Comparing the effect of several pretreatment steps, selected to steer (bio)chemical reactions, on the volatile profile of leek (Allium ampeloprasum var. porrum)

Delbaere, Sophie M.; Bernaerts, Tom; Vancoillie, Flore; Buvé, Carolien; Hendrickx, Marc; Grauwet, Tara; Loey, Ann M. Van

doi:10.1016/j.lwt.2022.114205

Cited by 6 publications

(1 citation statement)

References 51 publications

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“…Over the last ten years, nanotechnology has rapidly advanced in creating and producing several types of nanoparticles for magnetic resonance imaging purposes. Gd (III)-loaded nanocarriers, such as dendrimers ( 88 ), liposomes ( 89 ), micelles ( 90 ), and inorganic NPs ( 91 ), provide significantly improved image contrast compared to tiny-molecule Gd (III) complexes. Furthermore, due to the extended blood circulation time and easy surface modification of NPs, NP systems can achieve increased imaging duration, desirable compatibility with living organisms, and enhanced imaging accuracy.…”

Section: Application Of Antifouling Materials In Oncologymentioning

confidence: 99%

Current status, challenges and prospects of antifouling materials for oncology applications

Zhang,

Sun

2024

Front. Oncol.

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Targeted therapy has become crucial to modern translational science, offering a remedy to conventional drug delivery challenges. Conventional drug delivery systems encountered challenges related to solubility, prolonged release, and inadequate drug penetration at the target region, such as a tumor. Several formulations, such as liposomes, polymers, and dendrimers, have been successful in advancing to clinical trials with the goal of improving the drug’s pharmacokinetics and biodistribution. Various stealth coatings, including hydrophilic polymers such as PEG, chitosan, and polyacrylamides, can form a protective layer over nanoparticles, preventing aggregation, opsonization, and immune system detection. As a result, they are classified under the Generally Recognized as Safe (GRAS) category. Serum, a biological sample, has a complex composition. Non-specific adsorption of chemicals onto an electrode can lead to fouling, impacting the sensitivity and accuracy of focused diagnostics and therapies. Various anti-fouling materials and procedures have been developed to minimize the impact of fouling on specific diagnoses and therapies, leading to significant advancements in recent decades. This study provides a detailed analysis of current methodologies using surface modifications that leverage the antifouling properties of polymers, peptides, proteins, and cell membranes for advanced targeted diagnostics and therapy in cancer treatment. In conclusion, we examine the significant obstacles encountered by present technologies and the possible avenues for future study and development.

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Section: Application Of Antifouling Materials In Oncologymentioning

confidence: 99%

Current status, challenges and prospects of antifouling materials for oncology applications

Zhang,

Sun

2024

Front. Oncol.

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The classification of leek seeds based on fluorescence spectroscopic data using machine learning

Ropelewska,

Sabanci,

Slavova

et al. 2023

Eur Food Res Technol

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The objective of this study was to distinguish leek seeds belonging to the Starozagorski kamush variety and two breeding lines based on the selected fluorescence spectroscopic data. The classification models were developed for three classes of Starozagorski kamush vs. breeding line 4 vs. breeding line 39 and pairs of classes of Starozagorski kamush vs. breeding line 4, Starozagorski kamush vs. breeding line 39, and breeding line 4 vs. breeding line 39. The traditional machine learning algorithms, such as PART, Logistic, Naive Bayes, Random Forest, IBk, and Filtered Classifier were applied. All three classes were distinguished with an average accuracy of up to 93.33% for models built using IBk and Filtered Classifier. In the case of each model, Starozagorski kamush variety was completely different (accuracy of 100%, precision, and F-measure, MCC (Matthews correlation coefficient), and ROC (receiver operating characteristic) area of 1.000) from breeding lines, and the mixing of cases was observed between breeding line 4 and breeding line 39. The models built for pairs of leek seed classes distinguished Starozagorski kamush and breeding line 4 with an average accuracy reaching 100% (Logistic, Naive Bayes, Random Forest, IBk). The classification accuracy of Starozagorski kamush and breeding line 39 also reached 100% (Logistic, Naive Bayes, Random Forest, IBk), whereas breeding line 4 and breeding line 39 were classified with an average accuracy of up to 80% (Logistic, Naive Bayes, Random Forest, Filtered Classifier). The proposed approach combining fluorescence spectroscopy and machine learning may be used in practice to distinguish leek seed varieties and breeding lines.

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Engineering strategies to modulate nutrient digestion kinetics and bioaccessibility of plant-based foods

Verkempinck¹,

Hendrickx²,

Loey³

et al. 2023

Current Opinion in Food Science

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Comparing the effect of several pretreatment steps, selected to steer (bio)chemical reactions, on the volatile profile of leek (Allium ampeloprasum var. porrum)

Cited by 6 publications

References 51 publications

Current status, challenges and prospects of antifouling materials for oncology applications

Current status, challenges and prospects of antifouling materials for oncology applications

The classification of leek seeds based on fluorescence spectroscopic data using machine learning

Engineering strategies to modulate nutrient digestion kinetics and bioaccessibility of plant-based foods

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