Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods

Alizadeh, Seyed Mehdi Seyed; Parhizi, Zahra; Alibak, Ali Hosin; Vaferi, Behzad; Hosseini, Saleh

doi:10.1016/j.ijhydene.2022.05.038

Cited by 11 publications

(3 citation statements)

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“…The authors showed that ODA should be considered the primary analytic approach in dose−response applications. 15 Having this in mind, several zeolite-based delivery systems (ZDS) with silver (Ag, antimicrobial, and anticancer agents) and 5-fluorouracil (5-FU, classical antineoplastic agent) were prepared. Both Ag-and 5-FU-containing zeolites have already been described in the literature for anticancer and antimicrobial applications.…”

Section: ■ Introductionmentioning

confidence: 99%

“…For example, the optimal discriminant analysis (ODA) machine-learning algorithm was used to analyze the data from a study measuring the responses of the blood flow in the forearm to the intra-arterial administration of isoproterenol. The authors showed that ODA should be considered the primary analytic approach in dose–response applications …”

Section: Introductionmentioning

confidence: 99%

“…The authors showed that ODA should be considered the primary analytic approach in dose–response applications. 15 …”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Assisted Optimization of Drug Combinations in Zeolite-Based Delivery Systems for Melanoma Therapy

Bertão,

Teixeira,

Ivasiv

et al. 2024

ACS Appl. Mater. Interfaces

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Two independent artificial neural network (ANN) models were used to determine the optimal drug combination of zeolite-based delivery systems (ZDS) for cancer therapy. The systems were based on the NaY zeolite using silver (Ag + ) and 5-fluorouracil (5-FU) as antimicrobial and antineoplastic agents. Different ZDS samples were prepared, and their characterization indicates the successful incorporation of both pharmacologically active species without any relevant changes to the zeolite structure. Silver acts as a counterion of the negative framework, and 5-FU retains its molecular integrity. The data from the A375 cell viability assays, involving ZDS samples (solid phase), 5-FU, and Ag + aqueous solutions (liquid phase), were used to train two independent machine learning (ML) models. Both models exhibited a high level of accuracy in predicting the experimental cell viability results, allowing the development of a novel protocol for virtual cell viability assays. The findings suggest that the incorporation of both Ag and 5-FU into the zeolite structure significantly potentiates their anticancer activity when compared to that of the liquid phase. Additionally, two optimal AgY/5-FU@Y ratios were proposed to achieve the best cell viability outcomes. The ZDS also exhibited significant efficacy against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus); the predicted combination ratio is also effective against S. aureus, underscoring the potential of this approach as a therapeutic option for cancer-associated bacterial infections.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%