Traditional Chinese medicine as a promising choice for future control of PEDV

Virus Research, Journal Year: 2025, Volume and Issue: unknown, P. 199572 - 199572

Published: April 1, 2025

Porcine epidemic diarrhea virus (PEDV) is the major agent of recent outbreaks in piglets, which has caused huge economic losses to global swine industry. Since traditional vaccine strategies cannot provide complete protection for development safe, effective, and economical antiviral drugs urgently needed. For many years, Chinese medicines (TCMs) have been broadly applied viral infectious diseases, exhibiting advantages such as abundant resources, lower toxicity, minimal drug resistance. Many herbal monomers, single extracts derived from these drugs, recipes exhibit significant anti-PEDV effects vitro and/or vivo by targeting multiple sites perspectives, including inhibition life cycle, anti-inflammation effects, enhancement host immune response, modulation reactive oxygen species, apoptosis. However, date, no review published on TCM. Therefore, this summarizes current control PEDV systematically analyses research progress TCMs against PEDV. Furthermore, future directions integration nanotechnology artificial intelligence with are also discussed. This will a valuable reference studies research.

Language: Английский

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Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling

Frontiers in Pharmacology, Journal Year: 2025, Volume and Issue: 16

Published: April 15, 2025

Traditional Chinese Medicine (TCM) utilizes multi-metabolite and multi-target interventions to address complex diseases, providing advantages over single-target therapies. However, the active metabolites, therapeutic targets, especially combination mechanisms remain unclear. The integration of advanced data analysis nonlinear modeling capabilities artificial intelligence (AI) is driving transformation TCM into precision medicine. This review concentrates on application AI in target prediction, including multi-omics techniques, TCM-specialized databases, machine learning (ML), deep (DL), cross-modal fusion strategies. It also critically analyzes persistent challenges such as heterogeneity, limited model interpretability, causal confounding, insufficient robustness validation practical applications. To enhance reliability scalability future research should prioritize continuous optimization algorithms using zero-shot learning, end-to-end architectures, self-supervised contrastive learning.

Language: Английский

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NB-TCM-CHM: Image dataset of the Chinese herbal medicine fruits and its application in classification through deep learning

Data in Brief, Journal Year: 2024, Volume and Issue: 54, P. 110405 - 110405

Published: April 10, 2024

Chinese herbal medicine (CHM) is integral to a traditional (TCM) system. Accurately identifying crucial for quality control and prescription compounding verification. However, with many medicines some similar appearances but different therapeutic effects, achieving precise identification challenging task. Traditional manual methods have certain limitations, including labor-intensive, inefficient. Deep learning techniques can enhance accuracy, improve efficiency lower coats. few high-quality datasets are currently available deep applications. To alleviate this problem, study constructed dataset (Dataset 1) containing 3,384 images of 20 common fruits through web crawling. All annotated by TCM experts, making them suitable training testing methods. Furthermore, establishes another 2) 400 taking pictures using smartphones provide materials the practical efficacy evaluation The two form Ningbo Medicine Herb (NB-TCM-CHM) Dataset. In Dataset 1 2, each type herb stored in separate folder, folder named after its name. be used develop algorithms based on evaluate performance

Language: Английский

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New vision for TCM quality control: Elemental fingerprints and key ingredient combination strategy for identification and evaluation of TCMs

European Journal of Medicinal Chemistry, Journal Year: 2024, Volume and Issue: 281, P. 117006 - 117006

Published: Oct. 30, 2024

Language: Английский

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Network pharmacology speaking to ethnopharmacology: new data on an ancient remedy

Review of Clinical Pharmacology and Pharmacokinetics - International Edition, Journal Year: 2024, Volume and Issue: 38(Sup2), P. 27 - 29

Published: May 5, 2024

Network pharmacology as a “green approach”, predicting metabolite behaviours chemically and biologically guid¬ing biological experimental design, is new strategy aiming to uncover the mechanism of action natural products drug candidates. It provides powerful way identify novel mechanisms with potential thera¬peutic effects. This approach has emerged tool overcome limitations traditional methods, such ability predict adverse effects likelihood failure during clinical trials, by applying systems biology principles field pharmacology. method combines multi-omics dataset, computer modeling, chemical so reveal pharmaceutical actions guide discovery. Therefore, computer-aided design combined network can be viewed in silico screening ap¬proach discovery, utilising chemoinformatics, bioinformatics, structure biology, biology. includes target-based virtual - molecular docking, ligand similarity-based screening, inverse (Inver-dock), providing for target identification candidates, multitarget dis¬covery, bioactive product profiling. also used selectivity profiling drugs, repositioning, safety profiling, metabolism prediction (ADMET).

Language: Английский

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元宇宙技术在中医诊疗中应用的共识指南制定方法

元宇宙医学., Journal Year: 2024, Volume and Issue: unknown, P. 46 - 52

Published: March 28, 2024

随着元宇宙技术的不断发展，其在中医领域的应用也将越来越广泛。为了确保元宇宙技术的正确应用，保障患 者的安全和权益，需要制定相应的共识指南以规范元宇宙技术的使用。本文总结元宇宙技术在中医诊疗应用中的共识指南制 定方法，为后续共识指南的制定提供思路与借鉴。

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Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

Deleted Journal, Journal Year: 2024, Volume and Issue: 23(1)

Published: Oct. 15, 2024

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), robotic automation, AI enhanced efficiency, accuracy, innovation in field. The purpose of this review is to shed light on practical applications potential various fields. These fields include medicinal chemistry, pharmaceutics, pharmacology toxicology, clinical pharmacy, biotechnology, nanotechnology, pharmacognosy, management economics. By leveraging technologies such as ML, NLP, delves into role enhancing development processes, It analyzes AI's impact specific areas synthesis planning, formulation toxicology predictions, pharmacy market analysis. integration sciences significantly improved planning. In advanced medicine development. offers predictive capabilities for mechanisms toxic effects. facilitated automation Additionally, contributed protein engineering, gene therapy, nanocarrier design, discovery product therapeutics, economics, including marketing research trials management. transformed pharmaceuticals, innovation. This highlights care, serving a reference professionals. future promises field with AI-driven methodologies.

Language: Английский

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Research on resource allocation methods for traditional Chinese medicine services based on deep reinforcement learning

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 29, 2024

Language: Английский

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The integration of machine learning into traditional Chinese medicine

Journal of Pharmaceutical Analysis, Journal Year: 2024, Volume and Issue: unknown, P. 101157 - 101157

Published: Dec. 1, 2024

Language: Английский

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