Harnessing Hafnium‐Based Nanomaterials for Cancer Diagnosis and Therapy

Small, Год журнала: 2023, Номер 19(32)

Опубликована: Апрель 8, 2023

Abstract With the rapid development of nanotechnology and nanomedicine, there are great interests in employing nanomaterials to improve efficiency disease diagnosis treatment. The clinical translation hafnium oxide (HfO 2 ), commercially namedas NBTXR3, as a new kind nanoradiosensitizer for radiotherapy (RT) cancers has aroused extensive interest researches on Hf‐based biomedical application. In past 20 years, have emerged potential important nanomedicine computed tomography (CT)‐involved bioimaging RT‐associated cancer treatment due their excellent electronic structures intrinsic physiochemical properties. this review, bibliometric analysis method is employed summarize progress synthesis technology various nanomaterials, including HfO , ‐based compounds, Hf‐organic ligand coordination hybrids, such metal‐organic frameworks or nanoscaled polymers. Moreover, current states application CT‐involved contrasts tissue imaging reviewed detail. Importantly, recent advances nanomaterials‐mediated radiosensitization synergistic RT with other mainstream treatments also generalized. Finally, challenges future perspectives view maximize research translational medicine discussed.

Язык: Английский

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Current applications and future impact of machine learning in emerging contaminants: A review

Critical Reviews in Environmental Science and Technology, Год журнала: 2023, Номер 53(20), С. 1817 - 1835

Опубликована: Март 23, 2023

With the continuous release into environments, emerging contaminants (ECs) have attracted widespread attention for potential risks, and numerous studies been conducted on their identification, environmental behavior bioeffects, removal. Owing to superiority of dealing with high-dimensional unstructured data, a new data-driven approach, machine learning (ML), has gradually applied in research ECs. This review described fundamental principle, algorithms, workflow ML, summarized advances ML applications typical ECs (per- polyfluoroalkyl substances, nanoparticles, antibiotic resistance genes, endocrine-disrupting chemicals, microplastics, antibiotics, pharmaceutical personal care products). methods showed practicability, reliability, effectiveness predicting or analyzing occurrence, distribution, removal ECs, various algorithms derived models were developed optimized obtain better performance. Moreover, size homogeneity data set strongly influence application choosing appropriate different characteristics is crucial addressing specific problems related sets. Future efforts should focus improving quality adopting more advanced developing quantitative structure-activity relationship, promoting applicability domains interpretability models. In addition, development codeless tools will benefit accessibility

Язык: Английский

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Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach

International Journal of Pharmaceutics, Год журнала: 2023, Номер 640, С. 123012 - 123012

Опубликована: Май 2, 2023

Язык: Английский

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Development potential of nanoenabled agriculture projected using machine learning

Proceedings of the National Academy of Sciences, Год журнала: 2023, Номер 120(25)

Опубликована: Июнь 14, 2023

The controllability and targeting of nanoparticles (NPs) offer solutions for precise sustainable agriculture. However, the development potential nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets predict ( R 2 higher than 0.8 13 random forest models) response uptake/transport various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven total NP exposure dose duration age at exposure, as well size zeta potential. Feature interaction covariance further improve interpretability model reveal hidden factors (e.g., potential). Integration model, laboratory, field data suggests Fe O 3 application may inhibit bean growth in Europe due to low night temperatures. In contrast, risks oxidative stress Africa because high According prediction, is suitable area regional differences temperature changes make complicated. future, increase reduce African European maize induced NPs. This study projects learning, although many more studies needed address country continental scales.

Язык: Английский

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Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

The Science of The Total Environment, Год журнала: 2024, Номер 946, С. 174201 - 174201

Опубликована: Июнь 25, 2024

Язык: Английский

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