Academic literature on the topic 'Changzhou gong xue yuan'

Abstract Background: The IMpower133 represented the current standard of care in the 1L setting for patients (pts) with ES-SCLC (extensive-stage small cell lung cancer). However, there are still unmet needs for ES-SCLC treatment. LDRT could play a key role in the priming effect of immune system by acting as an immune adjuvant and having sensitive cytotoxic activity to SCLC. The interim analysis of MATCH study after stage I showed promising benefit and tolerability of combination of Atezo + chemotherapy + LDRT in pts with ES-SCLC. Here we report the primary efficacy and safety results of this study. Methods: The MATCH study was a single-arm phase II trial conducted in 8 centers across China. Previously untreated ES-SCLC pts with measureable disease per RECIST v1.1 at baseline, age≥18, ECOG 0-1 were eligible. Atezo (1200 mg IV, D1) + Cisplatin (75 mg/m2 IV, D1)/Carboplatin (AUC = 5 IV, D1) +Etoposide (100 mg/m2 IV, D1-D3) were administrated on a 21-day cycle for four cycles. Concurrent LDRT (15 Gy/5f) were conducted from D1-D5 in the first cycle. Then pts received Atezo maintenance until loss of clinical benefit or unacceptable toxicity. The primary endpoint was objective response rate (ORR), defined as the proportion of patients with a complete response or partial response on two consecutive occasions ≥ 4 weeks apart, as determined by the investigator according to RECIST v1.1. The secondary endpoints included disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and safety. A Simon’s minimax 2-stage design was adopted. Results: As the cutoff date of 30th Nov. 2022, 56 pts have been enrolled. 49 (87.5%) were males; mean age was 58.9 years with 78.6% pts had ECOG PS of 1. 80.4% pts had smoking history. Most pts were staged T4 (n = 33, 58.9%), N3 (n = 37, 66.1%) and M1(n = 40, 71.4%). Median follow-up was 14.8 months (range: 11.6-17.8 m). The confirmed ORR was 87.5% (95% CI: 75.9%-94.8%), all partial response. DCR was 94.6% (95% CI: 85.1%-98.9%). Median PFS was 6.9 m (95% CI: 5.4-9.3 m). The 6-month and 12-month PFS rate were 56.5% and 27.7%. Median OS was not reached (NR, 95% CI: 13.3m, NR). The 12-month OS rate was 71.9%. The safety profile, analyzed in all 56 pts, was consistent with the previous reports. Neutrophil count decreased (60.7%), white blood cell count decreased (58.9%) and platelet count decreased (23.2%) were the most common grade (G) 3-4 adverse events (AE). G5 AE occurred in 1 pt (pneumonia and pulmonary embolism). 4 pts experienced AEs leading to treatment discontinuation. IrAEs were reported in 21 (37.5%) pts, most common irAEs were hyperthyroidism (5.4%) and rash (5.4%). Radiation pneumonitis (G1) was observed in 1 pt. Conclusions: Adding LDRT to Atezo + chemotherapy shows impressive antitumor activity, potential survival benefit and well tolerability in 1L treatment of ES-SCLC. Clinical registration: NCT04622228. Citation Format: Lin Zhou, Jianguo Sun, Conghua Xie, Youling Gong, Meijuan Huang, Zhiyong Yuan, Lin Wu, Hui Wang, Nan Bi, Yaping Xu, Jiang Zhu, Yongmei Liu, Yan Zhang, Min Fan, Bingwen Zou, Min Yu, Yanying Li, Feifei Na, Weigang Xiu, Yong Xu, Jin Wang, Xuanwei Zhang, Jianxin Xue, You Lu. Efficacy and safety of Low dose radiotherapy (LDRT) concurrent Atezolizumab (Atezo) plus chemotherapy as first line (1L) therapy for ES-SCLC: Primary analysis of Phase II MATCH study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT219.

Graphene-based composites have received a great deal of attention in recent year because the presence of graphene can enhance the conductivity, strength of bulk materials and help create composites with superior qualities. Moreover, the incorporation of metal oxide nanoparticles such as Fe3O4 can improve the catalytic efficiency of composite material. In this work, we have synthesized a composite material with the combination of reduced graphene oxide (rGO), and Fe3O4 modified tissue-paper (mGO-PP) via a simple hydrothermal method, which improved the removal efficiency of the of methylene blue (MB) in water. MB blue is used as the model of contaminant to evaluate the catalytic efficiency of synthesized material by using a Fenton-like reaction. The obtained materials were characterized by SEM, XRD. The removal of materials with methylene blue is investigated by UV-VIS spectroscopy, and the result shows that mGO-PP composite is the potential composite for the color removed which has the removal efficiency reaching 65% in acetate buffer pH = 3 with the optimal time is 7 h. Keywords Graphene-based composite, methylene blue, Fenton-like reaction. References [1] Ma Joshi, Rue Bansal, Reng Purwar, Colour removal from textile effluents, Indian Journal of Fibre & Textile Research, 29 (2004) 239-259 http://nopr.niscair.res.in/handle/123456789/24631.[2] Kannan Nagar, Sundaram Mariappan, Kinetics and mechanism of removal of methylene blue by adsorption on various carbons-a comparative study, Dyes and pigments, 51 (2001) 25-40 https://doi.org/10.1016/S0143-7208(01)00056-0.[3] K Rastogi, J. N Sahu, B. C Meikap, M. N Biswas, Removal of methylene blue from wastewater using fly ash as an adsorbent by hydrocyclone, Journal of hazardous materials, 158 (2008) 531-540.https://doi.org/10.1016/j.jhazmat.2008.01. 105.[4] Qin Qingdong, Ma Jun, Liu Ke, Adsorption of anionic dyes on ammonium-functionalized MCM-41, Journal of Hazardous Materials, 162 (2009) 133-139 https://doi.org/10.1016/j.jhazmat. 2008.05.016.[5] Mui Muruganandham, Rps Suri, Sh Jafari, Mao Sillanpää, Lee Gang-Juan, Jaj Wu, Muo Swaminathan, Recent developments in homogeneous advanced oxidation processes for water and wastewater treatment, International Journal of Photoenergy, 2014 (2014). http://dx. doi.org/10.1155/2014/821674.[6] Herney Ramirez, Vicente Miguel , Madeira Luis Heterogeneous photo-Fenton oxidation with pillared clay-based catalysts for wastewater treatment: a review, Applied Catalysis B: Environmental, 98 (2010) 10-26 https://doi.org/ 10.1016/j.apcatb.2010.05.004.[7] Guo Rong, Jiao Tifeng, Li Ruifei, Chen Yan, Guo Wanchun, Zhang Lexin, Zhou Jingxin, Zhang Qingrui, Peng Qiuming, Sandwiched Fe3O4/carboxylate graphene oxide nanostructures constructed by layer-by-layer assembly for highly efficient and magnetically recyclable dye removal, ACS Sustainable Chemistry & Engineering, 6 (2017) 1279-1288 https://doi.org/10.1021/acssuschemeng.7b03635.[8] Sun Chao, Yang Sheng-Tao, Gao Zhenjie, Yang Shengnan, Yilihamu Ailimire, Ma Qiang, Zhao Ru-Song, Xue Fumin, Fe3O4/TiO2/reduced graphene oxide composites as highly efficient Fenton-like catalyst for the decoloration of methylene blue, Materials Chemistry and Physics, 223 (2019) 751-757 https://doi.org/ 10.1016/j.matchemphys.2018.11.056.[9] Guo Hui, Ma Xinfeng, Wang Chubei, Zhou Jianwei, Huang Jianxin, Wang Zijin, Sulfhydryl-Functionalized Reduced Graphene Oxide and Adsorption of Methylene Blue, Environmental Engineering Science, 36 (2019) 81-89 https://doi. org/10.1089/ees.2018.0157.[10] Zhao Lianqin, Yang Sheng-Tao, Feng Shicheng, Ma Qiang, Peng Xiaoling, Wu Deyi, Preparation and application of carboxylated graphene oxide sponge in dye removal, International journal of environmental research and public health, 14 (2017) 1301 https://doi.org/10.3390/ijerph14111301.[11] Yu Dandan, Wang Hua, Yang Jie, Niu Zhiqiang, Lu Huiting, Yang Yun, Cheng Liwei, Guo Lin, Dye wastewater cleanup by graphene composite paper for tailorable supercapacitors, ACS applied materials & interfaces, 9 (2017) 21298-21306 https://doi.org/10.1021/acsami.7b05318.[12] Wang Hou, Yuan Xingzhong, Wu Yan, Huang Huajun, Peng Xin, Zeng Guangming, Zhong Hua, Liang Jie, Ren MiaoMiao, Graphene-based materials: fabrication, characterization and application for the decontamination of wastewater and wastegas and hydrogen storage/generation, Advances in Colloid and Interface Science, 195 (2013) 19-40 https://doi. org/10.1016/j.cis.2013.03.009.[13] Marcano Daniela C, Kosynkin Dmitry V, Berlin Jacob M, Sinitskii Alexander, Sun Zhengzong, Slesarev Alexander, Alemany Lawrence B, Lu Wei, Tour James M, Improved synthesis of graphene oxide, ACS nano, 4 (2010) 4806-4814 https://doi.org/10.1021/nn1006368.[14] Zhang Jiali, Yang Haijun, Shen Guangxia, Cheng Ping, Zhang Jingyan, Guo Shouwu, Reduction of graphene oxide via L-ascorbic acid, Chemical Communications, 46 (2010) 1112-1114 http://doi. org/10.1039/B917705A [15] Gong Ming, Zhou Wu, Tsai Mon-Che, Zhou Jigang, Guan Mingyun, Lin Meng-Chang, Zhang Bo, Hu Yongfeng, Wang Di-Yan, Yang Jiang, Nanoscale nickel oxide/nickel heterostructures for active hydrogen evolution electrocatalysis, Nature communications, 5 (2014) 4695 https:// doi.org/10.1038/ncomms5695.[16] Wu Zhong-Shuai, Yang Shubin, Sun Yi, Parvez Khaled, Feng Xinliang, Müllen Klaus, 3D nitrogen-doped graphene aerogel-supported Fe3O4 nanoparticles as efficient electrocatalysts for the oxygen reduction reaction, Journal of the American Chemical Society, 134 (2012) 9082-9085 https://doi.org/10.1021/ja3030565.[17] Nguyen Son Truong, Nguyen Hoa Tien, Rinaldi Ali, Nguyen Nam Van, Fan Zeng, Duong Hai Minh, Morphology control and thermal stability of binderless-graphene aerogels from graphite for energy storage applications, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 414 (2012) 352-358 https://doi.org/ 10.1016/j.colsurfa.2012.08.048.[18] Deng Yang, Englehardt James D, Treatment of landfill leachate by the Fenton process, Water research, 40 (2006) 3683-3694 https://doi.org/ 10.1016/j.watres.2006.08.009.

Object segmentation is an important task which is widely employed in many computer vision applications such as object detection, tracking, recognition, and retrieval. It can be seen as a two-phase process: object detection and segmentation. Object segmentation becomes more challenging in case there is no prior knowledge about the object in the scene. In such conditions, visual attention analysis via saliency mapping may offer a mean to predict the object location by using visual contrast, local or global, to identify regions that draw strong attention in the image. However, in such situations as clutter background, highly varied object surface, or shadow, regular and salient object segmentation approaches based on a single image feature such as color or brightness have shown to be insufficient for the task. This work proposes a new salient object segmentation method which uses a depth map obtained from the input image for enhancing the accuracy of saliency mapping. A deep learning-based method is employed for depth map estimation. Our experiments showed that the proposed method outperforms other state-of-the-art object segmentation algorithms in terms of recall and precision. KeywordsSaliency map, Depth map, deep learning, object segmentation References[1] Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on pattern analysis and machine intelligence 20(11) (1998) 1254-1259.[2] Goferman, L. Zelnik-Manor, A. Tal, Context-aware saliency detection, IEEE transactions on pattern analysis and machine intelligence 34(10) (2012) 1915-1926.[3] Kanan, M.H. Tong, L. Zhang, G.W. Cottrell, Sun: Top-down saliency using natural statistics, Visual cognition 17(6-7) (2009) 979-1003.[4] Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, H.-Y. Shum, Learning to detect a salient object, IEEE Transactions on Pattern analysis and machine intelligence 33(2) (2011) 353-367.[5] Perazzi, P. Krähenbühl, Y. Pritch, A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 733-740.[6] M. Cheng, N.J. Mitra, X. Huang, P.H. Torr, S.M. Hu, Global contrast based salient region detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3) (2015) 569-582.[7] Borji, L. Itti, State-of-the-art in visual attention modeling, IEEE transactions on pattern analysis and machine intelligence 35(1) (2013) 185-207.[8] Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, arXiv preprint arXiv:1312.6034.[9] Li, Y. Yu, Visual saliency based on multiscale deep features, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 5455-5463.[10] Liu, J. Han, Dhsnet: Deep hierarchical saliency network for salient object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 678-686.[11] Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned saliency detection model, CVPR: Proc IEEE, 2009, pp. 1597-604.Fu, J. Cheng, Z. Li, H. Lu, Saliency cuts: An automatic approach to object segmentation, in: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, IEEE, 2008, pp. 1-4Borenstein, J. Malik, Shape guided object segmentation, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1, IEEE, 2006, pp. 969-976.Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, S. Li, Automatic salient object segmentation based on context and shape prior., in: BMVC. 6 (2011) 9.Ciptadi, T. Hermans, J.M. Rehg, An in depth view of saliency, Georgia Institute of Technology, 2013.Desingh, K.M. Krishna, D. Rajan, C. Jawahar, Depth really matters: Improving visual salient region detection with depth., in: BMVC, 2013.Li, J. Ye, Y. Ji, H. Ling, J. Yu, Saliency detection on light field, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2806-2813.Koch, S. Ullman, Shifts in selective visual attention: towards the underlying neural circuitry, in: Matters of intelligence, Springer, 1987, pp. 115-141.Laina, C. Rupprecht, V. Belagiannis, F. Tombari, N. Navab, Deeper depth prediction with fully convolutional residual networks, in: 3D Vision (3DV), 2016 Fourth International Conference on, IEEE, 2016, pp. 239-248.Bruce, J. Tsotsos, Saliency based on information maximization, in: Advances in neural information processing systems, 2006, pp. 155-162.Ren, X. Gong, L. Yu, W. Zhou, M. Ying Yang, Exploiting global priors for rgb-d saliency detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 25-32.Fang, J. Wang, M. Narwaria, P. Le Callet, W. Lin, Saliency detection for stereoscopic images., IEEE Trans. Image Processing 23(6) (2014) 2625-2636.Hou, L. Zhang, Saliency detection: A spectral residual approach, in: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, IEEE, 2007, pp. 1-8.Guo, Q. Ma, L. Zhang, Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform, in: Computer vision and pattern recognition, 2008. cvpr 2008. ieee conference on, IEEE, 2008, pp. 1-8.Fang, W. Lin, B.S. Lee, C.T. Lau, Z. Chen, C.W. Lin, Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum, IEEE Transactions on Multimedia 14(1) (2012) 187-198.Lang, T.V. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, S. Yan, Depth matters: Influence of depth cues on visual saliency, in: Computer vision-ECCV 2012, Springer, 2012, pp. 101-115.Zhang, G. Jiang, M. Yu, K. Chen, Stereoscopic visual attention model for 3d video, in: International Conference on Multimedia Modeling, Springer, 2010, pp. 314-324.Wang, M.P. Da Silva, P. Le Callet, V. Ricordel, Computational model of stereoscopic 3d visual saliency, IEEE Transactions on Image Processing 22(6) (2013) 2151-2165.Peng, B. Li, W. Xiong, W. Hu, R. Ji, Rgbd salient object detection: A benchmark and algorithms, in: European Conference on Computer Vision (ECCV), 2014, pp. 92-109.Wu, L. Duan, L. Kong, Rgb-d salient object detection via feature fusion and multi-scale enhancement, in: CCF Chinese Conference on Computer Vision, Springer, 2015, pp. 359-368.Xue, Y. Gu, Y. Li, J. Yang, Rgb-d saliency detection via mutual guided manifold ranking, in: Image Processing (ICIP), 2015 IEEE International Conference on, IEEE, 2015, pp. 666-670.Katz, A. Adler, Depth camera based on structured light and stereo vision, uS Patent App. 12/877,595 (Mar. 8 2012).Chatterjee, G. Molina, D. Lelescu, Systems and methods for determining depth from multiple views of a scene that include aliasing using hypothesized fusion, uS Patent App. 13/623,091 (Mar. 21 2013).Matthies, T. Kanade, R. Szeliski, Kalman filter-based algorithms for estimating depth from image sequences, International Journal of Computer Vision 3(3) (1989) 209-238.Y. Schechner, N. Kiryati, Depth from defocus vs. stereo: How different really are they?, International Journal of Computer Vision 39(2) (2000) 141-162.Delage, H. Lee, A.Y. Ng, A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 2, IEEE, 2006, pp. 2418-2428.Saxena, M. Sun, A.Y. Ng, Make3d: Learning 3d scene structure from a single still image, IEEE transactions on pattern analysis and machine intelligence 31(5) (2009) 824-840.Hedau, D. Hoiem, D. Forsyth, Recovering the spatial layout of cluttered rooms, in: Computer vision, 2009 IEEE 12th international conference on, IEEE, 2009, pp. 1849-1856.Liu, S. Gould, D. Koller, Single image depth estimation from predicted semantic labels, in: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE, 2010, pp. 1253-1260.Ladicky, J. Shi, M. Pollefeys, Pulling things out of perspective, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 89-96.K. Nathan Silberman, Derek Hoiem, R. Fergus, Indoor segmentation and support inference from rgbd images, in: ECCV, 2012.Liu, J. Yuen, A. Torralba, Sift flow: Dense correspondence across scenes and its applications, IEEE transactions on pattern analysis and machine intelligence 33(5) (2011) 978-994.Konrad, M. Wang, P. Ishwar, 2d-to-3d image conversion by learning depth from examples, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE, 2012, pp. 16-22.Liu, C. Shen, G. Lin, Deep convolutional neural fields for depth estimation from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5162-5170.Wang, X. Shen, Z. Lin, S. Cohen, B. Price, A.L. Yuille, Towards unified depth and semantic prediction from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2800-2809.Geiger, P. Lenz, C. Stiller, R. Urtasun, Vision meets robotics: The kitti dataset, International Journal of Robotics Research (IJRR).Achanta, S. Süsstrunk, Saliency detection using maximum symmetric surround, in: Image processing (ICIP), 2010 17th IEEE international conference on, IEEE, 2010, pp. 2653-2656.E. Rahtu, J. Kannala, M. Salo, J. Heikkilä, Segmenting salient objects from images and videos, in: Computer Vision-ECCV 2010, Springer, 2010, pp. 366-37.