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6. Úlceras de Pressão

Author

Sheila Rampazzo Luz, André Cleocir Lopacinski, Rogério de Fraga, and Cícero de Andrade Urban

Subjects
úlcera de pressão, revisão de literatura, fatores de risco, tratamento, prevenção, controle, Nursing, RT1-120, Geriatrics, RC952-954.6, Public aspects of medicine, RA1-1270
Abstract

As úlceras de pressão representam um problema de alta relevância na prática médica. Os pacientes acometidos apresentam pior evolução clínica, exigindo maior tempo de internamento e gerando maiores custos. Este artigo de revisão tem por objetivo apresentar e discutir, com base em evidências científicas, as medidas que visam à prevenção das úlceras de pressão. As recomendações incluídas neste artigo são baseadas em dados extraídos a partir de revisão bibliográfica de livros técnicos e artigos científicos obtidos nas seguintes bases de dados: 1. PubMed, 2. SciELO e 3. UpToDate Inc. Foram priorizados artigos publicados nos últimos cinco anos e eles foram avaliados criticamente, de modo a objetivar a certificação de que os métodos e os resultados apresentados eram válidos. Os pesquisadores incluíram, ainda, no estudo outros artigos consagrados pela literatura, com data de publicação superior a cinco anos, cujos dados foram analisados criticamente de modo a somente incluir informações atemporais.

Published
2024

7. Filtração Glomerular Em Idosos Coronarianos Agudos: Cockcroft-Gault é O Melhor Método?

Author

Lucas Rampazzo Diniz, Jessica Myrian de Amorim Garcia, Rafaella Italiano Peixoto, Keila Lima de Oliveira Diniz, and Verônica Soares Monteiro

Subjects
idosos, taxa de filtração glomerular, síndrome coronariana aguda, Nursing, RT1-120, Geriatrics, RC952-954.6, Public aspects of medicine, RA1-1270
Abstract

INTRODUÇÃO: Indivíduos com síndrome coronariana aguda e menores taxas de filtração glomerular são associados a piores desfechos. Apesar de diversas fórmulas terem sido propostas para estimar a taxa de filtração glomerular, ainda não está definido qual delas teria o melhor poder de predição dos desfechos em pacientes idosos com síndrome coronariana aguda. OBJETIVO: Comparar três fórmulas para a taxa de filtração glomerular a fim de predizer a mortalidade intra-hospitalar e outros desfechos. MÉTODOS: Coorte retrospectiva intra-hospitalar com idosos acometidos com síndrome coronariana aguda, admitidos em Unidade de Terapia Intensiva Cardiológica, no período de 1 de janeiro a 31 de dezembro de 2013. Foram utilizadas as fórmulas Cockcroft-Gault, MDRD e CKD-EPI para o cálculo da taxa de filtração glomerular. RESULTADOS: Foram analisados 122 indivíduos, 51,6% do gênero masculino, com média de 73,5 anos. A mortalidade foi de 19,6%. As médias da taxa de filtração glomerular para Cockcroft-Gault, MDRD e CKD-EPI foram, respectivamente, de 70,24 mL/min. (± 33,35), 74,70 mL/min./1,73 m2 (± 30,91) e 69,79 mL/min./1,73 m2 (± 23,82). Verificou-se a relação de todas as fórmulas com óbito (p = 0,001 versus 0,028 versus 0,028), uso de antibióticos (p = 0,002 versus < 0,001 versus 0,001) e hemodiálise (p ≤ 0,001 versus 0,003 versus 0,003). A fórmula Cockcroft-Gault teve maior área sob a curva pelo método ROC (0,701 versus 0,634 versus 0,639) e foi a única com significância estatística (IC95% 0,579 - 0,822, p = 0,006) para mortalidade intra-hospitalar. CONCLUSÃO: A Cockcroft-Gault foi a fórmula com melhor acurácia para predizer mortalidade.

Published
2024

8. 'UPDATE: I’m pregnant!': Inferring global downloads and reasons for using menstrual tracking apps

Author

Francesco Rampazzo, Alyce Raybould, Pietro Rampazzo, Ross Barker, and Douglas Leasure

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Objective The market for smartphone apps tracking menstrual cycles has expanded in recent years. These apps market themselves as empowering users to achieve their reproductive goals and maximize the chance of pregnancy. This paper presents the first open-access quantification of menstrual tracking app downloads globally, covering both the Global North and South. We also explore macro and micro-level reasons for app usage, testing national associations with downloads and analyzing user reviews. Methods We use data on app installations, reviews, and ratings from the Google Play Store and Apple App Store to estimate global app downloads using a Bayesian model. We perform regressions to test for national predictors of use, and multilingual topic models to analyze/cluster reviews left by users to understand individual reasons for use. Results We find that the majority of downloads are for three apps: Clue, Flo, and Period Tracker. Higher modern contraceptive prevalence and internet access are associated with more downloads, while low-income countries tend to have fewer. In low-income countries, a higher unmet need for family planning and total fertility rate are associated with more downloads. Individual reviews reveal the most common reasons for use are menstrual cycle tracking, achieving a pregnancy, community engagement, and avoiding pregnancy. Conclusion Existing research on menstrual tracking apps is largely confined to the Global North, but our study finds the use of these apps to be as prevalent throughout the Global South. Future research needs to urgently understand the implications these apps could have in a diversity of contexts.

Published
2024
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11. Measurement of the background in the CMS muon detector in $${p}{p}$$ pp -collisions at $$\sqrt{s} = 13$$ s = 13 $$\,\text {Te}\hspace{-.08em}\text {V}$$ Te V

Author

CMS Muon Group, M. Tytgat, A. Muhammad, G. De Lentdecker, J. Jaramillo, L. Moureaux, L. Pétré, Y. Yang, C. Rendón, G. Gokbulut, Y. Hong, A. Samalan, G. A. Alves, F. Marujo da Silva, E. Alves Coelho, M. Barroso Ferreira Filho, E. M. Da Costa, D. De Jesus Damiao, B. C. Ferreira, S. Fonseca De Souza, K. Mota Amarilo, H. Nogima, A. Santoro, M. Thiel, A. Aleksandrov, L. Dimitrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, G. Mitev, L. Ratchev, G. Rashevski, M. Shopova, G. Sultanov, A. Dimitrov, L. Litov, B. Pavlov, P. Petkov, A. Petrov, E. Shumka, S. Keshri, S. Thakur, M. Chen, X. Dong, W. Gong, Q. Hou, C. Jiang, H. Kou, Z.-A. Liu, W. Luo, J. Song, L. Sun, N. Wang, Y. Wang, Z. Wang, C. Zhang, Y. Zhang, H. Zhang, J. Zhao, A. Agapitos, Y. Ban, A. Levin, Q. Li, S. J. Qian, D. Wang, K. Wang, Z. You, C. Avila, D. A. Barbosa Trujillo, A. Cabrera, C. A. Florez, J. Fraga, J. A. Reyes Vega, F. Ramirez, M. Rodriguez, J. D. Ruiz, N. Vanegas, H. Abdalla, A. A. Abdelalim, Y. Assran, A. Radi, I. Crotty, M. A. Mahmoud, L. Balleyguier, X. Chen, C. Combaret, G. Galbit, M. Gouzevitch, G. Grenier, I. B. Laktineh, A. Luciol, L. Mirabito, W. Tromeur, I. Bagaturia, I. Lomidze, O. Kemularia, Z. Tsamalaidze, U. Böttger, D. Eliseev, T. Hebbeker, K. Hoepfner, M. Merschmeyer, F. Ivone, S. Mukherjee, F. Nowotny, B. Philipps, H. Reithler, A. Sharma, F. Torres Da Silva De Araujo, S. Wiedenbeck, S. Zaleski, F. P. Zantis, M. Abbas, S. Mallows, G. Bencze, N. Beni, J. Molnar, Z. Szillasi, D. Teyssier, B. Ujvari, G. Zilizi, J. Babbar, S. Bansal, V. Bhatnagar, S. Chauhan, A. Kaur, H. Kaur, A. Kaur Sahota, S. Kumar, T. Sheokand, J. Singh, B. C. Choudhary, A. Kumar, M. Kumar Saini, M. Naimuddin, N. Majumdar, S. Mukhopadhyay, P. Rout, V. Amoozegar, B. Boghrati, M. Ebraimi, M. Mohammadi Najafabadi, E. Zareian, M. Abbrescia, R. Aly, M. Buonsante, A. Colaleo, N. De Filippis, D. Dell’Olio, G. De Robertis, W. Elmetenawee, N. Ferrara, M. Franco, G. Iaselli, N. Lacalamita, F. Licciulli, F. Loddo, M. Maggi, S. Martiradonna, S. Nuzzo, L. Longo, A. Pellecchia, G. Pugliese, R. Radogna, D. Ramos, A. Ranieri, F. M. Simone, A. Stamerra, D. Troiano, R. Venditti, P. Verwilligen, A. Zaza, G. Abbiendi, C. Baldanza, C. Battilana, A. Benvenuti, L. Borgonovi, V. Cafaro, F. R. Cavallo, A. Crupano, M. Cuffiani, G. M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, V. Giordano, C. Guandalini, L. Guiducci, S. Lo Meo, L. Lunerti, S. Marcellini, G. Masetti, F. L. Navarria, G. Paggi, A. Perrotta, F. Primavera, A. M. Rossi, T. Rovelli, G. Torromeo, L. Benussi, S. Bianco, R. Campagnola, M. Caponero, S. Colafranceschi, S. Meola, L. Passamonti, D. Piccolo, D. Pierluigi, G. Raffone, A. Russo, G. Saviano, S. Buontempo, A. Cagnotta, F. Carnevali, F. Cassese, N. Cavallo, A. De Iorio, F. Fabozzi, A. O. M. Iorio, L. Lista, P. Paolucci, G. Passeggio, B. Rossi, L. Barcellan, M. Bellato, M. Benettoni, A. Bergnoli, A. Bragagnolo, R. Carlin, L. Castellani, P. Checchia, L. Ciano, A. Colombo, D. Corti, F. Gasparini, U. Gasparini, F. Gonella, A. Gozzelino, A. Griggio, G. Grosso, M. Gulmini, R. Isocrate, E. Lusiani, G. Maron, M. Margoni, A. T. Meneguzzo, M. Migliorini, L. Modenese, F. Montecassiano, M. Negrello, M. Passaseo, J. Pazzini, L. Ramina, M. Rampazzo, M. Rebeschini, P. Ronchese, R. Rossin, F. Simonetto, M. Toffano, N. Toniolo, A. Triossi, S. Ventura, M. Zanetti, P. G. Zatti, P. Zotto, A. Zucchetta, S. AbuZeid, C. Aimè, A. Braghieri, S. Calzaferri, D. Fiorina, S. Gigli, P. Montagna, C. Riccardi, P. Salvini, I. Vai, P. Vitulo, N. Amapane, G. Cotto, D. Dattola, P. De Remigis, B. Kiani, C. Mariotti, S. Maselli, M. Pelliccioni, F. Rotondo, A. Staiano, D. Trocino, G. Umoret, E. Asilar, T. J. Kim, J. A. Merlin, S. Choi, B. Hong, K. S. Lee, J. Goh, J. Choi, J. Kim, U. Yang, I. Yoon, W. Jang, J. Heo, D. Kang, Y. Kang, D. Kim, S. Kim, B. Ko, J. S. H. Lee, I. C. Park, I. J. Watson, S. Yang, Y. Jeong, Y. Lee, I. Yu, G. Alasfour, T. Beyrouthy, Y. Gharbia, Y. Maghrbi, M. Otkur, H. Castilla-Valdez, H. Crotte Ledesma, R. Lopez-Fernandez, A. Sánchez Hernández, M. Ramírez García, E. Vazquez, M. A. Shah, N. Zaganidis, I. Pedraza, C. Uribe Estrada, A. Ahmad, W. Ahmed, M. I. Asghar, H. R. Hoorani, S. Muhammad, A. Wajid, J. Alcaraz Maestre, A. Álvarez Fernández, Cristina F. Fernandez Bedoya, L. C. Blanco Ramos, E. Calvo, C. A. Carrillo Montoya, J. M. Cela Ruiz, M. Cepeda, M. Cerrada, N. Colino, S. Cuadrado Calzada, J. Cuchillo Ortega, B. De La Cruz, C. I. de Lara Rodríguez, D. Fernández Del Val, J. P. Fernández Ramos, M. C. Fouz, D. Francia Ferrero, J. García Romero, O. Gonzalez Lopez, S. Goy Lopez, M. I. Josa, J. León Holgado, O. Manzanilla Carretero, I. Martín Martín, J. J. Martínez Morales, E. Martín Viscasillas, D. Moran, Á. Navarro Tobar, R. Paz Herrera, J. C. Puras Sánchez, J. Puerta Pelayo, S. Pulido Ferrero, I. Redondo, D. D. Redondo Ferrero, V. Salto Parra, S. Sánchez Navas, J. Sastre, L. Urda Gómez, J. Vazquez Escobar, J. F. de Trocóniz, F. Frias Garcia-Lago, R. Reyes-Almanza, B. Alvarez Gonzalez, J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, P. Leguina López, E. Palencia Cortezon, C. Ramón Álvarez, J. Prado Pico, V. Rodríguez Bouza, A. Soto Rodríguez, A. Trapote, C. Vico Villalba, B. Kailasapathy, K. Malagalage, D. U. J. Sonnadara, D. D. C. Wickramarathna, W. G. D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage, P. Aspell, M. Bianco, D. Bozzato, S. Brachet, A. Conde Garcia, A. Dabrowski, R. De Oliveira, F. Fallavollita, P. Kicsiny, E. Hazen, S. May, A. Peck, K. Salyer, I. Suarez, S. Abbott, J. Bonilla, R. Breedon, H. Cai, P. T. Cox, R. Erbacher, O. Kukral, C. McLean, G. Mocellin, M. Mulhearn, B. Regnery, M. Tripathi, G. Waegel, Y. Yao, J. Carlson, R. Cousins, A. Dasgupta, A. Datta, J. Hauser, M. Ignatenko, M. A. Iqbal, C. Lo, D. Saltzberg, C. Schnaible, V. Valuev, R. Clare, M. Gordon, G. Hanson, N. Amin, J. Bradmiller-Feld, C. Campagnari, T. Danielson, A. Dishaw, A. Dorsett, B. Marsh, H. Mei, M. Oshiro, J. Richman, F. Setti, M. F. Sevilla, P. Siddireddy, S. Wang, C. Aruta, V. Barashko, V. Cherepanov, M. Dittrich, A. Korytov, E. Kuznetsova, A. Madorsky, G. Mitselmakher, A. Muthirakalayil Madhu, N. Rawal, N. Terentyev, J. Wang, B. Alsufyani, S. Butalla, T. Elkafrawy, M. Hohlmann, E. Yanes, J. Eysermans, E. Barberis, Y. Haddad, Y. Han, G. Madigan, D. M. Morse, V. Nguyen, D. Wood, S. Bhattacharya, J. Bueghly, Z. Chen, K. A. Hahn, Y. Liu, Y. Miao, D. G. Monk, M. H. Schmitt, A. Taliercio, M. Velasco, B. Bylsma, M. Carrigan, R. De Los Santos, L. S. Durkin, C. Hill, K. Banicz, J. Liu, M. Matveev, B. P. Padley, D. Aebi, M. Ahmad, T. Akhter, A. Bolshov, O. Bouhali, R. Eusebi, J. Gilmore, T. Huang, E. Juska, T. Kamon, H. Kim, M. Kizlov, S. Malhotra, R. Mueller, R. Rabadan, D. Rathjens, A. Safonov, P. E. Karchin, A. Aravind, K. Black, I. De Bruyn, P. Everaerts, C. Galloni, M. Herndon, A. Lanaro, R. Loveless, J. Madhusudanan Sreekala, S. Mondal, D. Teague, W. Vetens, A. Warden, I. Azhgirey, V. Borshch, L. Chtchipunov, A. Egorov, G. Gavrilov, V. Golovtcov, M. Ivanov, V. Ivantchenko, Y. Ivanov, V. Karjavine, A. Khodinov, V. Kim, I. A. Kurochkin, P. Levchenko, V. Murzin, S. Nasybulin, V. Oreshkin, V. Palichik, V. Perelygin, A. Riabchikova, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, and A. Vorobyev

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The CMS detector, including its muon system, has been operating at the CERN LHC in increasingly challenging conditions for about 15 years. The muon detector was designed to provide excellent triggering and track reconstruction for muons produced in proton–proton collisons at an instantaneous luminosity ( $$\mathcal {L}$$ L ) of $$1 \times 10^{34}$$ 1 × 10 34 cm $$^{-2}$$ - 2 s $$^{-1}$$ - 1 . During the Run 2 data-taking period (2015–2018), the LHC achieved an instantaneous luminosity of twice its design value, resulting in larger background rates and making the efficient detection of muons more difficult. While some backgrounds result from natural radioactivity, cosmic rays, and interactions of the circulating protons with residual gas in the beam pipe, the dominant source of background hits in the muon system arises from proton–proton interactions themselves. Charged hadrons leaving the calorimeters produce energy deposits in the muon chambers. In addition, high-energy particles interacting in the hadron calorimeter and forward shielding elements generate thermal neutrons, which leak out of the calorimeter and shielding structures, filling the CMS cavern. We describe the method used to measure the background rates in the various muon subsystems. These rates, in conjunction with simulations, can be used to estimate the expected backgrounds in the High-Luminosity LHC. This machine will run for at least 10 years starting in 2029 reaching an instantaneous luminosity of $$\mathcal {L} = 5 \times \text {10}^\text {34}\,\text {cm}^\text {-2}\,\text {s}^\text {-1}$$ L = 5 × 10 34 cm -2 s -1 and increasing ultimately to $$\mathcal {L} = 7.5 \times \text {10}^\text {34}\,\text {cm}^\text {-2}\,\text {s}^\text {-1}$$ L = 7.5 × 10 34 cm -2 s -1 . These background estimates have been a key ingredient for the planning and design of the muon detector upgrade.

Published
2024
Full Text
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12. Measurement of the background in the CMS muon detector in pps=13TeV-collisions at pps=13TeV pps=13TeV

Author

Tytgat, M., Muhammad, A., De Lentdecker, G., Jaramillo, J., Moureaux, L., Pétré, L., Yang, Y., Rendón, C., Gokbulut, G., Hong, Y., Samalan, A., Alves, G. A., Marujo da Silva, F., Alves Coelho, E., Barroso Ferreira Filho, M., Da Costa, E. M., De Jesus Damiao, D., Ferreira, B. C., Fonseca De Souza, S., Mota Amarilo, K., Nogima, H., Santoro, A., Thiel, M., Aleksandrov, A., Dimitrov, L., Hadjiiska, R., Iaydjiev, P., Misheva, M., Mitev, G., Ratchev, L., Rashevski, G., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Keshri, S., Thakur, S., Chen, M., Dong, X., Gong, W., Hou, Q., Jiang, C., Kou, H., Liu, Z.-A., Luo, W., Song, J., Sun, L., Wang, N., Wang, Y., Wang, Z., Zhang, C., Zhang, Y., Zhang, H., Zhao, J., Agapitos, A., Ban, Y., Levin, A., Li, Q., Qian, S. J., Wang, D., Wang, K., You, Z., Avila, C., Barbosa Trujillo, D. A., Cabrera, A., Florez, C. A., Fraga, J., Reyes Vega, J. A., Ramirez, F., Rodriguez, M., Ruiz, J. D., Vanegas, N., Abdalla, H., Abdelalim, A. A., Assran, Y., Radi, A., Crotty, I., Mahmoud, M. A., Balleyguier, L., Chen, X., Combaret, C., Galbit, G., Gouzevitch, M., Grenier, G., Laktineh, I. B., Luciol, A., Mirabito, L., Tromeur, W., Bagaturia, I., Lomidze, I., Kemularia, O., Tsamalaidze, Z., Böttger, U., Eliseev, D., Hebbeker, T., Hoepfner, K., Merschmeyer, M., Ivone, F., Mukherjee, S., Nowotny, F., Philipps, B., Reithler, H., Sharma, A., Torres Da Silva De Araujo, F., Wiedenbeck, S., Zaleski, S., Zantis, F. P., Abbas, M., Mallows, S., Bencze, G., Beni, N., Molnar, J., Szillasi, Z., Teyssier, D., Ujvari, B., Zilizi, G., Babbar, J., Bansal, S., Bhatnagar, V., Chauhan, S., Kaur, A., Kaur, H., Kaur Sahota, A., Kumar, S., Sheokand, T., Singh, J., Choudhary, B. C., Kumar, A., Kumar Saini, M., Naimuddin, M., Majumdar, N., Mukhopadhyay, S., Rout, P., Amoozegar, V., Boghrati, B., Ebraimi, M., Mohammadi Najafabadi, M., Zareian, E., Abbrescia, M., Aly, R., Buonsante, M., Colaleo, A., De Filippis, N., Dell’Olio, D., De Robertis, G., Elmetenawee, W., Ferrara, N., Franco, M., Iaselli, G., Lacalamita, N., Licciulli, F., Loddo, F., Maggi, M., Martiradonna, S., Nuzzo, S., Longo, L., Pellecchia, A., Pugliese, G., Radogna, R., Ramos, D., Ranieri, A., Simone, F. M., Stamerra, A., Troiano, D., Venditti, R., Verwilligen, P., Zaza, A., Abbiendi, G., Baldanza, C., Battilana, C., Benvenuti, A., Borgonovi, L., Cafaro, V., Cavallo, F. R., Crupano, A., Cuffiani, M., Dallavalle, G. M., Fabbri, F., Fanfani, A., Fasanella, D., Giacomelli, P., Giordano, V., Guandalini, C., Guiducci, L., Lo Meo, S., Lunerti, L., Marcellini, S., Masetti, G., Navarria, F. L., Paggi, G., Perrotta, A., Primavera, F., Rossi, A. M., Rovelli, T., Torromeo, G., Benussi, L., Bianco, S., Campagnola, R., Caponero, M., Colafranceschi, S., Meola, S., Passamonti, L., Piccolo, D., Pierluigi, D., Raffone, G., Russo, A., Saviano, G., Buontempo, S., Cagnotta, A., Carnevali, F., Cassese, F., Cavallo, N., De Iorio, A., Fabozzi, F., Iorio, A. O. M., Lista, L., Paolucci, P., Passeggio, G., Rossi, B., Barcellan, L., Bellato, M., Benettoni, M., Bergnoli, A., Bragagnolo, A., Carlin, R., Castellani, L., Checchia, P., Ciano, L., Colombo, A., Corti, D., Gasparini, F., Gasparini, U., Gonella, F., Gozzelino, A., Griggio, A., Grosso, G., Gulmini, M., Isocrate, R., Lusiani, E., Maron, G., Margoni, M., Meneguzzo, A. T., Migliorini, M., Modenese, L., Montecassiano, F., Negrello, M., Passaseo, M., Pazzini, J., Ramina, L., Rampazzo, M., Rebeschini, M., Ronchese, P., Rossin, R., Simonetto, F., Toffano, M., Toniolo, N., Triossi, A., Ventura, S., Zanetti, M., Zatti, P. G., Zotto, P., Zucchetta, A., AbuZeid, S., Aimè, C., Braghieri, A., Calzaferri, S., Fiorina, D., Gigli, S., Montagna, P., Riccardi, C., Salvini, P., Vai, I., Vitulo, P., Amapane, N., Cotto, G., Dattola, D., De Remigis, P., Kiani, B., Mariotti, C., Maselli, S., Pelliccioni , M., Rotondo, F., Staiano, A., Trocino, D., Umoret, G., Asilar, E., Kim, T. J., Merlin, J. A., Choi, S., Hong, B., Lee, K. S., Goh, J., Choi, J., Kim, J., Yang, U., Yoon, I., Jang, W., Heo, J., Kang, D., Kang, Y., Kim, D., Kim, S., Ko, B., Lee, J. S. H., Park, I. C., Watson, I. J., Yang, S., Jeong, Y., Lee, Y., Yu, I., Alasfour, G., Beyrouthy, T., Gharbia, Y., Maghrbi, Y., Otkur, M., Castilla-Valdez, H., Crotte Ledesma, H., Lopez-Fernandez, R., Sánchez Hernández, A., Ramírez García, M., Vazquez, E., Shah, M. A., Zaganidis, N., Pedraza, I., Uribe Estrada, C., Ahmad, A., Ahmed, W., Asghar, M. I., Hoorani, H. R., Muhammad, S., Wajid, A., Alcaraz Maestre, J., Álvarez Fernández, A., Fernandez Bedoya, Cristina F., Blanco Ramos, L. C., Calvo, E., Carrillo Montoya, C. A., Cela Ruiz, J. M., Cepeda, M., Cerrada, M., Colino, N., Cuadrado Calzada, S., Cuchillo Ortega, J., De La Cruz, B., de Lara Rodríguez, C. I., Fernández Del Val, D., Fernández Ramos, J. P., Fouz, M. C., Francia Ferrero, D., García Romero, J., Gonzalez Lopez, O., Goy Lopez, S., Josa, M. I., León Holgado, J., Manzanilla Carretero, O., Martín Martín, I., Martínez Morales, J. J., Martín Viscasillas, E., Moran, D., Navarro Tobar, Á., Paz Herrera, R., Puras Sánchez, J. C., Puerta Pelayo, J., Pulido Ferrero, S., Redondo, I., Redondo Ferrero, D. D., Salto Parra, V., Sánchez Navas, S., Sastre, J., Urda Gómez, L., Vazquez Escobar, J., de Trocóniz, J. F., Frias Garcia-Lago, F., Reyes-Almanza, R., Alvarez Gonzalez, B., Cuevas, J., Fernandez Menendez, J., Folgueras, S., Gonzalez Caballero, I., Leguina López, P., Palencia Cortezon, E., Ramón Álvarez, C., Prado Pico, J., Rodríguez Bouza, V., Soto Rodríguez, A., Trapote, A., Villalba, C. Vico, Kailasapathy, B., Malagalage, K., Sonnadara, D. U. J., Wickramarathna, D. D. C., Dharmaratna, W. G. D., Liyanage, K., Perera, N., Wickramage, N., Aspell, P., Bianco, M., Bozzato, D., Brachet, S., Conde Garcia, A., Dabrowski, A., De Oliveira, R., Fallavollita, F., Kicsiny, P., Sharma, A., Hazen, E., May, S., Peck, A., Salyer, K., Suarez, I., Abbott, S., Bonilla, J., Breedon, R., Cai, H., Cox, P. T., Erbacher, R., Kukral, O., McLean, C., Mocellin, G., Mulhearn, M., Regnery, B., Tripathi, M., Waegel, G., Yao, Y., Carlson, J., Cousins, R., Dasgupta, A., Datta, A., Hauser, J., Ignatenko, M., Iqbal, M. A., Lo, C., Saltzberg, D., Schnaible, C., Valuev, V., Clare, R., Gordon, M., Hanson, G., Amin, N., Bradmiller-Feld, J., Campagnari, C., Danielson, T., Dishaw, A., Dorsett, A., Kim, J., Marsh, B., Mei, H., Oshiro, M., Richman, J., Setti, F., Sevilla, M. F., Siddireddy, P., Wang, S., Aruta, C., Barashko, V., Cherepanov, V., Dittrich, M., Korytov, A., Kuznetsova, E., Madorsky, A., Mitselmakher, G., Muthirakalayil Madhu, A., Rawal, N., Terentyev, N., Wang, J., Alsufyani, B., Butalla, S., Elkafrawy, T., Hohlmann, M., Yanes, E., Eysermans, J., Barberis, E., Haddad, Y., Han, Y., Madigan, G., Morse, D. M., Nguyen, V., Wood, D., Bhattacharya, S., Bueghly, J., Chen, Z., Hahn, K. A., Liu, Y., Miao, Y., Monk, D. G., Schmitt, M. H., Taliercio, A., Velasco, M., Bylsma, B., Carrigan, M., De Los Santos, R., Durkin, L. S., Hill, C., Banicz, K., Liu, J., Matveev, M., Padley, B. P., Aebi, D., Ahmad, M., Akhter, T., Bolshov, A., Bouhali, O., Eusebi, R., Gilmore, J., Huang, T., Juska, E., Kamon, T., Kim, H., Kizlov, M., Malhotra, S., Mueller, R., Rabadan, R., Rathjens, D., Safonov, A., Karchin, P. E., Aravind, A., Black, K., De Bruyn, I., Everaerts, P., Galloni, C., Herndon, M., Lanaro, A., Loveless, R., Madhusudanan Sreekala, J., Mondal, S., Teague, D., Vetens, W., Warden, A., Azhgirey, I., Borshch, V., Chtchipunov, L., Egorov, A., Gavrilov, G., Golovtcov, V., Ivanov, M., Ivantchenko, V., Ivanov, Y., Karjavine, V., Khodinov, A., Kim, V., Kurochkin, I. A., Levchenko, P., Murzin, V., Nasybulin, S., Oreshkin, V., Palichik, V., Perelygin, V., Riabchikova, A., Sosnov, D., Sulimov, V., Uvarov, L., Vavilov, S., and Vorobyev, A.

Published
2024
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13. Generation of a human induced pluripotent stem cell line UGENTi002-A from an arrhythmogenic cardiomyopathy patient carrying the c.817C>T DSP heterozygous variant and isogenic control using CRISPR/Cas9 editing

Author

Laurens Léger, Jeffrey Aalders, Nina Heymans, Kiara Van Acker-Verberckt, Léa De Bleeckere, Paul Coucke, Björn Menten, Barbara Bauce, Libero Vitiello, Alessandra Rampazzo, Martina Calore, and Jolanda van Hengel

Subjects
Biology (General), QH301-705.5
Abstract

Arrhythmogenic cardiomyopathy is a severe genetic heart muscle disease characterized by fibro-fatty replacement of the myocardium. Pathogenic variants causal for this disease are mainly located in desmosomal genes, including desmoplakin (DSP). Renal epithelial cells were isolated from a patient carrying the heterozygous c.817C>T (p.Q273*, nonsense) pathogenic variant in DSP, and subsequently reprogrammed using the Cytotune®-iPS 2.0 Sendai Reprogramming Kit. An isogenic control line was generated using CRISPR/Cas9 genome editing. The resulting induced pluripotent stem cell lines were characterized and displayed the required traits for in vitro disease modeling.

Published
2024
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15. Integrated aerobic exercise with LDE-docetaxel treatment: a novel approach to combat prostate cancer progression

Author

Allice Santos Cruz Veras, Victor Rogério Garcia Batista, Rafael Ribeiro Correia, Maria Eduarda de Almeida Tavares, Rafael Jesus Gonçalves Rubira, Elaine Rufo Tavares, Inês Cristina Giometti, Raul Cavalcante Maranhão, and Giovana Rampazzo Teixeira

Subjects
Nanoemulsion, Raman spectroscopy, LDE, Endurance physical training, Taxanes, Medicine, Science
Abstract

Abstract The variability in response to conventional prostate cancer (PC) therapies, coupled with the emergent issue of drug resistance, underscores the critical need for innovative treatment strategies. Aerobic physical exercise reduced incidence of several cancers, but the mechanism underlying these effects associated the nanoemulsion not fully understood. The application of a lipid nanoemulsion (LDE) delivery system for docetaxel (DTX), showing marked enhancement in therapeutic efficacy when combined with aerobic physical exercise. This novel intervention potentiates the antitumor activity of LDE-delivered DTX by augmenting nanoparticle internalization and inducing cell cycle arrest. Our findings reveal that this synergistic treatment not only significantly reduces prostate weight and mitigates adenocarcinoma proliferation but also attenuates anti-apoptotic BCL-2 protein expression. Concurrently, it elevates pro-apoptotic proteins and diminishes inflammatory markers. Metabolic profiling of the combined therapy group disclosed additional benefits, such as reduced lipid and plasma glucose levels. Collectively, our data illuminate the profound impact of integrating LDE-mediated DTX delivery with structured physical exercise, which together spearhead a dual-front assault on PC. This multimodal approach heralds a new paradigm in PC management, accentuating the promise of combined pharmacological and non-pharmacological interventions to elevate tumor suppressor protein activity and refine patient outcomes.

Published
2024
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21. Adversarially Robust Fault Zone Prediction in Smart Grids With Bayesian Neural Networks

Author

Emad Efatinasab, Alberto Sinigaglia, Nahal Azadi, Gian Antonio Susto, and Mirco Rampazzo

Subjects
Adversarial attacks, Bayesian neural networks, fault prediction, smart grids, uncertainty quantification, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The rapid growth of the global population, economy, and urbanization is significantly increasing energy consumption, necessitating the integration of renewable energy sources. This integration presents challenges that demand innovative solutions to maintain grid stability and efficiency. Smart grids offer enhanced reliability, efficiency, sustainability, and bi-directional communication. However, the reliance on advanced technologies in smart grids introduces vulnerabilities, particularly concerning adversarial attacks. This paper addresses two critical issues in smart grid fault prediction: the vulnerability of machine learning models to adversarial attacks and the operational challenges posed by false alarms. We propose a Bayesian Neural Network (BNN) framework for fault zone prediction that quantifies uncertainty in predictions, enhancing robustness and reducing false alarms. Our BNN model achieves up to 0.958 accuracy and 0.960 precision in fault zone prediction. To counter adversarial attacks, we developed an uncertainty-based detection scheme that leverages prediction uncertainty. This framework distinguishes between normal and adversarial data using predictive entropy and mutual information as metrics. It detects complex white-box adversarial attacks, which are challenging due to attackers’ detailed knowledge of the model, with a mean accuracy of 0.891 using predictive entropy and 0.981 using mutual information. The model’s performance, combined with minimal computational overhead, underscores its practicality and robustness for enhancing smart grid security.

Published
2024
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22. The constitutive activation of STAT3 gene and its mutations are at the crossroad between LGL leukemia and autoimmune disorders

Author

Gianpietro Semenzato, Giulia Calabretto, Antonella Teramo, Vanessa Rebecca Gasparini, Elisa Rampazzo, Gregorio Barilà, and Renato Zambello

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Type T Large Granular Lymphocyte Leukemia (T-LGLL) is a chronic disorder characterized by the abnormal proliferation of clonal cytotoxic T cells. The intriguing association of T-LGLL with autoimmune and inflammatory diseases, the most prominent example being rheumatoid arthritis, raises questions about the underlying pathophysiologic relationships between these disorders which share several biological and clinical features, most notably neutropenia, which is considered as a clinical hallmark. Recent progress in molecular genetics has contributed to a better understanding of pathogenetic mechanisms, thus moving our knowledge in the field of LGL leukemias forward. Focusing on the constitutive activation of STAT3 pathway and the well-established role of STAT3 mutations in T-LGLL, we herein discuss whether the T cell clones occurring in comorbid conditions are the cause or the consequence of the immune-inflammatory associated events. Overall, this review sheds light on the intricate relationships between inflammation and cancer, emphasizing the importance of the STAT3 gene and its activation in the pathophysiology of these conditions. Gaining a deeper understanding of these underlying mechanisms seeks to pave the way for the development of novel targeted therapies for patients affected by inflammation-related cancers.

Published
2024
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24. Long exposure to a mixture of endocrine disruptors prediposes the ventral prostate of rats to preneoplastic lesions

Author

Sousa, Thaina Cavalleri, de Souza, Letícia Pereira, Ricardo, Maria Luiza Silva, Yoshigae, Andreia Yuri, Hinokuma, Karianne Delalibera, Gorzoni, Ana Beatriz Ratto, de Aquino, Ariana Musa, Scarano, Wellerson Rodrigo, de Sousa Castillho, Anthony César, Tavares, Maria Eduarda Almeida, Veras, Alice Santos Cruz, Teixeira, Giovana Rampazzo, Nai, Gisele Alborghetti, and de Oliveira Mendes, Leonardo

Published
2023
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32. Following a trail of breadcrumbs : a study of migration through digital traces

Author

Rampazzo, Francesco and Bijak, Jakub

Abstract

An accurate estimation of international migration is hampered by a lack of timely and comprehensive data, with different definitions and measures of migration adopted by different countries. The aim of this thesis is to understand whether information from digital traces can help measure international migration. One of the approaches implemented in this thesis is to complement traditional data sources for the United Kingdom (UK) with digital traces data. The Bayesian framework proposed in the Integrated Model of European Migration (IMEM) is used to combine data from the Labour Force Survey (LFS) and the Facebook Advertising Platform in order to study the number of European migrants in the UK, aiming to produce more accurate estimates of European migrants. The thesis suggests an extension of the IMEM model to disaggregate the estimate by age and sex. Additionally, weekly time series from the Facebook Advertising Platform are analysed to infer trends of change in migration stocks over time. The quality of the data is reviewed paying particular attention to the biases of these sources. The results indicate visible yet uncertain differences between the model estimates using the Bayesian framework and individual sources. The advantages and limitations of this approach, which can be applied in other contexts, are also discussed. It seems that any individual source cannot be completely trusted, but combining sources through modelling can offer valuable insights. The main conclusions are that the data generation process should be examined, digital traces data should be combined with traditional data sources, and that digital traces data might be used to infer trends of change in migration stocks.

Published
2022

33. On the effects of 30.5 GHz sinusoidal wave exposure on glioblastoma organoids

Author

Elena Rampazzo, Luca Persano, Nissar Karim, George Hodgking, Rosanna Pinto, Arianna Casciati, Mirella Tanori, Alessandro Zambotti, Silvia Bresolin, Alice Cani, Alessandro Pannicelli, Ilan W. Davies, Cristopher Hancock, Cristiano Palego, Giampietro Viola, Mariateresa Mancuso, and Caterina Merla

Subjects
millimeter waves, numerical and experimental dosimetry, transcriptomics, glioblastoma organoids, combined treatments, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

IntroductionGlioblastoma (grade IV) is the most aggressive primary brain tumor in adults, representing one of the biggest therapeutic challenges due to its highly aggressive nature. In this study, we investigated the impact of millimeter waves on tridimensional glioblastoma organoids derived directly from patient tumors. Our goal was to explore novel therapeutic possibilities in the fight against this challenging disease.MethodsThe exposure setup was meticulously developed in-house, and we employed a comprehensive dosimetry approach, combining numerical and experimental methods. Biological endpoints included a global transcriptional profiling analysis to highlight possible deregulated pathways, analysis of cell morphological changes, and cell phenotypic characterization which are all important players in the control of glioblastoma progression.Results and discussionOur results revealed a significant effect of continuous millimeter waves at 30.5 GHz on cell proliferation and apoptosis, although without affecting the differentiation status of glioblastoma cells composing the organoids. Excitingly, when applying a power level of 0.1 W (Root Mean Square), we discovered a remarkable (statistically significant) therapeutic effect when combined with the chemotherapeutic agent Temozolomide, leading to increased glioblastoma cell death. These findings present a promising interventional window for treating glioblastoma cells, harnessing the potential therapeutic benefits of 30.5 GHz CW exposure. Temperature increase during treatments was carefully monitored and simulated with a good agreement, demonstrating a negligible involvement of the temperature elevation for the observed effects. By exploring this innovative approach, we pave the way for improved future treatments of glioblastoma that has remained exceptionally challenging until now.

Published
2024
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34. DENV-2 Outbreak Associated With Cosmopolitan Genotype Emergence in Western Brazilian Amazon

Author

Jessiane Rodrigues Ribeiro, Tárcio Peixoto Roca, Gleense dos Santos Cartonilho, Ana Maísa Passos-Silva, Hillquias Monteiro Moreira, Karolaine Santos Teixeira, André Luiz Ferreira da Silva, Celina Aparecida Bertoni Lugtenburg, Alcione Oliveira dos Santos, Juan Miguel Villalobos Salcedo, Valdinete Alves do Nascimento, Victor Costa de Souza, Rosemary Aparecida Roque, Marco Aurélio Krieger, Felipe Gomes Naveca, Rita de Cássia Pontello Rampazzo, and Deusilene Souza Vieira

Subjects
Biology (General), QH301-705.5
Abstract

Background: Dengue virus (DENV) causes an important disease and directly affects public health, being the arbovirus that presents the highest number of infections and deaths in the Western Brazilian Amazon. This virus is divided into 4 serotypes that have already circulated in the region. Methodology: Molecular characterization of a cohort containing 841 samples collected from febrile patients between 2021 and 2023 was analyzed using a commercial kit to detect the main arboviruses circulating in Brazil: Zika, DENV-1, DENV-2, DENV-3, DENV-4 and, Chikungunya. Subsequently, Sanger sequencing was performed for positive samples. Results: The cohort detected 162 positive samples, 12 for DENV-1 and 150 identified as DENV-2, indicating co-circulation of serotypes. The samples were subjected to sequencing and the analysis of the sequences that obtained good quality revealed that 5 samples belonged to the V genotype of DENV-1 and 46 were characterized as DENV-2 Cosmopolitan genotype—lineage 5. Conclusion: The results allowed us to identify for the first time the Cosmopolitan genotype in Rondônia, Brazilian Western Amazon, and its fast spread dispersion.

Published
2024
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35. Machine and deep learning driven models for the design of heat exchangers with micro-finned tubes

Author

Emad Efatinasab, Nima Irannezhad, Mirco Rampazzo, and Andrea Diani

Subjects
Condensation, Micro-finned tube, Artificial neural network, Machine learning, Deep learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765
Abstract

The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insights, aid in optimization, and allow us to explore various design parameters efficiently. However, existing empirical models often fall short in facilitating an optimal design because of their limited accuracy, sensitivity to assumption, and context dependency. In this scenario, the use of Machine and Deep Learning (ML and DL) methods can enhance accuracy, manage nonlinearity, adjust to varying conditions, decrease dependence on assumptions, automatically extract pertinent features, and provide scalability. Indeed, ML and DL techniques can derive valuable insights from datasets, contributing to a comprehensive understanding. By means of multiple ML and DL methods, this paper addresses the challenge of estimating key parameters in micro-finned tube heat exchangers such as the heat transfer coefficient (HTC) and frictional pressure drop (FPD). The methods have been trained and tested using an experimental dataset consisting of over a thousand data points associated with flow condensation, involving various tube geometries. In this context, the Artificial Neural Network (ANN) demonstrates superior performance in accurately estimating parameters with MAEs in the range below 4.5% for both HTC and FPD. Finally, recognizing the importance of comprehending the internal mechanisms of the black-box ANN model, the paper explores its interpretability aspects.

Published
2024
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36. Molecular and functional profiling of chemotolerant cells unveils nucleoside metabolism-dependent vulnerabilities in medulloblastoma

Author

Elena Mariotto, Elena Rampazzo, Roberta Bortolozzi, Fatlum Rruga, Ilaria Zeni, Lorenzo Manfreda, Chiara Marchioro, Martina Canton, Alice Cani, Ruben Magni, Alessandra Luchini, Silvia Bresolin, Giampietro Viola, and Luca Persano

Subjects
Chemotherapy resistance, Medulloblastoma, High throughput drug screening, Antimetabolites, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Chemotherapy resistance is considered one of the main causes of tumor relapse, still challenging researchers for the identification of the molecular mechanisms sustaining its emergence. Here, we setup and characterized chemotherapy-resistant models of Medulloblastoma (MB), one of the most lethal pediatric brain tumors, to uncover targetable vulnerabilities associated to their resistant phenotype. Integration of proteomic, transcriptomic and kinomic data revealed a significant deregulation of several pathways in resistant MB cells, converging to cell metabolism, RNA/protein homeostasis, and immune response, eventually impacting on patient outcome. Moreover, resistant MB cell response to a large library of compounds through a high-throughput screening (HTS), highlighted nucleoside metabolism as a relevant vulnerability of chemotolerant cells, with peculiar antimetabolites demonstrating increased efficacy against them and even synergism with conventional chemotherapeutics. Our results suggest that drug-resistant cells significantly rewire multiple cellular processes, allowing their adaptation to a chemotoxic environment, nevertheless exposing alternative actionable susceptibilities for their specific targeting.

Published
2023
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37. Phenotype and Clinical Outcomes in Desmin-Related Arrhythmogenic Cardiomyopathy

Author

Bermudez-Jimenez, Francisco J., Protonotarios, Alexandros, García-Hernández, Soledad, Pérez Asensio, Ana, Rampazzo, Alessandra, Zorio, Esther, Brodehl, Andreas, Arias, Miguel A., Macías-Ruiz, Rosa, Fernández-Armenta, Juan, Remior Perez, Paloma, Muñoz-Esparza, Carmen, Pilichou, Kalliopi, Bauce, Barbara, Merino, Jose L., Moliner-Abós, Carlos, Ochoa, Juan P., Barriales-Villa, Roberto, Garcia-Pavia, Pablo, Lopes, Luis R., Syrris, Petros, Corrado, Domenico, Elliott, Perry M., McKenna, William J., and Jimenez-Jaimez, Juan

Published
2024
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44. Rose or Red, but Still under Threat: Comparing Microplastics Ingestion between Two Sympatric Marine Crustacean Species (Aristaeomorpha foliacea and Parapenaeus longirostris)

Author

Laura Ciaralli, Tommaso Valente, Eleonora Monfardini, Giovanni Libralato, Loredana Manfra, Daniela Berto, Federico Rampazzo, Giorgia Gioacchini, Giulia Chemello, Raffaella Piermarini, Cecilia Silvestri, and Marco Matiddi

Subjects
plastic pollution, MPs, trophic ecology, stable isotope analysis, decapoda, shrimps, Veterinary medicine, SF600-1100, Zoology, QL1-991
Abstract

Increasing plastic contamination poses a serious threat to marine organisms. Microplastics (MPs) ingestion can represent a risk for the organism itself and for the ultimate consumer. Through the analysis of the gastrointestinal tract, coupled with stable isotope analysis on the muscle tissue, this study provides insights into the relationship between MPs pollution and ecology in two commercial marine species caught in the Central Tyrrhenian Sea: Aristaeomorpha foliacea and Parapenaeus longirostris. Stable isotope analysis was conducted to determine the trophic position and the trophic niche width. The gastrointestinal tracts were processed, and the resultant MPs were analysed under FT-IR spectroscopy to estimate the occurrence, abundance, and typology of the ingested MPs. The trophic level of the species was similar (P. longirostris TP = 3 ± 0.10 and A. foliacea TP = 3.1 ± 0.08), with an important trophic niche overlap, where 38% and 52% of P. longirostris and A. foliacea has ingested MPs, respectively. Though species-level differences may not be evident regarding MP’s abundance per individual, a high degree of dissimilarity was noted in the typologies of ingested particles. This research provides valuable insights into how MPs enter marine trophic webs, stressing that isotopic niche analysis should be combined with other methods to explain in detail the differences in MPs ingestion.

Published
2024
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