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This research explores the intricate-coupling relationship between typhoons and the ionosphere by the high-frequency surface wave radar's (HFSWR) over-the-horizon capabilities. This letter describes the gravity wave features in travelling ionospheric disturbances (TIDs) and ionospheric drift information. Further discussion is raised on the chaotic properties of gravity wave features using the Lyapunov exponents and chaotic attractors, particularly as they are influenced by typhoon. To predict these gravity wave features more accurately, a new model is developed by synergizing phase space reconstruction theory and statistical learning theory, a chaotic prediction model for gravity wave features constructed on a third-order Volterra filter. The experimental findings confirm that the gravity waves features detected via HFSWR present the hyperchaotic behaviour. More importantly, the innovative gravity wave prediction model based on chaotic attractor can capture the dynamics of the original chaotic system. Notably, the model's prediction accuracy has been improved by 98.6% after the introduction of phase space reconstruction modelling. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the optimal control problem governed by linear parabolic equation with box constraints on control variables. We employ the Fenchel duality scheme to derive an unconstrained dual problem. Compared with the primal problem, the objective functional of the dual problem includes a projection onto the box constraints. We prove the existence and uniqueness of solutions to the dual problem and derive the first‐order optimality conditions. Furthermore, we investigate the saddle point property between the solutions of the primal problem and the solutions of the dual problem. To solve the dual problem, we design two implementable methods: the conjugate gradient method and the semi‐smooth Newton method. The solutions of the primal problem can be easily obtained through the solutions of the dual problem. We demonstrate the effectiveness and accuracy of the proposed methods by solving three example problems. [ABSTRACT FROM AUTHOR]

The combination of the time-scaling transformation and control parameterization has proven to be an effective approach in addressing optimal control problems involving switching systems with predefined subsystem sequences. However, this approach has certain limitations. First, the number of control switchings is required to be no less than the number of subsystem switchings. Second, the switching of the subsystem must be accompanied by the switching of the control. Third, this scheme introduces many hyperparameters, leading to combinatorial explosion. To address these drawbacks, we introduce a novel computational approach such that the control switching can be independent of subsystem switching. The superiority of this novel approach can be clearly observed from the solutions obtained using the proposed method for solving two illustrative examples. [ABSTRACT FROM AUTHOR]

Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification. [ABSTRACT FROM AUTHOR]

This paper proposes a method for the intelligent detection of high-frequency surface wave radar (HFSWR) targets. This method cascades the adaptive constant false alarm (CFAR) detector variability index (VI) with the convolutional neural network (CNN) to form a cascade detector (VI)CFAR-CNN. First, the (VI)CFAR algorithm is used for the first-level detection of the range–Doppler (RD) spectrum; based on this result, the two-dimensional window slice data are extracted using the window with the position of the target on the RD spectrum as the center, and input into the CNN model to carry out further target and clutter identification. When the detection rate of the detector reaches a certain level and cannot be further improved due to the convergence of the CNN model, this paper uses a dual-detection maps fusion method to compensate for the loss of detection performance. First, the optimized parameters are used to perform the weighted fusion of the dual-detection maps, and then, the connected components in the fused detection map are further processed to achieve an independent (VI)CFAR to compensate for the (VI)CFAR-CNN detection results. Due to the difficulty in obtaining HFSWR data that include comprehensive and accurate target truth values, this paper adopts a method of embedding targets into the measured background to construct the RD spectrum dataset for HFSWR. At the same time, the proposed method is compared with various other methods to demonstrate its superiority. Additionally, a small amount of automatic identification system (AIS) and radar correlation data are used to verify the effectiveness and feasibility of this method on completely measured HFSWR data. [ABSTRACT FROM AUTHOR]

Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of the ocean and ionosphere during the severe Typhoon Muifa. By employing the short-time Fourier transform (STFT) method, the HFSWR ocean and ionosphere echoes stimulated by typhoon-induced gravity waves are observed, and the joint gravity wave features of the ocean and ionosphere echoes at different time scales are extracted. Additionally, the phase-space reconstruction method is employed to characterize the dynamical evolution of the joint gravity wave features in higher-dimensional space. Furthermore, the chaotic dynamics behavior of the joint gravity wave features is analyzed using the largest Lyapunov exponents. By combining the gravity wave features with chaotic dynamics, this study introduces a method to characterize the joint gravity wave features. The extraction of joint gravity wave features in HFSWR echoes stimulated by typhoons, along with the construction of a chaotic characterization scheme for the gravity wave features, provides an innovative approach and a solid technical foundation for studying the ocean and ionosphere using HFSWR under sudden sea conditions. [ABSTRACT FROM AUTHOR]

The problem of accurate source localization has been an area of focus in high-frequency surface wave radar (HFSWR) applications. However, antenna pattern distortion (APD) decreases the direction-of-arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. Up to now, limited studies have been conducted on the calibration of antenna pattern distortion for phased arrays in HFSWR. In this paper, we first analyze the effect of APD on the performance of the MUSIC algorithm through estimation of accuracy and angular resolution. We demonstrate that using the actual pattern (or say APD) can improve DOA estimation performance. Based on this proposition, we propose a novel iterative calibration method that employs the first-order sea clutter data and can jointly estimate DOA and APD in an iterative way. To obtain available calibration points, we introduce the extraction methods of the first-order sea clutter spectrum and single-DOA spectrum points. Meanwhile, in each iteration, the Beamspace MUSIC algorithm and artificial hummingbird algorithm (AHA) are utilized to estimate the DOA and APD, respectively. Numerical results reveal a good coincidence between the actual pattern and the estimated APD. We also apply this method to process the experimental data of HFSWR. We obtain the APD vector of the real phased array and improve the direction-finding performance of several real ship targets using this vector. Both numerical and experimental results prove the correctness of our proposed calibration method. [ABSTRACT FROM AUTHOR]

Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the "ConsensusClusterPlus" package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the "glmnet" package in R, while the random forest algorithm was realized using the "randomForest" package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the "pRRophetic" package in R and the Genomics of Drug Sensitivity in Cancer database. The "rms" package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and CKS1B, HJURP, or TPX2 has not been reported in the literature so far. Thus, these findings indicated that CKS1B, HJURP, and TPX2 have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed. [ABSTRACT FROM AUTHOR]

The gravity wave produced by typhoons has been an essential subject of study that concerns numerous researchers. The damage to human beings and infrastructure in coastal regions caused by typhoon disasters annually is very severe, and analyzing gravity wave variation is a reliable approach to research typhoons. High-frequency surface wave radar (HFSWR) as an over-the-horizon radar can achieve real-time monitoring of an extensive sea area and space. This paper derived the gravity wave perturbation spectrum by handling high-frequency surface wave radar data during typhoons. The gravity wave spectrum data were examined by applying the chaos examination approaches of the Lyapunov exponent and phase-space reconstruction to the gravity wave spectrum data after processing and extraction. The reconstructed phase space had a specific shape in a certain direction, with the maximum Lyapunov exponent greater than zero. The gravity wave spectrum data are suggested to have chaotic properties through two chaos examination approaches. This paper demonstrated that the gravity waves observed by a radar have chaotic properties through the measurement data of HFSWR. While the chaotic properties suggest that observed gravity wave data are predictable in the short term, they are unpredictable in the long term. Predicting gravity wave data is important for understanding the chaotic properties of the atmosphere and for future gravity wave prediction. [ABSTRACT FROM AUTHOR]

Inactivation of the p53 pathway is linked to a variety of human cancers. As a critical component of the p53 pathway, ubiquitin‐specific protease 7 (USP7) acts as a deubiquitinase for both p53 and its ubiquitin E3 ligase mouse double minute 2 homolog. Here, myeloid leukemia factor 2 (MLF2) is reported as a new negative regulator of p53. MLF2 interacts with both p53 and USP7. Via these interactions, MLF2 inhibits the binding of USP7 to p53 and antagonizes USP7‐mediated deubiquitination of p53, thereby leading to p53 destabilization. Functionally, MLF2 plays an oncogenic role in colorectal cancer, at least partially, via the negative regulation of p53. Clinically, MLF2 is elevated in colorectal cancer and its high expression is associated with poor prognosis in patients with colorectal cancer. In wild‐type‐p53‐containing colorectal cancer, MLF2 and p53 expressions are inversely correlated. These findings establish MLF2 as an important suppressor of p53 function. The study also reveals a critical role for the MLF2–p53 axis in promoting colorectal carcinogenesis. [ABSTRACT FROM AUTHOR]

The ionosphere is an integral element of the Earth and reflects the variations of the Earth's space weather and solar activity. Since extreme weather can cause ionospheric disturbances, changes in the ionosphere can indirectly enable early warning of extreme weather. The major intention of predicting the peak height of the ionospheric F2 layer (hmF2) in this paper is to acquire ionospheric variations over a period of time in a local area to facilitate future extreme weather warning research. In this paper, a dual element LSTM-CNN (long short term memory-convolutional neural network) prediction model is proposed to predict the hmF2. The performance of the proposed model is assessed by comparing it with other popular models such as SARIMA (seasonal differential autoregressive moving average), LSTM (long short term memory), BP (back propagation neural network) and IRI2016 (international reference ionospheric model) models. The outcome demonstrates that the prediction effect with the proposed model is remarkably excellent in comparison with the remaining four models. Furthermore, the proposed model has better sensitivity to rapid changes in parameters. The outcomes indicate that the forecasting model of this study has high prediction capabilities. [ABSTRACT FROM AUTHOR]

In radar array signal processing, weak nonstationary polarization signal direction of arrival (DOA) estimation is a challenging and significant issue when both strong and weak nonstationary signals coexist. Time-frequency (T-F) analysis is an effective method to deal with nonstationary signals. In the last decade, spatial time-frequency distributions (STFDs) have been proposed for multiple dual-polarization antenna arrays and efficaciously used for nonstationary signal DOA estimation. In this article, we introduced a novelty means for estimating weak nonstationary polarization signal DOA by utilizing spatial polarimetric time-frequency distributions (SPTFDs) of cross terms when there are strong nonstationary polarization interference signals and additive Gaussian white noise. The cross terms' SPTFDs are considered via a replaceability matrix for data covariance in multiple signal classification (MUSIC) and the estimation of signal parameters using the rotational invariance technique (ESPRIT). Combining the STFDs of cross terms with polarization information about weak nonstationary signals improves signal DOA estimation accuracy. The combined MUSIC and ESPRIT are used in the algorithm to further ensure the success probability and accuracy of DOA estimation. Through simulation analyses, the proposed algorithm is more suitable for the required application scenarios than other algorithms and is superior to other algorithms. [ABSTRACT FROM AUTHOR]

In the practical application of high-frequency surface wave radar (HFSWR), ionospheric clutter has become the principal obstacle to target detection. Since ionospheric clutter has nonlinear and time-varying characteristics, the current suppression methods still require improvement. In this letter, we proposed a method merging the deep neural networks (DNNs) and linear Koopman operator from the dynamic perspective, combining the powerful learning capacity of neural networks and the interpretability of Koopman theory, and applied linear Koopman mode to represent nonlinear ionospheric clutter in the state space based on time-delayed coordinates to achieve the finite-dimensional linear approximate reconstruction of ionospheric clutter. The validation results of the measured data reveal that the method has satisfactory modeling accuracy, which proves the effectiveness of the approach and lays the foundations for the subsequent breakthrough in new methods for ionospheric clutter suppression. [ABSTRACT FROM AUTHOR]

Control parameterization technique is an effective method to solve optimal control problems. It works by approximating the control function by piece-wise constant (or linear) function. In this way, the optimal control problems are approximated by optimal parameter selection problems, which can be regarded as finite-dimensional optimization problems, where the control heights and switching times of the piece-wise constant function are taken as the decision variables. They can be solved by using gradient-based optimization methods. For this, it requires the gradient formulas of the objective and constraint functions with respect to the decision variables. There are two methods for deriving the required gradient formulas. The aim of this paper is to survey various numerical methods developed based on control parameterization for solving optimal control problems governed by time-delay systems. Particular focus is on those numerical methods for optimizing switching time points. This is because the process of optimizing the switching time points is much more complicated for optimal control problems with time delays. In addition, we shall also review the computational methods developed based on control parameterization technique for solving two optimal control problems involving more complicated time delays. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a worldwide public health concern. We aimed to investigate the association between preoperative prognostic scoring system based on the combination of age, American Society of Anesthesiologists physical status (ASA-PS), and prognostic nutritional index (PNI) and long-term survival outcomes in patients with (GC). Data from 513 patients were analyzed using Cox proportional hazards regression models to evaluate the association between this prognostic score system and risks of all-cause mortality. This simple prognostic score system (0–3 points) was an independent predictor of long-term survival outcomes in patients with GC after radical gastrectomy based on multivariate analyses. Prognostic 1-point score, 2-point score, and 3-point score significantly increased 50% (95% CI, 14%–98%; P = 0.004), 75% (95% CI, 22%–151%; P = 0.003), and 116% (95% CI, 26%–271%; P = 0.005) hazards of 5-year all-cause mortality, respectively, compared to prognostic 0-point score. Five-year overall survival rates were significantly decreased as prognostic scores increased, (0 point, 57.3%; 1-point, 41.3%; 2-ponint, 36.6%; 3-point, 25.9%; P < 0.01; area under the curve [AUC] = 0.62). A prognostic scoring method based on combination of age, ASA-PS, and PNI may serve as an independent risk stratification metric for long-term survival in patients with GC. [ABSTRACT FROM AUTHOR]

As a hot research topic, the gain-phase error self-calibration in MIMO radar systems has been investigated for many years. In this paper, we proposed a novel array error self-calibration method, termed online errors self-calibration based on feature learning (OES-FL). This method regards the statistical characteristics of the detected targets' DOA as a prior knowledge and does not require the calibrated antenna subarray or external reference source to correct the array disturbances in real time. First, we analyse the monostatic MIMO signal model suffering gain-phase error. Then, we exploit the statistical characteristics of DOA of many targets for correcting gain-phase error of antenna array. Next, the gain-phase error estimation scheme based on LMS and the DOA deviation estimation method based on LSTM are proposed, respectively. Using real-life radar data collected at the integrated transportation hubs to generate simulation data, the proposed approach is shown to be effective in correcting gain-phase errors and, therefore, provides a promising model for online error self-calibration in monostatic MIMO radars. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of time-delay optimal control problem (TDOCP) with canonical equality and inequality constraints. By applying control parameterization method together with time-scaling transformation, a TDOCP can be readily solved by gradient-based optimization methods. The partial derivative of the cost as well as the constraint functions with respect to the decision variables are obtained by variational approach, which is inefficient when the discretization for the control function is relatively dense. For general optimal control problem without time-delay, co-state approach is an effective way to compute the gradients, however, when time-delay is involved in the dynamic system, the co-state system is not known. In this paper, we derive the co-state system for TDOCP to compute the gradients of the cost and constraints. Numerical results show that the computational efficiency is much higher when compared with the traditional variational approach. [ABSTRACT FROM AUTHOR]

Background. Immunotherapy is an important treatment modality for gastric cancer, therefore, it is crucial to understand the regulators of the tumor microenvironment in gastric cancer. Numerous studies have shown that noncoding RNAs have a critical status in the tumor progression, and the influence of competing endogenous RNA (ceRNA) networks on gastric adenocarcinoma has been widely discussed over the years, but the connection between ceRNA networks and the immune microenvironment of cancer is unclear. This study was aimed at exploring how ceRNA networks influence the prognosis of patients with gastric cancer by modulating the tumor microenvironment. Methods. The Gene Expression Omnibus was analyzed to obtain differential expression matrixes of the noncoding RNAs (circular RNAs (circRNAs), microRNAs (miRNAs)), and mRNAs. The Circular RNA Interactome web tool and TargetScan were applied to determine the miRNA binding sites of the circRNAs and miRNA target genes. The Cancer Genome Atlas provided prognostic genes for gastric cancer, and Cytoscape created the ceRNA networks. Real-time quantitative reverse transcription polymerase chain reaction and western blot assay were adopted to find out how the ceRNA network regulates the expression of the hub gene. Additionally, the TISIDB and TIMER databases were used to assess the link between the hub gene and immunotherapy, with TISIDB providing the immune genes that are coexpressed with the hub gene. Furthermore, the immune-gene signature was constructed by using Cox regression analysis. Moreover, the nomogram, which could predict the prognostic role of gastric cancer patients was created on the basis of the immune-gene signature. Results. In gastric cancer, the circ-0007707/miR-429/PDGFD pathway had a differential expression. The results demonstrated that the pathway could regulate the progression and immune microenvironment of gastric cancer by modulating the immune-gene signature, which included two immune genes (TAB1 and CXCR4). Moreover, the low-risk group patients had better survival. Conclusion. The circ-0007707/miR-429/PDGFD pathway may play a regulatory role in the progression and prognosis of gastric cancer by interfering with the tumor microenvironment, and the PDGFD-related immune-gene signature could be considered a moderator of prognostic factor for gastric cancer and to guide immunotherapy programs. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of optimal control problems with control constraints on a set of characteristic time instants. By applying the control parameterization technique, these constraints are imposed on the subdomains that contain the characteristic time points. The values of the control functions as well as the lengths for their corresponding subdomains become decision variables. Time-scaling transformation is an effective technique to optimize the length of each subdomain in a new time horizon. However, the characteristic time instants in the original time horizon become variable time instants in the new time horizon, and hence the control constraints imposed on these characteristic time points are difficult to be formulated in the new time horizon. We propose a surrogate condition and show that the characteristic time control constraints will be satisfied once the surrogate condition holds. Moreover, this surrogate condition is easy to formulate in the new time horizon. The resulting approximate problem can be readily solved by many existing computational methods for solving constrained optimal control problems. Finally, we conclude this paper by solving two examples. [ABSTRACT FROM AUTHOR]

Worldwide, the incidence rate of gastric cancer ranks fifth, and the mortality rate of gastric cancer ranks third among all malignant tumors. However, the pathogenesis of gastric cancer remains poorly understood. In this study, we demonstrated that the expression level of NELFE is higher in human gastric cancer tissues than in adjacent nontumor tissues. A high level of NELFE is associated with worse postoperative overall survival (OS) and relapse-free survival (RFS) rates in patients with gastric cancer. Moreover, the expression of NELFE is correlated with high tumor grade and lymph node metastasis in gastric cancer patients. Knockdown of NELFE dramatically inhibits the cell proliferation and metastasis of gastric cancer xenografts in vivo. Furthermore, we found that NELFE binding to the 3'UTR of E2F2 affects the mRNA stability of E2F2 to regulate the expression level of E2F2. In gastric cancer, E2F2 also acts as an oncogene to inhibit the proliferation and migration of gastric cancer cells by knocking down the expression level of E2F2. However, overexpressing E2F2 in cells with NELFE knockdown significantly reverses the inhibition of cell proliferation and migration induced by NELFE knockdown. Therefore, NELFE at least partially functions as an oncogene through E2F2. Moreover, CIBERSORTx analysis of the proportion of tumor-infiltrating immune cells (TICs) revealed that immune cells are correlated with NELFE and E2F2 expression, suggesting that NELFE and E2F2 might be responsible for the preservation of the immunodominant status for gastric cancer. In conclusion, NELFE acts as an oncogene in gastric cancer and can be used as a potential therapeutic target. [ABSTRACT FROM AUTHOR]

Aiming to utilize CO2 and solid waste form coal chemical industry synchronously, we proposed a novel process which integrated CO2 mineral carbonation and utilization of gasification slags based on chemical looping. With NH4Cl as the loop chemical, which generated HCl and NH3 through pyrolysis, the aluminum, calcium, and so forth were extracted by HCl from the coal slag, and CO2 was mineralized by calcium and NH3. NH4Cl was recovered after mineralization, and chemical looping was closed. A pilot setup with a capacity of 1‐kg CO2/h was conducted to demonstrate the feasibility of the chemical looping process. An airlift reactor was employed for mineral carbonation reaction, and conditions for extraction and mineral carbonation were optimized. The process presented the CO2 conversion of more than 90% with a calcium overall utilization ratio of 74.8%. The CO2 sequestration potential of the gasification slag is 121‐g CO2/kg slag, which is higher than that of steel slag and red mud. Calcium carbonate with the particle size of 1–10 μm can be obtained during the mineral carbonation. Simultaneously, the process extracted and enriched 62.3% of aluminum in the slag and enabled the deep utilization of the slag. [ABSTRACT FROM AUTHOR]

Thoracic aortic dissection (TAD) is a severe and extremely dangerous cardiovascular disease. Proliferation, migration and phenotypic switching of vascular smooth muscle cells (SMCs) are major pathogenetic mechanisms involved in the development of TAD. The present study was designed to investigate the expression and potential function of serine peptidase inhibitor Kunitz type 2 (SPINT2) in TAD. The gene expression profile data for ascending aorta from patients with TAD were downloaded from the GEO database with the accession number GSE52093. Bioinformatics analysis using GEO2R indicated that the differentially expressed SPINT2 was prominently decreased in TAD. The expression levels of SPINT2 mRNA and protein in aortic dissection specimens and normal aorta tissues were measured using reverse transcription-quantitative PCR and western blotting. SPINT2 expression was downregulated in clinical samples from aortic dissection specimens of patients with TAD compared with the corresponding expression noted in tissues derived from patients without TAD. In vitro, platelet-derived growth factor BB (PDGF-BB) was applied to induce the isolated primary mouse aortic SMC phenotypic modulation (a significant upregulation in the expression levels of synthetic markers), and the SMCs were infected with the adenoviral vector, Ad-SPINT2, to construct SPINT2-overexpressed cell lines. SMC viability was detected by an MTT assay and SMC proliferation was detected via the presence of Ki-67-positive cells (immunofluorescence staining). To explore the effects of SPINT2 on SMC migration, a wound healing assay was conducted. ELISA and western blotting assays were used to measure the content and expression levels of MMP-2 and MMP-9. The expression levels of vimentin, collagen I, α-SMA and SM22α were measured using western blotting. The PDGF-BB-induced proliferation and migration of SMCs were recovered by SPINT2 overexpression. The increase in the expression levels of SPINT2 reduced the expression levels of active matrix metalloproteinases (MMPs), MMP-2 and MMP-9. Overexpression of SPINT2 suppressed SMC switching from a contractile to a synthetic type, as evidenced by decreased vimentin and collagen I expression levels along with increased α-smooth muscle actin and smooth muscle protein 22-α expression levels. Furthermore, activation of ERK was inhibited in SPINT2-overexpressing SMCs. A specific ERK agonist, 12-O-tetradecanoylphorbol-13-acetate, reversed the SPINT2-mediated inhibition of SMC migration and the phenotypic switching. Collectively, the data indicated that SPINT2 was implicated in the proliferation, migration and phenotypic switching of aortic SMCs, suggesting that it may be involved in TAD progression. [ABSTRACT FROM AUTHOR]