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  • MDPI AG  (2)
  • Le, Hong Nhung  (2)
  • 1
    Online Resource
    Online Resource
    Fabrication of a Charge-Conversion Polymer—Liposome for Enhancing Endosomal Escape of Drug Delivery System for α–Mangostin
    MDPI AG ; 2023
    In:  Processes Vol. 11, No. 8 ( 2023-08-04), p. 2344-
    Details
    In: Processes, MDPI AG, Vol. 11, No. 8 ( 2023-08-04), p. 2344-
    Abstract: α–Mangostin, which is a natural xanthone compound, inhibits the metastasis and survival of various cancer cell types. However, its therapeutic effectiveness is limited by low water solubility and very poor absorption. There are several studies that developed the drug delivery system for α–mangostin, but they are still a remaining challenge. Drug delivery techniques are severely hampered by the breakdown of nanoparticles inside endosomes. The abrasive chemical environment in these compartments causes both the nanoparticles and the encapsulated α–mangostin to degrade throughout the course of the voyage. Intracellular defenses against external materials refer to this collective mechanism. A pH-responsive liposome named PAsp(DET-Cit)–Toc, made of lipids and a charge-conversion polymer (CCP), has been created for the targeted transport of α–mangostin in order to avoid this deteriorative outcome. The average hydrodynamic size of CCP–liposome particles is 98.59 ± 5.1 nm with a PDI of 0.098 ± 0.02 and a negative zeta potential of 22.31 ± 2.4 mV. TEM showed the shape of the spherical CCP–liposomes. α–Mangostin is successfully captured inside CCP–liposome and the loading yield reached the highest encapsulation efficiency of 83% with 150 μg/mL of α–mangostin. In the acidic condition of pH 5.0, an initial burst of α–mangostin reached 50% after 6 h in buffer solution. CCP–liposomes could escape from endosomes even after 3 h, and almost 80% of CCP–liposomes escaped after 24 h. The cell ability of α–mangostin-loaded-CCP–liposome incubated in buffer solutions of 5.0 decreased significantly and was close to free α–mangostin. Our data proved that α–mangostin-loaded CCP–liposome delivered more effectively α–mangostin into cells and prevented the degradation of α–mangostin inside cells, especially endosomal degradation.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr11082344
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720994-5
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  • 2
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    Online Resource
    The Utilization of Machine Learning Algorithms for Assisting Physicians in the Diagnosis of Diabetes
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 12 ( 2023-06-16), p. 2087-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 12 ( 2023-06-16), p. 2087-
    Abstract: This paper investigates the use of machine learning algorithms to aid medical professionals in the detection and risk assessment of diabetes. The research employed a dataset gathered from individuals with type 2 diabetes in Ninh Binh, Vietnam. A variety of classification algorithms, including Decision Tree Classifier, Logistic Regression, SVC, Ada Boost Classifier, Gradient Boosting Classifier, Random Forest Classifier, and K Neighbors Classifier, were utilized to identify the most suitable algorithm for the dataset. The results of the present study indicate that the Random Forest Classifier algorithm yielded the most promising results, exhibiting a cross-validation score of 0.998 and an accuracy rate of 100%. To further evaluate the effectiveness of the selected model, it was subjected to a testing phase involving a new dataset comprising 67 patients that had not been previously seen. The performance of the algorithm on this dataset resulted in an accuracy rate of 94%, especially the study’s notable finding is the algorithm’s accurate prediction of the probability of patients developing diabetes, as indicated by the class 1 (diabetes) probabilities. This innovative approach offers a meticulous and quantifiable method for diabetes detection and risk evaluation, showcasing the potential of machine learning algorithms in assisting clinicians with diagnosis and management. By communicating the diabetes score and probability estimates to patients, the comprehension of their disease status can be enhanced. This information empowers patients to make informed decisions and motivates them to adopt healthier lifestyle habits, ultimately playing a crucial role in impeding disease progression. The study underscores the significance of leveraging machine learning in healthcare to optimize patient care and improve long-term health outcomes.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13122087
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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