Nanotechnology’s frontier in combatting infectious and inflammatory diseases: prevention and treatment

Signal Transduction and Targeted Therapy, Год журнала: 2024, Номер 9(1)

Опубликована: Фев. 21, 2024

Abstract Inflammation-associated diseases encompass a range of infectious and non-infectious inflammatory diseases, which continuously pose one the most serious threats to human health, attributed factors such as emergence new pathogens, increasing drug resistance, changes in living environments lifestyles, aging population. Despite rapid advancements mechanistic research development for these current treatments often have limited efficacy notable side effects, necessitating more effective targeted anti-inflammatory therapies. In recent years, nanotechnology has provided crucial technological support prevention, treatment, detection inflammation-associated diseases. Various types nanoparticles (NPs) play significant roles, serving vaccine vehicles enhance immunogenicity carriers improve targeting bioavailability. NPs can also directly combat pathogens inflammation. addition, facilitated biosensors pathogen imaging techniques This review categorizes characterizes different NPs, summarizes their applications It discusses challenges associated with clinical translation this field explores latest developments prospects. conclusion, opens up possibilities comprehensive management

Язык: Английский

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Artificial intelligence to bring nanomedicine to life

Advanced Drug Delivery Reviews, Год журнала: 2022, Номер 184, С. 114194 - 114194

Опубликована: Март 10, 2022

Язык: Английский

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Environmental behavior, human health effect, and pollution control of heavy metal(loid)s toward full life cycle processes

Eco-Environment & Health, Год журнала: 2022, Номер 1(4), С. 229 - 243

Опубликована: Ноя. 29, 2022

Heavy metal(loid)s (HMs) have caused serious environmental pollution and health risks. Although the past few years witnessed achievements of studies on behavior HMs, related toxicity mechanisms, control, their relationship remains a mystery. Researchers generally focused one topic independently without comprehensive considerations due to knowledge gap between science human health. Indeed, full life cycle control HMs is crucial should be reconsidered with combination occurrence, transport, fate in environment. Therefore, we started by reviewing behaviors which are affected variety natural factors as well physicochemical properties. Furthermore, mechanisms were discussed according exposure route, mechanism, adverse consequences. In addition, current state-of-the-art available technologies for wastewater solid wastes summarized. Finally, based research trend, proposed that advanced in-operando characterizations will help us better understand fundamental reaction big data analysis approaches aid establishing prediction model risk management.

Язык: Английский

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Fertilizer management for global ammonia emission reduction

Nature, Год журнала: 2024, Номер 626(8000), С. 792 - 798

Опубликована: Янв. 31, 2024

Язык: Английский

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Advancing Computational Toxicology by Interpretable Machine Learning

Environmental Science & Technology, Год журнала: 2023, Номер 57(46), С. 17690 - 17706

Опубликована: Май 24, 2023

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical are expensive, time-consuming, often fail detect toxicants in humans. Computational toxicology is promising alternative approach that utilizes machine learning (ML) deep (DL) techniques predict the potentials of chemicals. Although applications ML- DL-based computational predictions attractive, many "black boxes" nature difficult interpret by toxicologists, which hampers risk assessments using these models. The recent progress interpretable ML (IML) computer science field meets this urgent need unveil underlying mechanisms elucidate domain knowledge In review, we focused IML toxicology, including feature data, model interpretation methods, use base frameworks development, applications. challenges future directions modeling also discussed. We hope review can encourage efforts developing with new algorithms assist illustrating

Язык: Английский

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Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms

Environmental Science & Technology, Год журнала: 2023, Номер 57(46), С. 17786 - 17795

Опубликована: Фев. 2, 2023

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments induces high potential risks. Experimentally evaluating (eco)toxicity MNMs is time-consuming expensive due multiple environmental factors, complexity material properties, species diversity. Machine learning (ML) models provide an option deal with heterogeneous data sets complex relationships. present study established in silico model based on a machine properties-environmental conditions-multi species-toxicity prediction (ML-PEMST) that can be applied predict toxicity different toward species. Feature importance interaction analysis random forest method indicated exposure duration, illumination, primary size, hydrodynamic diameter were main factors affecting ecotoxicity variety organisms. Illumination was demonstrated have most other features. Moreover, incorporating additional detailed information ecological traits test will allow us further optimize improve predictive performance model. This provides new approach for predictions organisms environment help explore pathways risk assessment MNMs.

Язык: Английский

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Survey on Explainable AI: Techniques, challenges and open issues

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124710 - 124710

Опубликована: Июль 7, 2024

Язык: Английский

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Characterizing soil Cops Eco-risk in China

Journal of Hazardous Materials, Год журнала: 2025, Номер 489, С. 137588 - 137588

Опубликована: Фев. 11, 2025

Язык: Английский

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Machine Learning Boosts the Design and Discovery of Nanomaterials

ACS Sustainable Chemistry & Engineering, Год журнала: 2021, Номер 9(18), С. 6130 - 6147

Опубликована: Апрель 27, 2021

Nanomaterials (NMs) have developed quickly and cover various fields, but research on nanotechnology NMs largely relies costly experiments or complex calculations (e.g., density functional theory). In contrast, machine learning (ML) methods can address the large amount of time needed labor consumption in material testing achieve big-data, high-throughput screening, boosting design application NMs. ML is a powerful tool for NM research; however, knowledge gaps critical issues should be promptly addressed to promote from laboratory industry. With focus primary aspects, enhancements structures, properties, adsorption, catalysis by are reviewed discussed. Given emergent challenges nanobiology, predictions interactions between biology also analyzed. Subsequently, this perspective discusses how improve interpretability algorithms, which has been bottleneck recent years. led innovations development NMs, some problems remain, such as imperfect databases accuracy algorithm determination nanopattern image recognition, herein addressed. Overall, provides insights research.

Язык: Английский

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Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

International Journal of Nanomedicine, Год журнала: 2022, Номер Volume 17, С. 1365 - 1379

Опубликована: Март 1, 2022

Background: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in field cancer nanomedicine. Strategies on how improve NP remain be determined. Methods: This study analyzed roles physicochemical properties, models, and types using multiple machine learning artificial intelligence methods, data from recently published Nano-Tumor Database that contains 376 datasets generated physiologically based pharmacokinetic (PBPK) model. Results: The deep neural network model adequately predicted different NPs tumors it outperformed all other methods; including random forest, support vector machine, linear regression, bagged methods. adjusted determination coefficients (R 2 ) full training dataset were 0.92, 0.77, 0.77 0.76 for maximum (DE max ), at 24 h 168 last sampling time Tlast ). corresponding R values test 0.70, 0.46, 0.33 0.63, respectively. Also, this showed type was an important determinant predicting across endpoints (19– 29%). Among Zeta potential core material played greater role than such as type, shape, targeting strategy. Conclusion: provides quantitative design nanomedicine with efficiency. These results help our understanding causes low demonstrates feasibility integrating PBPK modeling approaches Graphical Abstract: Keywords: intelligence, learning, modeling, nanomedicine, drug delivery, nanotechnology

Язык: Английский

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