Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer‐related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)‐based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP‐based prediction models. Prediction performance evaluations and comparisons between the authors’ GEP models and three representative machine learning methods, support vector machine, multi‐layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross‐data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.Inspec keywords: lung, cancer, medical diagnostic computing, patient diagnosis, genetic algorithms, feature selection, learning (artificial intelligence), support vector machines, multilayer perceptrons, radial basis function networks, reliability, sensitivity analysisOther keywords: lung cancer prediction, cancer‐related death, cancer diagnosis, gene profiles, gene expression programming‐based model, gene selection, GEP‐based prediction models, prediction performance evaluations, representative machine learning methods, support vector machine, multilayer perceptron, radial basis function neural network, real microarray lung cancer datasets, cross‐data set validation, reliability, receiver operating characteristic curve     

Multi‐threshold and multi‐input DNA logic design style for profiling the microRNA biomarkers of real cancers

Early detection of cancer is very critical because it can reduce the treatment risk and cost. MicroRNAs (miRNAs) have been introduced in recent years as an efficient class of biomarkers for cancer early detection. Now, real‐time polymerase chain reaction has been used to profile the miRNA expression, which is costly, time consuming and low accuracy. Most recently, DNA logic gates are used to detect the miRNA expression level that is more accurate and faster than previous methods. The DNA‐based logic gates face with serious challenges such as the large complexity and low scalability. In this study, the authors proposed a methodology to design multi‐threshold and multi‐input DNA‐based logic gates in response to specific miRNA inputs in live mammalian cells. The proposed design style can simultaneously recognise multiple miRNAs with different rising and falling thresholds. The design style has been evaluated on the lung cancer biomarkers and the experimental results show the efficiency of the proposed method in terms of accuracy, efficiency and speed.Inspec keywords: DNA, logic design, biocomputing, RNA, molecular biophysics, logic gates, lung, genetics, cellular biophysics, cancer, biology computing, enzymes, biosensorsOther keywords: falling thresholds, specific miRNA inputs, multiinput DNA‐based logic gates, low scalability, DNA‐based logic gates face, miRNA expression level, DNA logic gates, low accuracy, time consuming, real‐time polymerase chain reaction, cancer early detection, treatment risk, cancers, microRNA biomarkers, multiinput DNA logic design style, multithreshold, lung cancer biomarkers     

Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach

The major intent of peptide vaccine designs, immunodiagnosis and antibody productions is to accurately identify linear B‐cell epitopes. The determination of epitopes through experimental analysis is highly expensive. Therefore, it is desirable to develop a reliable model with significant improvement in prediction models. In this study, a hybrid model has been designed by using stacked generalisation ensemble technique for prediction of linear B‐cell epitopes. The goal of using stacked generalisation ensemble approach is to refine predictions of base classifiers and to get rid of the worse predictions. In this study, six machine learning models are fused to predict variable length epitopes (6–49 mers). The proposed ensemble model achieves 76.6% accuracy and average accuracy of repeated 10‐fold cross‐validation is 73.14%. The trained ensemble model has been tested on the benchmark dataset and compared with existing sequential B‐cell epitope prediction techniques including APCpred, ABCpred, BCpred and AAPBCPred.Inspec keywords: generalisation (artificial intelligence), support vector machines, cellular biophysics, pattern classification, proteins, learning (artificial intelligence), bioinformaticsOther keywords: antigenic epitopes, stacked generalisation, peptide vaccine designs, immunodiagnosis, antibody productions, linear B‐cell epitopes, generalisation ensemble technique, generalisation ensemble approach, machine learning models, base classifiers     

RNA secondary structured logic gates for profiling the microRNA cancer biomarkers

Deregulation of microRNAs expression is symptomatic of cancer disease and occurs before the awareness of cancer signs. Early detection of cancer disease can improve or drop the disease entirely. DNA computing is an emerging field of detecting microRNAs based on toehold‐mediated strand displacement reactions, which is a more efficient method than the commonly used method like real‐time PCR. Accuracy and cost of diagnostic applications are essential criteria that are achieved by using the DNA logic gates based on the DNA computing method. In this study, the authors proposed the multi‐input liver cancer biosensor with the RNA secondary structure motifs as the computational module and two approaches are suggested.Inspec keywords: cancer, biocomputing, biochemistry, DNA, RNA, biosensors, logic gates, liver, macromolecules, genetics, molecular biophysics, diseasesOther keywords: RNA secondary structured logic gates, microRNA cancer biomarkers, microRNAs expression, cancer disease, cancer signs, detecting microRNAs, toehold‐mediated strand displacement reactions, DNA logic gates, DNA computing method, multiinput liver cancer biosensor, RNA secondary structure motifs     

Crosstalk between pathways enhances the controllability of signalling networks

The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow‐tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross‐pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors’ work provides a comprehensive understanding of the impact of network structures and properties on controllability.Inspec keywords: genetics, numerical analysis, control theoryOther keywords: signalling network controllability, interconnected pathways, bow‐tie architecture, structure controllability, biological mechanisms, cross‐pathway interactions, numerical simulations, biological networks, control theory, gene regulatory network     

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non‐smokers with lung adenocarcinoma

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non‐coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non‐smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co‐expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA–mRNA pairs which were significantly dysregulated in non‐smokers with lung adenocarcinoma. Also two pairs (miR‐133b/Protein Kinase C Zeta (PRKCZ) and miR‐557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors’ understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.Inspec keywords: cancer, molecular biophysics, patient diagnosis, tumours, RNA, proteins, lung, genetics, medical diagnostic computing, molecular configurationsOther keywords: miRNAs expression profiles, mRNAs expression profiles, smokers, nonsmokers, integrative analysis, lung adenocarcinoma, microRNAs, disease specific biomarkers, noncoding regulators, genetic abnormalities, weighted gene coexpression network analysis     

Deciphering the expression dynamics of ANGPTL8 associated regulatory network in insulin resistance using formal modelling approaches

ANGPTL8 is a recently identified novel hormone which regulates both glucose and lipid metabolism. The increase in ANGPTL8 during compensatory insulin resistance has been recently reported to improve glucose tolerance and a part of cytoprotective metabolic circuit. However, the exact signalling entities and dynamics involved in this process have remained elusive. Therefore, the current study was conducted with a specific aim to model the regulation of ANGPTL8 with emphasis on its role in improving glucose tolerance during insulin resistance. The main contribution of this study is the construction of a discrete model (based on kinetic logic of René Thomas) and its equivalent Stochastic Petri Net model of ANGPTL8 associated Biological Regulatory Network (BRN) which can predict its dynamic behaviours. The predicted results of these models are in‐line with the previous experimental observations and provide comprehensive insights into the signalling dynamics of ANGPTL8 associated BRN. The authors’ results support the hypothesis that ANGPTL8 plays an important role in supplementing the insulin signalling pathway during insulin resistance and its loss can aggravate the pathogenic process by quickly leading towards Diabetes Mellitus. The results of this study have potential therapeutic implications for treatment of Diabetes Mellitus and are suggestive of its potential as a glucose‐lowering agent.Inspec keywords: molecular biophysics, biomembranes, diseases, stochastic processes, biochemistry, patient treatment, Petri nets, genetics, sugar, cellular biophysics, biology computingOther keywords: ANGPTL8 associated regulatory network, formal modelling approaches, lipid metabolism, compensatory insulin resistance, glucose tolerance, equivalent Stochastic Petri Net model, ANGPTL8 associated BRN     

Facile synthesis of multi‐walled carbon nanotube via folic acid grafted nanoparticle for precise delivery of doxorubicin

The motive of work was to develop a multi‐walled carbon nanoplatform through facile method for transportation of potential anticancer drug doxorubicin (DOX). Folic acid (FA)‐ethylene diamine (EDA) anchored and acid functionalised MWCNTs were covalently grafted with DOX via π–π stacking interaction. The resultant composite was corroborated by ¹ H NMR, FTIR, XRD, EDX, SEM, and DSC study. The drug entrapment efficiency of FA‐conjugated MWCNT was found high and stability study revealed its suitability in biological system. FA‐EDA‐MWCNTs‐DOX conjugate demonstrated a significant in vitro anticancer activity on human breast cancer MCF‐7 cells. MTT study revealed the lesser cytotoxicity of folate‐conjugated MWCNTs. The obtained results demonstrated the targeting specificity of FA‐conjugate via overexpressed folate receptor deemed greater scientific value to overcome multidrug protection during cancer therapy. The proposed strategy is a gentle contribution towards development of biocompatible targeted drug delivery and offers potential to address the current challenges in cancer therapy.Inspec keywords: toxicology, nanoparticles, biomedical materials, scanning electron microscopy, drug delivery systems, nanofabrication, nanomedicine, nanocomposites, cellular biophysics, cancer, drugs, multi‐wall carbon nanotubes, Fourier transform infrared spectra, X‐ray chemical analysis, differential scanning calorimetry, proton magnetic resonance, organic compoundsOther keywords: facile synthesis, multiwalled carbon nanotube, precise delivery, multiwalled carbon nanoplatform, drug entrapment efficiency, FA‐conjugated MWCNT, stability study, biological system, human breast cancer MCF‐7 cells, MTT study, folate‐conjugated MWCNTs, overexpressed folate receptor, cancer therapy, biocompatible targeted drug delivery, anticancer drug doxorubicin, π‐π stacking interaction, composite material, ¹ H NMR, in vitro anticancer activity, folic acid grafted nanoparticle, folic acid‐ethylene diamine, acid functionalised MWCNT, FTIR spectra, XRD, EDX, SEM, FA‐EDA‐MWCNT‐DOX conjugate, cytotoxicity, DSC, C     

Antimicrobial activity of functionalised carbon nanotubes against pathogenic microorganisms

Carbon nanotubes represent one of the best examples of novel nanostructures, exhibit a range of extraordinary physical properties, strong antimicrobial activity and can pierce bacterial cell walls. This investigation handles the antimicrobial activity of functionalised multiwall carbon nanotubes (F‐MWNTs) as an alternative antimicrobial material compared to the commercial antibiotics. Antibacterial activities of F‐MWNTs are investigated through two different kinds of bacteria, E. coli and S. aureus. The results demonstrate that the best concentration of F‐MWNTs for the maximum inhibition and antibacterial functionality is 80 and 60 μg/ml for E. coli and S. aureus, respectively. The transmission electron microscope reveals the morphological changes damage mechanism for the cellular reliability on these microorganisms. F‐MWNTs are capable of biologically isolating the cell from their microenvironment, contributing to the development of toxic substances and placing the cell under oxidative stress leading to cellular death. The efficiency of F‐MWNTs is compared with the common antibiotics and shows an enhancement in the inhibitory effect with percentages reaches 85%. To account for the bactericidal performance of F‐MWNTs towards these pathogens, the dielectric conductivity and the bacterial growth measurements are conducted. The present study endeavour that F‐MWNTs could be exploited in biomedical devices and altering systems for hospital and industrial cleaning applications.Inspec keywords: antibacterial activity, biomedical materials, microorganisms, cellular biophysics, toxicology, nanomedicine, multi‐wall carbon nanotubes, transmission electron microscopy, electrical conductivity, permittivityOther keywords: F‐MWNTs, pathogenic microorganisms, antimicrobial activity, bacterial cell walls, functionalised multiwall carbon nanotubes, antibacterial activity, E. coli, S. aureus, antimicrobial material, physical properties, transmission electron microscopy, morphological changes, damage mechanism, cellular reliability, microenvironment, toxic substances, oxidative stress, cellular death, bactericidal performance, dielectric conductivity, bacterial growth measurements, biomedical devices, C     

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non‐linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10‐fold cross‐validation, for 110 real cases, and the results were compared with those of support vector machine and K ‐nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non‐commercial purposes.Inspec keywords: patient diagnosis, fuzzy logic, diseases, medical computing, cognition, learning (artificial intelligence), fuzzy set theory, Hebbian learning, neural nets, support vector machinesOther keywords: ischemic stroke, controllable factors, uncontrollable factors, blood pressure, heart disease, intelligent system, immediate treatment, soft computing method, fuzzy cognitive mapping, nonlinear Hebbian learning method, fuzzy cognitive maps training, risk rate     

Properties of scaffolds based on chitosan and collagen with bioglass 45S5

Scaffolds based on chitosan (CTS), collagen (Coll) and glycosaminoglycans (GAG) mixtures cross‐linked by tannic acid (TA) with bioglass 45S5 addition were obtained with the use of the freeze‐drying method. The prepared scaffolds were characterised for morphology, mechanical strength and degradation rate. Moreover, cell viability on the obtained scaffolds was measured with and without the presence of ascorbic acid and dexamethasone. The main purpose of the research was to compare the effectiveness of bioglass 45S5 influence on the physicochemical and biological properties of scaffolds. The results demonstrated that the scaffolds based on the blends of biopolymers cross‐linked by TA are stable in an aqueous environment. Scanning electron microscope images allowed the observation of a porous scaffold structure with interconnected pores. The addition of bioglass nanoparticles improved the mechanical properties and decreased the degradation rate of composite materials. The biological properties were improved for 20% tannic acid addition compared to 5%. However, the addition of bioglass 45S5 did not change to cells response significantly.Inspec keywords: biomedical materials, drying, porous materials, freezing, tissue engineering, proteins, nanofabrication, bone, scanning electron microscopy, polymers, molecular biophysics, cellular biophysics, nanoparticles, porosityOther keywords: chitosan, collagen, glycosaminoglycans, bioglass 45S5 addition, freeze‐drying method, degradation rate, ascorbic acid, dexamethasone, physicochemical properties, biological properties, porous scaffold structure, bioglass nanoparticles, mechanical properties, tannic acid addition, scanning electron microscopy     

Ensembled machine learning framework for drug sensitivity prediction

Drug sensitivity prediction is one of the critical tasks involved in drug designing and discovery. Recently several online databases and consortiums have contributed to providing open access to pharmacogenomic data. These databases have helped in developing computational approaches for drug sensitivity prediction. Cancer is a complex disease involving the heterogeneous behaviour of same tumour‐type patients towards the same kind of drug therapy. Several methods have been proposed in the literature to predict drug sensitivity. However, these methods are not efficient enough to predict drug sensitivity. The present study has proposed an ensemble learning framework for drug‐response prediction using a modified rotation forest. The proposed framework is further compared with three state‐of‐the‐art algorithms and two baseline methods using Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) drug screens. The authors have also predicted missing drug response values in the data set using the proposed approach. The proposed approach outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.14 and 0.404 is achieved using GDSC and CCLE drug screens, respectively. The obtained results show that the proposed framework has considerable potential to improve anti‐cancer drug response prediction.Inspec keywords: medical computing, molecular biophysics, genomics, genetics, learning (artificial intelligence), patient treatment, drugs, cellular biophysics, cancer, biology computing, tumours, diseasesOther keywords: ensembled machine learning framework, drug sensitivity prediction, drug therapy, ensemble learning framework, drug‐response prediction, Cancer Cell Line Encyclopedia drug screens, drug response values, CCLE drug screens, anti‐cancer drug response prediction     

Chitosan nanoparticles loaded with aspirin and 5‐fluororacil enable synergistic antitumour activity through the modulation of NF‐κB/COX‐2 signalling pathway

Based on the enhancement of synergistic antitumour activity to treat cancer and the correlation between inflammation and carcinogenesis, the authors designed chitosan nanoparticles for co‐delivery of 5‐fluororacil (5‐Fu: an as anti‐cancer drug) and aspirin (a non‐steroidal anti‐inflammatory drug) and induced synergistic antitumour activity through the modulation of the nuclear factor kappa B (NF‐κB)/cyclooxygenase‐2 (COX‐2) signalling pathways. The results showed that aspirin at non‐cytotoxic concentrations synergistically sensitised hepatocellular carcinoma cells to 5‐Fu in vitro. It demonstrated that aspirin inhibited NF‐κB activation and suppressed NF‐κB regulated COX‐2 expression and prostaglandin E2 (PGE2) synthesis. Furthermore, the proposed results clearly indicated that the combination of 5‐Fu and aspirin by chitosan nanoparticles enhanced the intracellular concentration of drugs and exerted synergistic growth inhibition and apoptosis induction on hepatocellular carcinoma cells by suppressing NF‐κB activation and inhibition of expression of COX‐2.Inspec keywords: proteins, molecular biophysics, cellular biophysics, biomedical materials, cancer, nanoparticles, drug delivery systems, enzymes, tumours, nanomedicine, drugsOther keywords: chitosan nanoparticles, aspirin, 5‐fluororacil, synergistic antitumour activity, anticancer drug, nonsteroidal antiinflammatory drug, hepatocellular carcinoma cells, NF‐κB activation, NF‐κB regulated COX‐2 expression, PGE2, synergistic growth inhibition, apoptosis induction, prostaglandin E2 synthesis, intracellular concentration, noncytotoxic concentrations, NF‐κB‐cyclooxygenase‐2 signalling pathways, cyclooxygenase‐2, nuclear factor kappa B     

CdO nanoparticles,c‐MWCNT nanoparticles and CdO nanoparticles/c‐MWCNT nanocomposite fibres: in vitro assessment of anti‐proliferative and apoptotic studies in HeLa cancer cell line

A simple ultrasonic assisted chemical technique was used to synthesise cadmium oxide (CdO) nanoparticles (NPs) and CdO NPs/c‐Multiwalled carbon nanotube (c‐MWCNT) nanocomposite fibres.To confirm the physio‐chemico properties and to analyse surface morphology of the obtained nanomaterials X‐Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM) were performed. To evaluate the anti‐cancer property of CdO NPs, c‐MWCNT NPs and CdO NPs/c‐MWCNT nanocomposite fibres, an anti‐proliferative assay test (Methylthiazolyl diphenyl‐ tetrazolium bromide ‐ MTT assay) were performed on HeLa cells which further estimated IC50 value (Least concentration of sample in which nearly 50% of cells remain alive) under in‐vitro conditions. On comparison, CdONPs/c‐MWCNT based system was found to be superior by achieving 52.3% cell viability with its minimal IC50 value of 31.2 μg/ml. Lastly, the CdO NPs based system was taken up for an apoptotic study using DNA fragmentation assay for estimating its ability to cleave the DNA of the HeLa cells into internucleosomal fragments using the agarose gel electrophoresis method. In conclusion, based on our observations, CdO NPs/c‐MWCNT hybrid based system can be further used for the development of efficient drug delivery and therapeutic systems.Inspec keywords: drug delivery systems, electrophoresis, oxidation, toxicology, DNA, nanoparticles, drugs, field emission electron microscopy, scanning electron microscopy, nanofabrication, surface morphology, cancer, X‐ray diffraction, nanomedicine, cellular biophysics, filled polymers, biomedical materials, molecular biophysics, biochemistry, Fourier transform infrared spectra, multi‐wall carbon nanotubesOther keywords: c‐MWCNT nanoparticles, apoptotic study, HeLa cancer cell line, cadmium oxide nanoparticles, c‐MWCNT NPs, anti‐proliferative assay test [methyl thiazolyl diphenyl‐tetrazolium bromide assay], human epithelioid cervix carcinoma cells, live cells, CdO NP‐based system, IC50 concentration, HeLa cell line, cell deaths, CdO‐C     

Bifurcation analysis of insulin regulated mTOR signalling pathway in cancer cells

Insulin induced mTOR signalling pathway is a complex network implicated in many types of cancers. The molecular mechanism of this pathway is highly complex and the dynamics is tightly regulated by intricate positive and negative feedback loops. In breast cancer cell lines, metformin has been shown to induce phosphorylation at specific serine sites in insulin regulated substrate of mTOR pathway that results in apoptosis over cell proliferation. The author models and performs bifurcation analysis to simulate cell proliferation and apoptosis in mTOR signalling pathway to capture the dynamics both in the presence and absence of metformin in cancer cells. Metformin is shown to negatively regulate PI3K through AMPK induced IRS1 phosphorylation and this brings about a reversal of AKT bistablity in codimension‐1 bifurcation diagram from S‐shaped, related to cell proliferation in the absence of drug metformin, to Z‐shaped, related to apoptosis in the presence of drug metformin. The author hypothesises and explains how this negative regulation acts a circuit breaker, as a result of which mTOR network favours apoptosis of cancer cells over its proliferation. The implication of reversing the shape of bistable dynamics from S to Z or vice‐versa in biological networks in general is discussed.Inspec keywords: bifurcation, molecular biophysics, drugs, enzymes, biochemistry, cellular biophysics, cancer, biomedical materialsOther keywords: intricate positive feedback loops, negative feedback loops, breast cancer cell lines, insulin regulated substrate, cell proliferation, cancer cells, AMPK induced IRS1 phosphorylation, codimension‐1 bifurcation diagram, drug metformin, mTOR network, insulin regulated mTOR signalling pathway, bifurcation analysis, PI3K, AKT bistablity     

Preparation and in vitro evaluation of novel cross‐linked chondroitin sulphate nanoparticles by aluminium ions for encapsulation of green tea flavonoids

Chondroitin sulphate is a sulphated glycosaminoglycan biopolymer composed over 100 individual sugars. Chondroitin sulphate nanoparticles (NPs) loaded with catechin were prepared by an ionic gelation method using AlCl₃ and optimised for polymer and cross‐linking agent concentration, curing time and stirring speed. Zeta potential, particle size, loading efficiency, and release efficiency over 24 h (RE₂₄ %) were evaluated. The surface morphology of NPs was investigated by scanning electron microscopy and their thermal behaviour by differential scanning calorimetric. Antioxidant effect of NPs was determined by chelating activity of iron ions. The cell viability of mesenchymal stem cells was determined by 3‐[4, 5‐dimethylthiazol‐2‐yl]‐2, 5‐diphenyl tetrazolium bromide assay and the calcification of osteoblasts was studied by Alizarin red staining. The optimised NPs showed particle size of 176 nm, zeta potential of −20.8 mV, loading efficiency of 93.3% and RE₂₄ % of 80.6%. The chatechin loaded chondroitin sulphate NPs showed 70‐fold more antioxidant activity, 3‐fold proliferation effect and higher calcium precipitation in osteoblasts than free catechin.Inspec keywords: nanoparticles, encapsulation, biomedical materials, particle size, nanofabrication, nanomedicine, electrokinetic effects, cellular biophysics, polymer blends, molecular biophysics, molecular configurations, biochemistry, curing, surface morphology, scanning electron microscopy, differential scanning calorimetry, dyes, precipitationOther keywords: in vitro evaluation, cross‐linked chondroitin sulphate nanoparticles, aluminium ions, nanoparticles, green tea flavonoids, sulphated glycosaminoglycan biopolymer, sugars, catechin, ionic gelation method, cross‐linking agent concentration, curing time, size 176 nm, time 24 h, calcium precipitation, 3‐fold proliferation effect, antioxidant activity, chatechin loaded chondroitin sulphate NPs, Alizarin red staining, osteoblasts, calcification, 3‐[4,5‐dimethylthiazol‐2‐yl]‐2,5‐diphenyl tetrazolium bromide assay, mesenchymal stem cells, cell viability, chelating activity, differential scanning calorimetry, thermal behaviour, scanning electron microscopy, surface morphology, release efficiency, loading efficiency, particle size, zeta potential, stirring speed     

Micro RNA‐30b (inhibitor) nanoparticles suppressed the lipopolysaccharide (LPS)‐induced acute kidney injury

The main aim of present study is to evaluate the effect of miR‐30b on the function of human proximal tubular epithelial cell line HK‐2 cells. For this purpose, miRNA was loaded in an ionically cross‐linked polysaccharide nanoparticle. The authors have demonstrated the influence of miR‐30b mimic and inhibitor in HK‐2 cell killing effect. Lipopolysaccharide (LPS) significantly increased the level of inflammatory cytokines of TNF‐α, IL‐1β and level was further increased with the treatment of PAg‐miR mimic consistent with the cell viability assay. Interestingly, PAg‐miR inhibitor significantly downregulated the expression of inflammatory cytokines and thereby reduced the inflammation in the body. Western blot analysis showed that LPS induced severe apoptosis of HK‐2 cells and the apoptosis was further promoted by the PAg‐miR (mimic). In contrast, PAg‐miR (inhibitor) alleviated the apoptosis of HK‐2 cells as indicated in the significantly reduced levels of Bax and c‐Caspase‐3 proteins. Overall, miR‐30b promoted LPS‐induced HK‐2 cell inflammatory injury by inducing the apoptosis and by releasing inflammatory cytokines, as well as by impairing autophagy process.Inspec keywords: biomedical materials, nanoparticles, molecular biophysics, enzymes, toxicology, injuries, nanomedicine, RNA, cellular biophysics, kidney, proteins, drugs, biochemistryOther keywords: microRNA‐30b, nanoparticles suppressed the lipopolysaccharide (LPS)‐induced, main aim, human proximal tubular epithelial cell line HK‐2 cells, polysaccharide nanoparticle, HK‐2 cell killing effect, inflammatory cytokines, IL‐1β, cell viability assay, PAg‐miR inhibitor, apoptosis, reduced levels, LPS‐induced HK‐2 cell inflammatory injury