific mechanism of gene interplay and the biological function in IS are not clear. Therefore, more research into IS is necessary. Dataset {"type":"entrez-geo","attrs":{"text":"GSE110993","term_id":"110993"}}GSE110993 including 20 ischemic stroke (IS) and 20 control specimens are used to establish both groups and the raw RNA‐seq data were analysed. Weighted gene co‐expression network analysis (WGCNA) was used to screen the key micro‐RNA modules. The centrality of key genes were determined by module membership (mm) and gene significance (GS). The key pathways were identified by enrichment analysis with Kyoto Protocol Gene and Genome Encyclopedia (KEGG), and the key genes were validated by protein‐protein interactions network. Result: Upon investigation, 1185 up‐ and down‐regulated genes were gathered and distributed into three modules in response to their degree of correlation to clinical traits of IS, among which the turquoise module show a trait‐correlation of 0.77. The top 140 genes were further identified by GS and MM. KEGG analysis showed two pathways may evolve in the progress of IS. Discussion: CXCL12 and EIF2a may be important biomarkers for the accurate diagnosis and treatment in IS.     

DOC‐LS,a new liposome for dermal delivery,and its endocytosis by HaCaT and CCC‐ESF‐1 cells

The main objective of this work was to investigate the uptake channels of skin cells through which coumarin 6, transported by deoxycholate‐mediated liposomes (DOC‐LS), was internalised; this was also compared against the action of conventional LS. Coumarin 6‐loaded DOC‐LS and LS were characterised for size distribution, zeta potential, and shape, and analysed in vitro in human epidermal immortal keratinocyte (HaCaT) (epidermal) and human embryonic skin fibroblast (CCC‐ESF‐1) (dermal) cell lines. Various endocytosis inhibitors were incubated with cells treated with the nanocarriers. Flow cytometry results indicated that HaCaT and CCC‐ESF‐1 cells internalise the tested preparations through pinocytotic vesicles, macropinocytosis, clathrin‐mediated endocytic pathways, and via lysosomes, which consume a considerable amount of energy. The endocytosis pathways of DOC‐LS and LS showed no difference. This study provides a basis for the application of LS being combined with a microneedle system for efficient intracellular drug delivery, targeting cutaneous histocyte disorders.Inspec keywords: drugs, nanoparticles, lipid bilayers, nanomedicine, biomedical materials, electrokinetic effects, biomembrane transport, drug delivery systems, skin, organic compoundsOther keywords: dermal delivery, CCC‐ESF‐1 cells, skin cells, deoxycholate‐mediated liposomes, coumarin 6‐loaded DOC‐LS, endocytosis inhibitors, clathrin‐mediated endocytic pathways, endocytosis pathways, HaCaT cell lines, size distribution, zeta potential, nanocarriers, flow cytometry, pinocytotic vesicles, macropinocytosis, microneedle system, efficient intracellular drug delivery, targeting cutaneous histocyte disorders     

Degree‐adjusted algorithm for prioritisation of candidate disease genes from gene expression and protein interactome

Computational methods play an important role in the disease genes prioritisation by integrating many kinds of data sources such as gene expression, functional annotations and protein–protein interactions. However, the existing methods usually perform well in predicting highly linked genes, whereas they work quite poorly for loosely linked genes. Motivated by this observation, a degree‐adjusted strategy is applied to improve the algorithm that was proposed earlier for the prediction of disease genes from gene expression and protein interactions. The authors also showed that the modified method is good at identifying loosely linked disease genes and the overall performance gets enhanced accordingly. This study suggests the importance of statistically adjusting the degree distribution bias in the background network for network‐based modelling of complex diseases.Inspec keywords: biochemistry, bioinformatics, diseases, genetics, genomics, medical computing, physiological models, proteins, statistical analysis, proteomicsOther keywords: degree‐adjusted algorithm, candidate disease genes prioritisation, gene expression, protein interactome, computational method, functional annotation, protein–protein interaction, highly linked genes prediction, disease genes prediction, loosely linked disease genes identification, degree distribution bias statistical adjustment, complex disease network‐based modelling     

Low noise patch‐clamp current amplification by nanoparticles plasmonic–photonic coupling (analysis and modelling)

In this article, a patch‐clamp low noise current amplification based on nanoparticles plasmonic radiation is analyzed. It is well‐known, a very small current is flowing from different membrane channels and so, for extra processing the current amplification is necessary. It is notable that there are some problems in traditional electronic amplifier due to its noise and bandwidth problem. Because of the important role of the patch‐clamp current in cancer research and especially its small amplitude, it is vital to intensify it without adding any noises. In this study, the current amplification is performed firstly: from the excitement of nanoparticles by the patch‐clamp pico‐ampere current and then, the effect of nanoparticles plasmonic far‐field radiation on conductor''s carriers, which will cause the current amplification. This relates to the plasmonic‐photonic coupling and their effect on conductor carriers as the current perturbation agent. In the steady state, the current amplification can reach to 1000 times of initial level. Furthermore, we investigated the nanoparticles morphology changing effect such as size, nanoparticles inter‐distance, and nanoparticles distance from the conductor on the amplifier parameters. Finally, it should note that the original aim is to use nanoparticles plasmonic engineering and their coupling to photonics for output current manipulating.Inspec keywords: nanoparticles, plasmonics, biomembranes, low noise amplifiers, cancer, bioelectric phenomena, nanomedicine, biomedical electronicsOther keywords: output current manipulation, NP plasmonic engineering, amplifier parameters, NP morphology changing effect, steady state, current perturbation agent, conductor carrier effect, NP plasmonic far‐field radiation effect, patch‐clamp picoampere current, bandwidth problem, noise, traditional electronic amplifier, membrane channels, nanoparticle plasmonic radiation, nanoparticle plasmonic‐photonic coupling analysis, low noise patch‐clamp current amplification     

Factorial design analysis and optimisation of chitosan‐based nanogels as controlled release system for gentamicin

The aim of this study was preparation and optimisation of a controlled‐release delivery system to decrease the dose‐dependent side effects of gentamicin. Hydrogel nanoparticles composed of a polycationic polymer (chitosan) and an inorganic polyanion (sodium tripolyphosphate) were fabricated in the presence of gentamicin. An experimental design was drawn upon to determine the optimum condition of nanoparticle preparation. Various features of the nanoparticles including drug loading parameters, particle size distribution, zeta potential and in vitro drug release profile were evaluated. Ultimately, the antimicrobial activity of the gentamicin‐loaded nanoparticles was analysed by determination of the minimum inhibitory concentration (MIC) and the potency test. As a result, the nanocarriers with an average size of about 250 nm (unloaded) and 493 nm (gentamicin‐loaded) were obtained with unimodal distribution and a notable polydispersity index (≤0.3). The drug loading efficiency was between 28 and 32%. The gradual and sustained releases (∼90%) of gentamicin were achieved in 24 h. The MIC and potency test showed no significant decrease in the antibacterial activity of gentamicin‐loaded nanoparticles. The outcomes demonstrated that the optimised chitosan nanogels prepared in this study can be considered as a suitable carrier for a controlled release system.Inspec keywords: hydrogels, nanoparticles, drug delivery systems, particle size, electrokinetic effects, antibacterial activity, nanomedicineOther keywords: factorial design analysis, chitosan‐based nanogels, gentamicin, controlled‐release delivery system, hydrogel nanoparticles, polycationic polymer, inorganic polyanion, sodium tripolyphosphate, particle size distribution, drug loading parameters, zeta potential, in vitro drug release profile, antimicrobial activity, minimum inhibitory concentration, polydispersity index, drug loading efficiency, antibacterial activity