Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

Medical diagnosis based on artificial intelligence and decision support system in the management of health development

BACKGROUND

Medical diagnosis plays a critical role in our daily lives. Every day, over 10 billion cases of both mental and physical health disorders are diagnosed and reported worldwide. To diagnose these disorders, medical practitioners and health professionals employ various assessment tools. However, these tools often face scrutiny due to their complexity, prompting researchers to increase their experimental parameters to provide accurate justifications. Additionally, it is essential for professionals to properly justify, interpret, and analyse the results from these prediction tools.

METHODS

This research paper explores the use of artificial intelligence and advanced analytics in developing Clinical Decision Support Systems (CDSS). These systems are capable of diagnosing and detecting patterns of various medical disorders. Various machine learning algorithms contribute to building these assessment tools, with the Network Pattern Recognition (NEPAR) algorithm being the first to aid in developing CDSS. Over time, researchers have recognised the value of machine learning-based prediction models in successfully justifying medical diagnoses.

RESULTS

The proposed CDSS models have demonstrated the ability to diagnose mental disorders with an accuracy of up to 89% using only 28 questions, without requiring human input. For physical health issues, additional parameters are used to enhance the accuracy of CDSS models.

CONCLUSIONS

Consequently, medical professionals are increasingly relying on these machine learning-based CDSS models, utilising these tools to improve medical diagnosis and assist in decision-making. The different cross-validation values are considered to remove the data biasness.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

A practical guide to implementing artificial intelligence in traditional East Asian medicine research

In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.

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

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

Network pharmacology to explore the mechanism of traditional Chinese medicine in the treatment of ground glass nodules

Background

Ground glass nodules (GGNs) in the lung are considered to be a high-risk factor of lung adenocarcinoma. Immediate surgery is not recommended for GGNs patients, and low-dose computed tomography (CT) is often used for observation and follow-up, which brings high psychological and economic burden to the patient.

Methods

Three traditional Chinese medicine (TCM) prescriptions for the treatment of GGNs were found through database including PubMed, Google Scholar, and China National Knowledge Infrastructure (CNKI), Scopus and so on. The possible targets of the active ingredients of the TCM preparations and the gene targets of GGNs were screened out from Traditional Chinese Medicine Systems Pharmacology (TCMSP), UniProt and GeneCards. Network visualization was realized via STRING, Cytoscape 3.7.2, Evenn, DAVID and Hiplot. Finally, molecular docking Vina and PyMOL software were performed to further explore the possibility of drug-target interactions using PubChem compounds, protein data bank (PDB) database, Autodocktools and Autodock.

Results

Three TCM preparations could target the same 13 potential therapeutic targets in GGNs. From network pharmacology, 14 signaling pathways, the functions of the significant targets, an effective ingredient in TCM prescriptions and its functions were obtained.

Conclusions

Chinese herbal formulas containing quercetin could be a potential treatment for GGNs, targeting C-reactive protein (CRP), tumor necrosis factor (TNF), interferon gamma (IFN-γ), intercellular adhesion molecule 1 (ICAM-1), and vascular endothelial growth factor A (VEGFA) through the hypoxia-inducible factor 1 (HIF-1) pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and leukocyte transendothelial migration.

A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

A Novel Network Pharmacology Strategy Based on the Universal Effectiveness-Common Mechanism of Medical Herbs Uncovers Therapeutic Targets in Traumatic Brain Injury

Purpose

Many herbs can promote neurological recovery following traumatic brain injury (TBI). There must lie a shared mechanism behind the common effectiveness. We aimed to explore the key therapeutic targets for TBI based on the common effectiveness of the medicinal plants.

Material and methods

The TBI-effective herbs were retrieved from the literature as imputes of network pharmacology. Then, the active ingredients in at least two herbs were screened out as common components. The hub targets of all active compounds were identified through Cytohubba. Next, AutoDock vina was used to rank the common compound-hub target interactions by molecular docking. A highly scored compound-target pair was selected for in vivo validation.

Results

We enrolled sixteen TBI-effective medicinal herbs and screened out twenty-one common compounds, such as luteolin. Ten hub targets were recognized according to the topology of the protein-protein interaction network of targets, including epidermal growth factor receptor (EGFR). Molecular docking analysis suggested that luteolin could bind strongly to the active pocket of EGFR. Administration of luteolin or the selective EGFR inhibitor AZD3759 to TBI mice promoted the recovery of body weight and neurological function, reduced astrocyte activation and EGFR expression, decreased chondroitin sulfate proteoglycans deposition, and upregulated GAP43 levels in the cortex. The effects were similar to those when treated with the selective EGFR inhibitor.

Conclusion

The common effectiveness-based, common target screening strategy suggests that inhibition of EGFR can be an effective therapy for TBI. This strategy can be applied to discover core targets and therapeutic compounds in other diseases.

Exploring Traditional Medicine Diagnostic Classification for Parkinson's Disease Using Hierarchical Clustering

INTRODUCTION

Personalized diagnosis and therapy for Parkinson's disease (PD) are needed due to the clinical heterogeneity of PD. Syndrome differentiation (SD) in traditional medicine (TM) is a diagnostic method for customized therapy that comprehensively analyzes various symptoms and systemic syndromes. However, research identifying PD classification based on SD is limited.

METHODS

Ten electronic databases were systematically searched from inception to August 10, 2021. Clinical indicators, including 380 symptoms, 98 TM signs, and herbal medicine for PD diagnosed with SD, were extracted from 197 articles; frequency statistics on clinical indicators were conducted to classify several subtypes using hierarchical clustering.

RESULTS

Four distinct cluster groups were identified, each characterized by significant cluster-specific clinical indicators with 95% confidence intervals of distribution. Subtype 2 had the most severe progression, longest progressive duration, and highest association with greater late-stage PD-associated motor symptoms, including postural instability and gait disturbance. The action properties of the herbal formula and original SD presented in the data sources for subtype 2 were associated with Yin deficiency syndrome.

DISCUSSION/CONCLUSION

Hierarchical clustering analysis distinguished various symptoms and TM signs among patients with PD. These newly identified PD subtypes may optimize the diagnosis and treatment with TM and facilitate prognosis prediction. Our findings serve as a cornerstone for evidence-based guidelines for TM diagnosis and treatment.

Einleitung

Eine personalisierte Diagnose und Therapie des Morbus Parkinson (MP) ist angesichts der ausgeprägten klinischen Heterogenität des MP unerlässlich. Die Syndromdifferenzierung (SD) ist in der traditionellen Medizin (TM) eine diagnostische Methode für eine maßgeschneiderte Therapie, bei der verschiedene Symptome und systemische Syndrome umfassend analysiert werden. Es liegen jedoch nur begrenzt Forschungsergebnisse in Bezug auf eine SD-basierte Klassifikation des MP vor.

Methoden

Zehn elektronische Datenbanken wurden systematisch durchsucht, von der Einrichtung bis zum 10. August 2021. Klinische Indikatoren einschließlich von 380 Symptomen, 98 TM-Zeichen sowie pflanzlichen Heilmitteln für mittels SD diagnostiziertem MP wurden aus 197 Artikeln extrahiert, und Häufigkeitsstatistiken der klinischen Indikatoren wurden erstellt, um mittels hierarchischem Clustering eine Reihe von Subtypen zu klassifizieren.

Ergebnisse

Vier verschiedene Cluster-Gruppen wurden identifiziert, die jeweils durch signifikante, Cluster-spezifische klinische Indikatoren mit 95% Konfidenzintervall der Verteilung gekennzeichnet waren. Subtyp 2 hatte den schwersten Verlauf, die längste Progressionsdauer und die stärkste Assoziation mit einem höheren Ausmaß von motorischen Symptomen des MP im Spätstadium, darunter Haltungsinstabilität und Gangstörungen. Die Wirkungseigenschaften der pflanzlichen Formulierung sowie die ursprüngliche SD, die in den Datenquellen für Subtyp 2 genannt wurden, waren mit Yin-Mangel-Syndrom assoziiert.

Diskussion/Schlussfolgerung

Die hierarchische Clustering-Analyse hob verschiedene Symptome und TM-Zeichen bei Patienten mit MP hervor. Die neu identifizierten MP-Subtypen könnten die Diagnose und Behandlung mittels TM optimieren und zur Prognoseerstellung beitragen. Unsere Ergebnisse sind ein Fundament für eine evidenzbasierte Leitlinie für die TM-Diagnostik und -Therapie.

Application of AI on cholangiocarcinoma

Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

Lycium barbarum Ameliorates Oral Mucositis via HIF and TNF Pathways: A Network Pharmacology Approach

BACKGROUND

Oral mucositis is the most common and troublesome complication for cancer patients receiving radiotherapy or chemotherapy. Recent research has shown that Lycium barbarum, an important economic crop widely grown in China, has epithelial protective effects in several other organs. However, it is unknown whether or not Lycium barbarum can exert a beneficial effect on oral mucositis. Network pharmacology has been suggested to be applied in "multi-component-multi-target" functional food studies. The purpose of this study is to evaluate the effect of Lycium barbarum on oral mucositis through network pharmacology, molecular docking and experimental validation.

AIMS

To explore the biological effects and molecular mechanisms of Lycium barbarum in the treatment of oral mucositis through network pharmacology and molecular docking combined with experimental validation.

METHODS

Based on network pharmacology methods, we collected the active components and related targets of Lycium barbarum from public databases, as well as the targets related to oral mucositis. We mapped protein- protein interaction (PPI) networks, performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, and constructed a 'components-disease-targets' network and 'components-pathways-targets' network using Cytoscape to further analyse the intrinsic molecular mechanisms of Lycium barbarum against oral mucositis. The affinity and stability predictions were performed using molecular docking strategies, and experiments were conducted to demonstrate the biological effects and possible mechanisms of Lycium barbarum against oral mucositis.

RESULTS

A network was established between 49 components and 61 OM targets. The main active compounds were quercetin, beta-carotene, palmatine, and cyanin. The predicted core targets were IL-6, RELA, TP53, TNF, IL10, CTNNB1, AKT1, CDKN1A, HIF1A and MYC. The enrichment analysis predicted that the therapeutic effect was mainly through the regulation of inflammation, apoptosis, and hypoxia response with the involvement of TNF and HIF pathways. Molecular docking results showed that key components bind well to the core targets. In both chemically and radiation-induced OM models, Lycium barbarum significantly promoted healing and reduced inflammation. The experimental verification showed Lycium barbarum targeted the key genes (IL-6, RELA, TP53, TNF, IL10, CTNNB1, AKT1, CDKN1A, HIF1A, and MYC) through regulating the HIF and TNF signaling pathways, which were validated using the RT-qPCR, immunofluorescence staining and western blotting assays.

CONCLUSION

In conclusion, the present study systematically demonstrated the possible therapeutic effects and mechanisms of Lycium barbarum on oral mucositis through network pharmacology analysis and experimental validation. The results showed that Lycium barbarum could promote healing and reduce the inflammatory response through TNF and HIF signaling pathways.

Hua-Feng-Dan Alleviates LPS-induced Neuroinflammation by Inhibiting the TLR4/Myd88/NF-κB Pathway: Integrating Network Pharmacology and Experimental Validation

BACKGROUND

Neuroinflammation is the pathological basis of many neurological diseases, including neurodegenerative diseases and stroke. Hua-Feng-Dan (HFD) is a well-established traditional Chinese medicine that has been used for centuries to treat stroke and various other brain-related ailments.

OBJECTIVE

Our study aims to elucidate the molecular mechanism by which HFD mitigates neuroinflammation by combining network pharmacology and in vitro experiments.

METHODS

TCMSP and SymMap databases were used to extract active compounds and their related targets. The neuroinflammation-related targets were obtained from the GeneCards database. The common targets of HFD and neuroinflammation were used to construct a protein-protein interaction (PPI) network. MCODE plug-in was used to find the hub module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to dissect the hub module genes. The lipopolysaccharide (LPS)-induced BV2 microglial neuroinflammation model was utilized to assess the therapeutic effects of HFD on neuroinflammation. Western blotting analysis was performed to examine the core target proteins in the TLR4/My- D88/NF-κB signaling pathway, potentially implicated in HFD's therapeutic effects on neuroinflammation. Hoechst 33342 staining and JC-1 staining were employed to evaluate neuronal apoptosis.

RESULTS

Through network pharmacology, 73 active compounds were identified, with quercetin, beta-sitosterol, luteolin, and (-)-Epigallocatechin-3-Gallate recognized as important compounds. Meanwhile, 115 common targets of HFD and neuroinflammation were identified, and 61 targets were selected as the hub targets utilizing the MCODE algorithm. The results of in vitro experiments demonstrated that HFD significantly inhibited microglial-mediated neuronal inflammation induced by LPS. Integrating the predictions from network pharmacology with the in vitro experiment results, it was determined that the mechanism of HFD in mitigating neuroinflammation is closely related to the TLR4/MyD88/NF-κB pathway. Furthermore, HFD demonstrated the capacity to shield neurons from apoptosis by curbing the secretion of pro-inflammatory factors subsequent to microglial activation.

CONCLUSION

The findings demonstrated that HFD had an inhibitory effect on LPS-induced neuroinflammation in microglia and elucidated its underlying mechanism. These findings will offer a theoretical foundation for the clinical utilization of HFD in treating neurodegenerative diseases associated with neuroinflammation.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Bioactive, Mineral and Antioxidative Properties of Gluten-Free Chicory Supplemented Snack: Impact of Processing Conditions

This study aimed to investigate the impact of chicory root addition (20-40%) and extrusion conditions (moisture content from 16.3 to 22.5%, and screw speed from 500 to 900 rpm) on bioactive compounds content (inulin, sesquiterpene lactones, and polyphenols) of gluten-free rice snacks. Chicory root is considered a potential carrier of food bioactives, while extrusion may produce a wide range of functional snack products. The mineral profiles were determined in all of the obtained extrudates in terms of Na, K, Ca, Mg, Fe, Mn, Zn, and Cu contents, while antioxidative activity was established through reducing capacity, DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) tests. Chicory root addition contributed to the improvement of bioactive compounds and mineral contents, as well as antioxidative activities in all of the investigated extrudates in comparison to the pure-rice control sample. An increase in moisture content raised sesquiterpene lactones and minerals, while high screw speeds positively affected polyphenols content. The achieved results showed the important impact of the extrusion conditions on the investigated parameters and promoted chicory root as an attractive food ingredient in gluten-free snack products with high bioactive value.

Polypharmacology-based approach for screening TCM against coinfection of Mycoplasma gallisepticum and Escherichia coli

Natural products and their unique polypharmacology offer significant advantages for finding novel therapeutics particularly for the treatment of complex diseases. Meanwhile, Traditional Chinese Medicine exerts overall clinical benefits through a multi-component and multi-target approach. In this study, we used the previously established co-infection model of Mycoplasma gallisepticum and Escherichia coli as a representative of complex diseases. A new combination consisting of 6 herbs were obtained by using network pharmacology combined with transcriptomic analysis to reverse screen TCMs from the Chinese medicine database, containing Isatdis Radix, Forsythia Fructus, Ginkgo Folium, Mori Cortex, Licorice, and Radix Salviae. The results of therapeutic trials showed that the Chinese herbal compounds screened by the target network played a good therapeutic effect in the case of co-infection. In summary, these data suggested a new method to validate target combinations of natural products that can be used to optimize their multiple structure-activity relationships to obtain drug-like natural product derivatives.

Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Identifying effective drug combinations to treat cancer is a challenging task, either experimentally or computationally. Here, the authors develop a bipartite network modelling approach to propose drug combination strategies in acute myeloid leukaemia using patient and cell line drug screening data.

Mechanism of Improving Aspirin Resistance: Blood-Activating Herbs Combined With Aspirin in Treating Atherosclerotic Cardiovascular Diseases

Atherosclerotic thrombotic disease continues to maintain a high morbidity and mortality rate worldwide at present. Aspirin, which is reckoned as the cornerstone of primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVDs), has been applied in clinics extensively. However, cardiovascular events continue to occur even though people utilize aspirin appropriately. Therefore, the concept of aspirin resistance (AR) was put forward by scholars, which is of great significance for the prediction of the clinical outcome of diseases. The pathogenesis of AR may be incorporated with low patient compliance, insufficient dose, genetic polymorphism, increased platelet transformation, inflammation, and the degenerative changes and calcification of platelets. The improvement of AR in the treatment of ASCVDs has gradually become a research hot spot in recent years. Traditional Chinese medicine (TCM) regards individuals as a whole and treats them from a holistic view, which has been found to have advantages in clinical studies on the treatment of AR. Many kinds of blood-activating TCM have the effect of improving AR. The potential mechanism for the improvement of AR by blood-activating herbs combined with aspirin was explored. The combination of blood-activating herbs and aspirin to improve AR is likely to turn into a hot topic of research in the future.

The Material Basis and Mechanism of Xuefu Zhuyu Decoction in Treating Stable Angina Pectoris and Unstable Angina Pectoris

METHODS

Firstly, we used a network proximity approach to calculate and compare the effectiveness of the formula with that of Western drugs for each type of angina, including all targets and intersecting targets, from a topological perspective. Secondly, we compared the mechanisms of action of the two angina pectoris at three levels and five aspects, including conventional and modular analysis approaches. Thirdly, based on the unique functions of each angina in the complex heterogeneous network, we designed a reverse process for finding the material basis using dynamic, static, and enriched items as well as a total item. Finally, the designed inverse process, material basis, and mechanism of action were validated.

RESULTS

The target network of Xuefu Zhuyu decoction is closer to the target network of each type of angina than that of Western drugs, and the intersection targets have a closer proximity. Comparison of the mechanisms of action showed that stable angina and unstable angina had 158 common targets, while the unique targets were 34 and 1, respectively. Modularity analysis showed that the GO similarity of target modules was highly correlated with KEGG similarity. We ended up with 67 compounds upregulated for stable angina and 47 compounds upregulated for unstable angina. Our results were validated by literature mining, high-volume molecular docking, and miRNA enrichment analysis.

CONCLUSIONS

For both types of angina pectoris, Xuefu Zhuyu decoction is superior to Western drugs. A comparison of various aspects led to the unique mechanisms of action, from which the material basis of each type of angina was deduced.

Reinforcement Learning for Precision Oncology

Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology.

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.

Network differentiation: A computational method of pathogenesis diagnosis in traditional Chinese medicine based on systems science

Resembling the role of disease diagnosis in Western medicine, pathogenesis (also called Bing Ji) diagnosis is one of the utmost important tasks in traditional Chinese medicine (TCM). In TCM theory, pathogenesis is a complex system composed of a group of interrelated factors, which is highly consistent with the character of systems science (SS). In this paper, we introduce a heuristic definition called pathogenesis network (PN) to represent pathogenesis in the form of the directed graph. Accordingly, a computational method of pathogenesis diagnosis, called network differentiation (ND), is proposed by integrating the holism principle in SS. ND consists of three stages. The first stage is to generate all possible diagnoses by Cartesian Product operated on specified prior knowledge corresponding to the input symptoms. The second stage is to screen the validated diagnoses by holism principle. The third stage is to pick out the clinical diagnosis by physician-computer interaction. Some theorems are stated and proved for the further optimization of ND in this paper. We conducted simulation experiments on 100 clinical cases. The experimental results show that our proposed method has an excellent capability to fit the holistic thinking in the process of physician inference.

Predicting biochemical and physiological effects of natural products from molecular structures using machine learning

Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.

IrGO: Iranian traditional medicine General Ontology and knowledge base

Background

Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been proposed for the concepts of this medicine up to the present time. Considering the extensive terminology in the numerous textbooks written by the scholars over centuries, comprehending the totality of concepts is obviously a very challenging task. To resolve this issue, overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity.

Construction and content

Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of the concepts used to describe medicinal substances. The steps followed to accomplish this task included (1) compiling the list of classes via examination of textbooks, and text mining the resource followed by manual review to ensure comprehensiveness of extracted terms; (2) arranging the classes in a taxonomy; (3) determining object and data properties; (4) specifying annotation properties including ID, labels (English and Persian), alternative terms, and definitions (English and Persian); (5) ontology evaluation. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry.

Utility and discussion

The ontology was finalized with inclusion of 3521 classes, 15 properties, and 20,903 axioms in the Iranian traditional medicine General Ontology (IrGO) database, freely available at http://ir-go.net/. An indented list and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in UNaProd database to create a knowledge base of ITM materia medica.

Conclusion

We constructed an ontology-based knowledge base of ITM concepts in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. This ontology will aid Persian medicine practitioners in clinical decision-making to select drugs. Extending IrGO will bridge the gap between traditional and conventional schools of medicine, helping guide future research in the process of drug discovery.

Network-based modeling of herb combinations in traditional Chinese medicine

Traditional Chinese medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein–protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.

In vitro biological activity of Salvia fruticosa Mill. infusion against amyloid β-peptide-induced toxicity and inhibition of GSK-3β, CK-1δ, and BACE-1 enzymes relevant to Alzheimer's disease

Salvia species have been traditionally used to improve cognition and have been proved to be a potential natural treatment for Alzheimer’s disease. Salvia fruticosa Mill. (Turkish sage or Greek sage) demonstrated to have anticholinergic effects in vitro.

The aim of this study was to understand the mechanism underlying the neuroprotective effects of S. fruticosa infusion and its representative compound rosmarinic acid, which was detected by LC-DAD-ESI-MS/MS. The protective effects of the S. fruticosa infusion (SFINF) and its major substance rosmarinic acid (RA) on amyloid beta 1–42 -induced cytotoxicity on SH-SY5Y cells together with p-GSK-3β activation were investigated. Their in vitro inhibitory effects against glycogen synthase kinase 3β, β-secretase, and casein kinase 1δ enzymes were also evaluated.

The results showed that treatment with the all tested concentrations, SFINF significantly decreased Aβ 1–42-induced cytotoxicity and exhibited promising in vitro glycogen synthase kinase 3β inhibitory activity below 10 µg/mL (IC₅₀ 6.52 ± 1.14 µg/mL), in addition to β-secretase inhibition (IC₅₀ 86 ± 2.9 µg/mL) and casein kinase 1δ inhibition (IC₅₀ 121.57 ± 4.00). The SFINF (100 µg/mL and 250 µg/mL) also activated the expression of p-GSK-3β in amyloid beta 1–42 treated SH-SY5Y cells.

The outcomes of this study demonstrated that the S. fruticosa infusion possessed activity to prevent amyloid beta 1–42 -induced neurotoxicity and provided proof that its mechanism may involve regulation of p-GSK-3β protein.

Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

Background

Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning.

Methods

First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest.

Results

Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.

Conclusions

The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

To Explore the Mechanism and Equivalent Molecular Group of Fuxin Mixture in Treating Heart Failure Based on Network Pharmacology

Fuxin mixture (FXHJ) is a prescription for the treatment of heart failure. It has been shown to be effective in clinical trials, but its active ingredients and mechanism of action are not completely clear, which limits its clinical application and international promotion. In this study, we used network pharmacology to find, conclude, and summarize the mechanism of FXHJ in the treatment of heart failure. From FXHJ, we found 39 active ingredients and 47 action targets. Next, we constructed the action network and was conducted enrichment analysis. The results showed that FXHJ mainly treated heart failure by regulating the MAPK signaling pathway, PI3KAkt signaling pathway, cAMP signaling pathway, TNF signaling pathway, toll-like receptor signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, and the apoptotic signaling molecule BCL2. Through the research method of network pharmacology, this study summarized the preliminary experiments of the research group and revealed the probable mechanism of FXHJ in the treatment of heart failure to a certain extent, which provided some ideas for the development of new drugs.