1- Introduction

2- Materials and methods

3- Results and discussion

4- Conclusions

References

Abstract

Nowadays, a combinatorial drug delivery system that simultaneously transports two or more drugs to the targeted site in a human body, also recognized as a dual-drugs delivery system, represents a promising strategy to overcome drug resistance. Solid lipid nanoparticles loaded with clotrimazole (CLZ) and alphalipolic acid (ALA), considered as an effective agent in the reduction of reactive oxygen species, can enhance anti-infective immunity being proposed as a non-toxic and mainly non-allergic dual-drugs delivery system. In this study, uncoated and cationic CLZ-ALA-loaded SLN were prepared and compared. Suspensions with a narrow size distribution of particles of mean size below 150 nm were obtained, having slight negative or highly positive zeta potential values, due to the presence of the cationic lipid, which also increased nanoparticles stability, as confirmed by Turbiscan® results. Calorimetric studies confirmed the rationale of separately delivering the two drugs in a dual-delivery system. Furthermore, they confirmed the formation of SLN, without significant variation in presence of the cationic lipid. In vitro release studies showed a prolonged drug release without the occurrence of any burst effect. In vitro studies performed on 25 strains of Candida albicans showed the antimicrobial drug activity was not altered when it was loaded into lipid nanoparticles. The study has proved the successfully encapsulation of CLZ and ALA in solid lipid nanoparticles that may represent a promising strategy to combine ALA protective effect in the treatment with CLZ.

Introduction

Fungal infections, most of all candidiasis, represent an increasing challenge affecting global health, that in many cases produces morbidity and mortality in immunocompromised and intensive care unit patients [1,2]. Among different types of fungi, Candida albicans represents the main cause of fungal diseases, responsible of both orally and vaginally yeast infections, overall when a decrease in Lactobacillus concentration appears [3]. Usually, polyenes, echinocandins and azoles are the most used drugs for the treatment of fungal infections [4], even if the requirement of early treatment and the occurrence of side effects still limit the efficiency of the therapy. Among azoles, Clotrimazole (CLZ) is one of the most broad-spectrum antimycotic drug used for the treatment of vulvovaginal and oropharyngeal candidiasis [5]. Moreover, CLZ has been claimed to show a selective antibacterial activity [6,7] and ability to inhibit in vitro Streptococcus mutans biofilm and virulence [8]. CLZ antifungal activity is related to its capability to interfere with the biosynthesis of ergosterol, the major component of the fungal cytoplasmic membrane, with the consequent depletion of ergosterol and its replacement with the aberrant sterol species, 14-amethylsterol, thus disturbing the membrane permeability and fluidity [9]. However, the increasing antifungal resistance to azoles, mainly among immunocompromised patients, strongly limits the effective treatment of mycoses [10]. Combinatorial drug delivery is a promising strategy to prevent potential drug resistance [11]. We have recently demonstrated the possibility to improve CLZ effectiveness exploiting its nanoencapsulation in lipid nanoparticles prepared by additionally using of essential oils from Mediterranean area, highlighting their synergistic effect [12].

Accurately evaluating opportunities in new and emerging science and technologies is a growing concern. This study proposes an integrated framework for identifying a range of potential innovation pathways and commercial applications for solid lipid nanoparticles – one particularly promising contender within the field of nanoenabled drug delivery. Several text mining techniques – term clumping, SAO technique, and net effect analysis – as well as technology roadmapping, are combined with expert judgment to identify the main areas of R&D in this field, and to track their evolution over time. Through analysis, data from multiple sources, including research publications, patents, and commercial press, reveal possible future applications and commercialization opportunities for this emerging technology. We find that research is moving away from materials and delivery outcomes toward clinical applications. The most promising markets are pharmaceuticals and cosmetics; however, the “time-to-market” is much shorter for cosmetics than it is for pharmaceuticals. The most significant contributions of this paper have been highlighted as follows. One innovation is extracting the intelligence from three kinds of data sources after in-depth considering their characteristics and matching with the features of different technology development stages to identify innovative research topics. The second one is combining SAO technique with net effect analysis to identify what the evolutionary links between research topics are, and then to use TRM to visualize the evolution of the main areas of R&D over time.

Introduction

New and emerging science and technology has attracted great attention among scholars because of its tremendous potential to improve society and stimulate economic development. However, the threats and opportunities inherent in such technologies can cause developers to proceed with caution. On the one hand, the existing competitive advantages inherent in current technological competencies offer stability, and new technology may threaten these advantages or even eliminate entire markets. On the other hand, early analysis of new technical areas may present opportunities to take the lead before other competitors become entrenched (Guo et al., 2015). Therefore, developing ways to assess the current research focus and future development directions of new and emerging science and technologies is a compelling issue. Technology opportunity analysis (Porter et al., 1994), which applies data mining and text mining tools to ST&I resources to detect technological innovation (Ma et al., 2014 and Ma et al., 2016) offers one possible solution to this problem. The process allows analysts to explore opportunities for transforming new technologies into new products and, thus, provides decision support to researchers, R&D planners and managers, and science policy-makers (Lee et al., 2015). A number of technology opportunity studies that rely on text mining technologies have been conducted to help derive information for competitive technical intelligence analysis, technology development trend analysis (Ma and Porter, 2015), and forecasting (Ailem et al., 2016; Song et al., 2017).

Abstract

Lipogenic and lipolytic pathways are tightly regulated by nuclear receptors and binding proteins, such as farnesoid x receptor (FXR) and sterol regulatory element binding protein 1c (SREBP-1c). We designed this research to study the effects of high-intensity interval training (HIIT) and high-fat diet (HFD) on hepatic and adipose FXR and SREBP-1c gene expression beside the plasma levels of lipid profile and insulin. 24 male Wistar rats were randomly divided into normal (~12% fat) and HFD (~56% fat) groups with or without participating in the 8 weeks HIIT protocol. Results from two-way ANOVA and Pearson tests (P<0.05) showed that the HFD rats experienced a larger weight gain correlated with dyslipidemia and hyperinsulinemia, higher hepatic and adipose SREBP-1c expression and lower hepatic FXR expression compared with normal diet fed rats. Although HIIT rats showed higher hepatic FXR and lower hepatic and adipose SREBP-1c expression and lower weight gain compared with untrained rats, plasma lipid profile levels had not any significant difference between trained and untrained rats. Interestingly, hepatic FXR expression was negatively correlated with weight gain and SREBP1c expression in both tissues while only the hepatic SREBP-1c was positively correlated to insulin levels. In conclusion, HFD-induced dyslipidemia could occur via the activation of the hepatic SREBP-1c pathway under the insulin effect. Although HIIT rats showed lower SREBP-1c correlated to hepatic FXR activation it seems diet is more effective on lipid profile than HIIT. Also, in presence of HFD, HIIT only affects adipose lipolysis.

Introduction

Postprandial high fat diet (HFD)-induced dyslipidemia has been shown to be an independent risk factor for metabolic and cardiovascular diseases (Tan et al., 2014). Lifestyle interventions are mostly used for lipid lowering effects. It has been shown in human studies, that even one session of aerobic (Kolifa et al., 2004) or resistant (Petitt et al., 2003) exercise can reduce postprandial triglyceride (TG) content in a sex-independent manner that apparently is related to increased lipoprotein lipase activity and decreased endogenous hepatic very low-density lipoprotein (VLDL) secretion (Tan et al., 2014). Increased hepatic lipolytic pathways after exercise appear to decrease liver free fatty acid stores, resulting in decreased endogenous hepatic synthesis and secretion of TG-rich VLDLs (Magkos, 2009).

Sources of potential variability in metabolomics analysis can be ascribed to analytical or biological issues, the latter desirably correlated with physiological states or dietary, environmental, genetic or pathophysiological conditions. For this reason, metabolomics studies require in-depth validation in three progressive domains: (i) analytical issues, (ii) inter-/intra-personal biological variation, and (iii) correspondence with a given phenotype [1,2]. The control of the potential sources of variability is crucial to avoid errors in data interpretation.

Although metabolomics studies developed with different types of samples have revealed that biological variation is usually higher than that attributed to analytical variation, it is impor- tant to identify the main sources of analytical variability [3–5]. Thus, in addition to inter-individual and intra-individual variations, the sources of error in metabolomics are mainly associated to sampling and post-collection procedures such as freeze–thaw cycles or inadequate storage conditions [6]. It is well-known that unsuitable sampling and sample preparation protocols can lead to biased results owing anabolic or catabolic processes [7]. There is increased interest in rapid collection and handling of samples for metabolomics purposes, as turnover kinetics of some metabolites is known to be extremely fast. Accordingly, the time window between sampling and analysis should be as short as possible. For instance, many intracellular metabolites such as ATP and glucose- 6-phosphate are extremely labile, with turnover rates of less than 2 s [8]. Thus, with identification purposes cellular metabolism must be stopped immediately upon sampling of the cells to prevent/minimize metabolite turnover. In fact, sample representation in metabolomics is only achieved when metabolism is efficiently interrupted during sampling by quenching. This step aims at instantaneous stop ofmetabolism by inhibiting the activity of endogenous enzymes. In this way, changes in the metabolic profile duringsampling are suppressed. The common strategy for quenching is based on rapid modification of sample conditions, usually by a temperature shock [9].

Immediate analysis of the samples is not always possible and, therefore, sample storage is required (e.g. in banks of biological samples for research purposes). Sample storage is other critical source of error in metabolomics analysis. Most metabolites are preserved if samples are immediately frozen at temperature close to −80 ◦C (e.g. by liquid nitrogen). However, itis worth noting that differences in storage time or frequent thaw–freeze cycles may have a strong influence on the development of metabolomics models. Metabolic activity during sampling and storage requires stopping or minimizing changes in the metabolic profile either in concentration or structure. For this purpose, decreased temperatures during sample preparation (4 ◦C) and storage (−80 ◦C) are frequently implemented in analysis protocols [10,11]. For instance, appropriate storage and preparatory measures must immediately follow urine collection to ensure the quality of results in metabolomics analysis throughout the collection and analytical process. Thus, apart from samples storage at −80 ◦C, freeze–thaw cycles should be avoided whenever possible. The addition of a bacteriostatic preservative such as sodium azide can be crucial to avoid bacterial contamination [12].