Merging data curation and machine learning to improve nanomedicines

Advanced Drug Delivery Reviews, Journal Year: 2022, Volume and Issue: 183, P. 114172 - 114172

Published: Feb. 18, 2022

Language: Английский

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Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences

Science Advances, Journal Year: 2022, Volume and Issue: 8(42)

Published: Oct. 19, 2022

Machine learning (ML) methodology used in the social and health sciences needs to fit intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping questions appropriate ML approaches by incorporating necessary requirements statistical analysis these disciplines. We map established classification into counterfactual structural common goals, such as estimating prevalence adverse outcomes, predicting risk an event, identifying factors causes explain performance metrics. Such mapping may help fully exploit benefits while considering domain-specific aspects relevant hopefully contribute acceleration uptake applications advance both basic applied research.

Language: Английский

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Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning

NPG Asia Materials, Journal Year: 2022, Volume and Issue: 14(1)

Published: Aug. 12, 2022

Abstract Nanoparticles play irreplaceable roles in optoelectronic sensing, medical therapy, material science, and chemistry due to their unique properties. There are many synthetic pathways used for the preparation of nanoparticles, different can produce nanoparticles with Therefore, it is crucial control properties precisely impart desired functions. In general, influenced by sizes morphologies. Current technology on microfluidic chips requires repeated experimental debugging significant resources synthesize Machine learning-assisted synthesis a sensible choice addressing this challenge. paper, we review recent studies syntheses assisted machine learning. Moreover, describe working steps learning, main algorithms, ways obtain datasets. Finally, discuss current problems research provide an outlook.

Language: Английский

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Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects

Journal of Hazardous Materials, Journal Year: 2022, Volume and Issue: 432, P. 128730 - 128730

Published: March 17, 2022

Language: Английский

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Combinatorial Polycation Synthesis and Causal Machine Learning Reveal Divergent Polymer Design Rules for Effective pDNA and Ribonucleoprotein Delivery

JACS Au, Journal Year: 2022, Volume and Issue: 2(2), P. 428 - 442

Published: Feb. 7, 2022

The development of polymers that can replace engineered viral vectors in clinical gene therapy has proven elusive despite the vast portfolios multifunctional generated by advances polymer synthesis. Functional delivery payloads such as plasmids (pDNA) and ribonucleoproteins (RNP) to various cellular populations tissue types requires design precision. Herein, we systematically screen a combinatorially designed library 43 well-defined polymers, ultimately identifying lead polycationic vehicle (P38) for efficient pDNA delivery. Further, demonstrate versatility P38 codelivering spCas9 RNP mediate homology-directed repair well facilitating ARPE-19 cells. achieves nuclear import eludes lysosomal processing far more effectively than structural analogue does not deliver efficiently. To reveal physicochemical drivers P38's performance, SHapley Additive exPlanations (SHAP) are computed nine polyplex features, causal model is applied evaluate average treatment effect most important features selected SHAP. Our machine learning interpretability inference approach derives structure-function relationships underlying efficiency, uptake, viability probes overlap criteria between payloads. Together, combinatorial synthesis, parallelized biological screening, establish demands careful tuning polycation protonation equilibria while delivered efficaciously deprotonate cooperatively via hydrophobic interactions. These payload-specific guidelines will inform further bespoke specific therapeutic contexts.

Language: Английский

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Intelligent control of nanoparticle synthesis through machine learning

Nanoscale, Journal Year: 2022, Volume and Issue: 14(18), P. 6688 - 6708

Published: Jan. 1, 2022

The synthesis of nanoparticles is affected by many reaction conditions, and their properties are usually determined factors such as size, shape surface chemistry. In order for the synthesized to have functions suitable different fields (for example, optics, electronics, sensor applications so on), precise control essential. However, with current technology preparing on a microreactor, it time-consuming laborious achieve synthesis. improve efficiency synthesizing expected functionality, application machine learning-assisted an intelligent choice. this article, we mainly introduce typical methods microreactors, explain principles procedures learning, well main ways obtaining data sets. We studied three types representative nanoparticle preparation assisted learning. Finally, problems in future development prospects discussed.

Language: Английский

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Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Journal of Hazardous Materials, Journal Year: 2022, Volume and Issue: 438, P. 129487 - 129487

Published: June 28, 2022

Language: Английский

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Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification

Environmental Science & Technology, Journal Year: 2023, Volume and Issue: 57(46), P. 17990 - 18000

Published: May 16, 2023

In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited best performances prediction reaction rate (k) based on training data set relevant to pollutant characteristics and conditions, indicated by Rext2 0.84 RMSEext 0.79. Based 315 points collected from literature, current density, concentration, gap energy (Egap) were identified be most impactful parameters available EO process. particular, adding conditions as input features allowed provision more information an increase in sample size improve accuracy. feature importance analysis was performed revealing pattern interpretation using Shapley additive explanations (SHAP). ML-based generalized random case tailoring optimum with phenol 2,4-dichlorophenol (2,4-DCP) serving pollutants. resulting predicted k values close experimental verification, accounting relative error lower than 5%. This study provides paradigm shift conventional trial-and-error mode data-driven advancing research development time-saving, labor-effective, environmentally friendly strategy, which makes purification efficient, economic, sustainable context global carbon peaking neutrality.

Language: Английский

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Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(13), P. 8575 - 8637

Published: June 1, 2023

Decades of nanotoxicology research have generated extensive and diverse data sets. However, is not equal to information. The question how extract critical information buried in vast streams. Here we show that artificial intelligence (AI) molecular simulation play key roles transforming nanotoxicity into information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, elucidating toxicity-related mechanisms. For AI realize their full impacts this mission, several obstacles must be overcome. These include paucity high-quality nanomaterials (NMs) standardized data, lack model-friendly databases, scarcity specific universal nanodescriptors, inability simulate NMs at realistic spatial temporal scales. This review provides a comprehensive representative, but exhaustive, summary current capability gaps tools required fill these formidable gaps. Specifically, discuss applications simulation, which can address large-scale challenge for research. need powerful new modeling approaches, mechanism analysis, design next-generation are also critically discussed. Finally, provide perspective on future trends challenges.

Language: Английский

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Harnessing Hafnium‐Based Nanomaterials for Cancer Diagnosis and Therapy

Small, Journal Year: 2023, Volume and Issue: 19(32)

Published: April 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.

Language: Английский

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