Do surface active parenteral formulations cause inflammation?

Söderberg, Lars; Engblom, Johan; Lanbeck, Peter; Wahlgren, Marie

doi:10.1016/j.ijpharm.2015.02.045

Cited by 4 publications

(3 citation statements)

References 28 publications

(37 reference statements)

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“…For example, imipenem-cilastatin, which induces phlebitis without causing direct cell killing, may induce other toxicity mechanisms. Other risk factors causing phlebitis, like the interfacial activity of some parenteral preparations, need to be explored (25).…”

Section: Fig 5 Cell Viability Of Huvec After a 24-h Contact With Vancmentioning

confidence: 99%

Endothelial Cell Toxicity of Vancomycin Infusion Combined with Other Antibiotics

Drouet

Chai

Barthélémy

et al. 2015

Antimicrob Agents Chemother

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Section: Fig 5 Cell Viability Of Huvec After a 24-h Contact With Vancmentioning

confidence: 99%

Endothelial Cell Toxicity of Vancomycin Infusion Combined with Other Antibiotics

Drouet

Chai

Barthélémy

et al. 2015

Antimicrob Agents Chemother

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“…The list of surfactants suitable for pharmaceutical application is restricted, especially in the case of emulsions aimed for parenteral or ocular administration. Even under such restrictions, surfactants may give rise to various side effects, including acute hypersensitivity reactions, peripheral neurotoxicity, and membranedamaging effects leading to haemolysis and tissue irritation [3][4][5]. Furthermore, some of the surfactants can be potentially hazardous for the environment [6].…”

Section: Introductionmentioning

confidence: 99%

Cyclodextrin-based Pickering emulsions: functional properties and drug delivery applications

Jug

Yoon

Jackman

2021

J Incl Phenom Macrocycl Chem

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Cyclodextrins (CDs) are biocompatible, cyclic oligosaccharides that are widely used in various industrial applications and have intriguing interfacial science properties. While CD molecules typically have low surface activity, they are capable of stabilizing emulsions by inclusion complexation of oil-phase components at the oil/water interface, which results in Pickering emulsion formation. Such surfactant-free formulations have gained considerable attention in recent years, owing to their enhanced physical stability, improved tolerability, and superior environmental compatibility compared to conventional, surfactant-based emulsions. In this review, we critically describe the latest insights into the molecular mechanisms involved in CD stabilization of Pickering emulsions, including covering practical aspects such as methods to prepare CD-based Pickering emulsions, lipid encapsulation, and relevant stability issues. In addition, the rheological and textural features of CD-based Pickering emulsions are discussed and particular attention is focused on promising examples for drug delivery, cosmetic, and nutraceutical applications. The functionality of currently developed CD-based Pickering emulsions is also summarised, including examples such as antifungal uses, and we close by discussing emerging possibilities to utilize the molecular encapsulation of CD-based emulsions for translational medicine applications in the antiviral and antibacterial spaces.

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“…The kind of descriptions found can be used for prediction, explanation, and understanding [13]. At the forefront of this research is the work done by [14] who proposed a method of identifying previously unseen malware by collectively classifying them. From their work, we see that most machine learning models try to identify malicious software by training classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning

Babaagba

Adesanya

2019

Proceedings of the 2019 8th International Conference on Educational and Information Technology

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In this paper, the effect of feature selection in malware detection using machine learning techniques is studied. We employ supervised and unsupervised machine learning algorithms with and without feature selection. These include both classification and clustering algorithms. The algorithms are compared for effectiveness and efficiency using their predictive accuracy, among others, as performance metric. From the studies, we observe that the best detection rate was attained for supervised learning with feature selection. The supervised learning algorithm used was Multilayer Perceptron (MLP) algorithm. The analysis also reveals that our system can detect viruses from varying sources. CCS Concepts• Computing methodologies➝Machine learning; Feature selection • Security and privacy➝Malware and its mitigation.

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Do surface active parenteral formulations cause inflammation?

Cited by 4 publications

References 28 publications

Endothelial Cell Toxicity of Vancomycin Infusion Combined with Other Antibiotics

Endothelial Cell Toxicity of Vancomycin Infusion Combined with Other Antibiotics

Cyclodextrin-based Pickering emulsions: functional properties and drug delivery applications

A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning

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