Osthole accelerates osteoporotic fracture healing by inducing the osteogenesis-angiogenesis coupling of BMSCs via the Wnt/ß-catenin pathway.

Osthole, a natural coumarin derivative, has been shown to have multiple pharmacological activities. However, its effect on osteoporotic fracture has not yet been examined. This research was designed to explore the unknown role and potential mechanism of osthole on osteoporotic fracture healing. We first evaluated the osteogenic and angiogenic abilities of osthole. Then angiogenesis-related assays were conducted to investigate the relationship between osteogenesis and angiogenesis, and further explore its molecular mechanism. After that, we established osteoporotic fracture model in ovariectomy-induced osteoporosis rats and treated the rats with osthole or placebo. Radiography, histomorphometry, histology, and sequential fluorescent labeling were used to evaluate the effect of osthole on osteoporotic fracture healing. In vitro research revealed that osthole promoted osteogenesis and up-regulated the expression of angiogenic-related markers. Further research found that osthole couldn't facilitate the angiogenesis of human umbilical vein endothelial cells in a direct manner, but it possessed the ability to induce the osteogenesis-angiogenesis coupling of bone marrow mesenchymal stem cells (BMSCs). Mechanistically, this was conducted through activating the Wnt/ß-catenin pathway. Subsequently, using ovariectomy-induced osteoporosis tibia fracture rat model, we observed that osthole facilitated bone formation and CD31hiEMCNhi type H-positive capillary formation. Sequential fluorescent labeling confirmed that osthole could effectively accelerate bone formation in the fractured region. The data above indicated that osthole could accelerate osteoporotic fracture healing by inducing the osteogenesis-angiogenesis coupling of BMSCs via the Wnt/ß-catenin pathway, which implied that osthole may be a potential drug for treating osteoporosis fracture.

Icariin accelerates bone regeneration by inducing osteogenesis-angiogenesis coupling in rats with type 1 diabetes mellitus.

BACKGROUND: Icariin (ICA), a natural flavonoid compound monomer, has multiple pharmacological activities. However, its effect on bone defect in the context of type 1 diabetes mellitus (T1DM) has not yet been examined. AIM: To explore the role and potential mechanism of ICA on bone defect in the context of T1DM. METHODS: The effects of ICA on osteogenesis and angiogenesis were evaluated by alkaline phosphatase staining, alizarin red S staining, quantitative real-time polymerase chain reaction, Western blot, and immunofluorescence. Angiogenesis-related assays were conducted to investigate the relationship between osteogenesis and angiogenesis. A bone defect model was established in T1DM rats. The model rats were then treated with ICA or placebo and micron-scale computed tomography, histomorphometry, histology, and sequential fluorescent labeling were used to evaluate the effect of ICA on bone formation in the defect area. RESULTS: ICA promoted bone marrow mesenchymal stem cell (BMSC) proliferation and osteogenic differentiation. The ICA treated-BMSCs showed higher expression levels of osteogenesis-related markers (alkaline phosphatase and osteocalcin) and angiogenesis-related markers (vascular endothelial growth factor A and platelet endothelial cell adhesion molecule 1) compared to the untreated group. ICA was also found to induce osteogenesis-angiogenesis coupling of BMSCs. In the bone defect model T1DM rats, ICA facilitated bone formation and CD31hiEMCNhi type H-positive capillary formation. Lastly, ICA effectively accelerated the rate of bone formation in the defect area. CONCLUSION: ICA was able to accelerate bone regeneration in a T1DM rat model by inducing osteogenesis-angiogenesis coupling of BMSCs.

Bayesian spatial cluster signal learning with application to adverse event (AE).

There is growing interest in understanding geographic patterns of medical device-related adverse events (AEs). A spatial scan method combined with the likelihood ratio test (LRT) for spatial-cluster signal detection over the geographical region is universally used. The spatial scan method used a moving window to scan the entire study region and collected some candidate sub-regions from which the spatial-cluster signal(s) will be found. However, it has some challenges, especially in computation. First, the computational cost increased when the number of sub-regions increased. Second, the computational cost may increase if a large spatial-cluster pattern is present and a flexible-shaped window is used. To reduce the computational cost, we propose a Bayesian nonparametric method that combines the ideas of Markov random field (MRF) to leverage geographical information to find potential signal clusters. Then, the LRT is applied for the detection of spatial cluster signals. The proposed method provides an ability to capture both locally spatially contiguous clusters and globally discontiguous clusters, and is manifested to be effective and tractable using hypothetical Left Ventricular Assist Device (LVAD) data as an illustration.

A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data.

We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set of conditions. Second, we show that our model can correctly identify question-level clusters asymptotically, and the parameters of interest that measure the proficiency of examinees in solving certain questions can be estimated at a n rate (up to a log term). Third, we present a tractable sampling algorithm to obtain valid posterior samples from our proposed model. Compared to the existing methods, our model manages to reveal the multi-dimensionality of the examinees' proficiency level in handling different types of questions parsimoniously by imposing a nested clustering structure. The proposed model is evaluated via a series of simulations as well as apply it to an English proficiency assessment data set. This data analysis example nicely illustrates how our model can be used by test makers to distinguish different types of students and aid in the design of future tests.

Time fused coefficient SIR model with application to COVID-19 epidemic in the United States.

In this paper, we propose a Susceptible-Infected-Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR model's transmission rate and removal rate via Bayesian shrinkage priors. MCMC sampling for the proposed method is facilitated by the nimble package in R. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze different levels of COVID-19 data in the United States.

Feeding Strategies and Trophic Niche Divergence of Three Groups of Dosidicus gigas off Peru: Based on Stable Carbon and Nitrogen Isotopes and Morphology of Feeding Apparatuses.

Dosidicus gigas (D. gigas) is a pelagic cephalopod of ecological and economic importance widely distributed in the eastern Pacific Ocean. Generally, small-, medium-, and large-sized groups of the squids have been respectively identified on the basis of the mantle length (ML) of adults. Intraspecific feeding variability maximizes the utilization of available food resources by D. gigas. However, the coexistence mechanism of three groups has not been fully understood yet. In our study, based on the analyses of beak morphology and stable carbon and nitrogen isotopes, the feeding strategies and coexistence patterns of large-, medium-, and small-sized groups of D. gigas were investigated. D. gigas had a wide range of 13C/12C (δ13C) and 15N/14N (δ15N) values in muscle tissue, variable feeding behaviors, and wide food sources. The δ13C or δ15N values showed no significant difference between the small- and medium-sized groups, which shared the same habitats and fed on preys with the similar trophic level. Compared to small- and medium-sized groups, the large-sized group had a smaller range of habitats and consumed more nearshore foods. Both isotopes and feeding apparatus morphology indicated a high degree of niche overlap between the small- and medium-sized groups, whereas the large-sized group differed significantly from other groups. In addition, the niche width of the female was larger than that of the male in all three groups. We inferred that the sex differences in body length and reproductive behavior led to the difference in niche width. The isotopic niche overlap between female and male samples was the most significant in large-sized group and the least significant in the small-sized group, indicating that different feeding strategies were adopted by the three groups. These findings proved that the three groups of D. gigas off Peruvian waters adopted a feeding strategy with inter- and intra-group regulation. This feeding strategy maximizes the use of food and habitat resources and ensures that different size groups can coexist in the same waters.

A novel optimization method for belief rule base expert system with activation rate.

Although the belief rule base (BRB) expert system has many advantages, such as the effective use of semi-quantitative information, objective description of uncertainty, and efficient nonlinear modeling capability, it is always limited by the problem of combinatorial explosion. The main reason is that the optimization of a BRB with many rules will consume many computing resources, which makes it unable to meet the real-time requirements in some complex systems. Another reason is that the optimization process will destroy the interpretability of those parameters that belong to the inadequately activated rules given by experts. To solve these problems, a novel optimization method for BRB is proposed in this paper. Through the activation rate, the rules that have never been activated or inadequately activated are pruned during the optimization process. Furthermore, even if there is a complete data set and all rules are activated, the activation rate can also be used in the parallel optimization process of the BRB expert system, where the training data set is divided into some subprocesses. The proposed method effectively solves the combinatorial explosion problem of BRB and can make full use of quantitative data without destroying the original interpretability provided by experts. Case studies prove the advantages and effectiveness of the proposed method, which greatly expands the application fields of the BRB expert system.

Sample size re-estimation in Phase 2 dose-finding: Conditional power versus Bayesian predictive power.

Unblinded sample size re-estimation (SSR) is often planned in a clinical trial when there is large uncertainty about the true treatment effect. For Proof-of Concept (PoC) in a Phase II dose finding study, contrast test can be adopted to leverage information from all treatment groups. In this article, we propose two-stage SSR designs using frequentist conditional power (CP) and Bayesian predictive power (PP) for both single and multiple contrast tests. The Bayesian SSR can be implemented under a wide range of prior settings to incorporate different prior knowledge. Taking the adaptivity into account, all type I errors of final analysis in this paper are rigorously protected. Simulation studies are carried out to demonstrate the advantages of unblinded SSR in multi-arm trials.

Benchmarking cell-type clustering methods for spatially resolved transcriptomics data.

Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data.

Performance Evaluation of Complex Equipment Considering Resume Information.

It is of great significance to obtain the performance state of complex equipment to protect equipment and maintain its normal operation. The majority of the performance evaluation methods are based on test data, but resume information is not considered. With its wide applicability and completeness, the resume information can be used in the comprehensive evaluation of equipment in various non-testing situations. By incorporating resume information into the performance evaluation of complex equipment, the flexible use of test data and resume information can result in a more comprehensive and accurate evaluation. Therefore, this paper focuses on the evaluation method of complex equipment performance based on evidential reasoning (ER) considering resume information. In order to unify the test data and resume information in the same framework, a novel method is proposed to transform them into the ER-based performance evaluation. On this basis, according to the index types, different reliability calculation methods are put forward, with one being based on the first-order fitting coefficient of variation, and the other being based on average time to failure; the index weight is analyzed based on the method of expert weight construction. Then, the transformed information with reliability and weight are fused by the ER rule. Finally, a performance evaluation case of a certain inertial measurement unit (IMU) is conducted to verify the effectiveness of the proposed method.

Acupuncture for shoulder-hand syndrome after stroke: An overview of systematic reviews.

BACKGROUND: To provide evidence, this review evaluated the methodological quality, risk of bias, and reporting quality of SRs/MAs in the treatment of shoulder-hand syndrome after stroke with acupuncture. METHODS: Systematic reviews and Meta analyses (SRs/MAs) of acupuncture for shoulder and hand syndromes after stroke were retrieved from 6 databases from inception to May 1, 2022. Two reviewers independently screened the literature and extracted the data, then used Assessment of Multiple Systematic Reviews-2 (AMSTAR-2), Bias Risk in Systematic Review (ROBIS), and Preferred Report Item for Systematic review and Meta-analysis (PRISMA), Grading of Recommendations, Assessment, Development and Evaluation (GRADE) to assess methodological quality, risk of bias, quality of reporting, and quality of evidence. RESULTS: We included 7 SRs/MAs, of which all SRs/MAs had very low AMSTAR-2 assessment quality and one study had a very low assessment bias risk. According to the PRISMA checklist, Protocol and registration, Synthesis of results, Summary of evidence, Conclusions and Funding were the main reporting limitations. GRADE evaluation showed a total of 37 results, but no high-quality evidence results, 6 results (16.22%) of the evidence quality were moderate, and supported acupuncture combined with exercise rehabilitation and drug therapy was better than exercise joint drug rehabilitation and rehabilitation, we also found that the result of limitations were the main factors that influence the evidence of low quality, followed by imprecision, inconsistency, and publication bias. CONCLUSIONS: Acupuncture is a relatively safe and effective adjuvant therapy for shoulder and hand syndromes after stroke. However, because of the low quality of SRs/MAs evidence supporting these findings, high-quality randomized controlled trials should be conducted, and the quality of relevant SRs should be improved to provide evidence for clinical application.

External treatment of traditional Chinese medicine for functional dyspepsia in children: Protocol for a systematic review and network meta-analysis.

BACKGROUND: Functional dyspepsia (FD) is a common digestive system disease in pediatrics, usually affects normal growth and increases health care expenditure. Many investigations have demonstrated that external treatment of traditional Chinese medicine (TCM) has certain advantages in the treatment of FD children, but the effectiveness, safety, and advantages of various methods have not been confirmed by high-quality meta-analyses. This study will conduct a systematic review and network meta-analysis to evaluate the differences and effectiveness in external treatments of TCM, in order to provide a reference for further clinical treatment for FD in children. METHODS: Nine electronic databases, including PubMed, Medline, Embase, Web of Science, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Scientific Journal Database, Wan-Fang Database and one clinical trial register platforms: ClinicalTrials.gov (www.ClinicalTrials.gov/) will be searched using English and Chinese search strategies. All eligible studies are randomized controlled trials of TCM external treatment for FD in children, published on or before July 20, 2022. The screening process will be developed by 2 independent authors, and network meta-analysis will be performed with RevMan (V5.3) software. RESULTS: This study will provide a high-quality synthesis to assess the effectiveness and safety on the external treatment of TCM for children with FD. CONCLUSION: The results of this study will provide evidence to judge whether the external treatment of TCM are effective interventions for children with FD. ETHICS AND DISSEMINATION: The results of this meta-analysis and meta-regression will be disseminated through publication in a peer-reviewed journal and presented at a relevant conference. The information used in the network meta-analysis does not contain individual patient data. Therefore, ethical approval was not required. PROSPERO REGISTRATION NUMBER: CRD42022360429.

Anatomical observation, classification, fracture and finite element analysis of the posterior process of the Asian adult talus.

BACKGROUND: Fractures of the posterior process of the talus are rarely seen and frequently overlooked. In our study, anatomical observation and classification of the posterior process of the talus were carried out, and related imaging and finite element methods were combined. The study aimed to observe and provide anatomical data related to posterior process of talus in Asian adults and explore the potential relationships between the different types with fracture of posterior process of talus. METHODS: Combined with the anatomical morphology and imaging data, the posterior process of talus was divided into four types, and the incidence and fracture situation were statistically analyzed. The finite element models of four different types of talus processes were established and verified, and the stress and strain were simulated and analyzed. RESULTS: The total incidence of the posterior process of the talus was 97.47%. The proportions of the four types were neck-like 10.13%, flat 36.29%, pointy 12.66% and round blunt 38.39%. The overall incidence of bone cracks of the posterior process of the talus was 4.98%; the most common type was neck-like type. Compared with the value on the other types, the maximum von Mises stress increased by 67.66%, 83.90% and 111.18% on the neck-like posterior process of talus respectively. CONCLUSIONS: It is speculated that different types of the posterior process of the talus may be related to the probability of fracture, and it may be better to consider different treatment strategies for different types of fractures.

Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data.

In this work, we propose a new Bayesian spatial homogeneity pursuit method for survival data under the proportional hazards model to detect spatially clustered patterns in baseline hazard and regression coefficients. Specially, regression coefficients and baseline hazard are assumed to have spatial homogeneity pattern over space. To capture such homogeneity, we develop a geographically weighted Chinese restaurant process prior to simultaneously estimating coefficients and baseline hazards and their uncertainty measures. An efficient Markov chain Monte Carlo (MCMC) algorithm is designed for our proposed methods. Performance is evaluated using simulated data, and further applied to a real data analysis of respiratory cancer in the state of Louisiana.

Orthogonal Optimal Design of Multiple Parameters of a Magnetically Controlled Capsule Robot.

Magnetically controlled capsule robots are predominantly used in the diagnosis and treatment of the human gastrointestinal tract. In this study, based on the permanent magnet method, magnetic driving and fluid measurement systems for in-pipe capsule robots were established. Using computational fluid dynamics (CFD) and particle image velocimetry (PIV), the fluid velocity and vorticity in the pipe of the capsule robot were calculated and measured. The running characteristics of the capsule robot were numerically analyzed in the curved pipe and the peristaltic flow. Furthermore, the range and variance method of orthogonal design was used to analyze the influence of four typical parameters (namely, pipe diameter, robotic translational speed, robotic rotational speed, and fluid viscosity) on the three operating performance indicators of the capsule robot (namely, the forward resistance of the robot, fluid turbulent intensity near the robot, and maximum fluid pressure to the pipe wall). In this paper, the relative magnitude and significance of the influence of each typical parameter on different performance indicators of the robot are presented. According to the different performance requirements of the robot, the different four parameter combinations are optimized. It is hoped that this work provides a reference for the selection of the appropriate mucus, translational speed, and rotational speed of the robot when it is working in pipes with different diameters.

Bayesian variable selection for the Cox regression model with spatially varying coefficients with applications to Louisiana respiratory cancer data.

The Cox regression model is a commonly used model in survival analysis. In public health studies, clinical data are often collected from medical service providers of different locations. There are large geographical variations in the covariate effects on survival rates from particular diseases. In this paper, we focus on the variable selection issue for the Cox regression model with spatially varying coefficients. We propose a Bayesian hierarchical model which incorporates a horseshoe prior for sparsity and a point mass mixture prior to determine whether a regression coefficient is spatially varying. An efficient two-stage computational method is used for posterior inference and variable selection. It essentially applies the existing method for maximizing the partial likelihood for the Cox model by site independently first and then applying an Markov chain Monte Carlo algorithm for variable selection based on results of the first stage. Extensive simulation studies are carried out to examine the empirical performance of the proposed method. Finally, we apply the proposed methodology to analyzing a real dataset on respiratory cancer in Louisiana from the Surveillance, Epidemiology, and End Results (SEER) program.

Non-pharmacological intervention for rehabilitation of post-stroke spasticity: A protocol for systematic review and network meta-analysis.

BACKGROUND: Post-stroke spasticity (PSS) is a major worldwide health problem, and timely and effective rehabilitation is associated with the risk of diabetes development; there are a variety of non-pharmacological interventions applied to the rehabilitation of PSS in these treatments; however, the relative efficacy and safety of different therapies remain uncertain, and we will conduct a systematic review and network meta-analysis to evaluate different non-pharmacological interventions. The relative efficacy and safety of intervention in PSS rehabilitation, thus providing evidence to support the optimization of the PSS rehabilitation program. METHODS: We searched the following databases electronically, including four English literature databases (i.e., PubMed, Medline, Embase, and Cochrane Library) and two Chinese literature databases (i.e., China National Knowledge Infrastructure and VIP). We will also search for randomized controlled trials on non-pharmacological interventions for post-stroke spasticity, and the search time limit is from its establishment to May 2020. Two reviewers working independently will screen the titles, abstracts, and full papers. Data extraction will be completed by two independent authors. The primary outcome was the motor function. The secondary outcome was the assessment of daily living ability. We will use RevMan V.5.3 software to compute the data synthesis carefully when a meta-analysis is allowed. We will conduct Bayesian network meta-analysis using the Markov Chain Monte Carlo random effects model in Aggregate Data Drug Information System version 1.16.8 (Drugis, Groningen, NL). RESULTS: This study provides a high-quality synthesis to assess the effectiveness and safety of non-pharmacological interventions for patients with PSS. CONCLUSION: The results of this study will provide evidence to judge whether non-pharmacological interventions are effective interventions for patients with post-stroke spasticity. ETHICS AND DISSEMINATION: The results of this meta-analysis and meta-regression will be disseminated through publication in a peer-reviewed journal and presented at a relevant conference. The data used in the network meta-analysis did not contain individual patient data. Therefore, ethical approval was not required. INPLASY REGISTRATION NUMBER: INPLASY202140059.

Tuina plus acupuncture for post-stroke depression: A protocol for systematic review and meta-analysis.

BACKGROUND: Post-stroke depression (PSD) is the most common mental health issue, affecting approximately 33% of stroke survivors. Tuina and acupuncture treatments are often combined to treat PSD; however, there has been no meta-analysis on their synergistic effect. Therefore, we aimed to perform a systematic review and meta-analysis to estimate the effectiveness of Tuina and acupuncture in PSD treatment. METHODS: The following electronic databases will be searched: PubMed, the Cochrane Library, Embase, Web of Science, Medline, CNKI, Chinese Biomedical Literature Database, VIP, and Wan Fang databases. We will consider articles published between database initiation and April 2021. Clinical randomized controlled trials related to Tuina combined with acupuncture for post-stroke depression will be included in the study. Language is limited to Chinese and English. Research selection, data extraction, and research quality assessment were independently completed by 2 researchers. Data were synthesized using a fixed effect model or random effect model, depending on the heterogeneity test. The Hamilton depression rating scale (HDRS) and effective rate were the primary outcomes. The post-stroke depression rating scale (PSDRS), patient health questionnaire-9 (PHQ-9), and the incidence of adverse events will also be assessed as secondary outcomes. RevMan V.5.4 statistical software will be used for meta-analysis. If it is not appropriate for a meta-analysis, a descriptive analysis will be conducted. Data synthesis uses the risk ratio and the standardized or weighted average difference of continuous data to represent the results. RESULTS: This study provides a high-quality synthesis to assess the effectiveness and safety of Tuina for post-stroke depression. CONCLUSION: This systematic review will provide evidence to determine whether Tuina plus acupuncture is an effective and safe intervention for patients with post-stroke depression. ETHICS AND DISSEMINATION: The protocol of the systematic review does not require ethical approval because it does not involve humans. This article will be published in peer-reviewed journals and presented at relevant conferences. SYSTEMATIC REVIEW REGISTRATION: INPLASY202140098.

Electroacupuncture for patients with spasticity after stroke: A protocol for systematic review and meta-analysis.

BACKGROUND: The purpose of this paper is to evaluate the effectiveness and safety of electroacupuncture in the treatment of spasticity after stroke. METHODS: We will electronically search PubMed, Medline, Embase, Web of Science, the Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Scientific Journal Database, and Wan-Fang Database from the date of creation to November 2020. In addition, we will manually retrieve other resources including the reference lists of identified publications, conference articles, and gray literature. The clinical randomized controlled trials or quasi-randomized controlled trials related to electroacupuncture in the treatment of spasticity after stroke will be included in the study. The language is limited to Chinese and English. Research selection, data extraction, and research quality assessment will be independently completed by 2 researchers. Data were synthesized by using a fixed effect model or random effect model depend on the heterogeneity test. The modified Ashworth scale was the primary outcomes. Simplified Fugl-Meyer assessment scale (FMA), Stroke specific quality of life scale (SS-QOL) and adverse events will also be assessed as secondary outcomes. RevMan V.5.3 statistical software will be used for meta-analysis. If it is not appropriate for a meta- analysis, then a descriptive analysis will be conducted. Data synthesis will use the risk ratio and the standardized or weighted average difference of continuous data to represent the results. RESULTS: This study will provide a high-quality synthesis to assess the effectiveness and safety of electroacupuncture in the treatment of spasticity after stroke. CONCLUSION: This systematic review will provide evidence to judge whether electroacupuncture is an effective and safety intervention for patients with spasticity after stroke. ETHICS AND DISSEMINATION: The protocol of the systematic review does not require ethical approval because it does not involve humans. We will publish this article in peer-reviewed journals and presented at relevant conferences. SYSTEMATIC REVIEW REGISTRATION: CRD42021220300.

Reliability assessment of train control and management system based on evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm.

The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the reliability assessment of TCMS, we propose a new quantitative model based on the evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm, the proposed model offers the following advantages: it has a strong modeling capability for the TCMS reliability, it has an interpretable model assessment process, it can describe the assessment result under probabilistic uncertainty and ignorance uncertainty, and it possesses considerable robustness. To make the model interpretable, an assessment hierarchy is established for the TCMS; to improve model robustness, weights interval is applied to replace the trained weights as the model weights. Several traditional methods are compared with the proposed model to demonstrate its performance, the results show that the proposed model has a better training accuracy. Moreover, a case study is conducted to verify the model's functional feasibility.