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.

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.

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.

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.

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.

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.

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.

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.

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.

The interaction between intestinal microenvironment and stroke.

BACKGROUND: Stroke is not only a major cause of disability but also the third leading cause of death, following heart disease and cancer. It has been established that stroke causes permanent disability in 80% of survivors. However, current treatment options for this patient population are limited. Inflammation and immune response are major features that are well-recognized to occur after a stroke. The gastrointestinal tract hosts complex microbial communities, the largest pool of immune cells, and forms a bidirectional regulation brain-gut axis with the brain. Recent experimental and clinical studies have highlighted the importance of the relationship between the intestinal microenvironment and stroke. Over the years, the influence of the intestine on stroke has emerged as an important and dynamic research direction in biology and medicine. AIMS: In this review, we describe the structure and function of the intestinal microenvironment and highlight its cross-talk relationship with stroke. In addition, we discuss potential strategies aiming to target the intestinal microenvironment during stroke treatment. CONCLUSION: The structure and function of the intestinal environment can influence neurological function and cerebral ischemic outcome. Improving the intestinal microenvironment by targeting the gut microbiota may be a new direction in treating stroke.

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.

Effect of leave-on cosmetic antimicrobial preservatives on healthy skin resident Staphylococcus epidermidis.

BACKGROUND: The effect of high doses preservatives in the leave-on cosmetic products to the skin microbiota is not clear. Studies have shown that the preservatives might alter the balance of the skin microbiota. AIM: In this study, we aimed to evaluate antimicrobial effect of nine cosmetic chemical preservatives. MATERIAL & METHOD: A total of 77 Staphylococcus epidermidis isolates from 46 healthy zygomatic skin samples were characterized by multilocus sequence typing (MLST). Nine preservatives used in leave-on cosmetics were analyzed by testing the minimal inhibitory concentrations (MICs) against S. epidermidis isolates. We also determined the mutant prevention concentration (MPC) and bactericidal kinetics on selected isolates. RESULTS: More than 17 sequence types were recognized among 77 S. epidermidis isolates. Our data demonstrated that the maximum permitted doses of 2-bromo-2-nitro-1,3-propanediol, ethyl 4-hydroxybenzoate, hexadecyltrimethylammonium bromide, and imidazolidinyl urea were significantly higher than both their MICs and MPCs. We showed that, at the maximum permitted doses, two preservatives could completely kill 107 CFU/mL S. epidermidis in less than 1 h in MH broth. CONCLUSION: Our data demonstrated that certain preservatives of the leave-on cosmetics might inhibit or kill S. epidermidis cells and perturb the skin microbiota balance. The determination of the maximum permitted doses of the preservatives should not only be based on the toxicological data, but also antimicrobial susceptibility analysis. This comprehensive evaluation would ensure a balanced and healthy skin microbiota.

Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images.

Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels).

Stem Cell-derived Extracellular Vesicles: A Promising Nano Delivery Platform to the Brain?

A very important cause of the frustration with drug therapy for central nervous system (CNS) diseases is the failure of drug delivery. The blood-brain barrier (BBB) prevents most therapeutic molecules from entering the brain while maintaining CNS homeostasis. Scientists are keen to develop new brain drug delivery systems to solve this dilemma. Extracellular vesicles (EVs), as a class of naturally derived nanoscale vesicles, have been extensively studied in drug delivery due to their superior properties. This review will briefly present current brain drug delivery strategies, including invasive and non-invasive techniques that target the brain, and the application of nanocarriers developed for brain drug delivery in recent years, especially EVs. The cellular origin of EVs affects the surface protein, size, yield, luminal composition, and other properties of EVs, which are also crucial in determining whether EVs are useful as drug carriers. Stem cell-derived EVs, which inherit the properties of parental cells and avoid the drawbacks of cell therapy, have always been favored by researchers. Thus, in this review, we will focus on the application of stem cell-derived EVs for drug delivery in the CNS. Various nucleic acids, proteins, and small-molecule drugs are loaded into EVs with or without modification and undergo targeted delivery to the brain to achieve their therapeutic effects. In addition, the challenges facing the clinical application of EVs as drug carriers will also be discussed. The directions of future efforts may be to improve drug loading efficiency and precise targeting.

Mining of novel secondary metabolite biosynthetic gene clusters from acid mine drainage.

Acid mine drainage (AMD) is usually acidic (pH < 4) and contains high concentrations of dissolved metals and metalloids, making AMD a typical representative of extreme environments. Recent studies have shown that microbes play a key role in AMD bioremediation, and secondary metabolite biosynthetic gene clusters (smBGCs) from AMD microbes are important resources for the synthesis of antibacterial and anticancer drugs. Here, 179 samples from 13 mineral types were used to analyze the putative novel microorganisms and secondary metabolites in AMD environments. Among 7,007 qualified metagenome-assembled genomes (MAGs) mined from these datasets, 6,340 MAGs could not be assigned to any GTDB species representative. Overall, 11,856 smBGCs in eight categories were obtained from 7,007 qualified MAGs, and 10,899 smBGCs were identified as putative novel smBGCs. We anticipate that these datasets will accelerate research in the field of AMD bioremediation, aid in the discovery of novel secondary metabolites, and facilitate investigation into gene functions, metabolic pathways, and CNPS cycles in AMD.

Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images.

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at "pollen localization problem") and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at "pollen classification problem"). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.

Knowledge Guided Feature Aggregation for the Prediction of Chronic Obstructive Pulmonary Disease With Chinese EMRs.

The automatic disease diagnosis utilizing clinical data has been suffering from the issues of feature sparse and high probability of missing values. Since the graph neural network is a effective tool to model the structural information and infer the missing values, it is becoming the dominant method for the predictive model construction from electronic medical records. Existing graph neural network based solutions usually adopt the medical concepts (e.g., symptoms) the feature representation of clinical data without considering their underlying semantic relations. The limited discriminative capability of the medical concept cannot provide sufficient indicative information about the disease. This paper proposes a knowledge- guided graph attention network for the disease prediction. Beside extracting the attribute-value structure as a large-size medical concept, the mutual information between multiple medical concepts mentioned in the electronic medical records are taken into account in the graph construction. Meanwhile, the defined diseases and their associations with the medical concepts in the medical knowledge graph are incorporated into the graph, which provides the potentials to enhance the indicative impacts of the medical concepts directly related to a target disease. Then, the spatial and attention based graph encoders are employed to aggregate information from directly neighbor nodes to generate node embeddings as the compact features to be used for disease diagnosis. The approach itself is a general one that can utilized to build the predictive model using Chinese EMRs for different diseases. The empirical experiments for its performance evaluation are conducted on the real-world COPD EMR dataset. The comparison study results show that the proposed model outperforms baseline methods, which illustrates the effectiveness of our proposed model.

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.