Hydrophilicity and hydrophobicity: Key aspects for biomedical and technological purposes

Corsaro, Carmelo; Mallamace, Domenico; Neri, G.; Fazio, Eugenio

doi:10.1016/j.physa.2021.126189

Cited by 22 publications

(20 citation statements)

References 152 publications

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“…Drug carriers can be used to load hydrophilic or hydrophobic drugs. The hydrophilicity and hydrophobicity of drug carriers not only determine the absorption, distribution, metabolism, and excretion of drugs in vivo, but they also directly affect the value of drugs [133]. For hydrophilic drug carriers, the drug release rate is commonly fast because the surface tension of hydrophilic drug carriers is easy to collapse in aqueous solution.…”

Section: Drug Release Behaviors Of Nanocellulose-based Materialsmentioning

confidence: 99%

Nanocellulose-Based Composite Materials Used in Drug Delivery Systems

et al. 2022

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Nanocellulose has lately emerged as one of the most promising “green” materials due to its unique properties. Nanocellulose can be mainly divided into three types, i.e., cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial cellulose (BC). With the rapid development of technology, nanocellulose has been designed into multidimensional structures, including 1D (nanofibers, microparticles), 2D (films), and 3D (hydrogels, aerogels) materials. Due to its adaptable surface chemistry, high surface area, biocompatibility, and biodegradability, nanocellulose-based composite materials can be further transformed as drug delivery carriers. Herein, nanocellulose-based composite material used for drug delivery was reviewed. The typical drug release behaviors and the drug release mechanisms of nanocellulose-based composite materials were further summarized, and the potential application of nanocellulose-based composite materials was prospected as well.

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Section: Drug Release Behaviors Of Nanocellulose-based Materialsmentioning

confidence: 99%

Nanocellulose-Based Composite Materials Used in Drug Delivery Systems

et al. 2022

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“…For a cation exchange process, adsorbent (or stationary phase) consisting of large number of negative charges was required. Highly hydrophilic surface negates the possibility of protein adsorption and any weak cation exchange between the surface and proteins ( [14], [23], [24]). Hence, a suitable copolymer that could adhere to the chromatography column walls was to be chosen without being highly hydrophilic (ca.…”

Section: Surface Morphologymentioning

confidence: 99%

Amphiphilic diblock copolymers as functional surfaces for protein chromatography

Moorthy¹,

D’Souza²,

Sunthar³

et al. 2022

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Stationary phase plays a crucial role in the operation of a protein chromatography column. Conventional resins composed of acrylic polymers and their derivatives contribute to heterogeneity of the packing of stationary phase inside these columns. Alternative polymer combinations through customized surface functionalization schemes which consist of multiple steps using static coating techniques are well known. In comparison, it is hypothesized that a single-step scheme is sufficient to obtain porous adsorbents as stationary phase for tuning surface morphology and protein immobilization. To overcome the challenge of heterogeneous packing and ease of fabrication at a laboratory scale, a change in the form factor of separation materials has been proposed in the form of functional copolymer surfaces. In the present work, an amphiphilic, block copolymer, poly(methyl methacrylate-co-methacrylic acid) has been chosen and fully characterized for its potential usage in protein chromatography. Hydrophilicity of the acrylic copolymer and abundance of carboxyl groups inherently on the copolymer surface have been successfully demonstrated through contact angle measurements, Fourier transform infrared (FTIR) and X-ray photoelectron spectroscopy (XPS) studies. Morphological studies indicate presence of a microporous region (nearly 1 to 1.5 µm pore size) that could be beneficial as a cation exchange media as part of the stationary phase in protein chromatography.

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“…For the obtained maps, the coordinate for the bulk water peak (slowest component) is shown at the upper left of the map indicating T 2 and T 1 relaxations (in ms) and T 1 /T 2 -ratio, respectively. Instead, the coordinates of the fastest components, due to hydration and bound water molecules [131], are reported close to the corresponding correlation peak (Figure 26).…”

Section: Biomedicalmentioning

confidence: 87%

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

et al. 2022

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NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

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Hydrophilicity and hydrophobicity: Key aspects for biomedical and technological purposes

Cited by 22 publications

References 152 publications

Nanocellulose-Based Composite Materials Used in Drug Delivery Systems

Nanocellulose-Based Composite Materials Used in Drug Delivery Systems

Amphiphilic diblock copolymers as functional surfaces for protein chromatography

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

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