The pathogenicity of SARS-CoV-2 in hACE2 transgenic mice.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19), which has become a public health emergency of international concern1. Angiotensin-converting enzyme 2 (ACE2) is the cell-entry receptor for severe acute respiratory syndrome coronavirus (SARS-CoV)2. Here we infected transgenic mice that express human ACE2 (hereafter, hACE2 mice) with SARS-CoV-2 and studied the pathogenicity of the virus. We observed weight loss as well as virus replication in the lungs of hACE2 mice infected with SARS-CoV-2. The typical histopathology was interstitial pneumonia with infiltration of considerable numbers of macrophages and lymphocytes into the alveolar interstitium, and the accumulation of macrophages in alveolar cavities. We observed viral antigens in bronchial epithelial cells, macrophages and alveolar epithelia. These phenomena were not found in wild-type mice infected with SARS-CoV-2. Notably, we have confirmed the pathogenicity of SARS-CoV-2 in hACE2 mice. This mouse model of SARS-CoV-2 infection will be valuable for evaluating antiviral therapeutic agents and vaccines, as well as understanding the pathogenesis of COVID-19.

The total xanthones extracted from Gentianella acuta alleviates HFpEF by activating the IRE1α/Xbp1s pathway.

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterized by pulmonary and systemic congestion resulting from left ventricular diastolic dysfunction and increased filling pressure. Currently, however, there is no evidence on effective pharmacotherapy for HFpEF. In this study, we aimed to investigate the therapeutic effect of total xanthones extracted from Gentianella acuta (TXG) on HFpEF by establishing an high-fat diet (HFD) + L-NAME-induced mouse model. Echocardiography was employed to assess the impact of TXG on the cardiac function in HFpEF mice. Haematoxylin and eosin staining, wheat germ agglutinin staining, and Masson's trichrome staining were utilized to observe the histopathological changes following TXG treatment. The results demonstrated that TXG alleviated HFpEF by reducing the expressions of genes associated with myocardial hypertrophy, fibrosis and apoptosis. Furthermore, TXG improved cardiomyocyte apoptosis by inhibiting the expression of apoptosis-related proteins. Mechanistic investigations revealed that TXG could activate the inositol-requiring enzyme 1α (IRE1α)/X-box-binding protein 1 (Xbp1s) signalling pathway, but the knockdown of IRE1α using the IRE1α inhibitor STF083010 or siRNA-IRE1α impaired the ability of TXG to ameliorate cardiac remodelling in HFpEF models. In conclusion, TXG alleviates myocardial hypertrophy, fibrosis and apoptosis through the activation of the IRE1α/Xbp1s signalling pathway, suggesting its potential beneficial effects on HFpEF patients.

AIM2 inflammasome: A potential therapeutic target in ischemic stroke.

Ischemic stroke (IS) is a significant global public health issue with a high incidence, disability, and mortality rate. A robust inflammatory cascade with complex and wide-ranging mechanisms occurs following ischemic brain injury. Inflammasomes are multiprotein complexes in the cytoplasm that modulate the inflammatory response by releasing pro-inflammatory cytokines and inducing cellular pyroptosis. Among these inflammasomes, the Absent in Melanoma 2 (AIM2) inflammasome shows the ability to detect a wide range of pathogen DNAs, thereby triggering an inflammatory response. Recent studies have indicated that the aberrant expression of AIM2 inflammasome in various cells is closely associated with the pathological processes of ischemic brain injury. This paper summarizes the expression and regulatory role of AIM2 in CNS and peripheral immune cells and discusses current therapeutic approaches targeting AIM2 inflammasome. These findings aim to serve as a reference for future research in this field.

Interleukins and Psoriasis.

Psoriasis is an immune-mediated chronic inflammatory skin disease that affects 2% to 3% of the world's population. It is widely assumed that immune cells and cytokines acting together play a crucial part in the pathophysiology of psoriasis by promoting the excessive proliferation of skin keratinocytes and inflammatory infiltration. Interleukins (ILs), as a critical component of cytokines, have been closely associated with the pathogenesis and progression of psoriasis. This review summarizes the current contribution of ILs to psoriasis and describes the role each IL performs in psoriasis. Furthermore, the paper presents the therapeutic effects and application prospects of biologics developed for ILs in clinical treatment and experiments. The study aims to further the research on ILs in the treatment of psoriasis.

The gut microbiome: an important role in neurodegenerative diseases and their therapeutic advances.

There are complex interactions between the gut and the brain. With increasing research on the relationship between gut microbiota and brain function, accumulated clinical and preclinical evidence suggests that gut microbiota is intimately involved in the pathogenesis of neurodegenerative diseases (NDs). Increasingly studies are beginning to focus on the association between gut microbiota and central nervous system (CNS) degenerative pathologies to find potential therapies for these refractory diseases. In this review, we summarize the changes in the gut microbiota in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and amyotrophic lateral sclerosis and contribute to our understanding of the function of the gut microbiota in NDs and its possible involvement in the pathogenesis. We subsequently discuss therapeutic approaches targeting gut microbial abnormalities in these diseases, including antibiotics, diet, probiotics, and fecal microbiota transplantation (FMT). Furthermore, we summarize some completed and ongoing clinical trials of interventions with gut microbes for NDs, which may provide new ideas for studying NDs.

SiBERT: A Siamese-based BERT network for Chinese medical entities alignment.

Entity alignment aims at associating semantically similar entities in knowledge graphs from different sources. It is widely used in the integration and construction of professional medical knowledge. The existing deep learning methods lack term-level embedding representation, which limits the performance of entity alignment and causes a massive computational overhead. To address these problems, we propose a Siamese-based BERT (SiBERT) for Chinese medical entities alignment. SiBERT generates term-level embedding based on word embedding sequences to enhance the features of entities in similarity calculation. The process of entity alignment contains three steps. Specifically, the SiBERT is firstly pre-trained with synonym dictionary in the public domain, and transferred to the task of medical entity alignment. Secondly, four different categories of entities (disease, symptom, treatment, and examination) are labeled based on the standard terms selected from standard terms dataset. The entities and their standard terms form term pairs to train SiBERT. Finally, combined with the entity alignment algorithm, the most similar standard term is selected as the final result. To evaluate the effectiveness of our method, we conduct extensive experiments on real-world datasets. The experimental results illustrate that SiBERT network is superior to other compared algorithms both in alignment accuracy and computational efficiency.

New Insights into Stroke Prevention and Treatment: Gut Microbiome.

Stroke, a lethal neurological disease, accounts for a grave economic burden on society. Despite extensive basic and clinical studies on stroke prevention, a precise effective treatment approach for stroke at this stage remains unavailable. The majority of our body's gut microbiota plays a vital role in food digestion, immune regulation, and nervous system development, which is highly associated with the development of some diseases. Multiple clinical studies have documented variation in the composition of gut microbiota between stroke patients and healthy counterparts. Moreover, the intervention of intestinal symbiotic microorganisms via several mechanisms plays an active role in stroke prognosis. In the prevention and treatment of stroke, the gut microbiota gives off a seductive glow, this is a promising therapeutic target. This paper summarizes the current knowledge of stroke and gut microbiota, and systematically describes the possible mechanisms of interaction between stroke and gut microbiota, the relationship between stroke-related risk factors and gut microbiota, and the treatment of gut flora using microorganisms. Thus, it could valuably elucidate the correlation of gut microbiota with stroke incidence, providing stroke researchers with a new strategy for stroke prevention and treatment by regulating gut microbiota.

GLA-Net: A global-local attention network for automatic cataract classification.

Cataracts are the most crucial cause of blindness among all ophthalmic diseases. Convenient and cost-effective early cataract screening is urgently needed to reduce the risks of visual loss. To date, many studies have investigated automatic cataract classification based on fundus images. However, existing methods mainly rely on global image information while ignoring various local and subtle features. Notably, these local features are highly helpful for the identification of cataracts with different severities. To avoid this disadvantage, we introduce a deep learning technique to learn multilevel feature representations of the fundus image simultaneously. Specifically, a global-local attention network (GLA-Net) is proposed to handle the cataract classification task, which consists of two levels of subnets: the global-level attention subnet pays attention to the global structure information of the fundus image, while the local-level attention subnet focuses on the local discriminative features of the specific regions. These two types of subnets extract retinal features at different attention levels, which are then combined for final cataract classification. Our GLA-Net achieves the best performance in all metrics (90.65% detection accuracy, 83.47% grading accuracy, and 81.11% classification accuracy of grades 1 and 2). The experimental results on a real clinical dataset show that the combination of global-level and local-level attention models is effective for cataract screening and provides significant potential for other medical tasks.

EWASdb: epigenome-wide association study database.

DNA methylation, the most intensively studied epigenetic modification, plays an important role in understanding the molecular basis of diseases. Furthermore, epigenome-wide association study (EWAS) provides a systematic approach to identify epigenetic variants underlying common diseases/phenotypes. However, there is no comprehensive database to archive the results of EWASs. To fill this gap, we developed the EWASdb, which is a part of 'The EWAS Project', to store the epigenetic association results of DNA methylation from EWASs. In its current version (v 1.0, up to July 2018), the EWASdb has curated 1319 EWASs associated with 302 diseases/phenotypes. There are three types of EWAS results curated in this database: (i) EWAS for single marker; (ii) EWAS for KEGG pathway and (iii) EWAS for GO (Gene Ontology) category. As the first comprehensive EWAS database, EWASdb has been searched or downloaded by researchers from 43 countries to date. We believe that EWASdb will become a valuable resource and significantly contribute to the epigenetic research of diseases/phenotypes and have potential clinical applications. EWASdb is freely available at http://www.ewas.org.cn/ewasdb or http://www.bioapp.org/ewasdb.

Isolation and Characterization of Shiga Toxin-Producing Escherichia coli from Retail Beef Samples from Eight Provinces in China.

While Shiga toxin-producing Escherichia coli (STEC) is a major foodborne pathogen worldwide, data on the molecular and phylogenetic properties of STEC isolates from retail beef samples in China remain scant. Fresh retail beef samples (n = 1062) were collected from eight provinces, and STEC isolates were recovered and characterized. PCR data showed that more than 50% of the samples were stx positive, and 82 STEC isolates were recovered from 14.8% (79/535) stx-positive enriched broths. In contrast, all ciprofloxacin resistant isolates (n = 19) and 13 cefotaxime (CTX) resistant isolates were eae positive and belonged to three serotypes: O111:H8, O26:H11, or O157:H7. Point mutations in quinolone resistance-determining regions and plasmid-mediated quinolone resistance determinants were identified in 16 and 20 isolates, respectively. BlaCTX-M and a point mutation (C-42T) in ampC promoter were detected in 15 and 8 of the CTX resistant isolates, respectively. In addition, macrolide resistance gene mphA was identified in eight azithromycin resistant O111:H8 isolates and one O26:H11 isolate. Single nucleotide polymorphism analysis demonstrated that the O26 and O157 isolates had multiple origins, but the O111 isolates were closely related. Taken together, our data demonstrated that several sequence types associated with hemolytic uremic syndrome from the retail beef samples in China had developed into dangerous multidrug resistant pathogens. The resistant phenotype can facilitate their transmission among the farm animals and human beings when there is an antimicrobial selective pressure.

Comparison of the general co-expression landscapes between human and mouse.

The murine model serves as an important experimental system in biomedical science because of its high degree of similarities at the sequence level with human. Recent studies have compared the transcriptional landscapes between human and mouse, but the general co-expression landscapes have not been characterized. Here, we calculated the general co-expression coefficients and constructed the general co-expression maps for human and mouse. The differences and similarities of the general co-expression maps between the two species were compared in detail. The results showed low similarities in the human and mouse, with only about 36.54% of the co-expression relationships conserved between the two species. These results indicate that researchers should pay attention to these differences when performing research using the expression data of human and mouse. To facilitate use of this information, we also developed the human-mouse general co-expression difference database (coexpressMAP) to search differences in co-expression between human and mouse. This database is freely available at http://www.bioapp.org/coexpressMAP.

The framework for population epigenetic study.

At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines). We analyzed the differences and similarities between CEU and YRI from the following aspects: SMP (single methylation polymorphism) allele frequency, SMP allele association, MD, MD block and methylation haplotype (meplotype) frequency. The results showed that CEU and YRI had similar distribution of SMP allele frequency, and shared many MD block region. We believe that the framework of population genetics can be used in the population epigenetics. The population epigenetic framework also has potential prospects in the study of complex diseases, such as epigenome-wide association study.

EWAS: epigenome-wide association study software 2.0.

Motivation: With the development of biotechnology, DNA methylation data showed exponential growth. Epigenome-wide association study (EWAS) provide a systematic approach to uncovering epigenetic variants underlying common diseases/phenotypes. But the EWAS software has lagged behind compared with genome-wide association study (GWAS). To meet the requirements of users, we developed a convenient and useful software, EWAS2.0. Results: EWAS2.0 can analyze EWAS data and identify the association between epigenetic variations and disease/phenotype. On the basis of EWAS1.0, we have added more distinctive features. EWAS2.0 software was developed based on our 'population epigenetic framework' and can perform: (i) epigenome-wide single marker association study; (ii) epigenome-wide methylation haplotype (meplotype) association study and (iii) epigenome-wide association meta-analysis. Users can use EWAS2.0 to execute chi-square test, t-test, linear regression analysis, logistic regression analysis, identify the association between epi-alleles, identify the methylation disequilibrium (MD) blocks, calculate the MD coefficient, the frequency of meplotype and Pearson's correlation coefficients and carry out meta-analysis and so on. Finally, we expect EWAS2.0 to become a popular software and be widely used in epigenome-wide associated studies in the future. Availability and implementation: The EWAS software is freely available at http://www.ewas.org.cn or http://www.bioapp.org/ewas.

Bone marrow mesenchymal stem cells therapy regulates sphingolipid and glycerophospholipid metabolism to promote neurological recovery in stroke rats: A metabolomics analysis.

Bone marrow mesenchymal stem cells (BMSCs) have therapeutic potential in the subacute/chronic phase of acute ischemic stroke (AIS), but the underlying mechanisms are not yet fully elucidated. There is a knowledge gap in understanding the metabolic mechanisms of BMSCs in stroke therapy. In this study, we administered BMSCs intravenously 24 h after reperfusion in rats with transient cerebral artery occlusion (MCAO). The treatment with BMSCs for 21 days significantly reduced the modified neurological severity score of MCAO rats (P < 0.01) and increased the number of surviving neurons in both the striatum and hippocampal dentate gyrus region (P < 0.01, respectively). Moreover, BMSCs treatment resulted in significant enhancements in various structural parameters of dendrites in layer V pyramidal neurons in the injured hemispheric motor cortex, including total length (P < 0.05), number of branches (P < 0.05), number of intersections (P < 0.01), and spine density (P < 0.05). Then, we performed plasma untargeted metabolomics analysis to study the metabolic changes of BMSCs on AIS. There were 65 differential metabolites identified in the BMSCs treatment group. Metabolic profiling analysis revealed that BMSCs modulate abnormal sphingolipid metabolism and glycerophospholipid metabolism, particularly affecting core members such as sphingomyelin (SM), ceramide (Cer) and sphingosine-1-phosphate (S1P). The metabolic network analysis and pathway-based compound-reaction-enzyme-gene network analysis showed that BMSCs inhibited the Cer-induced apoptotic pathway and promoted the S1P signaling pathway. These findings suggest that the enhanced effects of BMSCs on neuronal survival and synaptic plasticity after stroke may be mediated through these pathways. In conclusion, our study provides novel insight into the potential mechanisms of BMSCs treatment in stroke and sheds light on the possible clinical translation of BMSCs.

Buyang huanwu decoction alleviates stroke-induced immunosuppression in MCAO mice by reducing splenic T cell apoptosis triggered by AIM2 inflammasome.

ETHNOPHARMACOLOGICAL RELEVANCE: Ischemic stroke is a serious disabling and fatal disease that places a heavy burden on the world. Stroke induces a state of systemic immunosuppression that is strongly associated with an increased risk of infection and severe outcomes. Buyang Huanwu Decoction (BYHWD) is an ancient Chinese traditional formula with a good clinical and experimental basis. However, the role of BYHWD on post-stroke immunomodulation, especially immunosuppression, is unclear. AIM OF THE STUDY: The aim of this study was to investigate the pharmacological mechanism of BYHWD to alleviate ischemic stroke by analyzing splenic T cells apoptosis triggered by the AIM2 inflammasome activation cascade. MATERIALS AND METHODS: An ischemic stroke model in C57BL/6 J mice was constructed using the MCAO method. The mNSS test and the hanging wire test were conducted to evaluate neurological impairment in mice. Histopathological damage was visualized by Nissl staining and HE staining. The protective effects of BYHWD on the spleen were determined by splenic index and spleen HE staining. The inhibition of AIM2 inflammasome cascade by BYHWD were explored through immunofluorescence (IF), flow cytometry, enzyme-linked immunosorbent assay (ELISA) and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Flow cytometry was used to assess the apoptosis of splenic T cells. RESULTS: BYHWD significantly reduced infarct size, improved neurological function scores, and alleviated histopathological damage in middle cerebral artery occlusion (MCAO) mice. At the same time, BYHWD salvaged spleen atrophy. BYHWD significantly ameliorated apoptosis of splenic T lymphocytes. Key proteins and factors in the AIM2/IL-1ß/FasL/Fas axis are effectively inhibited from expression after BYHWD treatment. CONCLUSION: It is the first study to demonstrate that BYHWD can improve stroke-induced immunosuppression by down-regulating Fas-dependent splenic T-cell apoptosis triggered by peripheral AIM2 inflammasome-driven signaling cascade.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

Antimicrobial Resistance and the Genomic Epidemiology of Multidrug-Resistant Salmonella enterica serovar Enteritidis ST11 in China.

BACKGROUND: With the recent evolution of multidrug-resistant strains, the genetic characteristics of foodborne Salmonella enterica serovar Enteritidis and clinical isolates have changed. ST11 is now the most common genotype associated with S. Enteritidis isolates. METHODS: A total of 83 strains of S. Enteritidis were collected at the General Hospital of the People's Liberation Army. Of these, 37 were from aseptic sites in patients, 11 were from the feces of patients with diarrhea, and the remaining 35 were of chicken-origin. The minimum inhibitory concentration of S. Enteritidis was determined by the broth microdilution method. Genomic DNA was extracted using the QiAamp DNA Mini Kit, and whole-genome sequencing (WGS) was performed using an Illumina X-ten platform. Prokka was used for gene prediction and annotation, and bioinformatic analysis tools included Resfinder, ISFinder, Virulence Factor Database, and PlasmidFinder. IQ-TREE was used to build a maximum likelihood phylogenetic tree. The phylogenetic relationship and distribution of resistance genes was displayed using iTOL. Comparative population genomics was used to analyze the phenotypes and genetic characteristics of antibiotic resistance in clinical and chicken-origin isolates of S. Enteritidis. RESULTS: The chicken-origin S. Enteritidis isolates were more resistant to antibiotics than clinical isolates, and had a broader antibiotic resistance spectrum and higher antibiotic resistance rate. A higher prevalence of antibiotic-resistance genes was observed in chicken-origin S. Enteritidis compared to clinical isolates, along with distinct patterns in the contextual characteristics of these genes. Notably, genes such as blaCTX-M and dfrA17 were exclusive to plasmids in clinical S. Enteritidis, whereas in chicken-origin S. Enteritidis they were found in both plasmids and chromosomes. Additionally, floR was significantly more prevalent in chicken-origin isolates than in clinical isolates. Careful analysis revealed that the delayed isolation of chicken-origin S. Enteritidis contributes to accelerated gene evolution. Of note, certain resistance genes tend to integrate seamlessly and persist steadfastly within the chromosome, thereby expediting the evolution of resistance mechanisms against antibiotics. Our comparative analysis of virulence genes in S. Enteritidis strains from various sources found no substantial disparities in the distribution of other virulence factors. In summary, we propose that chicken-origin S. Enteritidis has the potential to cause clinical infections. Moreover, the ongoing evolution and dissemination of these drug-resistant genes poses a formidable challenge to clinical treatment. CONCLUSIONS: Constant vigilance is needed to monitor the dynamic patterns of drug resistance in S. Enteritidis strains sourced from diverse origins.

Genome-based surveillance reveals cross-transmission of MRSA ST59 between humans and retail livestock products in Hanzhong, China.

Methicillin-resistant Staphylococcus aureus (MRSA) has been recognized in hospitals, community and livestock animals and the epidemiology of MRSA is undergoing a major evolution among humans and animals in the last decade. This study investigated the prevalence of MRSA isolates from ground pork, retail whole chicken, and patient samples in Hanzhong, China. The further characterization was performed by antimicrobial susceptibility testing and in-depth genome-based analysis to identify the resistant determinants and their phylogenetic relationship. A total of 93 MRSA isolates were recovered from patients (n = 67) and retail livestock products (n = 26) in Hanzhong, China. 83.9% (78/93) MRSA isolates showed multiple drug resistant phenotype. Three dominant livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) sequence types were identified: ST59-t437 (n = 47), ST9-t899 (n = 10) and ST398 (n = 7). There was a wide variation among sequence types in the distribution of tetracycline-resistance, scn-negative livestock markers and virulence genes. A previous major human MRSA ST59 became the predominant interspecies MRSA sequence type among humans and retail livestock products. A few LA-MRSA isolates from patients and livestock products showed close genetic similarity. The spreading of MRSA ST59 among livestock products deserving special attention and active surveillance should be enacted for the further epidemic spread of MRSA ST59 in China. Data generated from this study will contribute to formulation of new strategies for combating spread of MRSA.

OMGMed: Advanced System for Ocular Myasthenia Gravis Diagnosis via Eye Image Segmentation.

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of "network structure" and "loss function", and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of "nnUNet" network structure and "Cross-Entropy + Iou + Boundary" hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services.

Network Pharmacology Revealed the Mechanisms of Action of Lithospermum erythrorhizon Sieb on Atopic Dermatitis.

Aim: The application of network analysis algorithms promoted the development of network pharmacology. This study aimed to combine network pharmacology and signed random walk with restart (SRWR) to reveal the mechanism by which Lithospermum erythrorhizon Sieb (LES) exerts effects on atopic dermatitis (AD). Methods: The compounds and targets of LES were retrieved from Traditional Chinese Medicine Integrated Database (TCMID) and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and important compounds and targets were identified by intersection analysis and protein-protein interaction (PPI) network. Results: We found that active LES-derived compounds such as caffeic acid, Isovaleric acid, Arnebinol, and Alannan may inhibit PTGS2, HSP90AA1 and MAPK14, which are key mediators involved in PI3K-Akt pathway, vascular endothelial growth factor signaling pathway, Fc epsilon RI signaling pathway, and calcium signaling pathway. Conclusion: The application of SRWR could identify potential targets of LES with a low false-positive rate and help elucidate the mechanism of action of traditional Chinese medicine.