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In the world, cancer is listed as the second leading cause of death. Breast cancer is one of the types that affects women more often than men, and because it has a high mortality rate, the early detection for breast cancer is crucial. The demand for early breast cancer diagnosis and detection has led to a number of creative research avenues in recent years. But even if artificial intelligence techniques have improved in precision, their exactness still has to be increased to allow for their inevitable implementation in practical applications. This paper provides a Salp Swarm and Grey Wolf Optimization-based technique for diagnosing breast cancer that is inspired by nature. Data analysis for breast cancer was done using both SVM and KNN algorithms. For the purpose of diagnosis, we made use of the Wisconsin Breast Cancer Dataset (WBCD). The study also describes the proposed model's actual implementation in the field of computational biology, together with its characteristics, assessments, evaluations, and conclusions. Specificity, precision, F1-score, recall, and accuracy were some of the metrics used to evaluate how well the approach in question performed. When used on the WBCD-dataset, the proposed SSA-GWO model had an accuracy of 99.42%. The outcomes of the actual applications demonstrate the suggested hybrid algorithm's applicability to difficult situations involving unidentified search spaces. [ABSTRACT FROM AUTHOR]

Credit scoring is a mathematical and statistical tool that aids financial institutions in deciding suitable candidates for the issuance of loans, based on the analysis of the borrower’s financial history. Distinct groups of borrowers have unique characteristics that must be identified and trained on to increase the accuracy of classification models for all credit borrowers that financial institutions serve. Numerous studies have shown that models based on diverse base-classifier models outperform other statistical and AI-based techniques for related classification problems. This paper proposes a novel multi-layer clustering and soft-voting-based ensemble classification model, aptly named Self Organizing Map Clustering with Metaheuristic Voting Ensembles (SCMVE) which uses a self-organizing map for clustering the data into distinct clusters with their unique characteristics and then trains a sailfish optimizer powered ensemble of SVM-KNN base classifiers for classification of each distinct identified cluster. We train and evaluate our model on the standard public credit scoring datasets—namely the German, Australian and Taiwan datasets and use multiple evaluation scores such as precision, F1 score, recall to compare the results of our model with other prominent works in the field. On evaluation, SCMVE shows outstanding results (95% accuracy on standard datasets) when compared with popular works in the field of credit scoring. [ABSTRACT FROM AUTHOR]

With the rise of traffic over wide networks, particularly the internet, and the cloud-based transactions and interactions, database security is important for any organisation. The detection of, and protection from, unauthorised external attacks and insiders abusing privileges is an integral part of database security. To that end, we propose Outlier based Intrusion Detection in Databases for User Behaviour Analysis using Weighted Sequential Pattern Mining (BWSPM), a novel method for the detection of malicious transactions through a sequential flow from outlier detection followed by different behavioural checks at the role-based rule mining component, and finally a user level behavioural check. In the worst case, a transaction has to go through a triple-fold security validation directing the model from generalisation to specification. The Outlier Detection module generates clusters based on the syntactic characteristics of transactions and detects transactions that do not adhere to their closest cluster. Role-level analysis is based upon mining rules that capture dynamic usage of attributes local to every role domain, and the transactions are verified against these rules. Finally, User behaviour profiling models user behaviour based on past transactions, and the incoming transaction is flagged if it diverges from that. Security checks are made at every level to prevent further transaction analysis to reduce false positive rate and achieve a higher degree of optimisation. Encouraging results, with levels of accuracy of around 86.4%, were obtained through our approach after conducting experiments on a dataset generated using the TPC-C (Transaction Processing Performance Council) benchmark. [ABSTRACT FROM AUTHOR]

Forever and a day, breast cancer has caused significant negative impacts on the quality of lives of number of women, more often than not turning into a fatal disease. The growth in the number of such cases has constantly been a major concern for the community as well as medical experts. To prevent irreversible damages caused by the disease, early identification of breast cancer is essential. Various researches and techniques have been devised in the past as an attempt to achieve this task with appreciable accuracy. As an advancement to these pre-existing algorithms and methods, we have devised a model by exploiting the techniques of nature-inspired metaheuristics in order to efficiently detect breast cancer at an early stage while maintaining acceptable levels of accuracy. In this paper, we propose a hybrid model, namely "hybrid artificial bee colony and black hole with genetic operators (GBHABC)", for the early detection of breast cancer. In the proposed model, we employed a support vector machine (SVM) ensemble technique, optimized using the proposed GBHABC model. This model combines the techniques of two major algorithms, namely artificial bee colony (ABC) and black hole (BH), guided through crossover and mutation genetic operators. Datasets from the well-known UCI breast cancer repository have been used to train the models and evaluate test result. For a fair and accurate evaluation of the model, a number of metrics have been examined including accuracy, sensitivity, specificity, F1-score and precision. An impeccable accuracy of 99.42% was obtained on the UCI dataset, clearly outperforming any literature in the same field. [ABSTRACT FROM AUTHOR]

Background: Emulsomes are a type of lipid-based nanoparticle that consists of a solid lipid core surrounded by a phospholipid bilayer and have shown promise as drug delivery systems for a variety of applications. The major advantageous aspect of employing lipid-based carriers is their ability to enhance the solubility and bioavailability of poorly water-soluble drugs, mandatorily in case of intranasal drug delivery. These structures have portrayed significant ability to encapsulate drug with poor water solubility and low oral bioavailability, further leading to a completely enhanced drug delivery systems for achieving stability and controlled release of drug. The selection of lipid components and their physiochemical properties can be tailored to optimize drug solubility, blood brain barrier permeability, and enhanced targeting. Intranasal drug delivery systems offer several advantages over other routes of administration. Intranasal delivery of drugs can provide rapid and efficient absorption into the bloodstream, bypassing first-pass metabolism in the liver and potentially reducing the risk of systemic side effects. Nasal mucosa comprises of dense network of blood vessels, that allow much enhanced rapid drug absorption and direct systemic delivery once the medication is being insufflated through the nasal route. Emulsomes can be used to encapsulate a wide range of drugs, including hydrophobic compounds that are difficult to formulate using traditional delivery methods. By incorporating targeting ligands or other components into the emulsome structure, it is possible to create formulations that are highly selective for specific tissues or cells. The characterization parameters majorly particle size, zeta potential, and encapsulation efficiency play a significant role while demonstrating the effectiveness of emulsome formulation and further its nasal route of administration. Therefore, by assessing and evaluating the parameters, researchers could effectively gain insights into the quality, stability, and enhanced therapeutic effects of emulsome drug carrier, leading to impactful information which would help in future intranasal emulsome preparation preparations, optimization and ensuring the overall effectiveness of the drug delivery systems. Short conclusion: This review discusses the idea of emulsomes drug delivery systems, reviews the effectiveness of emulsomes for the delivery of small molecules, and pays particular attention to its structural and formulation design including benefits of intranasal emulsome delivery with recent advancements, stability aspects, and various considerations related to drug delivery and comprising of future prospects. [ABSTRACT FROM AUTHOR]

Organizations today are employing databases on a large scale to store data essential for their functioning. Malicious access and modifications of the databases may lead to adverse financial and legal implications. In recent years, security researchers have focused on detecting abuse of access privileges by employees of an organization. Identifying threats from insiders is hard because they are aware of the organization of the database in addition to having authorised access privileges. To detect insider attacks effectively and efficiently, we present a novel approach to dynamically determine the malicious transactions using historical data. We propose Trust factor-based user behavior analysis using sequential pattern mining for database intrusion detection systems (TFUBID). Since, groups of users access the organizational database for similar purposes, we cluster user behavior vectors using fuzzy clustering and define a class of Integral Data Attributes using sequential pattern mining to model trust factor-based behavioral patterns of employees accessing the database assigning higher weight to critical elements and Directly Correlated Attributes. A comprehensive experimental evaluation on our synthetic dataset adhering to TPC-C standard benchmark revealed that TFUBID achieved an accuracy of 94% for detecting malicious transactions and outperforms competing state-of-the-art techniques on several performance measures. [ABSTRACT FROM AUTHOR]

Database systems have become imperative for organisations around the world to store and analyse information. However, as one of the ramifications of a massive surge in cloud-based activities and interactions brought forth by the advent of the internet era, the data is exposed to an audience broader than ever and there are a variety of new challenges putting database security in jeopardy. To be able to address the problem of data security, we propose a unique method for Database Intrusion Detection System build on frequent sequential pattern mining and a modified metaheuristic hybrid clustering of Grey Wolf and Whale optimization algorithm to determine malicious transactions in Role Based Access Control and non-RBAC supervised databases. Our proposed approach extracts data dependency rules from the database logs using CM-SPADE mining algorithm to detect outsider threats. It then assigns role profiles to the users based on the previous user activities using the modified metaheuristic clustering to detect insider threats. Thereby, identifying incoming transactions as malicious by matching the role profile of the user and comparing the adherence of the transaction pattern to the generated dependency rules. To evaluate the efficiency of the model we generated a synthetic dataset including malicious and non-malicious transactions adhering to the TPC-C benchmark, and the findings were encouraging, with levels of accuracy of around 97.8 percent. [ABSTRACT FROM AUTHOR]

Elevated blood serotonin levels have been observed in patients with heart failure and serotonin has a role in pathological cardiac function. The serotonin receptor system was examined in adult rat isolated cardiac fibroblast and myofibroblast cells. This is one of the first studies that has investigated serotonin receptors and other proteins involved in the serotonin receptor system in rat cardiac fibroblast and myofibroblast cells. Rat primary cardiac fibroblasts were isolated and transformed into myofibroblasts using 5 ng/ml TGF-β1. Transformation of cells to myofibroblasts was confirmed with the presence of α-smooth muscle actin using Western blot. Serotonin metabolism and receptor protein expression was assessed using Western blot techniques and serotonin levels measured using ELISA. The 5-HT1A, 5-HT2A and 5-HT2B receptors were found to be present in both rat cardiac fibroblasts and myofibroblast cells, however no significance in protein expression between the two cell types was found (P > 0.05). In this study a significant increase in the serotonin transporter (SERT), tryptophan hydroxylase 1 and extracellular serotonin levels was observed in rat cardiac myofibroblasts when compared to fibroblasts (P < 0.05). These results suggest that serotonin levels may rise in parallel with cardiac myofibroblast populations and contribute to the pathogenesis of heart failure via serotonin receptors. [ABSTRACT FROM AUTHOR]

Database security is pertinent to every organisation with the onset of increased traffic over large networks especially the internet and increase in usage of cloud based transactions and interactions. Greater exposure of organisations to the cloud implies greater risks for the organisational as well as user data. In this paper, we propose a novel approach towards database intrusion detection systems (DIDS) based on Expectation maximization Clustering and Sequential Pattern Mining (EMSPM). This approach unlike any other does not have records and assumes a predetermined policy to be maintained in an organisational database and can operate seamlessly on databases that follow Role Based Access Control as well as on those which do not conform to any such access control and restrictions. This is achieved by focusing on pre-existing logs for the database and using the Expectation maximization clustering algorithm to allot role profiles according to the database user's activities. These clusters and patterns are then processed into an algorithm that prevents generation of unwanted rules followed by prevention of malicious transactions. Assessment into the accuracy of EMSPM over sets of synthetically generated transactions yielded propitious results with accuracies over 93%. [ABSTRACT FROM AUTHOR]

Objective: The present research aimed to investigate the anti-inflammatory potential of dietary anthocyanin (ACN) in type 2 diabetic (T2D), T2D-at-risk and healthy individuals. Furthermore, dietary inflammatory index (DII) was used to study the association of diet with biomarkers of inflammation. Research methods: An open-label clinical trial was conducted at Griffith University investigating the efficacy of 320 mg ACN supplementation per day over the course of 4 weeks. Diabetes-associated inflammatory biomarkers and relevant biochemical and physical parameters were tested pre-and post-intervention, and participants' dietary inflammatory potential was estimated. Results: A significant reduction in the pro-inflammatory biomarkers' interleukin-6, interleukin-18, and tumour necrosis factor-α was observed in the T2D group. In addition, some, but not all, biochemical parameters including fasting blood glucose, low-density lipoprotein cholesterol and uric acid were significantly improved in T2D-at-risk group. Moreover, a significant difference was detected between the DII scores of the healthy and T2D groups. DII score for the T2D group was consistent with an anti-inflammatory diet. Conclusion: Anti-inflammatory potential of dietary ACN in T2D participants was evidenced in the present study. Although, anti-inflammatory dietary patterns of T2D participants may have accelerated the anti-inflammatory effect of the ACN capsules supplemented in this trial. [ABSTRACT FROM AUTHOR]

Synthesis of metallic nanoparticles via green approach holds great potential in diverse fields of biotechnology and medicine with special mention to silver nanoparticles (AgNPs) which undoubtedly display antimicrobial, radical scavenging, and dye degradation properties. Currently, there is a need to explore more cost-effective and efficient methods to synthesize AgNPs. In this study, we have synthesized biogenic AgNPs using an aqueous extract of a flowering plant of the legume family, Fabaceae, subfamily Caesalpiniaceae, Cassia fistula, which is also well known for its medicinal values. Spectroscopically and physicochemically characterized AgNPs were evaluated for their cytocompatibility, antimicrobial effects, antioxidant and catalytic activity to establish their potential for various biomedical applications. DLS studies revealed their size ~ 237 nm with the surface charge of ~ − 30 mV. The results of the zone of inhibition and MIC assays showed the superiority of the activity of these particles over the pod extract. Catalytic reduction of toxic p-nitrophenol to benign p-aminophenol as well as degradation of hazardous industrial dyes (methyl orange and methylene blue) advocated their potential as environmental toxicant eradicators. Besides, these biogenic AgNPs displayed profound antibiofilm effects in static microtiter plates. Altogether, the results of various bioassays using these biogenic nanoparticles demonstrate their immense potential as antimicrobial, antioxidant, and antibiofilm agents. [ABSTRACT FROM AUTHOR]

The biogenic tailoring of silver nanoparticles using plant extract is becoming an attractive approach in the current scenario. Manilkara zapota (MZ) is well known for its antibacterial, hepato-protective, anti-inflammatory, anti-tussive, anti-fungal, anti-tumour, and free radical scavenging potential. Its plants extract is a rich source of secondary metabolites. Nowadays, silver nanoparticles (AgNPs) have been advocated for a variety of biomedical applications. In present work, silver nanoparticles have been synthesized using an aqueous extract of MZ, physicochemically characterized and finally evaluated for antimicrobial effects, catalytic reduction/degradation of organic dyes and cytotoxicity. The nanosized AgNPs (~ 84 nm) were found to possess prominent antibacterial potential against gram positive and gram negative pathogens (MIC 50 μg/ml) in comparison to native plant extract. Moreover, these particles were found to be non-toxic and efficient eradicators of environmental toxicants via rapid catalytic reduction of toxic chemicals and dyes. Altogether, these results suggest promising potential of these nanoparticles that can be used as multifunctional agents for future biomedical applications. [ABSTRACT FROM AUTHOR]

The impact of gamma irradiation on growth and physiology of Euryale ferox was described in the present investigation. E. ferox is an underutilized aquatic food crop that grows in shallow-water bodies in lower Assam regions and north Bihar of India. The seeds of E. ferox were irradiated with different doses of gamma irradiation ranging from 0 to 500 Gy. It was observed that the germination and survival percentage was inhibited by increasing the irradiation dose. However, plants developed from seed exposed to an irradiation dose beyond 100 Gy did not survive more than 1 month. Further growth parameters (leaf size and number, number of thorns, root number and length, and number of flower and seeds) were also compared with respect to non-irradiated plants. Physiological parameters, viz. chlorophyll a, chlorophyll b, total chlorophyll, photosynthetic rate, transpiration rate, stomatal conductance, and intracellular CO2 content was higher in the irradiation population of E. ferox. Superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities were observed low in irradiated population of E. ferox. The proline and glycine betaine content was enhanced with increasing the irradiation dose. The present investigation explores the potential use of gamma rays in genetic improvement of E. ferox and improves understanding of the physiological responses inflicted by gamma irradiation. [ABSTRACT FROM AUTHOR]

Experimental research has recognized the importance of cardiac fibroblast and myofibroblast cells in heart repair and function. In a normal healthy heart, the cardiac fibroblast plays a central role in the structural, electrical, and chemical aspects within the heart. Interestingly, the transformation of cardiac fibroblast cells to cardiac myofibroblast cells is suspected to play a vital part in the development of heart failure. The ability to differentiate between the two cells types has been a challenge. Myofibroblast cells are only expressed in the stressed or failing heart, so a better understanding of cell function may identify therapies that aid repair of the damaged heart. This paper will provide an outline of what is currently known about cardiac fibroblasts and myofibroblasts, the physiological and pathological roles within the heart, and causes for the transition of fibroblasts into myoblasts. We also reviewed the potential markers available for characterizing these cells and found that there is no single-cell specific marker that delineates fibroblast or myofibroblast cells. To characterize the cells of fibroblast origin, vimentin is commonly used. Cardiac fibroblasts can be identified using discoidin domain receptor 2 (DDR2) while α-smooth muscle actin is used to distinguish myofibroblasts. A known cytokine TGF-β1 is well established to cause the transformation of cardiac fibroblasts to myofibroblasts. This review will also discuss clinical treatments that inhibit or reduce the actions of TGF-β1 and its contribution to cardiac fibrosis and heart failure. [ABSTRACT FROM AUTHOR]

Purpose: To identify the underlying genetic defect for non-syndromic autosomal dominant retinitis pigmentosa (adRP) with incomplete penetrance in a North Indian family.Methods: Family history and clinical data were collected. Linkage analysis using 72 fluorescently labeled microsatellite markers flanking all the 26 candidate genes known for adRP was performed. Mutation screening in candidate gene at the mapped region was performed by bi-directional DNA sequencing.Results: Positive two-point lod scores > 1.0 (θ = 0.000) suggestive of linkage were obtained with markers D19S572, D19S927 and D19S926 at 19q13.42, in the vicinity of PRPF31 gene. Mutation screening in all the 14 exonic regions and intron-exon boundaries of PRPF31 revealed a novel change, i.e. c.896G>A (p.Cys299Tyr) in exon eight. The observed change segregated in heterozygous form in all the six affected members and in three carriers, consistent with incomplete penetrance. This substitution was not observed in tested 15 unaffected members and in 200 ethnically matched controls.Conclusion: Present study describes mapping of a locus for non-syndromic adRP with incomplete penetrance at 19q13.42 in a North Indian family and identifies a novel missense mutation (p.Cys299Tyr) in PRPF31 localized at the mapped interval. The observed substitution lies in the NOP domain of PRPF31 that exhibit RNA and protein binding surfaces and thus may interfere in the formation of spliceosome complex. Due to p.Cys299Tyr substitution hydrogen bonds are generated, which may result in conformational changes and PRPF31 protein deformity. Present findings further substantiate the role of PRPF31 in adRP with incomplete penetrance and expand the mutation spectrum of PRPF31. [ABSTRACT FROM AUTHOR]

The present study was aimed to test the hypothesis that paracetamol (PCM) can precipitate autistic like features when used to counteract vaccine-induced fever using experimental rat pups. The pups were treated with measles mumps rubella (MMR) vaccine, diphtheria tetanus and pertussis (DPT) vaccines and lipopolysaccharide (LPS) with subsequent PCM treatment. The pups were evaluated for postnatal growth (weight gain, eye opening) and behavior alterations (swimming performance, olfactory discrimination, negative geotaxis, nociception, and locomotor activity) by performing battery of neurobehavioral test. Significant correlation was observed between social behavioral domains (nociception, anxiety and motor coordination) and pro-inflammatory load in the pups when treated with MMR/LPS along with PCM. A significant change in pro and anti-inflammatory (IL-4, IL-6, IL-10) markers were observed in rats treated with PCM, MMR, LPS, DPS alone or in combination with MMR, LPS and DPT (5128.6 ± 0.000, 15,488 ± 0.000***, 9661.1 ± 157.29***a, 15,312 ± 249.29***, 10,471 ± 0.00***a, 16,789 ± 273.34*** and 12,882 ± 0.00***a). Pups were also scrutinized for the markers of oxidative stress, inflammation and histopathologically. All the treatment groups showed significant alteration in the behavioral changes, oxidative markers (TBARS-in control-4.33 ± 0.02, PCM-9.42 ± 0.18***, MMR-5.27 ± 0.15***, MMR + PCM-8.57 ± 0.18*** a, LPS-6.84 ± 0.10***, LPS + PCM-4.51 ± 0.30***a, DPT-5.68 ± 0.12***, DPT + PCM-7.26 ± 0.18***a) and inflammatory markers without following any specific treatment. These observation could be accorded to variable phenotypes of autistic spectrum disorders (ASDs). [ABSTRACT FROM AUTHOR]

Euryale ferox is native to Southeast Asia and China, and it is one of the important aquatic food crops propagated mostly in eastern part of India. The aim of the present study was to characterize and evaluate the genetic diversity of ex situ collections of E. ferox germplasm from different geographical states of India using microsatellite (simple sequence repeats (SSRs)) markers. Ten SSR markers were analyzed to assess DNA fingerprinting and genetic diversity of 16 cultivated germplasm of E. ferox. Total 37 polymorphic alleles were recorded with an average of 3.7 allele frequency per primer. The polymorphic information content value varied from 0.204 to 0.735 with mean of 0.448. A high range of heterozygosity (Ho 0.228; He 0.512) was detected in the present study. The neighbor-joining (N-J) tree and the principle coordinate analysis showed that the germplasm divided in to three main clusters. The results of the present investigation comply that SSR markers are effective for computing genetic assessment of genetic diversity and similarity with classifying cultivated varieties of E. ferox. Evaluation of genetic diversity among Indian E. ferox germplasm could provide useful information for genetic improvement. [ABSTRACT FROM AUTHOR]

Purpose: High fasting blood glucose (FBG) can lead to chronic diseases such as diabetes mellitus, cardiovascular and kidney diseases. Consuming probiotics or synbiotics may improve FBG. A systematic review and meta-analysis of controlled trials was conducted to clarify the effect of probiotic and synbiotic consumption on FBG levels.Methods: PubMed, Scopus, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases were searched for relevant studies based on eligibility criteria. Randomized or non-randomized controlled trials which investigated the efficacy of probiotics or synbiotics on the FBG of adults were included. Studies were excluded if they were review articles and study protocols, or if the supplement dosage was not clearly mentioned.Results: A total of fourteen studies (eighteen trials) were included in the analysis. Random-effects meta-analyses were conducted for the mean difference in FBG. Overall reduction in FBG observed from consumption of probiotics and synbiotics was borderline statistically significant (−0.18 mmol/L 95 % CI −0.37, 0.00; p = 0.05). Neither probiotic nor synbiotic subgroup analysis revealed a significant reduction in FBG. The result of subgroup analysis for baseline FBG level ≥7 mmol/L showed a reduction in FBG of 0.68 mmol/L (−1.07, −0.29; ρ < 0.01), while trials with multiple species of probiotics showed a more pronounced reduction of 0.31 mmol/L (−0.58, −0.03; ρ = 0.03) compared to single species trials.Conclusion: This meta-analysis suggests that probiotic and synbiotic supplementation may be beneficial in lowering FBG in adults with high baseline FBG (≥7 mmol/L) and that multispecies probiotics may have more impact on FBG than single species. [ABSTRACT FROM AUTHOR]

Euryale ferox Salisbury is an important aquatic food plant cultivated largely in eastern India. E. ferox is a monotypic genus, and breeding programmes have mostly relied on the variability present in the primary gene pool. Knowledge of the genetic structure of the population is limited, and there are very few reports available on the genetic diversity of E. ferox. In this study, comprehensive research on the genetic diversity of 16 germplasms of E. ferox was carried out using random amplified polymorphic DNA (RAPD) and inter-simple sequence repeat (ISSR) markers. Out of 320 RAPD and 95 ISSR primers screened initially, 61 primers (40 RAPD and 21 ISSR) gave reproducible bands and were selected for further work. Amplification of the 40 RAPD primers gave 533 polymorphic bands with an average of 13.32 polymorphic bands per primer. The percentage of polymorphism ranged from 37.5 to 100, with an average of 88.3 %. The 21 ISSR primers produced 259 bands, of which 214 were polymorphic, with an average of 10.19 polymorphic bands per primer. The percentage of polymorphism using ISSR primers ranged from 50 to 100, with a mean of 82.6 %. Jaccard's coefficient ranged from 0.45 to 0.69 (RAPD), 0.50 to 0.77 (ISSR) and 0.48 to 0.71 (RAPD and ISSR). Molecular characterization of different germplasms of E. ferox not only is essential for its conservation but also can be used in further breeding programmes. [ABSTRACT FROM AUTHOR]

The world is driving in to the era of transformation of chemical therapeutic molecules to biological genetic material therapeutics, and that is where the biological drugs especially 'genes' come into existence. These genes worked as 'magical bullets' to specifically silence faulty genes responsible for progression of diseases. Viral gene delivery research is far ahead of nonviral gene delivery technique. However, with more advancement in polymer science, new ways are opening for better and efficient nonviral gene delivery. But efficient delivery method is always considered as a bottleneck for gene delivery as success of which will decide the fate of gene in cells. During the past decade, it became evident that extracellular as well as intracellular barriers compromise the transfection efficiency of nonviral vectors. The challenge for gene therapy research is to pinpoint the rate-limiting steps in this complex process and implement strategies to overcome the biological physiochemical and metabolic barriers encountered during targeting. The synergy between studies that investigate the mechanism of breaking in and breaking out of nonviral gene delivery carrier through various extracellular and intracellular barriers with desired characteristics will enable the rational design of vehicles and revolutionize the treatment of various diseases. [ABSTRACT FROM AUTHOR]

Oxidative stress-induced reactive oxygen species are associated with the clinical manifestation of insulin resistance. Evidence suggests that antioxidant treatment may reduce this incidence. This study determined whether glucose oxidase (GO)-induced insulin resistance in cultured skeletal muscle cells could be ameliorated by pre-treatment with gamma-tocopherol (GT). Insulin sensitivity in L6 myotubes was assessed by 2-deoxy- d-[3H]-glucose uptake. The phosphorylation of distal insulin signaling proteins Akt and the Akt substrate AS160 were determined by western blot. One hour treatment with 100 mU/ml GO decreased insulin-stimulated glucose uptake (P < 0.001). Pre-treatment with GT either partially (100 µM) or completely (200 µM) restored insulin-stimulated glucose uptake in cells after GO-induced insulin resistance. GO-induced oxidative stress did not impair insulin stimulated phosphorylation of Akt or AS160, but 200 µM GT increased insulin-stimulated phosphorylation of these key signaling proteins ( P < 0.05). High-dose (200 µM) GT treatment ameliorated oxidative stress-induced insulin resistance in cultured rat L6 skeletal muscle cells. [ABSTRACT FROM AUTHOR]