In chemico methodology for engineered nanomaterial categorization according to number, nature and oxidative potential of reactive surface sites DOI Creative Commons
Víctor Alcolea-Rodriguez, Raquel Portela, Vanesa Calvino Casilda

и другие.

Environmental Science Nano, Год журнала: 2024, Номер 11(9), С. 3744 - 3760

Опубликована: Янв. 1, 2024

Methanol probe chemisorption quantifies the number of reactive sites at surface engineered nanomaterials, enabling normalization per site in reactivity and toxicity tests, rather than mass or physical area. Subsequent temperature-programmed reaction (TPSR) chemisorbed methanol identifies nature (acidic, basic, redox combination thereof) their reactivity. Complementary to assay, a dithiothreitol (DTT) oxidation is used evaluate capacity. These acellular approaches quantify number, nature, constitute new approach methodology (NAM) for site-specific classification nanomaterials. As proof concept, CuO, CeO2, ZnO, Fe3O4, CuFe2O4, Co3O4 two TiO2 nanomaterials were probed. A harmonized descriptor ENMs was obtained: DTT rate site, oxidative turnover frequency (OxTOF). CuO CuFe2O4 exhibit largest density possess highest oxidizing ability series, as estimated by reaction, followed CeO2 NM-211 then titania (DT-51 NM-101) Fe3O4. depletion ZnO NM-110 associated with dissolved zinc ions particles; however, basic characteristics particles evidenced TPSR. assays allow ranking eight into three categories statistically different potentials: are most reactive; ceria exhibits moderate reactivity; iron oxide titanias low potential.

Язык: Английский

AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity DOI
Julia Razlivina,

Andrei Dmitrenko,

Vladimir V. Vinogradov

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2024, Номер 15(22), С. 5804 - 5813

Опубликована: Май 23, 2024

Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, environmental monitoring, and beyond. In this work, we developed a machine learning (ML) approach to search new nanozymes deployed web platform, DiZyme, featuring state-of-the-art database of containing 1210 experimental samples, catalytic activity prediction, DiZyme Assistant interface powered by large language model (LLM). For the first time, enable prediction multiple activities training an ensemble algorithm achieving R2 = 0.75 Michaelis–Menten constant 0.77 maximum velocity on unseen test data. We envision accurate (peroxidase, oxidase, catalase) promoting novel wide range surface-modified inorganic nanozymes. The based ChatGPT provides users supporting information such as synthesis procedures, measurement protocols, etc. (dizyme.aicidlab.itmo.ru) is now openly available worldwide.

Язык: Английский

Процитировано

6

Unleashing novel horizons in advanced prostate cancer treatment: investigating the potential of prostate specific membrane antigen-targeted nanomedicine-based combination therapy DOI Creative Commons
Mingze He, Yu Cao,

Changliang Chi

и другие.

Frontiers in Immunology, Год журнала: 2023, Номер 14

Опубликована: Сен. 19, 2023

Prostate cancer (PCa) is a prevalent malignancy with increasing incidence in middle-aged and older men. Despite various treatment options, advanced metastatic PCa remains challenging poor prognosis limited effective therapies. Nanomedicine, its targeted drug delivery capabilities, has emerged as promising approach to enhance efficacy reduce adverse effects. Prostate-specific membrane antigen (PSMA) stands one of the most distinctive highly selective biomarkers for PCa, exhibiting robust expression cells. In this review, we explore applications PSMA-targeted nanomedicines management. Our primary objective bridge gap between cutting-edge nanomedicine research clinical practice, making it accessible medical community. We discuss mainstream strategies including chemotherapy, radiotherapy, immunotherapy, context nanomedicines. Additionally, elucidate novel concepts such photodynamic photothermal therapies, along nano-theragnostics. present content clear manner, appealing general physicians, those backgrounds biochemistry bioengineering. The review emphasizes potential benefits enhancing efficiency improving patient outcomes. While use nano-drug demonstrated results, further investigation required comprehend precise mechanisms action, pharmacotoxicity, long-term By meticulous optimization combination PSMA ligands, horizon nanomedicine-based therapy could bring renewed hope patients PCa.

Язык: Английский

Процитировано

13

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles DOI
Susan Jyakhwo, Nikita Serov,

Andrei Dmitrenko

и другие.

Small, Год журнала: 2023, Номер 20(6)

Опубликована: Сен. 28, 2023

Abstract Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily passive carriers, with limited selectivity. However, recent publications shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits concept cytotoxicity, first time proposes a high‐throughput in silico screening approach massive targeted discovery selectively cytotoxic inorganic NPs. In step, this work trains gradient boosting regression model predict viability NP‐treated lines. The achieves mean cross‐validation (CV) Q2 = 0.80 root square error (RMSE) 13.6. second develops machine learning (ML) reinforced genetic algorithm (GA), capable >14 900 candidates/min, identify best‐performing As proof‐of‐concept, DDS candidates treatment liver are screened HepG2 hepatocytes lines resulting Ag toxicity score 42%. opens door clinical translation NPs, expanding their therapeutic application wider range chemical space living organisms such bacteria fungi.

Язык: Английский

Процитировано

13

A prospective on machine learning challenges, progress, and potential in polymer science DOI Creative Commons

Daniel C. Struble,

Bradley G. Lamb, Boran Ma

и другие.

MRS Communications, Год журнала: 2024, Номер 14(5), С. 752 - 770

Опубликована: Июль 1, 2024

Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science engineering every year. Polymer is no different, though implementation of data-driven algorithms this subfield has unique challenges barring widespread application these techniques the study polymer systems. In Prospective, we discuss several critical ML science, including structure representation, high-throughput limitations, limited data availability. Promising studies targeting resolution issues are explored, contemporary research demonstrating potential despite existing obstacles discussed. Finally, present an outlook for moving forward. Graphical

Язык: Английский

Процитировано

4

Optimization and Interpretability Analysis of Machine Learning Methods for ZnO Colloid Particle Size Prediction DOI
Lin Fan, Honglei Yu, Yan He

и другие.

ACS Applied Nano Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 2, 2025

Язык: Английский

Процитировано

0

From detection to elimination: iron-based nanomaterials driving tumor imaging and advanced therapies DOI Creative Commons
Dongyue Xie,

Linglin Sun,

Manxiang Wu

и другие.

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Фев. 7, 2025

Iron-based nanomaterials (INMs), due to their particular magnetic property, excellent biocompatibility, and functionality, have been developed into powerful tools in both tumor diagnosis therapy. We give an overview here on how INMs such as iron oxide nanoparticles, element-doped nanocomposites, iron-based organic frameworks (MOFs) display versatility for imaging therapy improvement. In terms of imaging, improve the sensitivity accuracy techniques resonance (MRI) photoacoustic (PAI) support development multimodal platforms. Regarding treatment, play a key role advanced strategies immunotherapy, hyperthermia, synergistic combination therapy, which effectively overcome tumor-induced drug resistance reduce systemic toxicity. The integration with artificial intelligence (AI) radiomics further expands its capabilities precise identification, treatment optimization, amplifies monitoring. now link materials science computing clinical innovations enable next-generation cancer diagnostics therapeutics.

Язык: Английский

Процитировано

0

Artificial intelligence in tumor drug resistance: Mechanisms and treatment prospects DOI Creative Commons
Jianyou Gu, Junfeng Zhang,

Silüe Zeng

и другие.

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Blended cellulose nitrate/cellulose acetate membranes for enhanced water treatment performance in ultrafiltration DOI
Roman Dubovenko, Mariia Dmitrenko, Anna Mikulan

и другие.

Carbohydrate Polymers, Год журнала: 2025, Номер 363, С. 123713 - 123713

Опубликована: Май 8, 2025

Язык: Английский

Процитировано

0

Machine learning-driven nanoparticle toxicity DOI
Zied Hosni, Sofiene Achour,

Fatma Saâdi

и другие.

Ecotoxicology and Environmental Safety, Год журнала: 2025, Номер 299, С. 118340 - 118340

Опубликована: Май 19, 2025

Язык: Английский

Процитировано

0

Machine learning reshapes the paradigm of nanomedicine research DOI Creative Commons

Ziye Wei,

Shijie Zhuo,

Yixin Zhang

и другие.

Acta Pharmaceutica Sinica B, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0