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8,017 results on '"Deng S"'

201. LncRNA HOTAIR Promotes Cancer Stem-Like Cells Properties by Sponging miR-34a to Activate the JAK2/STAT3 Pathway in Pancreatic Ductal Adenocarcinoma

Author

Deng S, Wang J, Zhang L, Li J, and Jin Y

Subjects
mir-34a, cscs-like properties, pancreatic ductal adenocarcinoma, lncrna hotair, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282, jak2/stat3 pathway
Abstract

Shikang Deng,1,2 Junfeng Wang,2 Li Zhang,2 Jiao Li,2 Yan Jin2 1Medical School, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People’s Republic of China; 2Department of Hepatobiliary and Pancreatic Surgery, The First People’s Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of ChinaCorrespondence: Yan JinDepartment of Hepatobiliary and Pancreatic Surgery, The First People’s Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Xishan, Kunming, Yunnan, 650032, People’s Republic of ChinaEmail sciddg@sina.comIntroduction: Pancreatic Ductal Adenocarcinoma (PDAC) stem cells (CSCs) play a vital role in the occurrence, development and recurrence of PDAC. Previous studies have shown that long non-coding RNAs (lncRNA) are closely associated with occurrence and development of malignant tumors. Among them, a LncRNA called homeobox transcription antisense RNA (HOTAIR) plays a key role in cancer progression in a variety of malignant tumors, including PDAC. Numerous studies have associated HOTAIR with poor prognosis of malignant tumor treatment, owing to its role in regulating downstream microRNAs (miRNAs). However, its underlying mechanism of action on CSCs-like properties of PDAC remain unclear.Methods: We enriched CSCs of PDAC with a serum-free medium (SFM), and analyzed the expression levels of HOTAIR and miR-34a after enrichment. In addition, we evaluated the regulatory effects of HOTAIR and miR-34a on CSCs-like properties, invasion and migration of PDAC. Finally, we elucidated the role of HOTAIR in pancreatic tumor xenotransplantation.Results: HOTAIR was upregulated in CSCs following PDAC enrichment of PDAC. Conversely, miR-34a was downregulated and appeared to be a direct target of HOTAIR. Moreover, knocking down HOTAIR or overexpressing miR-34a significantly inhibited CSCs-like properties, invasion and migration of PDAC cells. Furthermore, HOTAIR activated the JAK2/STAT3 pathway through miR-34a, thereby promoting CSCs-like properties, invasion and migration of PDAC cells. In vivo experiments indicated that knocking down HOTAIR could inhibit the tumorigenicity of CFPAC-1 cells.Conclusion: This is the first report of HOTAIR-mediated activation of the JAK2/STAT3 pathway via miR-34a inhibition. This activation promotes CSCs-like properties, invasion and migration of PDAC.Keywords: pancreatic ductal adenocarcinoma, lncRNA HOTAIR, miR-34a, CSCs-like properties, JAK2/STAT3 pathway

Published
2021

204. Analysis of Institutional Characteristics and Implementation of Guangdong Province Laboratory Animals Ordinance in the Past Decade

Author

DENG Shaochang, LIN Danrong, LIANG Chujun, LEI Weiqiao, YANG Jinchun, and ZHAO Weibo

Subjects
guangdong province, laboratory animals, legal construction, implementation, Medicine
Abstract

The Guangdong Province Laboratory Animals Ordinance (the GPLAO), officially enacted and implemented in October 2010, marked a new phase of legal management in the administration of laboratory animals in Guangdong Province. The GPLAO clearly authorizes the laboratory animal monitoring institutes in Guangdong Province to conduct laboratory animal quality monitoring and assume corresponding legal responsibilities. It also specifies the standards for the production and use of laboratory animals, while bringing attention to laboratory animal welfare and ethics. Through the formulation of supporting documents, the active promotion of the standardization and regulation of administrative law enforcement, and the implementation of measures to delegate power, streamline administration, and optimize government services, significant improvements have been made in the standardized management of laboratory animals in Guangdong Province and in supporting the innovative development of biomedicine. Moreover, notable achievements have been made in areas such as the construction of experimental animal technology platforms, standardization work, the preservation and development of unique resources, and talent pool development. However, with the advancements in science and technology, social economy, and legislative work, some aspects in the GPLAO have been identified for improvement. These include the need for clearer provisions on laboratory animal welfare, more specific guidelines for the management of genetically engineered animals, improved regulations on the safety and welfare of practitioners, further refined implementation of the "one-vote veto system", continued promotion of laboratory animal standardization, and the exploration of effective mechanisms for resource sharing. This paper reviews and summarizes the institutional characteristics, post-legislation implementation, and effectiveness of the GPLAO, as well as the issues and countermeasures identified during the implementation. It concludes the laboratory animals management practices in Guangdong Province during the past 10 years, aiming to provide a reference for laboratory animal legislation.

Published
2024
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205. Calculating effective irrigation water use efficiency of Zhongjiang County using head-tail and whole-process methods

Author

XU Kai, YANG Kaijing, DENG Shunjie, LEI Bo, XU Lirong, GOU Huanhuan, and LI Yuanhao

Subjects
water efficiency of irrigation, dynamic water discharge method, kostiakov empirical formula, head-tail method, whole-process method, Agriculture (General), S1-972, Irrigation engineering. Reclamation of wasteland. Drainage, TC801-978
Abstract

【Objective】 Irrigation water use efficiency is an important parameter in irrigation design. It depends on various factors. In this paper, we compared irrigation water use efficiency calculated using the whole-process method and the head-tail method, from which we further studied the factors that influence water loss in irrigation districts. 【Method】 The study area was Zhongjiang County in Sichuan province. We divided the county into four regions (represented by TW, TH, ON and FO), each region having its typical canal systems and field conditions. The whole-process method calculated the irrigation water use efficiency in each region using a combination of hydrodynamic method, empirical formula and irrigation experiments. The head-tail method calculated the total irrigation amount in each region first by measuring the water flowing into and out of the region at the heads and tails, respectively, as well as other water sources flowing into and out of the region; these, along with the irrigation water consumed by crops, were used to calculate the irrigation water use efficiency. 【Result】 The canal water use efficiency in the largest TW regions was 0.596 and the smallest TH regions was 0.564, with a weight-average of 0.580. The water use coefficient in ON and TW regions in the north of the county was 0.90, thanks to their good hydraulic engineering projects. Affected by their topography, the water use coefficient in the TH and FO regions was 0.85. When the required irrigation guarantee was 75%, the irrigation water use efficiency calculated by the whole-process method was 0.503, compared to 0.492 calculated by the head-tail method. 【Conclusion】 The irrigation water use efficiency calculated by the two methods is comparable and their results are reasonable. The proposed method and the results can help to improve irrigation water use efficiency in regions where the irrigation water use efficiency is currently lower than the national average.

Published
2024
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206. Preparation and characterization of oxidized corn starch/gelatin composite capsules

Author

TAN Xin, LIANG Chunyan, DENG Songlin, CHEN Qijie, and LUO Yongqing

Subjects
oxidized corn starch, gelatin, capsules, preparation, characterization, Food processing and manufacture, TP368-456
Abstract

Objective: Modified starch was used to mix with gelatin to prepare the capsule shell, and the cost of preparing the capsule shell was reduced. Methods: Oxidized corn starch prepared by using 15% hydrogen peroxide was used as raw material to mix with part of gelatin. The oxidized corn starch/gelatin composite film and capsules were prepared by casting method and impregnation method, respectively, and were characterized by Fourier transform infrared spectroscopy （FT-IR）, X-ray diffraction （XRD） and Scanning electron microscopy （SEM）. Results: The results showed that the ratio of oxidized corn starch and gelatin was 5∶5, the drying weight loss of oxidized corn starch/gelatin composite capsules was 13.0%～15.0%, the disintegration time in distilled water and simulated artificial gastric juice was less than 10min, and the crunchability was not more than 5 granules. Conclusion: The properties of the composite capsule prepared by blending oxidized corn starch with gelatin met the requirements of Chinese Pharmacopoeia （2020）.

Published
2024
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207. Cumulative exposure to AHA Life's Essential 8 is associated with nonalcoholic fatty liver disease: a large cohort study

Author

Wang Yaqin, Deng Shuwen, Yuan Ting, Zhu Xiaoling, Deng Yuling, Liu Lei, and Wang Changfa

Subjects
AHA Life's essential 8, Nonalcoholic Fatty Liver Disease, Development, Regression, Cohort, Nutrition. Foods and food supply, TX341-641, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Background and Aim We aimed to explore the associations of baseline and cumulative cardiovascular health with nonalcoholic fatty liver disease (NAFLD) development and regression using the new Life’s Essential 8 score. Methods From a health screening database, participants who underwent at least 4 health examinations between 2012 and 2022 were recruited and categorized into two cohorts: (a) the NAFLD development cohort with no history of NAFLD prior to Exam 4 and (b) the NAFLD regression cohort with diagnosed NAFLD prior to Exam 4. The LE8 score was calculated from each component. The outcomes were defined as newly incident NAFLD or regression of existing NAFLD from Exam 4 to the end of follow-up. Results In the NAFLD development cohort, of 21,844 participants, 3,510 experienced incident NAFLD over a median follow-up of 2.3 years. Compared with the lowest quartile of cumulative LE8, individuals in the highest quartile conferred statistically significant 76% lower odds (hazard ratio [HR] 0.24, 95% confidence interval [CI], 0.21–0.28) of NAFLD incidence, and corresponding values for baseline LE8 were 42% (HR 0.58, 95% CI 0.53–0.65). In the NAFLD regression cohort, of 6,566 participants, 469 experienced NAFLD regression over a median follow-up of 2.4 years. Subjects with the highest quartile of cumulative LE8 had 2.03-fold (95% CI, 1.51–2.74) higher odds of NAFLD regression, and corresponding values for baseline LE8 were 1.61-fold (95% CI, 1.24–2.10). Conclusion Cumulative ideal cardiovascular health exposure is associated with reduced NAFLD development and increased NAFLD regression. Improving and preserving health behaviors and factors should be emphasized as an important part of NAFLD prevention and intervention strategies. Graphical Abstract

Published
2024
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208. InstaCropNet: An efficient Unet-Based architecture for precise crop row detection in agricultural applications

Author

Zhiming Guo, Yuhang Geng, Chuan Wang, Yi Xue, Deng Sun, Zhaoxia Lou, Tianbao Chen, Tianyu Geng, and Longzhe Quan

Subjects
Visual navigation, Deep learning, Maize crop row detection, Early corn crops, Row clustering, Crop row segmentation, Agriculture
Abstract

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize fields. Among various navigation techniques, visual navigation using widely available RGB images is a cost-effective solution. However, current mainstream methods for maize crop row detection often rely on highly specialized, manually devised heuristic rules, limiting the scalability of these methods. To simplify the solution and enhance its universality, we propose an innovative crop row annotation strategy. This strategy, by simulating the strip-like structure of the crop row's central area, effectively avoids interference from lateral growth of crop leaves. Based on this, we developed a deep learning network with a dual-branch architecture, InstaCropNet, which achieves end-to-end segmentation of crop row instances. Subsequently, through the row anchor segmentation technique, we accurately locate the positions of different crop row instances and perform line fitting. Experimental results demonstrate that our method has an average angular deviation of no more than 2°, and the accuracy of crop row detection reaches 96.5%.

Published
2024
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211. 03. Is ethnicity an appropriate measure of health care marginalization?: A systematic review and meta-analysis of the outcomes of diabetic foot ulceration in the Aboriginal population

Author

Johnson, G., Vergis, A., Unger, B., Park, J., Gillman, L., Hickey, K., Pace, D., Azin, A., Guidolin, K., Lam-Tin-Cheung, K., Chadi, S., Quereshy, F., Catton, J., Rubin, B., Bell, J., Marangos, J., Heesters, A., Stuart-McEwan, T., Shariff, F., Wright, F., Ahmed, N., Nadler, A., Hallet, J., Gentles, J., Chen, L., Hwang, H., Parapini, M., Hirpara, D., Sidhu, R., Scott, T., Karimuddin, A., Guo, R., Nguyen, A., Osborn, J., Wiseman, S., Nabata, K., Ertel, E., Lenet, T., Baker, L., Park, L., Vered, M., Zahrai, A., Shorr, R., Davis, A., McIsaac, D., Tinmouth, A., Fergusson, D., Martel, G., Rummel, S., Stefic-Cubic, M., Stewart, M., Melck, A., McKechnie, T., Anpalagan, T., Ichhpuniani, S., Lee, Y., Ramji, K., Eskicioglu, C., Zhu, A., Deng, S., Greene, B., Tsang, M., Palter, V., Jayaraman, S., Mann, A., Tittley, J., Cadeddu, M., Nguyen, M., Madani, A., Pasternak, J., Hong, D., Qu, L., Istl, A., Tang, E., Gray, D., Zuckerman, J., Coburn, N., Callum, J., McLeod, R., Pearsall, E., Lin, Y., Turgeon, A., Mahar, A., Kammili, A., Kriviraltcheva-Kaneva, P., Lee, L., Cools-Lartigue, J., Ferri, L., Mueller, C., Haas, B., Tillman, B., Guttman, M., Chesney, T., Zuk, V., Hsu, A., Chan, W., Vasdev, R., D’Souza, K., Huynh, C., Ling, L. Cadili J., Warburton, R., Hameed, M., Glass, L., Williamson, H., Murphy, P., Leslie, K., Hawel, J., Kerr, L., Zablotny, S., Roldan, H., He, W., Jiang, X., Zheng, B., Fiore, J., Feldman, L., Fried, G., Valanci, S., Balvardi, S., Cipolla, J., Kaneva, P., Demyttenaere, S., Boutros, M., Alhashemi, M., Miles, A., Purich, K., Verhoeff, K., Shapiro, J., Bigam, D., Kung, J., Fecso, A., Mosko, J., Skubleny, D., Hamilton, P., Ghosh, S., Widder, S., Schiller, D., Do, U., El Kefraoui, C., Pook, M., Barone, N., Montgomery, H., Nguyen-Powanda, P., Rajabiyazdi, F., Elhaj, H., Lapointe-Gagner, M., Olleik, G., Antoun, A., Safa, N., Di Lena, E., Meterissian, S., Meguerditchian, A., Lee, F., Baldini, G., Serrano Aybar, P.E., Parpia, S., Ruo, L., Tywonek, K., Lee, S., O’Neill, C., Faisal, N., Alfayyadh, A., Gundayao, M., Meyers, B.M., Habashi, R., Kruse, C., Levin, M., Aldrich, K., Grantcharov, T., Langerman, A., Forbes, H., Anantha, R., Fawcett, V., Hetherington, A., Pravong, V., Gervais, M., Rakovich, G., Selvam, R., Hu, R., Musselman, R., Raiche, I., Moloo, H., Liu, R., Elnahas, A., Alkhamesi, N., Alnumay, A., Schlachta, C., Walser, E., Zhang, C., Cristancho, S., Ott, M., Lee, A., Niu, B., Balaa, F., Gawad, N., Ren, K., Qiu, Y., Hamann, K., How, N., Leveille, C., Davidson, A., Eqbal, A., Sardiwalla, Y., Korostensky, M., Lee, E., Yang, I., Muaddi, H., Stukel, T., de Mestral, C., Nathens, A., Karanicolas, P., Frigault, J., Lemieux, S., Breton, D., Bouchard, P., Bouchard, A., Grégoire, R., Letarte, F., Bouchard, G., Drolet, S., Avoine, S., Gagné, J., Thibault, C., Jutras Bouthillette, N., Gosselin, M., Rosenzveig, A., Stuleanu, T., Jarrar, A., Kolozsvari, N., Skelhorne-Gross, G., Nenshi, R., Jerath, A., Gomez, D., Singh, K., Amir, T., Liu, E., Farquharson, S., Mao, R., Lan, L., Yan, J., Allard-Coutu, A., Mierzwa, A., Tin, R., Brisebois, R., Bradley, N., Wigen, R., Hartford, L., Van Koughnett, J., Vogt, K., Hilsden, R., Parry, N., Allen, L., Raskin, R., Jones, J., Neumann, K., Dwyer, C., Strickland, M., O’Dochartaigh, D., Lobay, K., Kabaroff, A., Chang, E., Sun, W., Beck, J., Davidson, J., Jones, S., Van Hooren, T., Schmitz, E., El Hafid, M., Dang, J., Mocanu, V., Lutzak, G., Sultanian, R., Wong, C., Karmali, S., Petrera, M., Pickell, M., Auer, R., Patro, N., Li, B., Wilson, H., Jogiat, U., Switzer, N., Li, C., Al Hinai, A., Cieply, A., Hawes, H., Joos, E., Saleh, A., Engels, P., Drung, J., Pang, G., Kwong, M., Ellsmere, J., Chang, D., Hutter, M., Spence, R., Abou Khalil, M., Vasilevsky, C., Morin, N., Longtin, Y., Liberman, S., Montpetit, P., Poirier, M., Mukherjee, K., Sebajang, H., Younan, R., Schwenter, F., De Broux, E., Larsen, K., Beckett, A., Nantais, J., Kay, J., Lohre, R., Ayeni, O., Goel, D., de SA, D., He, R., Hylton, D., Bedard, E., Johnson, S., Laing, B., Valji, A., Hanna, W., Turner, S., Akhtar-Danesh, G., Akhtar-Danesh, N., Shargall, Y., Gupta, V., Kidane, B., Limbachia, J., Sullivan, K., Farrokhyar, F., Leontiadis, G., Patel, Y., Churchill, I., Xie, F., Seely, A., Spicer, J., Yasufuku, K., Beauchamp, M., Wald, J., Mbuagbaw, L., Agzarian, J., Finley, C., Fahim, C., Abbas, M., Olaiya, O., Begum, H., Ednie, A., Palma, D., Warner, A., Malthaner, R., Fortin, D., Qiabi, M., Nayak, R., Nguyen, T., Louie, A., Rodrigues, G., Yaremko, B., Laba, J., Inculet, R., Alaichi, J., Mador, B., Lai, H., White, J., Kim, M., Darling, G., Rousseau, M., Samarasinghe, Y., Lee, M., Thiru, L., Levine, O., Juergens, R., Brogly, S., Li, W., Lougheed, D., Petsikas, D., Mistry, N., Gatti, A., Abdul, S., Anestee, C., Gilbert, S., Sundaresan, S., Villeneuve, P., Maziak, D., Razzak, R., Ashrafi, A., Tregobov, N., Hassanzadeh, N., Stone, S., Panjwani, A., Bong, T., Bond, R., Hafizi, A., De Meo, M., Rayes, R., Milette, S., Vagai, M., Usatii, M., Chandrasekaran, A., Giannias, B., Bourdeau, F., Sangwan, V., Bertos, N., Moraes, C., Huang, S., Quail, D., Walsh, L., Camilleri-Broet, S., Fiset, P., Bilgic, E., Quaiattini, A., Maurice-Ventouris, M., Najmeh, S., Esther, L., Lu, J., Malhan, R., Brophy, S., Brennan, K., French, D., Resende, V., Momtazi, M., Solaja, O., Sisson, D., Donahoe, L., Bedard, P., Hansen, A., De Perrot, M., Alghamedi, A., Simone, A., Huang, J., Murthy, S., Lin, J., Li, H., Crowther, M., Linkins, L., Lau, E., Schneider, L., Douketis, J., Greenberg, B., Allen-Avodabo, C., Davis, L., Zhao, H., Sirois, C., Mulder, D., Al Rawahi, A., Aftab Abdul, S., Nguyen, D., Anstee, C., Delic, E., Sasewich, H., Islam, T., Low, D., Humer, M., Le Nguyen, D., Kay, M., Shayegan, B., Adili, A., Kaafarani, M., Chouiali, F., Muthukrishnan, N., Maleki, F., Ovens, K., Gold, M., Sorin, M., Falutz, R., Forghani, R., Hunka, N., Kennedy, R., Bigsby, R., Bharadwaj, S., Gowing, S., Pearce, K., Jones, D., Kumar, S., Gingrich, M., Ahmadzai, Z., Thavorn, K., Namavarian, A., Mohammed, A., Uddin, S., Behzadi, A., Brar, A., Peters, E., Buduhan, G., Tan, L., Srinathan, S., Levy, J., Ringash, J., Sutradhar, R., Robinson, M., Bednarek, L., Wang, H., MacDonald, D., Graham, K., Enns, S., Tan, A., Bruinooge, A., Poole, E., Pascoe, C., Karakach, T., Halayko, A., Fang, B., Birch, D., Singh, H., Hershorn, O., Hochman, D., Helewa, R., Robertson, R., Cahill, C., Lipson, M., Afzal, A., Maclean, A., Roen, S., Buie, W., Chu, M., Amin, N., Jaffer, H., Rebello, R., Doumouras, A., Hajjar, R., Oliero, M., Cuisiniere, T., Fragoso, G., Calvé, A., Djediai, S., Annabi, B., Richard, C., Santos, M., Zhou, Y., Dodd), S., Ring, B., Yuan, Y., Garfinkle, R., Dell’Aniello, S., Bhatnagar, S., Ghitulescu, G., Faria, J., Brassard, P., Salama, E., Amar-Zifkin, A., Talwar, G., Daniel, R., AlSulaim, H., Alqahtani, M., Al-Masrouri, S., Chen, A., Patel, A., Brissette, V., Al Busaidi, N., Moon, J., Demian, M., MacRae, H., Alam, F., Holland, J., Cwintal, M., Rigas, G., Pang, A., Marinescu, D., Brown, C., Raval, M., Phang, P., Ghuman, A., Li, M., Muncner, S., Mihajlovic, I., Dykstra, M., Snelgrove, R., Monton, O., Smith, A., AlAamer, O., AlSelaim, N., AlMalki, M., Al-osail, A., Ruxton, R., Manuel, P., Mohamed, F., Motamedi, M.K., Serahati, S., Rajendran, L., Phang, T., Caminsky, N., Alavi, K., Paquette, I., MacLean, T., Wexner, S., Steele, S., Patel, S., Bordeianou, L., Sylla, P., Bayat, Z., Kennedy, E., Victor, C., Govindarajan, A., Liang, J., Roy, H., Baig, Z., Karimmudin, A., Gill, D., Ginther, N., Alrashid, F., Wiseman, V., Zhang, L., MacDonald, P., Merchant, S.M., Wattie Barnett, K., Caycedo-Marulanda, A., Patel, S.V., Harra, Z., Hegagi, M., Alghaithi, N., Papillon, E., Kasteel, N., Kaur, G., Bindra, S., Malhotra, A., Graham, C., MacLean, A., Beck, P., Jijon, H., Ferraz, J., Szwimer, R., Merchant, S., Kong, W., Gyawali, B., Hanna, T., Chung, W., Nanji, S., Booth, C., Li, V., Awan, A., Serrano, P., Jacobson, M., Chanco, M., Wen, V., Singh, N., Peiris, L., Pasieka, J., Ghatage, P., Buie, D., Bouchard-Fortier, A., Mack, L., Marini, W., Zheng, W., Swallow, C., Reedijk, M., DiPasquale, A., Prus-Czrnecka, Z., Delmar, L., Gagnon, N., Villiard, R., Martel, É., Cadrin-Chênevert, A., Ledoux, É., Racicot, C., Mysuria), S., Bazzarelli, A., Pao, J., Zhang, M., McKevitt, E., Kuusk, U., Van Laeken, N., Bovill, E., Isaac, K., Dingee, C., Hunter-Smith, A., Cuthbert, C., Fergus, K., Barbera, L., Efegoma, Y., Howell, D., Isherwood, S., Levasseur, N., Scheer, A., Simmons, C., Srikantham, A., Temple-Orberle, C., Xu, Y., Metcalfe, K., Quan, M., Alqaydi, A., la, J., Digby, G., Brind’Amour, A., Sidéris, L., Dubé, P., De Guerke, L., Fortin, S., Auclair, M., Trilling, B., Tremblay, J., Di Lena, É., Hopkins), B., Wong, S., Hopkins, B., Dumitra, S., Mysuria, S., Kapur, H., Bazarelli, A., Cadili, L., DeGirolamo, K., Ng, D., Ali, A., Eymae, D., Lee, K., Brar, S., Conner, J., Magalhaes, M., Allen, K., Baliski, C., Cyr, D., Sari, A., Messenger, D., Driman, D., Assarzadegan, N., Juda, A., Brar, M., Kirsch, R., Lamontagne, A., Gamache, Y., Mardinger, C., Lee, C., Duckworth, R., Brindle, M., Fraulin, F., Austen, L., Kortbeek, J., Hyndman, M., Jamjoum, G., Langer, S., Yuan Xu, Y., Kong, S., Lim, D., Retrouvey, H., Kerrebijn, I., Butler, K., O’Neill, A., Cil, T., Zhong, T., Hofer, S., McCready, D., Look Hong, N., Skipworth, J., Mah, A., Desai, S., Chung, S., Scudamore, C., Segedi, M., Vasilyeva, E., Li, J., Kim, P., Deprato, A., Dajani, K., Gilbert, R., Smoot, R., Tzeng, C., Rocha, F., Yohanathan, L., Cleary, S., Bertens, K., Reyna-Sepulveda, F., Badrudin, D., Gala-Lopez, B., Hanna, N., Wei, X., Kaliwal, Y., Wei, A., Henault, D., Barrette, B., Pelletier, S., Thebault, P., Beaudry-Simoneau, E., Rong, Z., Plasse, M., Dagenais, A. Roy M., Létourneau, R., Lapointe, R., Vandenbroucke-Menu, F., Nguyen, B., Soucy, G., Turcotte, S., Lemke, M., Waugh, E., Quan, D., Skaro, A., Lund, M., Glinka, J., Jada, G., Daza, J., Msallak, H., Zhang, B., Workneh, A., Faisal, S., Faisal, R., Fabbro, M., Gu, C., Claassen, M., Sapisochin, G., Breadner, D., Welch, S., Webb, A., Lester, E., Shapiro, A., Eurich, D., Essaji, Y., Shrader, H., Nayyar, A., Suraju, M., Williams-Perez, S., Ear, P., Chan, C., Smith, V., Rivers-Bowerman, M., Costa, A., Stueck, A., Campbell, N., Allen, S., Mir, Z., Golding, H., McKeown, S., Flemming, J., Groome, P., Djerboua, M., Elbekri, S., Girard, E., Morency-Potvin, P., Dagenais, M., Roy, A., Letourneau, R., Simoneau, E., Zuker, N., Oakley, M., Chartrand, G., Misheva, B., Bendavid, Y., Melland-Smith, M., Smith, L., Tan, J., Kahn, U., McLean, C., Fortin, M., Paré, X., Doyon, A., Keshavjee, S., Schwenger, K., Yadav, J., Fischer, S., Jackson, T., Allard, J., Okrainec, A., Anvari, S., Lovrics, O., Aditya, I., Khondker, A., Walsh, M., Hardy, K., Romanescu, R., Deaninck, F., Linton, J., Fowler-Woods, M., Fowler-Woods, A., Shingoose, G., Zmudzinski, M., Cloutier, Z., Archer, V., Shiroky, J., Abu Halimah, J., Boudreau, V., Marcil, G., Hardy-Henry, A., Lin, W., Hagen, J., and Connell, M.

Subjects
Abstracts, Canadian Association of General Surgeons, Canadian Hernia Society, Canada, Canadian Association of Thoracic Surgeons, Canadian Hepato-Pancreato-Biliary Association, Humans, Canadian Society of Surgical Oncology, Canadian Association of Bariatric Physicians and Surgeons, Canadian Society of Colon and Rectal Surgeons
Published
2022

212. Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Author

Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C

Abstract

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AG

Published
2022

214. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Author

Duncanson, L., Kellner, J.R., Armston, J., Dubayah, R., Minor, D.M., Hancock, S., Healey, S.P., Patterson, P.L., Saarela, S., Marselis, S., Silva, C.E., Bruening, J., Goetz, S.J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., Alonso, A., Andersen, H.-E., Aplin, P., Baker, T.R., Barbier, N., Bastin, J.F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Brehm Boucher, P., Boyd, D.S., Burslem, D.F.R.P., Calvo-Rodriguez, S., Chave, J., Chazdon, R.L., Clark, D.B., Clark, D.A., Cohen, W.B., Coomes, D.A., Corona, P., Cushman, K.C., Cutler, M.E.J., Dalling, J.W., Dalponte, M., Dash, J., de-Miguel, S., Deng, S., Jeffery, K.J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, Nikolai, Král, K., Krůček, M., Labrière, N., Lewis, S.L., Longo, M., Lucas, R.M., Main, R., Manzanera, J.A., Vásquez Martínez, R., Mathieu, R., Memiaghe, H., Meyer, V., Monteagudo Mendoza, A., Monerris, A., Montesano, P., Morsdorf, F., Næsset, E., Naidoo, L., Nilus, R., O’Brien, M., Orwig, D.A., Papathanassiou, K., Parker, G., Philipson, C., Phillips, O.L., Pisek, J., Poulsen, J.R., Pretzsch, H., Rüdiger, C., Saatchi, S., Sanchez-Azofeifa, A., Sanchez-Lopez, N., Scholes, R., Silva, C.A., Simard, M., Skidmore, A., Stereńczak, K., Tanase, M., Torresan, C., Valbuena, R., Verbeeck, H., Vrska, T., Wessels, K., White, J.C., White, L.J.T., Zahabu, E., Zgraggen, C., Duncanson, L., Kellner, J.R., Armston, J., Dubayah, R., Minor, D.M., Hancock, S., Healey, S.P., Patterson, P.L., Saarela, S., Marselis, S., Silva, C.E., Bruening, J., Goetz, S.J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., Alonso, A., Andersen, H.-E., Aplin, P., Baker, T.R., Barbier, N., Bastin, J.F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Brehm Boucher, P., Boyd, D.S., Burslem, D.F.R.P., Calvo-Rodriguez, S., Chave, J., Chazdon, R.L., Clark, D.B., Clark, D.A., Cohen, W.B., Coomes, D.A., Corona, P., Cushman, K.C., Cutler, M.E.J., Dalling, J.W., Dalponte, M., Dash, J., de-Miguel, S., Deng, S., Jeffery, K.J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, Nikolai, Král, K., Krůček, M., Labrière, N., Lewis, S.L., Longo, M., Lucas, R.M., Main, R., Manzanera, J.A., Vásquez Martínez, R., Mathieu, R., Memiaghe, H., Meyer, V., Monteagudo Mendoza, A., Monerris, A., Montesano, P., Morsdorf, F., Næsset, E., Naidoo, L., Nilus, R., O’Brien, M., Orwig, D.A., Papathanassiou, K., Parker, G., Philipson, C., Phillips, O.L., Pisek, J., Poulsen, J.R., Pretzsch, H., Rüdiger, C., Saatchi, S., Sanchez-Azofeifa, A., Sanchez-Lopez, N., Scholes, R., Silva, C.A., Simard, M., Skidmore, A., Stereńczak, K., Tanase, M., Torresan, C., Valbuena, R., Verbeeck, H., Vrska, T., Wessels, K., White, J.C., White, L.J.T., Zahabu, E., and Zgraggen, C.

Abstract

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AG

Published
2022

215. Development and validation of a noninvasive prediction model for identifying eosinophilic asthma.

Author

Li, M, Wen Ma, Z, Jun Deng, S, Oliver, BG, Wang, T, Ping Zhang, H, Wang, L, McDonald, VM, Wang, J, Liu, D, Gibson, PG, Ming Luo, F, Min Li, W, Jing Wan, H, Wang, G, Li, M, Wen Ma, Z, Jun Deng, S, Oliver, BG, Wang, T, Ping Zhang, H, Wang, L, McDonald, VM, Wang, J, Liu, D, Gibson, PG, Ming Luo, F, Min Li, W, Jing Wan, H, and Wang, G

Abstract

BACKGROUND: Identification of eosinophilic asthma (EA) using sputum analysis is important for disease monitoring and individualized treatment. But it is laborious and technically demanding. We aimed to develop and validate an effective model to predict EA with multidimensional assessment (MDA). METHODS: The asthma patients who underwent a successful sputum induction cytological analysis were consecutively recruited from March 2014 to January 2021. The variables assessed by MDA were screened by least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a nomogram and an online web calculator. Validation was performed internally by a bootstrap sampling method and externally in the validation cohort. Diagnostic accuracy of the model in different asthma subgroups were also investigated. RESULTS: In total of 304 patients in the training cohort and 95 patients in the validation cohort were enrolled. Five variables were identified in the EA prediction model: gender, nasal polyp, blood eosinophils, blood basophils and FeNO. The C-index of the model was 0.86 (95% CI: 0.81-0.90) in the training cohort and 0.84 (95% CI: 0.72-0.89) in the validation cohort. The calibration curve showed good agreement between the prediction and actual observation. The decision curve analysis (DCA) also demonstrated that the EA prediction model was clinically beneficial. An online publicly available web calculator was constructed (https://asthmaresearcherlimin.shinyapps.io/DynNomapp/). CONCLUSION: We developed and validated a multivariable model based on MDA to help the diagnosis of EA, which has good diagnostic performance and clinical practicability. This practical tool may be a useful alternative for predicting EA in the clinic.

Published
2022

216. Characterization of nitrous oxide and nitrite accumulation during iron (Fe(0))- and ferrous iron (Fe(II))-driven autotrophic denitrification: mechanisms, environmental impact factors and molecular microbial characterization

Author

Deng, S, Peng, S, Ngo, HH, Oh, SJA, Hu, Z, Yao, H, Li, D, Deng, S, Peng, S, Ngo, HH, Oh, SJA, Hu, Z, Yao, H, and Li, D

Abstract

The iron (Fe(0))-/ferrous iron (Fe(II))-driven autotrophic denitrification processes have been alternative methods for nitrogen removal from low organic carbon (OC) wastewater, but the accumulation of nitrous oxide (N2O) and nitrite (NO2−) along with these processes remains unclear. This research aimed to systematically characterize the N2O/NO2− accumulation in Fe(0)-/Fe(II)-ADN processes through investigating the mechanisms, impact factors, and molecular biological characteristics. Results showed that Fe(II)-ADN was effective in NO3− reduction but was less efficient in N2O reduction (k = 0.50 h−1) than Fe(0)-ADN (k = 1.82 h−1). NO2−/N2O accumulation in Fe(II)-ADN (28.6%/30.7%) was much higher than that in Fe(0)-ADN (12.6%/1.5%). Introducing hydrogenotrophic denitrification (H-ADN) into Fe(II)-ADN system significantly (p < 0.05) reduced NO2−/N2O accumulation. Fe(0)-ADN was proved a coupled process of Fe(II)- and H-ADN by in-situ generating Fe(II)/H2, and Fe(II)-ADN and H-ADN mainly contributed to NO3− and NO2−/N2O reduction, respectively. Optimum pH (7.5) and temperature (30–35 °C) were confirmed with controlled NO2–/N2O accumulation and effective denitrification. Dosing inorganic carbon (IC) and OC enhanced denitrification and reduced NO2–/N2O accumulation, where OC was more efficient with an optimum dosage of 0.25 mmol C/mmol N. 16S rRNA high-throughput sequencing and Pearson Correlation Coefficients verified that Thiobacillus was the main contributor to NO3− reduction, whereas Thauera and Acidovorax possessed high NO2−/N2O reduction capability. Real-time quantitative polymerase chain reaction and enzyme activity assay demonstrated that the nitrite reductase encoded by gene nirK and the nitrous oxide reductase encoded by gene nosZ were efficient in catalyzing the further reduction of NO2− and N2O, respectively. This study could provide an in-depth understanding of NO2−/N2O accumulation in Fe(II)-/Fe(0)-ADN processes and contribute to their application, optimizatio

Published
2022

217. Two-parameter dynamics of an autonomous mechanical governor system with time delay

Author

Deng, S, Ji, J, Wen, G, Xu, H, Deng, S, Ji, J, Wen, G, and Xu, H

Abstract

A deep understanding of the dynamical behavior in the parameter-state space plays a vital role in both the optimal design and motion control of mechanical systems. By combining the GPU parallel computing technique with two determinate indicators, namely the Lyapunov exponents and Poincaré section, this paper presents a detailed study on the two-parameter dynamics of a mechanical governor system with different time delays. By identifying different responses in the two-parameter plane, the effect of time delay on the complexity of the evolutionary process is fully revealed. The path-following calculation scheme and time domain collocation method are used to explore the detailed bifurcation mechanisms. An interesting phenomenon that the number of intersection points of some periodic responses on the specified Poincaré section differs from the actual period characteristics is found in classifying the dynamic behavior. For example, the commonly exhibited period-one orbit may have two or more intersection points on the Poincaré section rather than one point. The variations of the basins of attraction are also discussed in the plane of initial history conditions to demonstrate the multistability phenomena and chaotic transitions.

Published
2022

218. Impact factors and novel strategies for improving biohydrogen production in microbial electrolysis cells.

Author

Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Deng, S, An, D, Hoang, NB, Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Deng, S, An, D, and Hoang, NB

Abstract

Microbial electrolysis cell (MEC) system is an environmentally friendly method for clean biohydrogen production from a wide range of biowastes owing to low greenhouse gas emissions. This approach has relatively higher yields and lower energy costs for biohydrogen production compared to conventional biological technologies and direct water electrolysis, respectively. However, biohydrogen production efficiency and operating costs of MEC still need further optimization to realize its large-scale application.This paper provides a unique review of impact factors influencing biohydrogen production in MECs, such as microorganisms and electrodes. Novel strategies, including inhibition of methanogens, development of novel cathode catalyst, advanced reactor design and integrated systems, to enhance low-cost biohydrogen production, are discussed based on recent publications in terms of their opportunities, bottlenecks and future directions. In addition, the current challenges, and effective future perspectives towards the practical application of MECs are described in this review.

Published
2022

222. POS1045 MASS SPECTROMETRY-BASED PROTEOMICS FOR THE IDENTIFICATION OF CANDIDATE SERUM PROTEIN BIOMARKERS THAT MAY PREDICT TREATMENT RESPONSE IN PATIENTS WITH PSORIATIC ARTHRITIS

Author

Waddington, J., primary, Zhou, R., additional, Coleman, O., additional, Wundervald, B., additional, Parnell, A., additional, Mease, P. J., additional, Chandran, V., additional, Fallon, L., additional, Chapman, D., additional, Pollock, R., additional, Deng, S., additional, Fitzgerald, O., additional, and Pennington, S., additional

Published
2022
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229. A Novel lncRNA NONHSAT053785 Acts as an Independent Risk Factor for Intrahepatic Metastasis of Hepatocellular Carcinoma

Author

Li Y, Li G, Chen X, Huang H, Liao L, Yuan T, and Deng S

Subjects
risk factor, long noncoding rna, hepatocellular carcinoma, intrahepatic metastasis, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282
Abstract

Yuwei Li,1,* Guangyao Li,2,* Xia Chen,1 Hengliu Huang,1 Ling Liao,1 Tao Yuan,2 Shaoli Deng1 1Department of Laboratory Medicine, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, People’s Republic of China; 2Department of Hepatobiliary Surgery, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shaoli Deng Email dengshaoli@tmmu.edu.cnPurpose: Long noncoding RNAs (lncRNAs) in body fluids have been considered as promising novel biomarkers for tumor-related diseases. The present study aimed to investigate the expression level of lncRNA NONHSAT053785 in serum and its correlation with clinical characteristics of hepatocellular carcinoma (HCC) patients.Methods: The droplet digital PCR (ddPCR) was used to measure the serum levels of NONHSAT053785 in 112 HCC patients, 96 chronic hepatitis B (CHB) patients, and 99 healthy controls (HC). The correlation between NONHSAT053785 and clinical characteristics was analyzed by chi-square test and Spearman correlation test. The risk factors of intrahepatic metastasis (IM) were detected by univariate and multivariate analyses. Furthermore, the diagnostic value of NONHSAT053785 in HCC and its predictive ability in IM were evaluated by the receiver operating characteristic (ROC) curves.Results: The level of NONHSAT053785 was significantly increased in the serum of HCC patients and was higher in HCC patients with IM as compared to those without. Additionally, the expression level of NONHSAT053785 was significantly related to IM, Child–Pugh classification, and peripheral blood indicators such as liver metabolic enzymes and positively correlated to IM, Barcelona Clinic Liver Cancer (BCLC) staging, and some peripheral blood indicators. Furthermore, the serum NONHSAT053785 was indicated as an independent predictor for IM in the elderly, non-smoking, drinking, and tumor size ≥ 5 cm subjects. The area under the ROC curve (AUC) was 0.801 (P < 0.0001) for diagnosis of HCC and 0.678 (P =0.0015) for predicting IM.Conclusion: The increase in serum NONHSAT053785 levels was related to an increased risk of IM, and hence, may serve as a novel biomarker for the diagnosis of HCC and the prediction of IM.Keywords: long noncoding RNA, hepatocellular carcinoma, intrahepatic metastasis, risk factor

Published
2020

230. Alpha-1 Antitrypsin Induces Epithelial-to-Mesenchymal Transition, Endothelial-to-Mesenchymal Transition, and Drug Resistance in Lung Cancer Cells

Author

Wu D, Liu T, Deng S, Han R, Zhang T, Li J, and Xu Y

Subjects
alpha-1 antitrypsin, epithelial-to-mesenchymal transition, cisplatin resistance, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282, non-small cell lung cancer, endothelial-to-mesenchymal transition
Abstract

Dong-ming Wu,* Teng Liu,* Shi-hua Deng, Rong Han, Ting Zhang, Jing Li, Ying Xu Clinical Laboratory, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying Xu Tel +86 02883016723Email yingxu825@126.comPurpose: Alpha-1 antitrypsin (A1AT) is a secreted protein that plays an important role in various diseases. However, the role of A1AT in non-small cell lung cancer is obscure.Materials and Methods: A1AT expression in non-small cell lung cancer was analyzed using quantitative reverse transcription PCR, Western blotting (WB), immunohistochemistry (IHC), and ELISA.WB and IF were used to analyze markers of epithelial-to-mesenchymal transition (EMT), EndoMT, and cancer stem cell (CSC). Transwell and cell wound healing assays were used to analyze migration and invasion abilities. Colony formation and CCK-8 assays were used to analyze cell proliferation following cisplatin treatment.Results: A1AT expression was higher in lung cancer samples than in normal tissues and the increased expression was correlated with poor overall survival of patients. In vitro experiments showed that A1AT overexpressed by plasmid transfection significantly promoted migration, invasion, EMT, EndoMT, stemness, and colony formation in lung cancer cell lines, as opposed to A1AT downregulation by siRNA transfection, which significantly inhibited all these variables.Conclusion: A1AT is a novel therapeutic target and might be associated with tumor metastasis in lung carcinoma.Keywords: epithelial-to-mesenchymal transition, endothelial-to-mesenchymal transition, alpha-1 antitrypsin, non-small cell lung cancer, cisplatin resistance

Published
2020

231. Low-Dose Radiation Promotes Invasion and Migration of A549 Cells by Activating the CXCL1/NF-κB Signaling Pathway

Author

Li J, Wu D, Han R, Yu Y, Deng S, Liu T, Zhang T, and Xu Y

Subjects
cxcl1, emt, ionizing radiation, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282, non-small cell lung cancer
Abstract

Jing Li,* Dong-ming Wu,* Rong Han,* Ye Yu, Shi-hua Deng, Teng Liu, Ting Zhang, Ying Xu Clinical Laboratory, The First Affiliated Hospital, Collaborative Innovation Center of Sichuan for Elderly Care and Health of Chengdu Medical College, Chengdu, Sichuan 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying XuClinical Laboratory, The First Affiliated Hospital of Chengdu Medical College, 278 Baoguang Road, Chengdu, Sichuan 610500, People’s Republic of ChinaTel +86 2883016723Email yingxu825@126.comPurpose: Radiation has well-known and well-characterized direct toxic effects on cells and tissues. However, low-dose ionizing irradiation (LDIR) can also enhance the invasion and migration of tumor cells, and the mechanisms underlying these effects remain unclear. The present study aimed to investigate changes induced in the migration and invasion of A549 cells after LDIR and to explore the potential molecular mechanism.Materials and Methods: A549 cells were irradiated with X-rays at different doses (0, 2, 4, and 6 Gy) and cultured for 24 or 48 h. Apoptosis and proliferation were evaluated by lactate dehydrogenase release, CCK8, colony formation, and flow cytometry assays. Wound-healing and transwell assays were performed to detect migration and invasion ability. CXCL1 or p65 were knocked down using lentivirus-mediated siRNA in A549 cell lines. Knockdown efficiency of CXCL1 and p65 was assessed by RT-qPCR. Western blotting and immunofluorescence were used to determine the changes in protein levels.Results: In cells irradiated with a dose of 6 Gy, after 48 h, apoptosis was clearly induced while proliferation was inhibited. Irradiation with 4 Gy resulted in the upregulation of CXCL1 expression and activation of the NF-κB signaling pathway. Moreover, upon 4 Gy irradiation, migration, invasion, and epithelial–mesenchymal transition (EMT) were significantly enhanced in A549 cells. Importantly, CXCL1 or p65 knockdown inhibited radiation-induced migration, invasion, and EMT.Conclusion: Low-dose radiation upregulates CXCL1 expression and activates the NF-κB signaling to regulate EMT in A549 cells, thereby promoting invasion and migration. These results provide new insights into the prevention of tumor invasion and metastasis induced by radiotherapy.Keywords: ionizing radiation, non-small cell lung cancer, CXCL1, EMT

Published
2020

235. Correction to: Proposed nomenclature for Pseudallescheria, Scedosporium and related genera (Fungal Diversity, (2014), 67, 1, (1-10), 10.1007/s13225-014-0295-4)

Author

Lackner, M. de Hoog, G.S. Yang, L. Ferreira Moreno, L. Ahmed, S.A. Andreas, F. Kaltseis, J. Nagl, M. Lass-Flörl, C. Risslegger, B. Rambach, G. Speth, C. Robert, V. Buzina, W. Chen, S. Bouchara, J.-P. Cano-Lira, J.F. Guarro, J. Gené, J. Fernández Silva, F. Haido, R. Haase, G. Havlicek, V. Garcia-Hermoso, D. Meis, J.F. Hagen, F. Kirchmair, M. Rainer, J. Schwabenbauer, K. Zoderer, M. Meyer, W. Gilgado, F. Schwabenbauer, K. Vicente, V.A. Piecková, E. Regenermel, M. Rath, P.-M. Steinmann, J. de Alencar, X.W. Symoens, F. Tintelnot, K. Ulfig, K. Velegraki, A. Tortorano, A.M. Giraud, S. Mina, S. Rigler-Hohenwarter, K. Hernando, F.L. Ramirez-Garcia, A. Pellon, A. Kaur, J. Bergter, E.B. de Meirelles, J.V. da Silva, I.D. Delhaes, L. Alastruey-Izquierdo, A. Li, R.-Y. Lu, Q. Moussa, T. Almaghrabi, O. Al-Zahrani, H. Okada, G. Deng, S. Liao, W. Zeng, J. Issakainen, J. Liporagi Lopes, L.C.

Abstract

In this article the author name Ana Alastruey-Izquierdo was incorrectly written as Ana Alastruey-Izquerdo. © Mushroom Research Foundation 2022.

Published
2022

240. Impact factors and novel strategies for improving biohydrogen production in microbial electrolysis cells

Author

Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Deng, S, An, D, and Hoang, NB

Subjects
Bioelectric Energy Sources, Electrodes, Catalysis, Electrolysis, Biotechnology, Hydrogen
Abstract

Microbial electrolysis cell (MEC) system is an environmentally friendly method for clean biohydrogen production from a wide range of biowastes owing to low greenhouse gas emissions. This approach has relatively higher yields and lower energy costs for biohydrogen production compared to conventional biological technologies and direct water electrolysis, respectively. However, biohydrogen production efficiency and operating costs of MEC still need further optimization to realize its large-scale application.This paper provides a unique review of impact factors influencing biohydrogen production in MECs, such as microorganisms and electrodes. Novel strategies, including inhibition of methanogens, development of novel cathode catalyst, advanced reactor design and integrated systems, to enhance low-cost biohydrogen production, are discussed based on recent publications in terms of their opportunities, bottlenecks and future directions. In addition, the current challenges, and effective future perspectives towards the practical application of MECs are described in this review.

Published
2021

241. Hydrogen-Catalyzed Acid Transformation for the Hydration of Alkenes and Epoxy Alkanes over Co-N Frustrated Lewis Pair Surfaces

Author

Deng, Q, Li, X, Gao, R, Wang, J, Zeng, Z, Zou, J-J, Deng, S, and Tsang, SCE

Subjects
Colloid and Surface Chemistry, General Chemistry, Biochemistry, Catalysis
Abstract

Hydrogen (H2) is widely used as a reductant for many hydrogenation reactions; however, it has not been recognized as a catalyst for the acid transformation of active sites on solid surface. Here, we report the H2-promoted hydration of alkenes (such as styrenes and cyclic alkenes) and epoxy alkanes over single-atom Co-dispersed nitrogen-doped carbon (Co-NC) via a transformation mechanism of acid–base sites. Specifically, the specific catalytic activity and selectivity of Co-NC are superior to those of classical solid acids (acidic zeolites and resins) per micromole of acid, whereas the hydration catalysis does not take place under a nitrogen atmosphere. Detailed investigations indicate that H2can be heterolyzed on the Co–N bond to form Hδ−–Co–N–Hδ+and then be converted into OHδ−–Co–N–Hδ+accompanied by H2generation via a H2O-mediated path, which significantly reduces the activation energy for hydration reactions. This work not only provides a novel catalytic method for hydration reactions but also removes the conceptual barriers between hydrogenation and acid catalysis.

Published
2021

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