To explore the application value of three-dimensional breath-hold gradient spin echo sequence (3D BH-GRASE) in MRCP. We conducted MRCP imaging on 56 patients with pancreatic and biliary diseases via both 3D BH-GRASE and 3D NT-TSE. We compared and statistically analysed the acquisition time, signal-to-noise ratio (SNR), contrast ratio (CR), contrast-to-noise ratio (CNR), and image quality between the two techniques. The mean image acquisition time of 3D BH-GRASE was 16.4 s, which was significantly shorter than the (238.12 ± 43.85) seconds required for 3D NT-TSE (p < 0.05). Compared with3D NT-TSE, 3D BH-GRASE achieved superior scores in overall image quality, artifacts, and visualization of the common bile duct, hepatic duct, and gallbladder/cystic duct (p < 0.05), but not in the left/right hepatic ducts. In contrast, 3D BH-GRASE was significantly inferior to 3D NT-TSE in visualizing the left and right secondary hepatic ducts (p < 0.05), although not for the pancreatic duct. The image quality scores of the 3D NT-TSE group combined with the 3D BH-GRASE group were significantly greater than those of the individual sequences (p < 0.05). 3D BH-GRASE addresses image quality concerns arising from motion artefacts in 3D NT-TSE and serves as a valuable supplementary imaging modality.

Ultra-high performance concrete (UHPC) has gained a lot of attention lately because of its remarkable properties, even if its high cost and high carbon emissions run counter to the current development trend. To lower the cost and carbon emissions of UHPC, this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this issue. The key features of UHPC are filtered using the recursive feature elimination approach, and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction model. The optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-III and multi-objective evolutionary algorithm based on adaptive geometric estimation. The results are evaluated by technique for order preference by similarity to ideal solution and validated by experiments. The outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning models. The multi-objective optimization produces Pareto fronts, which illustrate the trade-off between the mix’s compressive strength, slump flow, cost, and environmental sustainability as well as the wide variety of possible solutions. The research contributes to the development of cost-effective and environmentally sustainable UHPC, and aids in robust, intelligent, and sustainable building practices.

ObjectiveTo explore the value of dual-accelerated simultaneous multi-slice (SMS) imaging in diffusion tensor imaging (DTI) of glioma.MethodsThirty-four patients with glioma who underwent magnetic resonance imaging (MRI) in our hospital from January 2022 to March 2023 were randomly selected. The results of dual-accelerated SMS-DTI and conventional DTI were retrospectively analyzed. All patients were scanned using a uMR790 3.0T MRI scanner, and the scanning technicians followed a predefined sequence to ensure consistency in scan parameters. The images were subjectively evaluated using a Likert 5-point scoring system. Objective evaluation was performed by measuring the required values of the images with b-value = 1000 s/mm2, primarily measuring the signal intensity in the tumor region and the contralateral normal brain white matter region. The standard deviation values were used to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the same encoding direction as the background noise. The number of generated fiber pathways, fractional anisotropy (FA), and mean diffusivity (MD) were measured and analyzed using post-processing software. The relative FA (rFA) and relative MD (rMD) were calculated.ResultsThe results of conventional DTI and SMS-accelerated DTI were compared. In terms of subjective evaluation, including overall image quality, tumor edge clarity, and magnetic sensitivity artifacts, both techniques showed no significant differences, indicating comparable diagnostic performance in anatomical visualization. In terms of objective evaluation and quantitative parameter measurement, there were statistically significant differences in SNR and CNR values, with slightly lower values in the dual-accelerated SMS-DTI compared with conventional DTI, a significant reduction in scanning time can be achieved through a slight loss in image quality. The number of fiber pathways and the rFA and rMD values did not show typical differences between the two techniques. The correlation between these measures was highly similar, with no significant differences observed.ConclusionThe application of dual-accelerated simultaneous multi-slice imaging in DTI of glioma is feasible.

Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material’s significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

The objective of this study was to investigate the utility of conventional imaging combined with diffusion-weighted magnetic resonance imaging (MRI) in identifying high-risk tumor characteristics in patients with localized prostate cancer. A retrospective cohort study was conducted on 194 patients who underwent surgery for localized prostate cancer. Patients were categorized into low-risk and high-risk groups based on clinical criteria. Imaging data were obtained using a MRI system, and various imaging parameters were analyzed, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) signal intensities, diffusion-weighted MRI parameters, and their correlations with clinical characteristics. Statistical methods such as logistic regression, and receiver operating characteristic (ROC) analysis were employed to assess the diagnostic performance of the imaging parameters and to construct joint prediction models. A verification set prediction model was established and compared. The comparison of demographic and clinical characteristics between the low and high-risk groups revealed significant differences in the prostate-specific antigen (PSA) level, Gleason score, Tumor Size and prostate volume (PV). Standard imaging parameters, T1WI and T2WI signal intensities, exhibited significant differences between the low and high-risk groups. Additionally, diffusion-weighted MRI parameters, including signal intensities at different b values, apparent diffusion coefficient (ADC), Ktrans, and Kep, were notably associated with high-risk tumor characteristics in localized prostate cancer. Logistic regression analysis identified both standard imaging and diffusion-weighted MRI parameters as independent predictors of high-risk tumor characteristics. Furthermore, the ROC analysis demonstrated the diagnostic potential of T2WI signal intensity, signal intensity at 800 s/mm2, and ADC in identifying high-risk tumors. Joint prediction models combining standard imaging and diffusion-weighted MRI parameters showed high predictive accuracy for high-risk tumor characteristics in localized prostate cancer, with Area Under the Curve (AUC) values of 0.777 for standard imaging, 0.826 for diffusion-weighted MRI, and 0.892 for the combined model. The AUC value for the prediction model in validation set was 0.860. In conclusion, this study underscores the diagnostic potential of conventional imaging combined with diffusion-weighted MRI in identifying high-risk tumor characteristics in patients with localized prostate cancer. Both standard imaging and diffusion-weighted MRI parameters were identified as non-invasive biomarkers for risk assessment and prognosis. These findings have implications for precision treatment of localized prostate cancer, highlighting the potential integration of imaging-based risk assessment tools into clinical practice for tailored treatment strategies and improved patient outcomes.

Wire nets woven from high-strength steel wires have been used as applique armor against attack by short-range weapons. In this study, the mechanical behavior of chain-link wire nets under pressure from a warhead was investigated by quasistatic experiments and simulations. First, the new rig, the warhead device, and the wire nets were designed; pressure tests were conducted; and the deformation, fracture of the wire nets, and pressure force vs. displacement curves were obtained and analyzed. Then, the numerical approach and Finite element (FE) model were developed, considering the contacts between the steel wires in the inner connections, the contacts between the warhead and the mesh of the wire nets, and the fracture of the steel wire material. By comparison with the experimental data, the numerical approach and FE model are shown to be reliable in predicting the behavior of the wire nets under pressure from a warhead. Finally, the parameters of the wire net size and the mesh angles were further investigated by using the validated numerical approach and FE model, and suggestions for the initial design of the wire nets are discussed.

Wire nets refers to the three-dimensional mesh-structure woven by high-strength steel wire, which has been used in the interception of short-range weapons (such as rocket and mortar shells). To understand the mechanical properties of the interaction between the wire nets and the warhead, combined with the intercepted tests and the fracture feature of the wire nets, the rigid warhead device and the static pressure experimental platform of the wire nets were designed, and the static tests had been carried out for the wire nets pressured by the rigid warhead. The results show that: when the wire nets were downing, the wire nets presented the funnel-shaped deformation on a whole and the diamond-shaped mesh at the contact part between the wire nets and the warhead presented the deformation gradually approaching to the profile shape of the warhead; and the fracture was located at the intersection of the steel wires in the diamond-shaped mesh which was belonged to the contact part. Based on the research of experimental phenomena and relevant results, one theoretical calculation method for static pressure critical force by the rigid warhead was deduced and the reasons of the errors were analyzed. Overall, the theoretical calculation method for the static pressure critical force was in a good agreement with that obtained from the tests and the error was within 10%. The research results can provide a reference for the preliminary design of the wire nets used for intercepting the short-range weapons.

Flexible rock sheds mainly consisting of flexible nets and vaulted steel structures are a new type of device engineered to stop rockfalls. Full-scale experiments have been conducted on a flexible single-module rock shed in the last few years. Because the mechanical characteristics of a flexible three-module rock shed are much different from those of a single-module system, a flexible three-module rock shed aimed at withstanding a rockfall with kinetic energy ranging from 100 to 250 kJ is designed and built. According to the European testing standards (ETAG 027), full-scale experiments on the three-module rock shed are carried out under the impact of rock blocks with a service energy level (SEL) of 100 kJ and a maximum energy level (MEL) of 250 kJ. The flexible three-module rock shed could be put into service again and required no repair after the SEL tests, but it needed simple maintenance before returning to service after the MEL test. Furthermore, the weakness parts of the three-module rock shed, including the arch beams between the longitudinal supports and the connections between the arch beams and the stand columns, needed extra reinforcement. In addition, the cross sections of the stand columns, the diameter of the hoop support cables and the windings in the ring sets could be reduced for optimal design.

To develop and validate radiomic models for preoperative prediction of intraductal component in invasive breast cancer (IBC-IC) using the intratumoral and peritumoral features derived from dynamic contrast-enhanced MRI (DCE-MRI). The prediction models were developed in a primary cohort of 183 consecutive patients from September 2017 to December 2018, consisting of 45 IBC-IC and 138 invasive breast cancers (IBC). The validation cohort of 111 patients (27 IBC-IC and 84 IBC) from February 2019 to January 2020 was enrolled to test the prediction models. A total of 208 radiomic features were extracted from the intratumoral and peritumoral regions of MRI-visible tumors. Then the radiomic features were selected and combined with clinical characteristics to construct predicting models using the least absolute shrinkage and selection operator. The area under the curve (AUC) of receiver operating characteristic, sensitivity, and specificity were used to evaluate the performance of radiomic models. Four radiomic models for prediction of IBC-IC were built including intratumoral radiomic signature, peritumoral radiomic signature, peritumoral radiomic nomogram, and combined intratumoral and peritumoral radiomic signature. The combined intratumoral and peritumoral radiomic signature had the optimal diagnostic performance, with the AUC, sensitivity, and specificity of 0.821 (0.758–0.874), 0.822 (0.680–0.920), and 0.739 (0.658–0.810) in the primary cohort and 0.815 (0.730–0.882), 0.778 (0.577–0.914), and 0.738 (0.631–0.828) in the validation cohort. The radiomic model based on the combined intratumoral and peritumoral features from DCE-MRI showed a good ability to preoperatively predict IBC-IC, which might facilitate the individualized surgical planning for patients with breast cancer before breast-conserving surgery. •·Preoperative prediction of intraductal component in invasive breast cancer is crucial for breast-conserving surgery planning. • Peritumoral radiomic features of invasive breast cancer contain useful information to predict intraductal components. •·Radiomics is a promising non-invasive method to facilitate individualized surgical planning for patients with breast cancer before breast-conserving surgery.

Background Non-invasive modalities for assessing axillary lymph node (ALN) are needed in clinical practice. Purpose To investigate the suspicious ALN on unenhanced T2-weighted (T2W) imaging and intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) for predicting ALN metastases (ALNM) in patients with T1-T2 stage breast cancer and clinically negative ALN. Material and Methods Two radiologists identified the most suspicious ALN or the largest ALN in negative axilla by T2W imaging features, including short axis (Size-S), long axis (Size-L)/S ratio, fatty hilum, margin, and signal intensity on T2W imaging. The IVIM parameters of these selected ALNs were also obtained. The Mann-Whitney U test or t-test was used to compare the metastatic and non-metastatic ALN groups. Finally, logistic regression analysis with T2W imaging and IVIM features for predicting ALNM was conducted. Results This study included 49 patients with metastatic ALNs and 50 patients with non-metastatic ALNs. Using the above conventional features on T2W imaging, the sensitivity and specificity in predicting ALNM were not high. Compared with non-metastatic ALNs, metastatic ALNs had lower pseudo-diffusion coefficient (D*) (P = 0.043). Logistic regression analysis showed that the most useful features for predicting ALNM were signal intensity and D*. The sensitivity and specificity predicting ALNM that satisfied abnormal signal intensity and lower D* were 73.5% and 84%, respectively. Conclusions The abnormal signal intensity on T2W imaging and one IVIM feature (D*) were significantly associated with ALNM, with sensitivity of 73.5% and specificity of 84%.

目的 探讨基于药代动力学动态增强MRI(dynamic contrast-enhanced MRI,DCE-MRI)的全肿瘤影像组学特征对三阴型乳腺癌的诊断价值.材料与方法 回顾性分析85例治疗前行DCE-MRI扫描的乳腺癌患者,Luminal型39例、人表皮生长因子受体2(human epidermal growth factor receptor,HER-2)过表达型16例、三阴(triple negative,TN)型30例.提取全肿瘤药代动力学及增强图像的影像组学特征.采用Spearman相关分析及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)筛选最优影像组学特征,并构建Logistic模型,对TN型与Luminal型、TN型与HER-2过表达型、TN型与非TN型进行鉴别,并绘制受试者工作特征曲线,计算AUC.利用五折交叉验证法验证预测性能.结果 TN型和Luminal型预测模型共筛选出6个重要特征,鉴别准确度和AUC分别为0.783、0.865.TN型和HER-2过表达型预测模型筛选出14个重要特征,鉴别准确度和AUC分别为0.870、0.923.TN型和非TN型预测模型共筛选17个重要特征,鉴别准确度和AUC分别为0.847、0.913.结论 基于药代动力学DCE-MRI的全肿瘤影像组学特征有利于鉴别三阴型与其他分子分型的乳腺癌.

OBJECTIVE:To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer.MATERIALS AND METHODS:A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models.RESULTS:The 106 radiomic features were reduced to 4 ALNM diagnosis-related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947-0.997]) in the training cohort and 0.979 (95% CI [0.952-1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05).CONCLUSION:Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.

目的 研究乳腺癌腋窝淋巴结的动态对比增强磁共振成像(DCE-MRI)影像组学特征对诊断其转移状态的价值.方法 回顾性选取67例经术后病理确诊乳腺癌且经腋窝淋巴结清扫存在3枚及以上腋窝淋巴结转移患者.每个患者选取乳腺癌同侧的最大可评价腋窝淋巴结作为转移性淋巴结组,选取同一患者对侧的可评价最大淋巴结作为非转移性淋巴结对照组.将所有MRI原始数据输入Omni-Kinetics后处理软件,经手动3D分割淋巴结,并基于药物动力学模型对分割的病灶进行全体素分析,自动生成全淋巴结内的药物动力学参数(Ktrans,Kep,Vp)和每个参数对应的直方图分布特征(共22个特征),以及增强后第一期的增强纹理特征(共75个特征).采用配对非参数检验比较转移淋巴结组和对照组间各影像组学特征差异.采用拉索回归分析筛选对转移性淋巴结诊断最有价值的特征,使用线性判别分析和留一交叉验证法分析这些影像组学特征对转移性淋巴结的判别诊断能力,采用受试者曲线分析评价判别模型的诊断效能.结果 配对非参数检验结果显示,转移性淋巴结组与对照组间,分别有6个(6/22)药物动力学参数及直方图特征和53个(53/75)增强后动态纹理特征存在统计学差异(P＜0.05).经过拉索回归分析结果显示,排列前4位的转移性淋巴结组和对照组间有差异的特征都是增强纹理特征.线性判别分析结果显示,基于腋窝淋巴结的影像组学特征对转移性腋窝淋巴结的判别准确性达到90％(60/67),对应的曲线下面积达到0.987(P=0.000).结论 乳腺癌腋窝淋巴结基于药物动力学模型DCE-MRI上的影像组学特征对诊断其转移状态具有很好的价值.

A series of gem-chlorosulfurization products bearing difluoromethyl substituents were synthesized in high to excellent yields directly from p-toluenesulfonyl difluorodiazoethane (TsCF2CHN2), disulfides and PhICl2 without any catalysts or additives. The mild reaction conditions and high functional group compatibility indicated the utility and sustainability of the method. In addition, the gem-chlorosulfurization products could be efficiently converted to sulfur-containing and aryl substituted difluoromethyl derivatives by a feasible multi-component operation.

Purpose To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients. Methods This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive ( n = 93) and negative ( n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong’s test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the “Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis” (TRIPOD) statement. Results The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717–0.849) and 0.680 (95% CI, 0.604–0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737–0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783–0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05). Conclusion FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.

OBJECTIVE:The aim of the present study was to use pharmacokinetic quantitative parameters with histogram and texture features on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate between the luminal A and luminal B molecular subtypes of breast cancer.METHODS:We retrospectively reviewed the data of 94 patients with histopathologically proven breast cancer. The pharmacokinetic quantitative parameters (Ktrans, Kep, and Ve) with their corresponding histogram and texture features based on preoperative DCE-MRI were obtained. The parameters were compared using the Mann-Whitney U-test between the luminal A and luminal B groups, the human epidermal growth factor receptor-2 (HER2)-positive luminal B and HER2-negative luminal B groups, and the lymph node metastasis (LNM)-positive and LNM-negative groups. Receiver operating characteristic curves were generated for parameters that presented significant between-group differences.RESULTS:The maximum values of Ktrans, Kep, and Ve, and the mean and 90th percentile values of Ve were significantly higher in the luminal B group than in the luminal A group. Among the texture features, only skewness of Ktrans significantly differed between the luminal A and B groups. All histogram features of Ktrans were higher in the HER2-positive luminal B group than in the HER2-negative luminal B group. However, no parameter differed between the LNM-positive and LNM-negative groups.CONCLUSION:Pharmacokinetic quantitative parameters with histogram and texture features obtained from DCE-MRI are associated with the molecular subtypes of breast cancer, and may serve as potential imaging biomarkers to differentiate between the luminal A and luminal B molecular subtypes.

Based on the engineering background of the heavy drop impacting metal tube, the scaling law without considering the strain rate effects was deduced according to the π theorem. Then, the scaling law considering the strain rate effects by modifying the initial velocity was derived. Finally, two scaling laws are compared by experiments and numerical simulation. The following conclusions are drawn: the scaling law without considering the strain rate effects is completely self-contained. However, if the strain rate effects are taken into account, the scaling law will be distorted. The scaling law for modifying the initial velocity can make the model test more accurately reflect the prototype test. The model test guided by the modified scaling law fits better with the prototype in terms of pipeline deformation, effective strain, effective stress, and particle vibration velocity, and the error is reduced by more than 50%.

In recent years, with the continuous improvement of people's living standards, tourism has become a fashionable way of leisure and vacation. Therefore, whether a star hotel is comfortable and elegant has become an important part of the needs of passengers. The star hotel interior design system not only requires the full display of the cultural connotation, but also pays more attention to the application of modern technology, and strives to create a comfortable, novel and intelligent hotel experience environment to cater to the modern tourists' psychological needs of star-rated hotel interior design for innovation, differentiation, comfort and intelligence. Taking this as an opportunity, the paper mainly discusses the innovative research mode of the star hotel interior design system.

Zhenjiang Dagang Ferry, located in the middle and upper reaches of the Yangtze River Jiangsu section, is full of different types of ships. The steam ferry crosses the waterway and frequently meets with the passing ships, inducing water collision accidents. This article, taking cross-river ferrys and normal navigation ships as research subjects, proposes an algorithm for water navigation collision avoidance based on position data. First, by analyzing the dynamic coordinate of the ferry and navigation vessel, their navigation trajectories are predicted respectively. Second, the time when they arrive at the possible collision point is calculated, the type of traffic conflict is determined, the drive’s collision avoidance response time is considered, and a warning criterion for early warning is established. Finally, the proposed warning algorithm is analyzed to check to what extent the warning can help the ship avoid the collision. Sixteen sets of data showing the navigation of the ships are selected to compare the effect of the warning algorithm with the radar warning method. The research results show that the application of the algorithm can avoid unnecessary alarm, false alarm, and alarm failure the radar warnings have, improving the accuracy and helping avoid the collision accidents.

OBJECTIVE: The aim of this study was to investigate the mechanism of simvastatin-induced apoptosis in nasopharyngeal carcinoma (NPC) cells. MATERIALS AND METHODS: CNE1 and HK1 cell lines were treated with different concentrations of simvastatin for different time course. Subsequently, Cell Counting Kit-8 (CCK-8), colony formation assay, and flow cytometry were conducted to evaluate cell activity, colony formation ability, as well as cell cycle of NPC cells, respectively. The mRNA expressions of p21, Bim, and cyclin D1 were examined by qPCR. Meanwhile, the protein expression levels of apoptosis-related proteins (including caspase-3, Bax, Bcl-2) were detected by Western blot. Caspase-3 activity was determined to estimate cell apoptosis. An NPC xenotransplantation model was constructed to further determine the role of simvastatin in vivo. In addition, NF-κB activity was assessed through Luciferase reporter gene assay and Western blot. RESULTS: Simvastatin treatment lead to significantly reduced viability of NPC cells and the number of cell colonies dose-dependently and time-dependently. Meanwhile, simvastatin treatment caused cell cycle arrest in G0/G1 phase, remarkably downregulated expression of cyclin D1, and upregulated expressions of p21 and Bim. In addition, simvastatin induced apoptosis of NPC cells and enhanced the Luciferase activity of caspase-3. Western blot results indicated that simvastatin promoted the protein level of Bax and caspase-3, whereas suppressed the protein expression of Bcl-2. In vivo experiments showed that simvastatin was able to suppress the growth of NPC cells. Further studies demonstrated that simvastatin remarkably attenuated the Luciferase activity of pNF-κB-Luc, thereby specifically inhibiting the NF-κB signaling pathway. CONCLUSIONS: Simvastatin inhibits proliferation and promotes apoptosis of NPC cells by inhibiting the NF-κB pathway.