Search

Objective: To explore the protective effects of 20-Hydroxyecdysone (20-HE) on high glucose induced HepG2 cells and its related molecular mechanism. Methods: In this study, high glucose (50 mmol/L glucose) was used to establish the oxidative damage model in HepG2 cells. The CCK-8 assay, caspase-3 assay, fluorescent probe method, and colorimetric method were used to assess the levels of cell viability, apoptosis, oxygen species (ROS), superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA), respectively. The signaling pathways involved in the regulation of 20-HE were predicted using bioinformatics analysis. The phosphorylation level of Akt protein was detected by Western blot to evaluate the activation level of the PI3K/Akt signaling pathway. The involvement of the PI3K/Akt signaling pathway in the regulatory effects of 20-HE was verified using the inhibitor LY294002. Results: Treatment with 20-HE had no significant toxic effect on HepG2 cells at concentrations lower than 20 μmol/L. In the injured cells, 20-HE could significantly improve the viability (P

Objective: To explore the protective effect and molecular mechanisms of noni (Morinda citrifolia) fruit juice on human renal cortical epithelial cells (HK-2) with hyperuricemi. Methods: Morinda citrifolia pulp was prepared and fermented by pectinase and Lactobacillus plantarum to obtain fermented noni juice. Hyperuricemia model of HK-2 cells was established based on by adenosine combined with xanthine oxidase (XO). The cytotoxicity of fermented noni juice on HK-2 cells was detected by CCK-8 experiment. The uric acid content in the supernatant of HK-2 cell culture solution after the treatment of fermented noni juice was detected and the expression levels of hyperuricemia marker proteins urate transporter 1 (URAT1) and glucose transporter 9 (GLUT9) were detected by Western blotting to verify the regulatory effect of fermented noni juice on HK-2 cells with hyperuricemia. By detecting the expression levels of interleukin-1β (IL-1β) and Tumor necrosis factor-α (TNF-α) in HK-2 cells, the effect of fermented noni juice on inflammatory reaction in HK-2 cells with hyperuricemia was determined. By detecting the expression level of phosphorylated nuclear transcription factor kappa B (NF-κB) p-p65/p65 in HK-2 cells, the regulatory effect of fermented noni juice on NF-κB signaling pathway was verified. Results: When the treatment concentration was lower than 60 μL/mL, the fermented noni juice did not show cytotoxicity. With the treatment of fermented noni juice, the uric acid content which was dose-dependent in the supernatant of cell culture solution was decreased significantly. The expressions of URAT1 and GLUT9 in the experimental group treated with fermented noni juice were significantly lower than those in model group. The fermented noni juice could significantly inhibit the expression levels of IL-1β and TNF-α in hyperuricemia model of HK-2 cell and significantly inhibited the expression of p-p65 in disease model. Conclusion: Fermented noni juice could reduce the content of uric acid in HK-2 cells with hyperuricemia, and then inhibit inflammatory reaction by regulating NF-κB signaling pathway, thus playing a protective role.

Background: The molecular mechanisms regulating the therapeutic effects of plant-based ingredients on the exercise-induced fatigue (EIF) remain unclear. The therapeutic effects of both tea polyphenols (TP) and fruit extracts of Lycium ruthenicum (LR) on mouse model of EIF were investigated.Methods: The variations in the fatigue-related biochemical factors, i.e., lactate dehydrogenase (LDH), superoxide dismutase (SOD), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-2 (IL-2), and interleukin-6 (IL-6), in mouse models of EIF treated with TP and LR were determined. The microRNAs involved in the therapeutic effects of TP and LR on the treatment of mice with EIF were identified using the next-generation sequencing technology.Results: Our results revealed that both TP and LR showed evident anti-inflammatory effect and reduced oxidative stress. In comparison with the control groups, the contents of LDH, TNF-α, IL-6, IL-1β, and IL-2 were significantly decreased and the contents of SOD were significantly increased in the experimental groups treated with either TP or LR. A total of 23 microRNAs (21 upregulated and 2 downregulated) identified for the first time by the high-throughput RNA sequencing were involved in the molecular response to EIF in mice treated with TP and LR. The regulatory functions of these microRNAs in the pathogenesis of EIF in mice were further explored based on Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses with a total of over 20,000–30,000 target genes annotated and 44 metabolic pathways enriched in the experimental groups based on GO and KEGG databases, respectively.Conclusion: Our study revealed the therapeutic effects of TP and LR and identified the microRNAs involved in the molecular mechanisms regulating the EIF in mice, providing strong experimental evidence to support further agricultural development of LR as well as the investigations and applications of TP and LR in the treatment of EIF in humans, including the professional athletes.

BackgroundHyperuricemia is generally defined as the high level of serum uric acid and is well known as an important risk factor for the development of various medical disorders. However, the medicinal treatment of hyperuricemia is frequently associated with multiple side-effects.MethodsThe therapeutic effect of noni (Morinda citrifolia L.) fruit juice on hyperuricemia and the underlying molecular mechanisms were investigated in mouse model of hyperuricemia induced by potassium oxonate using biochemical and high-throughput RNA sequencing analyses.ResultsThe levels of serum uric acid (UA) and xanthine oxidase (XOD) in mice treated with noni fruit juice were significantly decreased, suggesting that the noni fruit juice could alleviate hyperuricemia by inhibiting the XOD activity and reducing the level of serum UA. The contents of both serum creatinine and blood urine nitrogen of the noni fruit juice group were significantly lower than those of the model group, suggesting that noni fruit juice promoted the excretion of UA without causing deleterious effect on the renal functions in mice. The differentially expressed microRNAs involved in the pathogenesis of hyperuricemia in mice were identified by RNA sequencing with their target genes further annotated based on both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases to explore the metabolic pathways and molecular mechanisms underlying the therapeutic effect on hyperuricemia by noni fruit juice.ConclusionOur study provided strong experimental evidence to support the further investigations of the potential application of noni fruit juice in the treatment of hyperuricemia.

This paper proposes a new research method for braided river sedimentation on the beach shore based on the action of tidal currents. This study conducts a statistical analysis of the length and width of a single braided river and channel bar sand body, and establishes the relationship function model of the quantitative scale of a single braided river and the channel bar. According to the core and logging data of the Nanwu area of the target oilfield, a quantitative methodology based on the calculation of a single accretion scale is established from three perspectives: the architecture interface identification of the accretion, the occurrence and scale calculation of the interlayer, and the scale calculation of the single accretion. In the Nanwu area, the inclination angle of the accretion interface in the direction of the long axis is 0.78–1.32°, and the inclination angle of the accretion interface in the direction of the short axis is 2.02–3.78°. The density of a single well group is generally 2–3 per well. The length of the single accretion in the channel bar is 700–1500 m. Based on these findings, this paper completes the construction of the architecture of the channel bar, and establishes the quantitative scale calculation method for architecture elements for different levels of braided river reservoirs. The research results provide support for the prediction of the braided river reservoir architecture and the remaining oil in similar blocks.

Objective: To explore the molecular mechanism driving the tea polyphenol epigallocatechin-3-gallate (EGCG)-exerted protective effect on hydrogen peroxide (H2O2)-induced cell apoptosis of human cardiomyocyte HCM cell lines. Methods: HCM cells were stimulated with H2O2 to establish a vitro apoptosis model; the caspase-3 assay was used to assess the cell apoptosis of HCM cells; the CCK-8 assay was used to perform the cytotoxicity experiment; qPCR was used to detect the levels of AOC1 mRNA and miR-16-5p; Western blot was used to detect the protein level of AOC1; cells transfection was conducted to regulate the expression levels of AOC1 and miR-16-5p in HCM cells; TargetScan was used to predict the binding relationship and site between AOC1 mRNA 3’UTR and miR-16-5p, which was verified by the double luciferase reporter gene assay. Results: EGCG could significantly inhibit H2O2-induced apoptosis of HCM cells (P

Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.

Natural gas, as one of the main energy sources of the modern clean energy system, is also an important raw material for the chemical industry, and the stable extraction of natural gas reservoirs is often affected by bottom water. It is difficult to control water in natural gas reservoirs, while fractured gas reservoirs are even more demanding. This is due to the complexity of the seepage laws of gas and water in fractures, resulting in the poor applicability of conventional processes for water control. Continuous research is needed to propose a process with effective control capabilities for bottom-water fractured gas reservoirs. Aiming at the above difficulties, this paper is based on a large-scale three-dimensional physical simulation device to carry out physical model design and simulation results testing and analysis. The water control ability of the combination of density-segmented sieve tubes and continuous packers in fractured gas reservoirs is explored. The physical simulation results show that the fracture distribution characteristics control the upward transportation path of bottom water. According to the segmentation characteristics of the fractures at the horizontal section location, optimizing the number of horizontal well screen tube segments and the density of boreholes reduces the cone-in velocity of bottom water before connecting the fractures to a certain extent. And the combined process has different degrees of water control ability for the three stages of bottom water transportation from the fractured gas reservoir to the production well. As the degree of water in the production well increases, the water control ability of the process gradually decreases. After the implementation of the water control process, the water-free gas production period was extended by about 6.84%, and the total production time was extended by about 6.46%. After the shutdown of the horizontal wells, the reduction in daily water production can still reach 21% compared to the natural extraction. The results of this research can provide process suggestions for water control in offshore fractured reservoirs and further ensure stable production in offshore fractured gas reservoirs.

Immune cell infiltration that occurs at the site of colon tumors influences the course of cancer. Different immune cell compositions in the microenvironment lead to different immune responses and different therapeutic effects. This study analyzed single-cell RNA sequencing data in a normal colon with the aim of screening genetic markers of 25 candidate immune cell types and revealing quantitative differences between them. The dataset contains 25 classes of immune cells, 41,650 cells in total, and each cell is expressed by 22,164 genes at the expression level. They were fed into a machine learning-based stream. The five feature ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, minimum redundancy maximum relevance, and random forest) were first used to analyze the importance of gene features, yielding five feature lists. Then, incremental feature selection and two classification algorithms (decision tree and random forest) were combined to filter the most important genetic markers from each list. For different immune cell subtypes, their marker genes, such as KLRB1 in CD4 T cells, RPL30 in B cell IGA plasma cells, and JCHAIN in IgG producing B cells, were identified. They were confirmed to be differentially expressed in different immune cells and involved in immune processes. In addition, quantitative rules were summarized by using the decision tree algorithm to distinguish candidate immune cell types. These results provide a reference for exploring the cell composition of the colon cancer microenvironment and for clinical immunotherapy.

The spleen and lymph nodes are important functional organs for human immune system. The identification of cell types for spleen and lymph nodes is helpful for understanding the mechanism of immune system. However, the cell types of spleen and lymph are highly diverse in the human body. Therefore, in this study, we employed a series of machine learning algorithms to computationally analyze the cell types of spleen and lymph based on single-cell CITE-seq sequencing data. A total of 28,211 cell data (training vs. test = 14,435 vs. 13,776) involving 24 cell types were collected for this study. For the training dataset, it was analyzed by Boruta and minimum redundancy maximum relevance (mRMR) one by one, resulting in an mRMR feature list. This list was fed into the incremental feature selection (IFS) method, incorporating four classification algorithms (deep forest, random forest, K-nearest neighbor, and decision tree). Some essential features were discovered and the deep forest with its optimal features achieved the best performance. A group of related proteins (CD4, TCRb, CD103, CD43, and CD23) and genes (Nkg7 and Thy1) contributing to the classification of spleen and lymph nodes cell types were analyzed. Furthermore, the classification rules yielded by decision tree were also provided and analyzed. Above findings may provide helpful information for deepening our understanding on the diversity of cell types.

Protein–protein interactions (PPIs) are extremely important for gaining mechanistic insights into the functional organization of the proteome. The resolution of PPI functions can help in the identification of novel diagnostic and therapeutic targets with medical utility, thus facilitating the development of new medications. However, the traditional methods for resolving PPI functions are mainly experimental methods, such as co-immunoprecipitation, pull-down assays, cross-linking, label transfer, and far-Western blot analysis, that are not only expensive but also time-consuming. In this study, we constructed an integrated feature selection scheme for the large-scale selection of the relevant functions of PPIs by using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations of PPI participants. First, we encoded the proteins in each PPI with their gene ontologies and KEGG pathways. Then, the encoded protein features were refined as features of both positive and negative PPIs. Subsequently, Boruta was used for the initial filtering of features to obtain 5684 features. Three feature ranking algorithms, namely, least absolute shrinkage and selection operator, light gradient boosting machine, and max-relevance and min-redundancy, were applied to evaluate feature importance. Finally, the top-ranked features derived from multiple datasets were comprehensively evaluated, and the intersection of results mined by three feature ranking algorithms was taken to identify the features with high correlation with PPIs. Some functional terms were identified in our study, including cytokine–cytokine receptor interaction (hsa04060), intrinsic component of membrane (GO:0031224), and protein-binding biological process (GO:0005515). Our newly proposed integrated computational approach offers a novel perspective of the large-scale mining of biological functions linked to PPI.

BackgroundPulmonary infection is one of the common complications of long-term use of glucocorticoids. Severe infections not only increase the length of hospital stay and treatment costs but also cause progression or recurrence of the primary disease.Case descriptionHerein, we reported a case of mixed pulmonary infection secondary to glucocorticoid use. Rare pathogens such as Nocardia nova, Mycobacterium tuberculosis, Aspergillus fumigatus, and cytomegalovirus were detected by metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid and lung puncture tissue. Combining the results of conventional pathogen detection and clinical symptoms, the patient was diagnosed with mixed pulmonary infection by multiple pathogens. After timely targeted medication, the patient was finally discharged with a good prognosis.ConclusionTo our knowledge, this is the first case report on mixed pulmonary infection with pathogens including Nocardia nova, Mycobacterium tuberculosis, Aspergillus fumigatus, and human cytomegalovirus. As a new clinical diagnostic method, mNGS has great advantages in diagnosis of diseases such as mixed infections.

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

Mammalian cortical interneurons (CINs) could be classified into more than two dozen cell types that possess diverse electrophysiological and molecular characteristics, and participate in various essential biological processes in the human neural system. However, the mechanism to generate diversity in CINs remains controversial. This study aims to predict CIN diversity in mouse embryo by using single-cell transcriptomics and the machine learning methods. Data of 2,669 single-cell transcriptome sequencing results are employed. The 2,669 cells are classified into three categories, caudal ganglionic eminence (CGE) cells, dorsal medial ganglionic eminence (dMGE) cells, and ventral medial ganglionic eminence (vMGE) cells, corresponding to the three regions in the mouse subpallium where the cells are collected. Such transcriptomic profiles were first analyzed by the minimum redundancy and maximum relevance method. A feature list was obtained, which was further fed into the incremental feature selection, incorporating two classification algorithms (random forest and repeated incremental pruning to produce error reduction), to extract key genes and construct powerful classifiers and classification rules. The optimal classifier could achieve an MCC of 0.725, and category-specified prediction accuracies of 0.958, 0.760, and 0.737 for the CGE, dMGE, and vMGE cells, respectively. The related genes and rules may provide helpful information for deepening the understanding of CIN diversity.

Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.

AbstractAlthough research into water control completion techniques is now relatively mature, there are still difficulties in controlling water in fractured gas reservoirs in the sea. Unlike oil reservoirs, submerged gas reservoir development still faces some problems. To explore the water control mechanism of buried hill fractured gas reservoir, based on the parameters of well HZ26-6-2 in HZ 26-6 condensate gas field, the water control experiment of the continuous packer in the bottom water gas reservoir was completed through the design of experimental parameters, the design of buried hill fractured formation, and the process design of continuous packer. Studies have shown that the continuous packer water control technology delays the water coning rate and makes the water propulsion more uniform. The water breakthrough time and gas production are slightly different for different fractured gas reservoirs, and the network fracture model has a short gas recovery period and fast gas production speed. Continuous packer water control can improve natural gas recovery by 14.3% in fractured gas reservoir. Based on fracture dredging ability, sand accumulation and filling degree, and drilling and production strategy, the influencing factors of horizontal well water control effect in buried hill fractured gas reservoir are discussed and summarized. The research results are aimed at establishing a development model of water control for buried hill gas reservoirs and providing good technical support for rational and scientific water control of horizontal Wells in offshore condensate gas fields. The findings of this study can help for a better understanding of water control of horizontal wells in offshore condensate gas fields.

In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein functions is necessary. Considerable accumulation of protein knowledge and recent developments on computer science provide an alternative way to complete this task, that is, designing computational methods. Several efforts have been made in this field. Most previous methods have adopted the protein sequence features or directly used the linkage from a protein–protein interaction (PPI) network. In this study, we proposed some novel multi-label classifiers, which adopted new embedding features to represent proteins. These features were derived from functional domains and a PPI network via word embedding and network embedding, respectively. The minimum redundancy maximum relevance method was used to assess the features, generating a feature list. Incremental feature selection, incorporating RAndom k-labELsets to construct multi-label classifiers, used such list to construct two optimum classifiers, corresponding to two key measurements: accuracy and exact match. These two classifiers had good performance, and they were superior to classifiers that used features extracted by traditional methods.

The occurrence of coronavirus disease 2019 (COVID-19) has become a serious challenge to global public health. Definitive and effective treatments for COVID-19 are still lacking, and targeted antiviral drugs are not available. In addition, viruses can regulate host innate immunity and antiviral processes through the epigenome to promote viral self-replication and disease progression. In this study, we first analyzed the methylation dataset of COVID-19 using the Monte Carlo feature selection method to obtain a feature list. This feature list was subjected to the incremental feature selection method combined with a decision tree algorithm to extract key biomarkers, build effective classification models and classification rules that can remarkably distinguish patients with or without COVID-19. EPSTI1, NACAP1, SHROOM3, C19ORF35, and MX1 as the essential features play important roles in the infection and immune response to novel coronavirus. The six significant rules extracted from the optimal classifier quantitatively explained the expression pattern of COVID-19. Therefore, these findings validated that our method can distinguish COVID-19 at the methylation level and provide guidance for the diagnosis and treatment of COVID-19.

Alzheimer’s disease (AD) is a progressive disease that leads to irreversible behavioral changes, erratic emotions, and loss of motor skills. These conditions make people with AD hard or almost impossible to take care of. Multiple internal and external pathological factors may affect or even trigger the initiation and progression of AD. DNA methylation is one of the most effective regulatory roles during AD pathogenesis, and pathological methylation alterations may be potentially different in the various brain structures of people with AD. Although multiple loci associated with AD initiation and progression have been identified, the spatial distribution patterns of AD-associated DNA methylation in the brain have not been clarified. According to the systematic methylation profiles on different structural brain regions, we applied multiple machine learning algorithms to investigate such profiles. First, the profile on each brain region was analyzed by the Boruta feature filtering method. Some important methylation features were extracted and further analyzed by the max-relevance and min-redundancy method, resulting in a feature list. Then, the incremental feature selection method, incorporating some classification algorithms, adopted such list to identify candidate AD-associated loci at methylation with structural specificity, establish a group of quantitative rules for revealing the effects of DNA methylation in various brain regions (i.e., four brain structures) on AD pathogenesis. Furthermore, some efficient classifiers based on essential methylation sites were proposed to identify AD samples. Results revealed that methylation alterations in different brain structures have different contributions to AD pathogenesis. This study further illustrates the complex pathological mechanisms of AD.

Cell transplantation is an effective method for compensating for the loss of liver function and improve patient survival. However, given that hepatocytes cultivated in vitro have diverse developmental processes and physiological features, obtaining hepatocytes that can properly function in vivo is difficult. In the present study, we present an advanced computational analysis on single-cell transcriptional profiling to resolve the heterogeneity of the hepatocyte differentiation process in vitro and to mine biomarkers at different periods of differentiation. We obtained a batch of compressed and effective classification features with the Boruta method and ranked them using the Max-Relevance and Min-Redundancy method. Some key genes were identified during the in vitro culture of hepatocytes, including CD147, which not only regulates terminally differentiated cells in the liver but also affects cell differentiation. PPIA, which encodes a CD147 ligand, also appeared in the identified gene list, and the combination of the two proteins mediated multiple biological pathways. Other genes, such as TMSB10, TMEM176B, and CD63, which are involved in the maturation and differentiation of hepatocytes and assist different hepatic cell types in performing their roles were also identified. Then, several classifiers were trained and evaluated to obtain optimal classifiers and optimal feature subsets, using three classification algorithms (random forest, k-nearest neighbor, and decision tree) and the incremental feature selection method. The best random forest classifier with a 0.940 Matthews correlation coefficient was constructed to distinguish different hepatic cell types. Finally, classification rules were created for quantitatively describing hepatic cell types. In summary, This study provided potential targets for cell transplantation associated liver disease treatment strategies by elucidating the process and mechanism of hepatocyte development at both qualitative and quantitative levels.

Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease, and many other disease types, cause cognitive dysfunctions such as dementia via the progressive loss of structure or function of the body’s neurons. However, the etiology of these diseases remains unknown, and diagnosing less common cognitive disorders such as vascular dementia (VaD) remains a challenge. In this work, we developed a machine-leaning-based technique to distinguish between normal control (NC), AD, VaD, dementia with Lewy bodies, and mild cognitive impairment at the microRNA (miRNA) expression level. First, unnecessary miRNA features in the miRNA expression profiles were removed using the Boruta feature selection method, and the retained feature sets were sorted using minimum redundancy maximum relevance and Monte Carlo feature selection to provide two ranking feature lists. The incremental feature selection method was used to construct a series of feature subsets from these feature lists, and the random forest and PART classifiers were trained on the sample data consisting of these feature subsets. On the basis of the model performance of these classifiers with different number of features, the best feature subsets and classifiers were identified, and the classification rules were retrieved from the optimal PART classifiers. Finally, the link between candidate miRNA features, including hsa-miR-3184-5p, has-miR-6088, and has-miR-4649, and neurodegenerative diseases was confirmed using recently published research, laying the groundwork for more research on miRNAs in neurodegenerative diseases for the diagnosis of cognitive impairment and the understanding of potential pathogenic mechanisms.

In mammals, the cerebellum plays an important role in movement control. Cellular research reveals that the cerebellum involves a variety of sub-cell types, including Golgi, granule, interneuron, and unipolar brush cells. The functional characteristics of cerebellar cells exhibit considerable differences among diverse mammalian species, reflecting a potential development and evolution of nervous system. In this study, we aimed to recognize the transcriptional differences between human and mouse cerebellum in four cerebellar sub-cell types by using single-cell sequencing data and machine learning methods. A total of 321,387 single-cell sequencing data were used. The 321,387 cells included 4 cell types, i.e., Golgi (5,048, 1.57%), granule (250,307, 77.88%), interneuron (60,526, 18.83%), and unipolar brush (5,506, 1.72%) cells. Our results showed that by using gene expression profiles as features, the optimal classification model could achieve very high even perfect performance for Golgi, granule, interneuron, and unipolar brush cells, respectively, suggesting a remarkable difference between the genomic profiles of human and mouse. Furthermore, a group of related genes and rules contributing to the classification was identified, which might provide helpful information for deepening the understanding of cerebellar cell heterogeneity and evolution.

Cancer driver gene is a type of gene with abnormal alterations that initiate or promote tumorigenesis. Driver genes can be used to reveal the fundamental pathological mechanisms of tumorigenesis. These genes may have pathological changes at different omics levels. Thus, identifying cancer driver genes involving two or more omics levels is essential. In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. A following screen procedure was performed to extract essential driver genes, which contained three tests: permutation, association, and function tests, which can exclude false-positive genes and screen essential ones. Finally, the intersection operation was performed to obtain novel driver genes involving two omic levels. The analyses on obtained genes indicated that they were associated with fundamental pathological mechanisms of lung cancer at two corresponding omics levels.

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.

The geological estimated hydrocarbon reserves of the Mesozoic granite buried-hill reservoir in the PL oilfield exceed 200 million tons. During the exploration stage, it was commonly considered that the PL buried-hill reservoir should demonstrate a “layered” edge water reservoir mode, because of weathering and tectonics. However, during a later stage, the formation of water was found at structurally high locations, which does not fit the intuition. Therefore, a suitable model is still lacking. Based on the comprehensive information of thin-section, core plug, well logging, and seismic data, etc., the buried-hill of the PL oilfield was re-divided into three northeast–southwest-trending zones. In this model, the reservoir is layered, with each layer having the characteristics of bottom water and two kinds of horizontal and vertical seepage. This new model fits the field-drilling test very well. These insights can effectively guide the exploration and development practice for this kind of buried-hill.

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

Pregnancy is a complicated and long procedure during one or more offspring development inside a woman. A short period of oxygen shortage after birth is quite normal for most babies and does not threaten their health. However, if babies have to suffer from a long period of oxygen shortage, then this condition is an indication of pathological fetal intolerance, which probably causes their death. The identification of the pathological fetal intolerance from the physical oxygen shortage is one of the important clinical problems in obstetrics for a long time. The clinical syndromes typically manifest five symptoms that indicate that the baby may suffer from fetal intolerance. At present, liquid biopsy combined with high-throughput sequencing or mass spectrum techniques provides a quick approach to detect real-time alteration in the peripheral blood at multiple levels with the rapid development of molecule sequencing technologies. Gene methylation is functionally correlated with gene expression; thus, the combination of gene methylation and expression information would help in screening out the key regulators for the pathogenesis of fetal intolerance. We combined gene methylation and expression features together and screened out the optimal features, including gene expression or methylation signatures, for fetal intolerance prediction for the first time. In addition, we applied various computational methods to construct a comprehensive computational pipeline to identify the potential biomarkers for fetal intolerance dependent on the liquid biopsy samples. We set up qualitative and quantitative computational models for the prediction for fetal intolerance during pregnancy. Moreover, we provided a new prospective for the detailed pathological mechanism of fetal intolerance. This work can provide a solid foundation for further experimental research and contribute to the application of liquid biopsy in antenatal care.

Smooth muscles are a specific muscle subtype that is widely identified in the tissues of internal passageways. This muscle subtype has the capacity for controlled or regulated contraction and relaxation. Airway smooth muscles are a unique type of smooth muscles that constitute the effective, adjustable, and reactive wall that covers most areas of the entire airway from the trachea to lung tissues. Infection with SARS-CoV-2, which caused the world-wide COVID-19 pandemic, involves airway smooth muscles and their surrounding inflammatory environment. Therefore, airway smooth muscles and related inflammatory factors may play an irreplaceable role in the initiation and progression of several severe diseases. Many previous studies have attempted to reveal the potential relationships between interleukins and airway smooth muscle cells only on the omics level, and the continued existence of numerous false-positive optimal genes/transcripts cannot reflect the actual effective biological mechanisms underlying interleukin-based activation effects on airway smooth muscles. Here, on the basis of newly presented machine learning-based computational approaches, we identified specific regulatory factors and a series of rules that contribute to the activation and stimulation of airway smooth muscles by IL-13, IL-17, or the combination of both interleukins on the epigenetic and/or transcriptional levels. The detected discriminative factors (genes) and rules can contribute to the identification of potential regulatory mechanisms linking airway smooth muscle tissues and inflammatory factors and help reveal specific pathological factors for diseases associated with airway smooth muscle inflammation on multiomics levels.

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

Cancer can be generally defined as a cluster of systematic diseases triggered by abnormal cell proliferation and growth. With the development of biological sciences and biotechnologies, the etiology of cancer is partially revealed, including some of the most substantial pathogenic factors [either endogenous (genetics) or exogenous (environmental)]. However, some remaining factors that contribute to the tumorigenesis but have not been analyzed and discussed in detail remain. For instance, some typical correlations between microorganisms and tumorigenesis have been reported already, but previous studies are just sporadic studies on single microorganism–cancer subtype pairs and do not explain and validate the specific contribution of microbiome on tumorigenesis. On the basis of the systematic microbiome analyses of blood and cancer-associated tissues in cancer patients/controls in public domain, we performed interpretable analyses. We identified several core regulatory microorganisms that contribute to the classification of multiple tumor subtypes and established quantitative predictive models for interpretable prediction by using multiple machine learning methods. We also compared the optimal features (microorganisms) and rules identified from microbiome profiles processed using the Kraken and the SHOGUN. Collectively, our study identified new microbiome signatures and their interpretable classification rules for cancer discrimination and carried out reliable methodological comparison for robust cancer microbiome analyses, thereby promoting the development of tumor etiology at the microbiome level.

Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes’ methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). Representative functions, such as nervous system development, intrinsic plasma membrane component, calcium ion binding, systemic lupus erythematosus, and alcoholism, are potential pathogenic functions that participate in the initiation and progression of glioblastoma and its subtypes. With these sites, an efficient model can be built to classify the subtypes of glioblastoma.

Radiotherapy is a helpful treatment for cancer, but it can also potentially cause changes in many molecules, resulting in adverse effects. Among these changes, the occurrence of abnormal DNA methylation patterns has alarmed scientists. To explore the influence of region-specific radiotherapy on blood DNA methylation, we designed a computational workflow by using machine learning methods that can identify crucial methylation alterations related to treatment exposure. Irrelevant methylation features from the DNA methylation profiles of 2052 childhood cancer survivors were excluded via the Boruta method, and the remaining features were ranked using the minimum redundancy maximum relevance method to generate feature lists. These feature lists were then fed into the incremental feature selection method, which uses a combination of deep forest, k-nearest neighbor, random forest, and decision tree to find the most important methylation signatures and build the best classifiers and classification rules. Several methylation signatures and rules have been discovered and confirmed, allowing for a better understanding of methylation patterns in response to different treatment exposures.

Atopic dermatitis and psoriasis are members of a family of inflammatory skin disorders. Cellular immune responses in skin tissues contribute to the development of these diseases. However, their underlying immune mechanisms remain to be fully elucidated. We developed a computational pipeline for analyzing the single-cell RNA-sequencing profiles of the Human Cell Atlas skin dataset to investigate the pathological mechanisms of skin diseases. First, we applied the maximum relevance criterion and the Boruta feature selection method to exclude irrelevant gene features from the single-cell gene expression profiles of inflammatory skin disease samples and healthy controls. The retained gene features were ranked by using the Monte Carlo feature selection method on the basis of their importance, and a feature list was compiled. This list was then introduced into the incremental feature selection method that combined the decision tree and random forest algorithms to extract important cell markers and thus build excellent classifiers and decision rules. These cell markers and their expression patterns have been analyzed and validated in recent studies and are potential therapeutic and diagnostic targets for skin diseases because their expression affects the pathogenesis of inflammatory skin diseases.

The heart is an essential organ in the human body. It contains various types of cells, such as cardiomyocytes, mesothelial cells, endothelial cells, and fibroblasts. The interactions between these cells determine the vital functions of the heart. Therefore, identifying the different cell types and revealing the expression rules in these cell types are crucial. In this study, multiple machine learning methods were used to analyze the heart single-cell profiles with 11 different heart cell types. The single-cell profiles were first analyzed via light gradient boosting machine method to evaluate the importance of gene features on the profiling dataset, and a ranking feature list was produced. This feature list was then brought into the incremental feature selection method to identify the best features and build the optimal classifiers. The results suggested that the best decision tree (DT) and random forest classification models achieved the highest weighted F1 scores of 0.957 and 0.981, respectively. The selected features, such as NPPA, LAMA2, DLC1, and the classification rules extracted from the optimal DT classifier played a crucial role in cardiac structure and function in recent research and enrichment analysis. In particular, some lncRNAs (LINC02019, NEAT1) were found to be quite important for the recognition of different cardiac cell types. In summary, these findings provide a solid academic foundation for the development of molecular diagnostics and biomarker discovery for cardiac diseases.

Remotely operated vehicle is a reliable tool in an emergency rescue and routing inspection of a reactor pool. In practice, a cable has been considered as an important part of a vehicle system (i.e. winch, vehicle, and cable) to evaluate and predict the mechanical characteristics, and this article presents a study on a dynamic mechanism modeling of a cable partially in water and air based on an omnidirectional motion, and a numerical simulation is employed. In this work, we programmed the model governed by a partial differential equation set, at the discrete time node which was transformed into an ordinary differential equation set regarded as an initial value problem. The dynamic mechanical characteristics of the lower endpoint (i.e. a connection point between vehicle and cable) and the upper endpoint (i.e. a connection point between cable and winch) were, respectively, quantified with acceleration and a compounded motion including a uniform and rolling motion. A dynamic-state mechanism test was carried out to verify an authenticity of the three-dimensional mechanical model and numerical solution in a circulating tank. The results demonstrated that the presented method was used to evaluate the dynamic mechanism, and held a potential to improve a vehicle design and control strategy.

This article presents a centroid variability model–based controller of HITUWV (Underwater Welding Vehicle by Harbin Institute of Technology), an underwater welding vehicle, for automatic welding with high stability and accuracy. First, an accurate centroid variability model, which considers the coefficient changes of the HITUWV caused by the movements of a 3-degree-of-freedom manipulator, is presented to perform the dynamic characteristics of the HITUWV precisely. Second, a centroid variability model–based adaptive sliding model controller is developed for the HITUWV to complete centroid variability compensation. Experimental results indicate that the proposed centroid variability model–based adaptive sliding model controller demonstrates better performances in stability and accuracy than the conventional proportional–integral–derivative controller and the model-based proportional–integral–derivative controller. As a result, the centroid variability model–based adaptive sliding model controller holds great practicality and utility on the control of underwater operation with high stability and accuracy.

We investigated the functions of microRNAs in the therapeutic effects of noni (Morinda citrifolia L.) fruit juice on mouse models of acute gouty arthritis induced with monosodium urate (MSU). Compared with the model group (treated with MSU), mice in both the positive control group (treated with both MSU and colchicine) and noni fruit juice group (treated with MSU and noni fruit juice) showed a significantly decreased degree of paw swelling in 5 days, as well as the contents of two types of proinflammatory cytokines (i.e., NALP3 and TNF-α). Based on the next-generation sequencing technology, a total of 3896 microRNAs (234 known and 3662 novel) were identified in mice treated with noni fruit juice. A large amount of differentially expressed miRNAs were identified in the noni fruit juice group, suggesting the significant effects of noni fruit juice on the mice with acute gouty arthritis, while the different patterns of change in the numbers of both upregulated and downregulated miRNAs in both noni fruit juice and positive control groups indicated that the mice of acute gouty arthritis may be regulated by differential mechanisms between the treatments of noni fruit juice and colchicine. The target genes of microRNAs involved in the pathogenesis and pathology of acute gouty arthritis in mice were identified and further annotated by both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Our results revealed the therapeutic effects of noni fruit juice on acute gouty arthritis in mice with a group of microRNAs involved in the pharmacological mechanisms of noni fruit juice, providing scientific evidence to support both the agricultural cultivation and pharmacological significance of noni plants.

Antifreeze protein (AFP) is a proteinaceous compound with improved antifreeze ability and binding ability to ice to prevent its growth. As a surface-active material, a small number of AFPs have a tremendous influence on the growth of ice. Therefore, identifying novel AFPs is important to understand protein–ice interactions and create novel ice-binding domains. To date, predicting AFPs is difficult due to their low sequence similarity for the ice-binding domain and the lack of common features among different AFPs. Here, a computational engine was developed to predict the features of AFPs and reveal the most important 39 features for AFP identification, such as antifreeze-like/N-acetylneuraminic acid synthase C-terminal, insect AFP motif, C-type lectin-like, and EGF-like domain. With this newly presented computational method, a group of previously confirmed functional AFP motifs was screened out. This study has identified some potential new AFP motifs and contributes to understanding biological antifreeze mechanisms.

The general preparation method of large-area, continuous, uniform, and controllable vdW heterostructure materials is provided in this paper. To obtain the preparation of MoS2/h-BN and WS2/h-BN heterostructures, MoS2 and WS2 material are directly grown on the insulating h-BN substrate by atmospheric pressure chemical vapor deposition (APCVD) method, which does not require any intermediate transfer steps. The test characterization of MoS2/h-BN and WS2/h-BN vdW heterostructure materials can be accomplished by optical microscope, AFM, Raman and PL spectroscopy. The Raman peak signal of h-BN material is stronger when the h-BN film is thicker. Compared to the spectrum of MoS2 or WS2 material on SiO2/Si substrate, the Raman and PL spectrum peak positions of MoS2/h-BN heterostructure are blue-shifted, which is due to the presence of local strain, charged impurities and the vdW heterostructure interaction. Additionally, the PL spectrum of WS2 material shows the strong emission peak at 1.96 eV, while the full width half maximum (FWHM) is only 56 meV. The sharp emission peak indicates that WS2/h-BN heterostructure material has the high crystallinity and clean interface. In addition, the peak position and shape of IPM mode characteristic peak are not obvious, which can be explained by the Van der Waals interaction of WS2/h-BN heterostructure. From the above experimental results, the preparation method of heterostructure material is efficient and scalable, which can provide the important support for the subsequent application of TMDs/h-BN heterostructure in nanoelectronics and optoelectronics.

Background/Aims: The aim of this study was to identify the predictors for relapse after discontinuation of oral nucleos(t)ide analog treatment for chronic hepatitis B (CHB). Patients and Methods: We evaluated patients who were receiving long-term, regular antiviral therapy with nucleos(t)ide analogs, and subsequently achieved the discontinuation criteria from the Asia-Pacific guideline. After they voluntarily discontinued the drug therapy, data were prospectively collected to observe the potential virologic relapse, and the parameters that predicted recurrence were analyzed. Results: Sixty-five patients met the inclusion criteria, and were included in this study. Twenty-eight patients relapsed, and the accumulative recurrence rates at the 3-month, 6-month, and 1-year follow-ups were 13.85%, 32.31%, and 49.23%, respectively. There was no difference in the accumulative recurrence rate 12 months after discontinuation among patients who were positive or negative for the hepatitis B e antigen (HBeAg) before they received the medication. Logistic regression analysis revealed that the time to complete response, age at discontinuation, and HBsAg levels at discontinuation affected the rate of relapse. Conclusions: Among patients who received orally administrated nucleos(t)ide analogs, serum levels of HBsAg, age at discontinuation, and the time to complete response might be used as a guide to discontinue treatment. Among younger patients, those with low serum HBsAg levels, and those with an earlier complete response, the risk of relapse is lower and discontinuation is much safer.

In this article, a mathematical model of hydrodynamic force was established to model the influence of wall in realizing the precise control and maneuverability of a complex-shaped underwater robot. A hydrodynamic model of a robot for use in a nuclear reaction pool was presented containing not only hydrodynamics coefficients but also a wall hydrodynamic force term, which was often ignored for robots used at sea. First, hydrodynamic coefficients in the model, including viscous and inertial coefficients, were solved by simulating steady-state and unsteady-state motion by computational fluid dynamics. Next, the wall hydrodynamic force was calculated by computational fluid dynamics for different velocities and distances from the wall, and the scope of influence of the wall was identified. Finally, hydrodynamic coefficients without the wall effect and with the wall hydrodynamic force under different conditions were measured experimentally in a circulating water tank. The result demonstrated the accuracy of hydrodynamic calculation by computational fluid dynamics and verified the reliability of the hydrodynamic mathematical model.