11 results on '"Elsea, S"'
Search Results
2. OP13.01: *Effective aspirin treatment for women at risk of pre‐eclampsia delays the metabolic clock of gestation.
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Hoang, L. Nguyen, Kovacevic, V., Tlaye, K., Milosavljevic, A., Elsea, S., Fernando, S., Syngelaki, A., Nicolaides, K., Wang, C., and Poon, L.C.
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FATTY acids ,OBSTETRICS ,DISCRIMINANT analysis ,GESTATIONAL age ,ASPIRIN - Abstract
This article, titled "Effective aspirin treatment for women at risk of pre-eclampsia delays the metabolic clock of gestation," examines the impact of low-dose aspirin on the metabolic gestational clock in high-risk women for pre-eclampsia. The study analyzed plasma samples from two trials, one with aspirin-treated women and the other with placebo-treated women. The results showed that aspirin significantly decreased the metabolic clock of gestation, suggesting that it delays the gestational age at delivery with pre-eclampsia. The findings provide support for the use of aspirin in high-risk pregnancies. [Extracted from the article]
- Published
- 2024
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3. A functional network module for Smith–Magenis syndrome
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Girirajan, S, Truong, H T, Blanchard, C L, and Elsea, S H
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- 2009
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4. 17p11.2p12 triplication and del(17)q11.2q12 in a severely affected child with dup(17)p11.2p12 syndrome
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Girirajan, S, Williams, S R, Garbern, J Y, Nowak, N, Hatchwell, E, and Elsea, S H
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- 2007
5. Gender, genotype, and phenotype differences in Smith–Magenis syndrome: a meta-analysis of 105 cases
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Edelman, E A, Girirajan, S, Finucane, B, Patel, P I, Lupski, J R, Smith, A CM, and Elsea, S H
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- 2007
6. RAI1 variations in Smith–Magenis syndrome patients without 17p11.2 deletions
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Girirajan, S, Elsas, L J, II, Devriendt, K, and Elsea, S H
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- 2005
7. Siblings of individuals with Smith-Magenis syndrome: an investigation of the correlates of positive and negative behavioural traits.
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Moshier, M. S., York, T. P., Silberg, J. L., and Elsea, S. H.
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BEHAVIOR disorders ,SIBLINGS ,DEVELOPMENTAL disabilities ,QUESTIONNAIRES ,WORLD Wide Web ,PILOT projects ,MULTIPLE regression analysis ,CROSS-sectional method ,DATA analysis software ,DISEASE complications ,DISEASE risk factors - Abstract
Background Smith-Magenis syndrome (SMS) is a neurodevelopmental disorder that affects approximately one out of 25 000 births worldwide. To date, no research has been conducted to investigate how having an individual with SMS in a family is a positive or negative influence on siblings. Methods To investigate this question we conducted a study involving 79 siblings and 60 parents of individuals with SMS to assess perceptions of how having a sibling with SMS positively and negative influence siblings' behavioural traits. Results Our findings show that age of siblings of individuals with SMS was associated with a significant increase in positive behavioural traits and a significant decrease in negative behavioural traits. Additionally, siblings who perceive benefits from having a sibling with SMS demonstrate significantly more positive behavioural traits and significantly fewer negative behavioural traits. Parents accurately assess the changes in sibling behavioural traits with age, and parents who perceive their child as having experienced benefits from the sibling relationship report that siblings demonstrate significantly more positive behavioural traits and significantly fewer negative behavioural traits. Conclusions Our research shows that although individuals experience difficulties as a result of having a sibling with SMS, overall, siblings tend to fare well and parents appreciate both the positive and negative behavioural effects that result from having a sibling with SMS. [ABSTRACT FROM AUTHOR]
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- 2012
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8. New developments in Smith-Magenis syndrome (del 17p11.2)
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Gropman AL, Elsea S, Duncan WC Jr., and Smith ACM
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- 2007
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9. Human genome meeting 2016
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Srivastava, A. K., Wang, Y., Huang, R., Skinner, C., Thompson, T., Pollard, L., Wood, T., Luo, F., Stevenson, R., Polimanti, R., Gelernter, J., Lin, X., Lim, I. Y., Wu, Y., Teh, A. L., Chen, L., Aris, I. M., Soh, S. E., Tint, M. T., MacIsaac, J. L., Yap, F., Kwek, K., Saw, S. M., Kobor, M. S., Meaney, M. J., Godfrey, K. M., Chong, Y. S., Holbrook, J. D., Lee, Y. S., Gluckman, P. D., Karnani, N., Kapoor, A., Lee, D., Chakravarti, A., Maercker, C., Graf, F., Boutros, M., Stamoulis, G., Santoni, F., Makrythanasis, P., Letourneau, A., Guipponi, M., Panousis, N., Garieri, M., Ribaux, P., Falconnet, E., Borel, C., Antonarakis, S. E., Kumar, S., Curran, J., Blangero, J., Chatterjee, S., Akiyama, J., Auer, D., Berrios, C., Pennacchio, L., Donti, T. R., Cappuccio, G., Miller, M., Atwal, P., Kennedy, A., Cardon, A., Bacino, C., Emrick, L., Hertecant, J., Baumer, F., Porter, B., Bainbridge, M., Bonnen, P., Graham, B., Sutton, R., Sun, Q., Elsea, S., Hu, Z., Wang, P., Zhu, Y., Zhao, J., Xiong, M., Bennett, David A., Hidalgo-Miranda, A., Romero-Cordoba, S., Rodriguez-Cuevas, S., Rebollar-Vega, R., Tagliabue, E., Iorio, M., D’Ippolito, E., Baroni, S., Kaczkowski, B., Tanaka, Y., Kawaji, H., Sandelin, A., Andersson, R., Itoh, M., Lassmann, T., Hayashizaki, Y., Carninci, P., Forrest, A. R. R., Semple, C. A., Rosenthal, E. A., Shirts, B., Amendola, L., Gallego, C., Horike-Pyne, M., Burt, A., Robertson, P., Beyers, P., Nefcy, C., Veenstra, D., Hisama, F., Bennett, R., Dorschner, M., Nickerson, D., Smith, J., Patterson, K., Crosslin, D., Nassir, R., Zubair, N., Harrison, T., Peters, U., Jarvik, G., Menghi, F., Inaki, K., Woo, X., Kumar, P., Grzeda, K., Malhotra, A., Kim, H., Ucar, D., Shreckengast, P., Karuturi, K., Keck, J., Chuang, J., Liu, E. T., Ji, B., Tyler, A., Ananda, G., Carter, G., Nikbakht, H., Montagne, M., Zeinieh, M., Harutyunyan, A., Mcconechy, M., Jabado, N., Lavigne, P., Majewski, J., Goldstein, J. B., Overman, M., Varadhachary, G., Shroff, R., Wolff, R., Javle, M., Futreal, A., Fogelman, D., Bravo, L., Fajardo, W., Gomez, H., Castaneda, C., Rolfo, C., Pinto, J. A., Akdemir, K. C., Chin, L., Patterson, S., Statz, C., Mockus, S., Nikolaev, S. N., Bonilla, X. I., Parmentier, L., King, B., Bezrukov, F., Kaya, G., Zoete, V., Seplyarskiy, V., Sharpe, H., McKee, T., Popadin, K., Basset-Seguin, N., Chaabene, R. Ben, Andrianova, M., Verdan, C., Grosdemange, K., Sumara, O., Eilers, M., Aifantis, I., Michielin, O., de Sauvage, F., Antonarakis, S., Likhitrattanapisal, S., Lincoln, S., Kurian, A., Desmond, A., Yang, S., Kobayashi, Y., Ford, J., Ellisen, L., Peters, T. L., Alvarez, K. R., Hollingsworth, E. F., Lopez-Terrada, D. H., Hastie, A., Dzakula, Z., Pang, A. W., Lam, E. T., Anantharaman, T., Saghbini, M., Cao, H., Gonzaga-Jauregui, C., Ma, L., King, A., Rosenzweig, E. Berman, Krishnan, U., Reid, J. G., Overton, J. D., Dewey, F., Chung, W. K., Small, K., DeLuca, A., Cremers, F., Lewis, R. A., Puech, V., Bakall, B., Silva-Garcia, R., Rohrschneider, K., Leys, M., Shaya, F. S., Stone, E., Sobreira, N. L., Schiettecatte, F., Ling, H., Pugh, E., Witmer, D., Hetrick, K., Zhang, P., Doheny, K., Valle, D., Hamosh, A., Jhangiani, S. N., Akdemir, Z. Coban, Bainbridge, M. N., Charng, W., Wiszniewski, W., Gambin, T., Karaca, E., Bayram, Y., Eldomery, M. K., Posey, J., Doddapaneni, H., Hu, J., Sutton, V. R., Muzny, D. M., Boerwinkle, E. A., Lupski, J. R., Gibbs, R. A., Shekar, S., Salerno, W., English, A., Mangubat, A., Bruestle, J., Thorogood, A., Knoppers, B. M., Takahashi, H., Nitta, K. R., Kozhuharova, A., Suzuki, A. M., Sharma, H., Cotella, D., Santoro, C., Zucchelli, S., Gustincich, S., Mulvihill, J. J., Baynam, G., Gahl, W., Groft, S. C., Kosaki, K., Lasko, P., Melegh, B., Taruscio, D., Ghosh, R., Plon, S., Scherer, S., Qin, X., Sanghvi, R., Walker, K., Chiang, T., Muzny, D., Wang, L., Black, J., Boerwinkle, E., Weinshilboum, R., Gibbs, R., Karpinets, T., Calderone, T., Wani, K., Yu, X., Creasy, C., Haymaker, C., Forget, M., Nanda, V., Roszik, J., Wargo, J., Haydu, L., Song, X., Lazar, A., Gershenwald, J., Davies, M., Bernatchez, C., Zhang, J., Woodman, S., Chesler, E. J., Reynolds, T., Bubier, J. A., Phillips, C., Langston, M. A., Baker, E. J., Lin, N., Amos, C., Calhoun, V., Dobretsberger, O., Egger, M., Leimgruber, F., Sadedin, S., Oshlack, A., Antonio, V. A. A., Ono, N., Ahmed, Z., Bolisetty, M., Zeeshan, S., Anguiano, E., Sarkar, A., Nandineni, M. R., Zeng, C., Shao, J., Liang, T., Pham, K., Chee-Wei, Y., Dongsheng, L., Lai-Ping, W., Lian, D., Hee, R. O. Twee, Yunus, Y., Aghakhanian, F., Mokhtar, S. S., Lok-Yung, C. V., Bhak, J., Phipps, M., Shuhua, X., Yik-Ying, T., Kumar, V., Boon-Peng, H., Campbell, I., Young, M. -A., James, P., Rain, M., Mohammad, G., Kukreti, R., Pasha, Q., Akilzhanova, A. R., Guelly, C., Abilova, Z., Rakhimova, S., Akhmetova, A., Kairov, U., Trajanoski, S., Zhumadilov, Z., Bekbossynova, M., Schumacher, C., Sandhu, S., Harkins, T., Makarov, V., Glenn, R., Momin, Z., Dilrukshi, B., Chao, H., Meng, Q., Gudenkauf, B., Kshitij, R., Jayaseelan, J., Nessner, C., Lee, S., Blankenberg, K., Lewis, L., Han, Y., Dinh, H., Jireh, S., Buhay, C., Liu, X., Wang, Q., Ding, Y., Veeraraghavan, N., Yang, Y., Beaudet, A. L., Eng, C. M., Worley, K. C. C., Liu, Y., Hughes, D. S. T., Murali, S. C., Harris, R. A., English, A. C., Hampton, O. A., Larsen, P., Beck, C., Wang, M., Kovar, C. L., Salerno, W. J., Yoder, A., Richards, S., Rogers, J., Raveenedran, M., Xue, C., Dahdouli, M., Cox, L., Fan, G., Ferguson, B., Hovarth, J., Johnson, Z., Kanthaswamy, S., Kubisch, M., Platt, M., Smith, D., Vallender, E., Wiseman, R., Below, J., Yu, F., Lin, J., Zhang, Y., Ouyang, Z., Moore, A., Wang, Z., Hofmann, J., Purdue, M., Stolzenberg-Solomon, R., Weinstein, S., Albanes, D., Liu, C. S., Cheng, W. L., Lin, T. T., Lan, Q., Rothman, N., Berndt, S., Chen, E. S., Bahrami, H., Khoshzaban, A., Keshal, S. Heidari, Alharbi, K. K. R., Zhalbinova, M., Akilzhanova, A., Bekbosynova, M., Myrzakhmetova, S., Matar, M., Mili, N., Molinari, R., Ma, Y., Guerrier, S., Elhawary, N., Tayeb, M., Bogari, N., Qotb, N., McClymont, S. A., Hook, P. W., Goff, L. A., McCallion, A., Kong, Y., Charette, J. R., Hicks, W. L., Naggert, J. K., Zhao, L., Nishina, P. M., Edrees, B. M., Athar, M., Al-Allaf, F. A., Taher, M. M., Khan, W., Bouazzaoui, A., Harbi, N. A., Safar, R., Al-Edressi, H., Anazi, A., Altayeb, N., Ahmed, M. A., Alansary, K., Abduljaleel, Z., Kratz, A., Beguin, P., Poulain, S., Kaneko, M., Takahiko, C., Matsunaga, A., Kato, S., Bertin, N., Vigot, R., Plessy, C., Launey, T., Graur, D., Friis-Nielsen, J., Izarzugaza, J. M., Brunak, S., Chakraborty, A., Basak, J., Mukhopadhyay, A., Soibam, B. S., Das, D., Biswas, N., Das, S., Sarkar, S., Maitra, A., Panda, C., Majumder, P., Morsy, H., Gaballah, A., Samir, M., Shamseya, M., Mahrous, H., Ghazal, A., Arafat, W., Hashish, M., Gruber, J. J., Jaeger, N., Snyder, M., Patel, K., Bowman, S., Davis, T., Kraushaar, D., Emerman, A., Russello, S., Henig, N., Hendrickson, C., Zhang, K., Rodriguez-Dorantes, M., Cruz-Hernandez, C. D., Garcia-Tobilla, C. D. P., Solorzano-Rosales, S., Jäger, N., Chen, J., Haile, R., Hitchins, M., Brooks, J. D., Jiménez-Morales, S., Ramírez, M., Nuñez, J., Bekker, V., Leal, Y., Jiménez, E., Medina, A., Hidalgo, A., Mejía, J., Halytskiy, V., Naggert, J., Collin, G. B., DeMauro, K., Hanusek, R., Belhassa, K., Belhassan, K., Bouguenouch, L., Samri, I., Sayel, H., moufid, FZ., El Bouchikhi, I., Trhanint, S., Hamdaoui, H., Elotmani, I., Khtiri, I., Kettani, O., Quibibo, L., Ahagoud, M., Abbassi, M., Ouldim, K., Marusin, A. V., Kornetov, A. N., Swarovskaya, M., Vagaiceva, K., Stepanov, V., De La Paz, E. M. Cutiongco, Sy, R., Nevado, J., Reganit, P., Santos, L., Magno, J. D., Punzalan, F. E., Ona, D., Llanes, E., Santos-Cortes, R. L., Tiongco, R., Aherrera, J., Abrahan, L., Pagauitan-Alan, P., Morelli, K. H., Domire, J. S., Pyne, N., Harper, S., Burgess, R., Gari, M. A., Dallol, A., Alsehli, H., Gari, A., Gari, M., Abuzenadah, A., Thomas, M., Sukhai, M., Garg, S., Misyura, M., Zhang, T., Schuh, A., Stockley, T., Kamel-Reid, S., Sherry, S., Xiao, C., Slotta, D., Rodarmer, K., Feolo, M., Kimelman, M., Godynskiy, G., O’Sullivan, C., Yaschenko, E., Rangel-Escareño, C., Rueda-Zarate, H., Tayubi, I. A., Mohammed, R., Ahmed, I., Ahmed, T., Seth, S., Amin, S., Mao, X., Sun, H., Verhaak, R. G., Whiite, S. J., Farek, J., Kahn, Z., Kasukawa, T., Lizio, M., Harshbarger, J., Hisashi, S., Severin, J., Imad, A., Sahin, S., Freeman, T. C., Baillie, K., Shekar, S. N., Salem, A. H., Ali, M., Ibrahim, A., Ibrahim, M., Barrera, H. A., Garza, L., Torres, J. A., Barajas, V., Ulloa-Aguirre, A., Kershenobich, D., Mortaji, Shahroj, Guizar, Pedro, Loera, Eliezer, Moreno, Karen, De León, Adriana, Monsiváis, Daniela, Gómez, Jackeline, Cardiel, Raquel, Fernandez-Lopez, J. C., Bonifaz-Peña, V., Contreras, A. V., Polfus, L., Wang, X., Philip, V., Abuzenadah, A. A., Turki, R., Uyar, A., Kaygun, A., Zaman, S., Marquez, E., George, J., Hendrickson, C. L., Starr, D. B., Baird, M., Kirkpatrick, B., Sheets, K., Nitsche, R., Prieto-Lafuente, L., Landrum, M., Lee, J., Rubinstein, W., Maglott, D., Thavanati, P. K. R., de Dios, A. Escoto, Hernandez, R. E. Navarro, Aldrate, M. E. Aguilar, Mejia, M. R. Ruiz, Kanala, K. R. R., Shahzad, N., Huber, E., Dan, A., Herr, W., Sprotte, G., Köstler, J., Hiergeist, A., Gessner, A., Andreesen, R., Holler, E., Al-Allaf, F., Alashwal, A., Taher, M., Abalkhail, H., Al-Allaf, A., Bamardadh, R., Filiptsova, O., Kobets, M., Kobets, Y., Burlaka, I., Timoshyna, I., Kobets, M. N., Al-allaf, F. A., Mohiuddin, M. T., Zainularifeen, A., Mohammed, A., and Owaidah, T.
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10. Assignment1 of β-centractin (CTRN2) to human chromosome 2 bands q11.1→q11.2 with somatic cell hybrids and in situ hybridization.
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Elsea, S. H., Clark, I. B., Juyal, R. C., Meyer, D. J., Meyer, D. I., and Patel, P. I.
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MICROFILAMENT proteins , *CYTOPLASM , *ACTOMYOSIN , *SOMATIC cells , *HUMAN chromosomes , *IN situ hybridization - Abstract
Centractins are a group of actin-related proteins that comprise the majority of the mass of the dynactin complex, a regulator of cytoplasmic dynein-mediated functions. Centractins are 50% identical to actin at the amino acid level. Centractins are highly conserved molecules and multiple isoforms of centractin are known to exist. The &b.alpha; and &b.beta; isoforms are expressed in a wide variety of similar tissues, but the &b.beta;-isoform is expressed at a much lower level than the &b.alpha; isoform.
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- 1999
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11. A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data
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Diego di Bernardo, Francesco Napolitano, Sarah H. Elsea, Xin Gao, Gian Marco Messa, Messa, G. M., Napolitano, F., Elsea, S. H., di Bernardo, D., and Gao, X.
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Statistics and Probability ,Prioritization ,Process (engineering) ,Computer science ,Metabolic network ,Machine learning ,computer.software_genre ,Biochemistry ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Metabolomics ,Metabolic Diseases ,Humans ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,Artificial neural network ,business.industry ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,Simulated data ,Metabolome ,Neural Networks, Computer ,Artificial intelligence ,business ,computer ,030217 neurology & neurosurgery - Abstract
Motivation Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN). Results The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future. Availability and implementation Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).
- Published
- 2020
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