Advances in artificial intelligence for drug delivery and development: A comprehensive review

Computers in Biology and Medicine, Год журнала: 2024, Номер 178, С. 108702 - 108702

Опубликована: Июнь 7, 2024

Язык: Английский

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Leveraging artificial intelligence in vaccine development: A narrative review

Journal of Microbiological Methods, Год журнала: 2024, Номер 224, С. 106998 - 106998

Опубликована: Июль 15, 2024

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity mortality. However, traditional vaccine methods are often time-consuming, costly, inefficient. The advent artificial intelligence (AI) has ushered new era design, offering unprecedented opportunities to expedite the process. This narrative review explores role AI development, focusing on antigen selection, epitope prediction, adjuvant identification, optimization strategies. algorithms, including machine learning deep learning, leverage genomic data, protein structures, immune system interactions predict antigenic epitopes, assess immunogenicity, prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate rational design immunogens identification novel candidates with optimal safety efficacy profiles. Challenges such data heterogeneity, model interpretability, regulatory considerations must be addressed realize full potential development. Integrating emerging technologies, single-cell omics synthetic biology, promises enhance precision scalability. underscores transformative impact highlights need interdisciplinary collaborations harmonization accelerate delivery safe effective vaccines against diseases.

Язык: Английский

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Toward intelligent food drying: Integrating artificial intelligence into drying systems

Drying Technology, Год журнала: 2024, Номер 42(8), С. 1240 - 1269

Опубликована: Май 24, 2024

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, optimization within the drying industry. This paper presents a comprehensive overview of progress made ML from shallow to deep implications for food drying. Theoretical foundations, advantages, limitations various approaches employed this domain explored. Additionally, advancements models, particularly those enhanced by algorithms, reviewed. The review underscores role intelligent configuration which affects their accuracy ability solve problems high energy consumption, nutrient degradation, uneven Drawing upon research achievements, integrating AI models real-time measuring methods is discussed, enabling dynamic determination optimal conditions parameter adjustments. integration facilitates automated decision-making, reducing human errors enhancing operational efficiency Moreover, demonstrate proficiency predicting times analyzing usage patterns, thereby minimize resource consumption while preserving product quality. Finally, identifies current obstacles technology development proposes novel avenues sustainable technologies.

Язык: Английский

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Machine Learning-Based Molecular Dynamics Studies on Predicting Thermophysical Properties of Ethanol–Octane Blends

Energy & Fuels, Год журнала: 2025, Номер unknown

Опубликована: Янв. 7, 2025

This paper presents an innovative approach to predicting thermophysical properties of ethanol–octane blends by integrating molecular dynamics (MD) simulations with machine learning (ML) algorithms. The work addresses the growing interest in ethanol–gasoline as alternative fuels and need for efficient computational methods analyze their properties. Using MD various ML models such Decision Tree Regression (DTR), Random Forest (RFR) Gaussian Process (GPR), behavior 660-molecule systems mixtures was modeled. OPLS-AA force field employed accurately represent interactions. Among models, DTR demonstrated highest accuracy atomic displacements velocities. integration promises rapid accurate predictions, error rates consistently below 2.5% across different ethanol concentrations timesteps. Notably, model showcases remarkable speedup efforts, approximately 1.8, 2.7, 3.4 times faster E10, E20 E85 respectively compared traditional simulations. not only enhances understanding blend but also demonstrates potential accelerate complex findings this study have significant implications design optimization fuels, targeting sustainable energy demand.

Язык: Английский

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现代疫苗学赋能新突发病毒性传染病疫苗的快速“智造”——以猴痘疫情为例

Chinese Science Bulletin (Chinese Version), Год журнала: 2025, Номер 70(7), С. 789 - 798

Опубликована: Фев. 11, 2025

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Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

BioMedInformatics, Год журнала: 2024, Номер 4(1), С. 754 - 779

Опубликована: Март 4, 2024

Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, managing health issues like pulmonary diseases, heart failure, diabetes screening, intraoperative risks. However, a systematic review of learning’s use evidence-based is lacking, as practice aims to eliminate biases subjective opinions. Methods: The author conducted mixed methods explore applications care. A search literature was performed during 16 June 2023–27 2023 Google Scholar, PubMed, registry platform ClinicalTrials.gov. included articles if they were implemented by informatics concerned with technologies. Results: identifies 18 key (17 trials) from 175 citations found internet databases categorizes them using problem-specific groupings, medical/health domains, models, algorithms, techniques. Conclusions: Machine combined application practices healthcare can enhance strategies improving quality personalized care, early detection health-related problems, patient life, patient-physician communication, resource efficiency cost-effectiveness. this requires interdisciplinary expertise collaboration among stakeholders, including clinicians, informaticians, policymakers. Therefore, further research clinicall studies, reviews, analyses, meta-analyses required fully exploit potential area.

Язык: Английский

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Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network

Talanta, Год журнала: 2024, Номер 275, С. 126098 - 126098

Опубликована: Апрель 12, 2024

Язык: Английский

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Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis

Cognitive Computation, Год журнала: 2024, Номер 16(6), С. 3518 - 3556

Опубликована: Авг. 6, 2024

Abstract As cognitive-inspired computation approaches, deep neural networks or learning (DL) models have played important roles in allowing machines to reach human-like performances various complex cognitive tasks such as and sentiment analysis. This paper offers a thorough examination of the rapidly developing topic DL-assisted aspect-based analysis (DL-ABSA), focusing on its increasing importance implications for practice research advancement. Leveraging bibliometric indicators, social network analysis, modeling techniques, study investigates four questions: publication citation trends, scientific collaborations, major themes topics, prospective directions. The reveals significant growth DL-ABSA output impact, with notable contributions from diverse sources, institutions, countries/regions. Collaborative between countries/regions, particularly USA China, underscore global engagement research. Major syntax structure sequence modeling, specific aspects modalities emerge guiding future endeavors. identifies avenues practitioners, emphasizing strategic methodologies, domain-specific applications. Overall, this contributes understanding dynamics, providing roadmap practitioners researchers navigate evolving landscape drive innovations methodologies

Язык: Английский

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Neuroscience-Driven Solutions for Speech Impairment

Advances in medical education, research, and ethics (AMERE) book series, Год журнала: 2025, Номер unknown, С. 119 - 146

Опубликована: Янв. 10, 2025

Speech impairment presents a significant challenge for aged and chronically ill individuals. Emerging assistive technologies offer promising solutions to improve their communication abilities. This discussion explores established options like Augmentative Alternative Communication (AAC) devices cutting-edge approaches Brain-Computer Interfaces (BCIs). Neuroprosthetics, field merging neuroscience biomedical engineering, aims replace or modulate damaged parts of the nervous system. We delved into potential optogenetics, neuroengineering devices, biosensors, highlighting contributions future advancements in speech assistance. The dialogue emphasized importance comprehensive approach, combining with ongoing research on areas synthetic biology. multi-faceted approach holds key empowering speech-impaired individuals fostering improved communication.

Язык: Английский

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Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 197 - 228

Опубликована: Янв. 1, 2025

Язык: Английский

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