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

TCMNPAS: a comprehensive analysis platform integrating network formulaology and network pharmacology for exploring traditional Chinese medicine

The application of network formulaology and network pharmacology has significantly advanced the scientific understanding of traditional Chinese medicine (TCM) treatment mechanisms in disease. The field of herbal biology is experiencing a surge in data generation. However, researchers are encountering challenges due to the fragmented nature of the data and the reliance on programming tools for data analysis. We have developed TCMNPAS, a comprehensive analysis platform that integrates network formularology and network pharmacology. This platform is designed to investigate in-depth the compatibility characteristics of TCM formulas and their potential molecular mechanisms. TCMNPAS incorporates multiple resources and offers a range of functions designed for automated analysis implementation, including prescription mining, molecular docking, network pharmacology analysis, and visualization. These functions enable researchers to analyze and obtain core herbs and core formulas from herbal prescription data through prescription mining. Additionally, TCMNPAS facilitates virtual screening of active compounds in TCM and its formulas through batch molecular docking, allowing for the rapid construction and analysis of networks associated with "herb-compound-target-pathway" and disease targets. Built upon the integrated analysis concept of network formulaology and network pharmacology, TCMNPAS enables quick point-and-click completion of network-based association analysis, spanning from core formula mining from clinical data to the exploration of therapeutic targets for disease treatment. TCMNPAS serves as a powerful platform for uncovering the combinatorial rules and mechanism of TCM formulas holistically. We distribute TCMNPAS within an open-source R package at GitHub ( https://github.com/yangpluszhu/tcmnpas ), and the project is freely available at http://54.223.75.62:3838/ .

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation

Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (""), 322 patterns(""), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.

“Drying effect” of fructus aurantii components and the mechanism of action based on network pharmacology and in vitro pharmacodynamic validation

Background: Fructus aurantii (FA) is the dried, unripe fruit of the plant Citrus aurantium L. and its cultivated varieties. We investigated the drying effect of FA components and how this drying affect is achieved.

Methods: We employed systems pharmacology to predict the components and targets of FA that produce its drying effect. These predictions were verified by computer simulation and animal experiments. In the latter, we measured the bodyweight, water consumption, urine output, fecal water content, rate of salivary secretion, and cross-sectional area of the long axis of the submandibular gland of mice. Immunohistochemistry was used to measure expression of aquaporin (AQP)5 in the submandibular gland, AQP2 in the kidney, and AQP3 in the colon. ELISA kits were used to measure the horizontal variation of cyclic adenosine monsophosphate (cAMP), cyclic guanosine monophosphate (cGMP) and interferon-γ.

Results: Sixty-seven potentially active components of FA were screened out. FA could produce a drying effect after regulating 214 targets through 66 active components. A total of 870 gene ontology (GO) terms and 153 signaling pathways were identified. The hypoxia inducible factor-1 signaling pathway, phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway, calcium signaling pathway, and Ras signaling pathway may have important roles in the drying effect of FA. Four components of FA were identified: sinensetin, tangeretin, 5-demethylnobiletin and chrysin. These four components could increase the serum level of interferon-γ and ratio of cyclic adenosine monophosphate:cyclic guanosine monophosphate in mice, and affect their water consumption, urine output, fecal water content and rate of salivary secretion.

Conclusion: Four components of FA (tangeretin, sinensetin, chrysin, 5-Demethylmobiletin) were closely related to the Janus kinase-signal transducer and activator of transcription-3 (JAK-STAT3), PI3K-AKT, and the other signaling pathways. They can regulate the protein expression of JAK2, STAT3, PI3K, lymphocyte cell-specific protein-tyrosine kinase, vascular endothelial growth factor A, and protein kinase B1, affect water metabolism in the body and, finally, result in a drying effect.

High expression of homeobox B2 predicts poor survival of colon adenocarcinoma by enhancing tumor proliferation and invasion

OBJECTIVE

Homeobox B2 (HOXB2) is known to be correlated with the development and prognosis of various cancers. However, its role in colon cancer remains unclear.

AIMS

In this study, we explored the prognostic value of HOXB2 in colon adenocarcinoma (COAD).

METHODS

A total of 264 colon adenocarcinoma cases were retrospectively enrolled to evaluate HOXB2 expression and clinical significance. Chi-square test was applied to identify relationship between clinical features and HOXB2 expression. The effect of HOXB2 expression and clinical features on the survival of COAD patients was evaluated using Kaplan-Meier and Cox regression analyses. Cellular assays and mice models were conducted to validate the tumor-related role of HOXB2 in COAD.

RESULTS

Higher expression of HOXB2 in COAD tissues was significantly associated with tumor size, invasion depth, and lymph node metastasis (all P < 0.05). Univariate and multivariate analyses showed that high expression of HOXB2 was significantly correlated with a poor overall survival. In vitro cellular assays combined with knockdown strategies demonstrated that HOXB2 can promote tumor proliferation and invasion of COAD, which was further confirmed by in vivo xenograft experiments.

CONCLUSIONS

HOXB2 may be a valuable biomarker and potential therapeutic target for the treatment of COAD.

Interactive 3D Force/Torque Parameter Acquisition and Correlation Identification during Primary Trocar Insertion in Laparoscopic Abdominal Surgery: 5 Cases

Laparoscopic procedures have become indispensable in gastrointestinal surgery. As a minimally invasive process, it begins with primary trocar insertion. However, this step poses the threat of injuries to the gastrointestinal tract and blood vessels. As such, the comprehension of the insertion process is crucial to the development of robotic-assisted/automated surgeries. To sustain robotic development, this research aims to study the interactive force/torque (F/T) behavior between the trocar and the abdomen during the trocar insertion process. For force/torque (F/T) data acquisition, a trocar interfaced with a six-axis F/T sensor was used by surgeons for the insertion. The study was conducted during five abdominal hernia surgical cases in the Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University. The real-time F/T data were further processed and analyzed. The fluctuation in the force/torque (F/T) parameter was significant, with peak force ranging from 16.83 N to 61.86 N and peak torque ranging from 0.552 Nm to 1.76 Nm. The force parameter was observed to positively correlate with procedural time, while torque was found to be negatively correlated. Although during the process a surgeon applied force and torque in multiple axes, for a robotic system, the push and turn motion in a single axis was observed to be sufficient. For minimal tissue damage in less procedural time, a system with low push force and high torque was observed to be advantageous. These understandings will eventually benefit the development of computer-assisted or robotics technology to improve the outcome of the primary trocar insertion procedure.

Study on complications of osteoporosis based on network pharmacology

Osteoporosis is a serious threat to human life. Guben Zenggu Granule is an empirical prescription for clinical treatment of osteoporosis. MC3T3-E1 cells are mouse osteogenic precursor cells with osteogenic differentiation, and are classic cells for studying bone metabolism and osteogenic mechanism, as well as mechanical stimulation sensitive cells. Therefore, it can be inferred that Guben Zenggu granule can repair MC3T3-E1 cells under continuous static pressure overload. This study aims to through the network of pharmacology and gene sequencing method, reveal thrift increase bone particles under the condition of continuous static pressure overload on osteogenesis mechanism of MC3T3-E1 cells. In the process of analysis, from a variety of 98 compounds was predicted in the database, a collection of 474 goals, a total of 29,164 difference between two groups of genes. Then, construction of composite targets between cells and predict targets and protein - protein interaction networks, and through the cluster analysis to further explore the relationship between the target. In addition, linkages between target proteins and cells were further identified using Gene Ontology (GO) and Pathways (KEGG Pathway). Finally, the repair effect of Guben Zenggu granule on MC3T3-E1 cells under continuous static pressure overload was verified through experiments, so as to accurately explain the pharmacodynamic mechanism of Traditional Chinese medicine.

Study on the Adjustment of Cervical Spondylopathy in Middle-Aged and Elderly People Based on CT Image Analysis

Cervical spondylosis has caused serious distress to the patient's body and mind, and traditional treatment methods take time and effort. Based on this, this paper proposes a study on the regulation of cervical spondylopathy in the elderly by sports dance. In this article, 81 patients with cervical spondylosis treated in the rehabilitation department of the hospital from March 2019 to November 2019 were randomly divided into a control group of 39 cases and an experimental group of 42 cases. The control group received routine rehabilitation nursing intervention, including dietary nursing intervention. The patient received neck traction 3 times a week. In addition to the above treatments, patients with cervical spondylosis in the experimental group also received care for 20-30 minutes of sports and dance exercises. At first, the patient received exercise training once a day. After his symptoms resolved, he changed to twice a day for 180 days of continuous rehabilitation and exercise therapy. Before treatment, after treatment, and after 3 months of follow-up, NPS, NPP, and CROM scores were performed on the two groups of patients through CT images. Results. The effective rate of the control group was 66.0%, and the effective rate of the experimental group was 96.4%. There was no significant difference in NPRS, NPQ, and cr scores between the two groups before treatment (P > 0.05). After treatment, the improvement of the experimental group was significantly greater than that of the control group (P < 0.05). There was a significant difference in curative effect between the two groups (P < 0.05). It shows that when adjusting the cervical spondylosis of the elderly, the auxiliary application of sports dance therapy has a significant effect on improving the treatment efficiency. If this method is popularized in the current stage of medical treatment, it can effectively promote the diversified development of cervical spondylosis treatment methods for middle-aged and elderly people.

GC-CNNnet: Diagnosis of Alzheimer’s Disease with PET Images Using Genetic and Convolutional Neural Network

There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.

Sport Resource Classification Algorithm for Health Promotion Based on Cloud Computing: Rhythmic Gymnastics' Example

In the processing of rhythmic gymnastics resources, there are inefficiency problems such as confusion of teaching resources and lack of individuation. To improve the health access to teaching resource data, such as videos and documents, this study proposes a cloud computing-based personalized rhythmic gymnastics teaching resource classification algorithm for health promotion. First, personalized rhythmic gymnastics teaching resource database is designed based on cloud computing technology, and the teaching resources in the database are preprocessed to obtain a meta-sample set. Then, the characteristics of teaching resources are selected by the information acquisition method, and a vector space model is established to calculate the similarity of teaching resources. Finally, the distance-weighted k-NN method is used to classify the teaching resources for health promotion. The experimental results show that the classification accuracy of the proposed algorithm is high, the recall rate is high, and the F-measure value is high, which verifies the effectiveness of the algorithm.

Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data

Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k-means clustering through a cross-sectional study of sleep disturbance patient data. The data were obtained from an anonymous electronic survey in the Republic of Korea Army (ROKA) members from August 16, 2021, to September 20, 2021. The survey instrument consisted of six sections: demographics, medical history, military duty, sleep-related assessments (Pittsburgh sleep quality index (PSQI), Berlin questionnaire, and sleeping environment), diet/nutrition-related assessments [dietary habit survey questionnaire and nutrition quotient (NQ)], and gastrointestinal-related assessments [gastrointestinal symptom rating scale (GSRS) and Bristol stool scale]. Principal component analysis (PCA) and a deep autoencoder were used to extract features, which were then clustered using the k-means clustering method. The Calinski-Harabasz index, silhouette coefficient, and within-cluster sum of squares were used for internal cluster validation and the final PSQI, Berlin questionnaire, GSRS, and NQ scores were used for external cluster validation. One-way analysis of variance followed by the Tukey test and chi-squared test were used for between-cluster comparisons. Among 4,869 survey responders, 2,579 patients with sleep disturbances were obtained after filtering using a PSQI score of &gt;5. When comparing clustering performance using raw data and extracted features by PCA and the deep autoencoder, the best feature extraction method for clustering was the deep autoencoder (16 nodes for the first and third hidden layers, and two nodes for the second hidden layer). Our model could cluster three different PI types because the optimal number of clusters was determined to be three via the elbow method. After external cluster validation, three PI types were differentiated by changes in sleep quality, dietary habits, and concomitant gastrointestinal symptoms. This model may be applied to the development of artificial intelligence-based clinical decision support systems through electronic medical records and clinical trial protocols for evaluating the effectiveness of TEAM treatment.

Ginsenoside Rg3 induces apoptosis and inhibits proliferation by down-regulating TIGAR in rats with gastric precancerous lesions

BACKGROUND

Ginsenoside Rg3 (GRg3) is one of the main active ingredients in Chinese ginseng extract and has various biological effects, such as immune-enhancing, antitumour, antiangiogenic, immunomodulatory and anti-inflammatory effects. This study aimed to investigate the therapeutic effect of GRg3 on gastric precancerous lesion (GPL) induced by N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) and the potential mechanism of action.

METHODS

The MNNG-ammonia composite modelling method was used to establish a rat model of GPL. Histopathological changes in the rat gastric mucosa were observed by pathological analysis using haematoxylin-eosin staining to assess the success rate of the composite modelling method. Alcian blue-periodic acid Schiff staining was used to observe intestinal metaplasia in the rat gastric mucosa. Apoptosis was detected in rat gastric mucosal cells by terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling staining. The production level of reactive oxygen species (ROS) was determined by the dihydroethidium fluorescent probe method, and that of TP53-induced glycolysis and apoptosis regulator (TIGAR) protein was determined by immunohistochemical staining and western blotting. The production levels of nicotinamide adenine dinucleotide phosphate (NADP) and glucose-6-phosphate dehydrogenase (G6PDH) were determined by an enzyme-linked immunosorbent assay, and that of glutathione (GSH) was determined by microanalysis.

RESULTS

GRg3 significantly alleviated the structural disorganization and cellular heteromorphism in the form of epithelial glands in the gastric mucosa of rats with GPL and retarded the progression of the disease. Overexpression of TIGAR and overproduction of NADP, GSH and G6PDH occurred in the gastric mucosal epithelium of rats with GPL, which in turn led to an increase in the ROS concentration. After treatment with GRg3, the expression of TIGAR and production of NADP, GSH G6PDH decreased, causing a further increase in the concentration of ROS in the gastric mucosal epithelium, which in turn induced apoptosis and played a role in inhibiting the abnormal proliferation and differentiation of gastric mucosal epithelial cells.

CONCLUSION

Grg3 can induce apoptosis and inhibit cell proliferation in MNNG-induced GPL rats. The mechanism may be related to down-regulating the expression levels of TIGAR and production levels of GSH, NADP and G6PD, and up-regulating the concentration of ROS.

Understanding the mechanism of polybrominated diphenyl ethers reducing the anaerobic co-digestion efficiency of excess sludge and kitchen waste

Polybrominated diphenyl ethers (PBDEs) widely existing in the environment can pose a serious threat to the ecological safety. However, the influence of PBDEs on methane production by excess sludge (ES) and kitchen waste (KW) anaerobic co-digestion and its mechanism is not clear. To fill this gap, in this work, the co-digestion characteristics of ES and KW exposed to different levels of PBDEs at medium temperature were investigated in sequencing batch reactor, and the related mechanisms were also revealed. The results showed that PBDEs reduced methane production and the proportion of methane in the biogas. Methane yield decreased from 215.3 mL/g· volatile suspended solids (VSS) to 161.5 mL/(g·VSS), accompanied by the increase of PBDE content from 0 to 8.0 mg/Kg. Volatile fatty acid (VFA) yield was also inhibited by PBDEs; especially when PBDEs were 8.0 mg/Kg, VFA production was only 215.6 mg/g VSS, accounting for 75.7% of that in the control. Mechanism investigation revealed PBDEs significantly inhibited the processes of hydrolysis, acidogenesis, acetogenesis, and methanogenesis. Further study showed that PBDEs could inhibit the degradation and bioavailability of ES and KW, but it had a greater inhibition on the utilization of KW. Enzyme activity investigation revealed that all the key enzyme activities related to methane production were suppressed by PBDEs.

Migration, Transformation and Removal of Macrolide Antibiotics in The Environment: A Review

Macrolide antibiotics (MAs), as a typical emerging pollutant, are widely detected in environmental media. When entering the environment, MAs can interfere with the growth, development and reproduction of organisms, which has attracted extensive attention. However, there are few reviews on the occurrence characteristics, migration and transformation law, ecotoxicity and related removal technologies of MAs in the environment. In this work, combined with the existing relevant research, the migration and transformation law and ecotoxicity characteristics of MAs in the environment are summarized, and the removal mechanism of MAs is clarified. Currently, most studies on MAs are based on laboratory simulation experiments, and there are few studies on the migration and transformation mechanism between multiphase states. In addition, the cost of MAs removal technology is not satisfactory. Therefore, the following suggestions are put forward for the future research direction. The migration and transformation process of MAs between multiphase states (such as soil-water-sediment) should be focused on. Apart from exploring the new treatment technology of MAs, the upgrading and coupling of existing MAs removal technologies to meet emission standards and reduce costs should also be concerned. This review provides some theoretical basis and data support for understanding the occurrence characteristics, ecotoxicity and removal mechanism of MAs.

Construction and Clinical Application Effect of General Surgery Patient-Oriented Nursing Information Platform Using Cloud Computing

The paper aims to build a nursing information platform (NIP) for general surgery (GS) patients and explore its clinical application effect based on cloud computing (CC) technology. Specifically, the present work first analyzes and expounds on the characteristics of GS patients, the CC concept, the three-tier service mode of CC, and the cloud data center (CDC). Secondly, based on the principle of the overall system design, the evaluation indexes of medical care end, patient end, family end, and management end are constructed using Visual Studio 2010. Thirdly, the expert evaluation and user evaluation methods are selected to analyze the clinical application effect of the proposed system. Finally, SPSS is used to analyze the effect of the proposed system. The results of the first and second rounds of the expert evaluation show that the authority coefficient of experts is greater than 0.7, which indicates that the degree of expert authority is good. The proposed CC-based GS patient-oriented NIP system is universal. The evaluation results of 20 users have shown 15 doctors and nurses, 14 patients, and 18 family members, who mostly still support applying the proposed CC-based GS patient-oriented NIP system and believe that the system brings convenience and improves work efficiency. In short, more incentives should be taken to build a NIP for GS patients.

INPUT: an Intelligent Network Pharmacology Platform Unique for Traditional Chinese Medicine

The application of network pharmacology has greatly promoted the scientific interpretation of disease treatment mechanism of traditional Chinese medicine (TCM). However, the data required by network pharmacology analysis were scattered in different resources. In the present work, by integrating and reorganizing the data from multiple resources, we developed the intelligent network pharmacology platform unique for traditional Chinese medicine, called INPUT (http://cbcb.cdutcm.edu.cn/INPUT/), for automatically performing network pharmacology analysis. Besides the curated data collected from multiple resources, a series of bioinformatics tools for network pharmacology analysis were also embedded in INPUT, which makes it become the first automatic platform able to explore the disease treatment mechanisms of TCM. With the built-in tools, researchers can also analyze their own in-house data and obtain the results of pivotal ingredients, GO and KEGG pathway, protein-protein interactions, etc. In addition, as a proof-of-principle, INPUT was applied to decipher the antidepressant mechanism of a commonly used prescription. In summary, INPUT is a powerful platform for network pharmacology analysis and will facilitate the researches on drug discovery.

Increasing Aspartoacylase in the Central Amygdala: The Common Mechanism of Gastroprotective Effects of Monoamine-Based Antidepressants Against Stress

Monoamine-based antidepressants can prophylactically protect against stress-induced gastric ulcers. Although the central nucleus of amygdala (CeA) has been shown to modulate the severity of stress ulcers, little is known about the molecular mechanisms underlying the gastroprotective effect of this kind of drugs. Here, we first used proton magnetic resonance spectroscopy, a non-invasive tool, to explore the change of neurometabolites of the CeA of rats pretreated with the duloxetine of selective serotonin-norepinephrine reuptake inhibitors during 6 h of water-immersion restraint stress (WIRS). Duloxetine decreased N-acetyl-aspartate/creatine ratio (NAA/creatine) in CeA after WIRS, which was paralleled by the amelioration of gastric lesions. Meanwhile, the gastric ulcer index was negatively correlated with reduced NAA/creatine. Furthermore, the intra-CeA infusion of NAA aggravated WIRS-induced gastric mucosa damage, which suggested the crucial role of reduced NAA. Western blotting was performed to identify the specific enzymes responsible for the change of the contents of NAA at 0.5 h/3 h/6 h after WIRS, considering the preventative gastric protection of duloxetine. The NAA-catabolizing enzyme aspartoacylase (ASPA) was the only enzyme downregulated by 0.5 h WIRS and upregulated by duloxetine. Moreover, overexpressing ASPA in CeA alleviated stress ulcers. Additionally, all of the other three monoamine-based antidepressants, the fluoxetine of selective serotonin reuptake inhibitors, the amitriptyline of tricyclic agents, and the moclobemide of MAOs, increased ASPA expression in CeA. Together, these results indicate that increasing ASPA to hydrolyze NAA in CeA is a common mechanism of gastroprotective effects against stress exerted by monoamine-based antidepressants, and ASPA is a shared target more than monoamine regulation for this kind of drugs.

Risk Assessment-Oriented Design of a Needle Insertion Robotic System for Non-Resectable Liver Tumors

Medical robotics is a highly challenging and rewarding field of research, especially in the development of minimally invasive solutions for the treatment of the worldwide leading cause of death, cancer. The aim of the paper is to provide a design methodology for the development of a safe and efficient medical robotic system for the minimally invasive, percutaneous, targeted treatment of hepatocellular carcinoma, which can be extended with minimal modification for other types of abdominal cancers. Using as input a set of general medical requirements to comply with currently applicable standards, and a set of identified hazards and failure modes, specific methods, such as the Analytical Hierarchy Prioritization, Risk Analysis and fuzzy logic Failure Modes and Effect Analysis have been used within a stepwise approach to help in the development of a medical device targeting the insertion of multiple needles in brachytherapy procedures. The developed medical device, which is visually guided using CT scanning, has been tested for validation in a medical environment using a human-size ballistic gel liver, with promising results. These prove that the robotic system can be used for the proposed medical task, while the modular approach increases the chances of acceptance.

Chimeric Peptides/Proteins Encoded by circRNA: An Update on Mechanisms and Functions in Human Cancers

The discovery of circular RNAs and exploration of their biological functions are increasingly attracting attention in cell bio-sciences. Owing to their unique characteristics of being highly conserved, having a relatively longer half-life, and involvement in RNA maturation, transportation, epigenetic regulation, and transcription of genes, it has been accepted that circRNAs play critical roles in the variety of cellular processes. One of the critical importance of these circRNAs is the presence of small open reading frames that enable them to encode peptides/proteins. In particular, these encoded peptides/proteins mediate essential cellular activities such as proliferation, invasion, epithelial–mesenchymal transition, and apoptosis and develop an association with the development and progression of cancers by modulating diverse signaling pathways. In addition, these peptides have potential roles as biomarkers for the prognosis of cancer and are being used as drug targets against tumorigenesis. In the present review, we thoroughly discussed the biogenesis of circRNAs and their functional mechanisms along with a special emphasis on the reported chimeric peptides/proteins encoded by circRNAs. Additionally, this review provides a perspective regarding the opportunities and challenges to the potential use of circRNAs in cancer diagnosis and therapeutic targets in clinics.

Loss of ATP5A1 enhances proliferation and predicts poor prognosis of colon adenocarcinoma

BACKGROUND

ATP Synthase F1 Subunit Alpha (ATP5F1A), also named as ATP5A1, is a subunit of mitochondrial ATP synthase. Dysregulated expression of ATP5A1 has been reported in several malignancies, nevertheless it showed either oncogenic or tumor-suppressing roles in different cancer types. Here we aimed to initially investigate the expression and role of ATP5A1 in colon adenocarcinoma.

METHODS

We firstly evaluated the transcription and mRNA levels of ATP5A1 using data from The Cancer Genome Atlas (TCGA). Besides, we tested its mRNA and protein expression in our enrolled retrospective cohort (n = 115). Univariate and multivariate analyzes were conducted to assess its prognostic value. Cellular experiments and xenografts in mice model were performed to validate the role of ATP5A1 in colon cancer.

RESULTS

ATP5A1 showed a significant lower level in colon adenocarcinoma than in adjacent nontumorous tissue. Advanced tumor stage was characterized with lower ATP5A1 level. Lower ATP5A1 was associated with poor prognosis in both TCGA dataset (P = 0.041) and our cohort (P = 0.001). Furthermore, Cox regression analysis demonstrated that ATP5A1 was a novel independent prognostic factor for colon cancer patients (HR=0.43, P = 0.018). Finally, cellular and xenografts data confirmed that overexpressing ATP5A1 can remarkably attenuate colon cancer growth.

CONCLUSION

Low expression of ATP5A1 may be a potential molecular marker for poor prognosis in colon cancer.

DATA AVAILABILITY

Data will be available upon request.

Key Technologies of New Type of Intravascular Ultrasound Image Processing

Since entering the 21st century, the application of ultrasound technology has developed rapidly. Intravascular ultrasound technology has been widely used in the diagnosis and treatment of cardiovascular diseases. With the help of computer image processing technology, it can provide clinicians with more accurate diagnosis. Based on the information to improve the success rate of clinical treatment. Based on this, this article combines the development history of intravascular ultrasound technology, explores the principles of new intravascular ultrasound technology, and analyzes the application of new intravascular ultrasound technology. On this basis, the preprocessing of intravascular ultrasound image data is discussed, involving the acquisition of intravascular ultrasound image data and image analysis. On this basis, explore the combined application of new intravascular ultrasound technology and other imaging examination methods, such as X-rays to use three-dimensional image technology to reconstruct new intravascular ultrasound image sequences, and provide doctors with clearer morphology and properties of tube wall lesions. In order to make a more accurate diagnosis of the lesion, a more detailed and accurate treatment plan can be given, which has extremely high clinical application value.

Application and Feasibility Study of Integrated Nursing Information Construction in Nephrology Nursing

With the continuous deepening of medical reforms and the continuous attempts and explorations of various management models, the traditional health care model is undergoing tremendous changes, and patients' needs for medical institutions are becoming more and more comprehensive. Medical institutions are meeting the needs of providing medical services to patients at the same time. It is even more necessary to change our thinking and enhance the service concept. This article is based on case-based deep learning hospital nursing business process reengineering and the application and feasibility study of integrated nursing information construction in nephrology nursing. This article uses the literature analysis method, the social survey method, and other methods to discuss the construction of integrated nursing information. On the one hand, the content of this article uses the concept of process reengineering to analyze the current development status and existing problems of the hospital care industry and find countermeasures to solve problems. On the other hand, the main research content of this article is the construction of integrated nursing information and its analysis of the application and feasibility of nursing in the nephrology department. At the same time, under the background of the rapid development of the mobile Internet, we will carry out extended thinking on the continuous transformation of the construction of nursing information. According to the survey results, 87.5% of patients in the nephrology department are dissatisfied with the current hospital's work efficiency, and 85.7% of the nursing staff in the nephrology department are generally satisfied with the information management of the current department. After the implementation of the hospital information integration system, patient satisfaction is as high as 98.2%, and the satisfaction of medical staff reached 94.2%. The construction of integrated nursing information has played a great role in the application of nephrology nursing.

Impact of Early Intravenous Haemostatic Drugs on Brain Haemorrhage Patients and Their Image Segmentation Based on RGB-D Images

Cerebral haemorrhage is a serious subtype of stroke, with most patients experiencing short-term haematoma enlargement leading to worsening neurological symptoms and death. The main hemostatic agents currently used for cerebral haemorrhage are antifibrinolytics and recombinant coagulation factor VIIa. However, there is no clinical evidence that patients with cerebral haemorrhage can benefit from hemostatic treatment. We provide an overview of the mechanisms of haematoma expansion in cerebral haemorrhage and the progress of research on commonly used hemostatic drugs. To improve the semantic segmentation accuracy of cerebral haemorrhage, a segmentation method based on RGB-D images is proposed. Firstly, the parallax map was obtained based on a semiglobal stereo matching algorithm and fused with RGB images to form a four-channel RGB-D image to build a sample library. Secondly, the networks were trained with 2 different learning rate adjustment strategies for 2 different structures of convolutional neural networks. Finally, the trained networks were tested and compared for analysis. The 146 head CT images from the Chinese intracranial haemorrhage image database were divided into a training set and a test set using the random number table method. The validation set was divided into four methods: manual segmentation, algorithmic segmentation, the exact Tada formula, and the traditional Tada formula to measure the haematoma volume. The manual segmentation was used as the "gold standard," and the other three algorithms were tested for consistency. The results showed that the algorithmic segmentation had the lowest percentage error of 15.54 (8.41, 23.18) % compared to the Tada formula method.

U-Net Modelling-Based Imaging MAP Score for Tl Stage Nephrectomy: An Exploratory Study

We evaluate the stability of the clinical application of the MAP scoring system based on anatomical features of renal tumour images, explore the relevance of this scoring system to the choice of surgical procedure for patients with limited renal tumours, and investigate the effectiveness of automated segmentation and reconstruction 3D models of renal tumour images based on U-net for interpretative cognitive navigation during laparoscopy Tl stage radical renal tumour cancer surgery. A total of 5 000 kidney tumour images containing manual annotations were applied to the training set, and a stable and efficient full CNN algorithm model oriented to clinical needs was constructed to regionalism and multistructure and to finely automate segmentation of kidney tumour images, output modelling information in STL format, and apply a tablet computer to intraoperatively display the Tl stage kidney tumour model for cognitive navigation. Based on a training sample of MR images from 201 patients with stage Tl renal tumour cancer, an adaptation of the classical U-net allows individual segmentation of important structures such as renal tumours and 3D visualisation to visualise the structural relationships and the extent of tumour invasion at key surgical sites. The preoperative CT and clinical data of 225 patients with limited renal tumours treated surgically at our hospital from August 2011 to August 2012 were retrospectively analysed by three imaging physicians using the MAP scoring system for the total score and the variables R (maximum diameter), E (exogenous/endogenous), N (distance from the renal sinus), A (ventral/dorsal), L (relationship along the longitudinal axis of the kidney), and h (whether in contact with the renal hilum). The score for each variable (contact with the renal hilum) was statistically compared with each other for the three observers. Patients were divided into three groups according to the total score-low, medium, and high-and according to the surgical procedure-radical and partial resection. The correlation between the total score and the score of each variable and the choice of surgical procedure was analysed. The agreement rate of the total score and the score of each variable for all three observers was over 90% (P ≤ 0.001). The map scoring system based on the anatomical features of renal tumour imaging was well stabilized, and the scores were significantly correlated with the surgical approach.

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.

An effective model for predicting serum albumin level in hemodialysis patients

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients.

A High-Dimensional and Small-Sample Submersible Fault Detection Method Based on Feature Selection and Data Augmentation

The fault detection of manned submersibles plays a very important role in protecting the safety of submersible equipment and personnel. However, the diving sensor data is scarce and high-dimensional, so this paper proposes a submersible fault detection method, which is made up of feature selection module based on hierarchical clustering and Autoencoder (AE), the improved Deep Convolutional Generative Adversarial Networks (DCGAN)-based data augmentation module and fault detection module using Convolutional Neural Network (CNN) with LeNet-5 structure. First, feature selection is developed to select the features that have a strong correlation with failure event. Second, data augmentation model is conducted to generate sufficient data for training the CNN model, including rough data generation and data refiners. Finally, a fault detection framework with LeNet-5 is trained and fine-tuned by synthetic data, and tested using real data. Experiment results based on sensor data from submersible hydraulic system demonstrate that our proposed method can successfully detect the fault samples. The detection accuracy of proposed method can reach 97% and our method significantly outperforms other classic detection algorithms.

Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm

Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model can be applied in practical areas. In this study, to improve optimization performance, a new parameter optimization strategy is proposed, based on a disperse foraging sine cosine algorithm (DFSCA), which is utilized to force some portions of search agents to explore other potential regions. Meanwhile, DFSCA is integrated into KELM to establish a new machine learning model named DFSCA-KELM. Firstly, using the CEC2017 benchmark suite, the exploration and exploitation capabilities of DFSCA were demonstrated. Secondly, evaluation of the model DFSCA-KELM on six medical datasets extracted from the UCI machine learning repository for medical diagnosis proved the effectiveness of the proposed model. At last, the model DFSCA-KELM was applied to solve two real medical cases, and the results indicate that DFSCA-KELM can also deal with practical medical problems effectively. Taken together, these results show that the proposed technique can be regarded as a promising tool for medical diagnosis.

Revealing the efficacy-toxicity relationship of Fuzi in treating rheumatoid arthritis by systems pharmacology

In recent decades, herbal medicines have played more and more important roles in the healthcare system in the world because of the good efficacy. However, with the increasing use of herbal medicines, the toxicity induced by herbal medicines has become a global issue. Therefore, it is needed to investigate the mechanism behind the efficacy and toxicity of herbal medicines. In this study, using Aconiti Lateralis Radix Praeparata (Fuzi) as an example, we adopted a systems pharmacology approach to investigate the mechanism of Fuzi in treating rheumatoid arthritis and in inducing cardiac toxicity and neurotoxicity. The results showed that Fuzi has 25 bioactive compounds that act holistically on 61 targets and 27 pathways to treat rheumatoid arthritis, and modulation of inflammation state is one of the main mechanisms of Fuzi. In addition, the toxicity of Fuzi is linked to 32 compounds that act on 187 targets and 4 pathways, and the targets and pathways can directly modulate the flow of Na+, Ca2+, and K+. We also found out that non-toxic compounds such as myristic acid can act on targets of toxic compounds and therefore may influence the toxicity. The results not only reveal the efficacy and toxicity mechanism of Fuzi, but also add new concept for understanding the toxicity of herbal medicines, i.e., the compounds that are not directly toxic may influence the toxicity as well.

Induction of PI3K/Akt-Mediated Apoptosis in Osteoclasts Is a Key Approach for Buxue Tongluo Pills to Treat Osteonecrosis of the Femoral Head

The Buxue Tongluo pill (BTP) is a self-made pill with the functions of nourishing blood, promoting blood circulation, dredging collaterals, and relieving pain. It consists of Angelica sinensis (Oliv.) Diels, Pheretima aspergillum (E.Perrier), Panax notoginseng (Burk.) F. H. Chen, Astragalus membranaceus (Fisch.) Bge, and Glycyrrhiza uralensis Fisch. Various clinical practices have confirmed the therapeutic effect of BTP on osteonecrosis of the femoral head (ONFH), but little attention has been paid to the study of its bioactive ingredients and related mechanisms of action. In this study, UPLC/MS-MS combined with GEO data mining was used to construct a bioactive ingredient library of BTP and a differentially expressed gene (DEG) library for ONFH. Subsequently, Cytoscape (3.7.2) software was used to analyze the protein-protein interaction between BTP and DEGs of ONFH to screen the key targets, and functional annotation analysis and pathway enrichment analysis were carried out. Finally, 34 bioactive compounds were screened, which acted on 1,232 targets. A total of 178 DEGs were collected, and 17 key genes were obtained after two screenings. By bioinformatics annotation on these key genes, a total of 354 gene ontology (GO) functional annotation analyses and 42 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained. The present study found that GO and KEGG enrichment were mainly related to apoptosis, suggesting that BTP may exert an anti-ONFH effect by promoting osteoclast apoptosis. Experiments in vitro demonstrated that BTP could increase the mitochondrial membrane potential (MMP) and induce remarkable apoptosis in osteoclasts. Furthermore, we determined the apoptosis marker of cleaved(C)-caspase-3, bcl-2, and bax and found that BTP could upregulate the C-caspase-3 and bax expression in osteoclasts and decrease the expression of bcl-2, p-Akt, and p-PI3K in a dose-dependent manner, indicating that BTP could induce PI3K/Akt-mediated apoptosis in osteoclasts to treat ONFH. This study explored the pharmacodynamic basis and mechanism of BTP against ONFH from the perspective of systemic pharmacology, laying a foundation for further elucidating the therapeutic effects of BTP against ONFH.

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID-19 Detection

Image medical semantic segmentation has been employed in various areas, including medical imaging, computer vision, and intelligent transportation. In this study, the method of semantic segmenting images is split into two sections: the method of the deep neural network and previous traditional method. The traditional method and the published dataset for segmentation are reviewed in the first step. The presented aspects, including all-convolution network, sampling methods, FCN connector with CRF methods, extended convolutional neural network methods, improvements in network structure, pyramid methods, multistage and multifeature methods, supervised methods, semiregulatory methods, and nonregulatory methods, are then thoroughly explored in current methods based on the deep neural network. Finally, a general conclusion on the use of developed advances based on deep neural network concepts in semantic segmentation is presented.

Identification of Active Compounds and Mechanism of Huangtu Decoction for the Treatment of Ulcerative Colitis by Network Pharmacology Combined with Experimental Verification

Introduction

Huangtu decoction (HTD) has been widely used in the treatment of gastrointestinal bleeding, ulcerative colitis (UC) and gastrointestinal tumors in China, but its active compounds and mechanism are still not clear yet. The present research aimed to identify the active compounds and mechanism of HTD for the treatment of UC.

Methods

Firstly, the chemical compounds of HTD were qualitatively identified based on Q Exactive Orbitrap LC-MS/MS, and their potential targets were predicted through SwissTargetPrediction. Secondly, the differential expressed genes (DEGs) in colon tissues of UC patients and normal controls were retrieved from the GEO database. Thirdly, the overlapping targets of DEGs and the predicted targets were obtained and subjected to GO and KEGG analysis. Finally, the key targets in the most significantly enriched pathway were verified by in vivo experiment, and the protein and mRNA expressions of matrix metalloproteinase-1 (MMP1), MMP3, MMP7, MMP9 and MMP12 were determined by immunohistochemistry (IHC), Western blotting (WB) and quantitative real-time-PCR (qRT-PCR).

Results

A total of 47 compounds were identified and 29 overlapping targets were obtained from HTD extract. The most significantly enriched pathway of overlapping targets involved was MMP. HTD improved the pathological damage in colon tissues of DSS-induced UC model and significantly decreased the serum levels of IL-1β and IL-6. The protein and mRNA expressions of MMP1, MMP3 and MMP9 in colon tissues were significantly decreased after HTD treatment.

Conclusion

HTD treatment can alleviate the colonic inflammation via inhibiting MMPs including MMP1, MMP3 and MMP9.

A Traditional Chinese Medicine Syndrome Classification Model based on Cross-FGCNN: Model Development and Validation (Preprint)

Background

Nowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy.

Objective

This study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases.

Methods

The relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data.

Results

The data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%.

Conclusions

The main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.