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1.
Safety of two-dose schedule of COVID-19 adsorbed inactivated vaccine (CoronaVac; Sinovac/Butantan) and heterologous additional doses of mRNA BNT162b2 (Pfizer/BioNTech) in immunocompromised and immunocompetent individuals
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Miyaji, Karina Takesaki
; Ibrahim, Karim Yaqub
; Infante, Vanessa
; Moreira, Raquel Megale
; Santos, Carolina Ferreira dos
Belizário, Juliana de Cássia
Pinto, Maria Isabel de Moraes
Marinho, Ana Karolina Barreto Berselli
Pereira, Juliana Marquezi
Mello, Liliane Saraiva de
Silva, Vitor Gabriel Lopes da
Sato, Paula Keiko
Strabelli, Tânia Mara Varejão
Ragiotto, Lucas
Pacheco, Pedro Henrique Theotonio de Mesquita
Braga, Patricia Emilia
Loch, Ana Paula
Precioso, Alexander Roberto
Sartori, Ana Marli Christovam
França, João Ítalo
Lima, Marcos Alves de
Ando, Mauricio Cesar Sampaio
Rodrigues, Camila Cristina Martini
Song, Alice Tung Wan
Lara, Amanda Nazareth
Belizário, Ana Cristina
Lima, Anna Helena Simões Bortulucci de
Zanetti, Ariane Cristina Barboza
Paulo, Audrey Rose da Silveira Amancio de
Rosa, Barbara Miranda dos Santos
Moraes, Bruna Del Guerra de Carvalho
Oliveira, Bruna Ribeiro de
Picone, Camila de Melo
Aranda, Carolina Sanches
Troli, Carolinne Paioli
Kokron, Cristina M.
Terrabuio, Debora Raquel Benedita
Abdala, Edson
David Neto, Elias
Nakanishi, Érika Yoshie Shimoda
Lima, Fabiana Mascarenhas Souza
Firmino, Fabio Batista
Santos, Fernanda Barone Alves dos
Bacal, Fernando
Fatobene, Giancarlo
Santana, Jaqueline Oliveira
Kalil, Jorge
Barbosa, Julia
Gonçalves, Leandro Peres
Otuyama, Leonardo Jun
Pierrotti, Ligia Camera
Compte, Livia Caroline Mariano
Marinho, Livia
Chaer, Livia Netto
Seguro, Luis Fernando
Azevedo, Luiz Sergio
Ueda, Márcia Aiko
Terreri, Maria Teresa
Barros, Myrthes Anna Maragna Toledo
Grecco, Octávio
Sejas, Odeli Nicole Encinas
Musqueira, Priscila Tavares
Ito, Raquel Keiko de Luca
Teixeira, Samia Silveira Souza
Fidalgo, Serafim
Costa, Silvia Figueiredo
Campos, Silvia Vidal
Fernandes, Tamiris Hinsching
Rocha, Vanderson Geraldo
Coelho, Vivian Caso




Revista do Instituto de Medicina Tropical de São Paulo
- Métricas do periódico
ABSTRACT Immunocompromised individuals were considered high-risk for severe disease due to SARS COV-2 infection. This study aimed to describe the safety of two doses of COVID-19 adsorbed inactivated vaccine (CoronaVac; Sinovac/Butantan), followed by additional doses of mRNA BNT162b2 (Pfizer/BioNTech) in immunocompromised (IC) adults, compared to immunocompetent/healthy (H) individuals. This phase 4, multicenter, open label study included solid organ transplant and hematopoietic stem cell transplant recipients, cancer patients and people with inborn errors of immunity with defects in antibody production, rheumatic, end-stage chronic kidney or liver disease, who were enrolled in the IC group. Participants received two doses of CoronaVac and additional doses of mRNA BNT162b2. Adverse reactions (AR) data were collected within seven days after each vaccination. Serious adverse events and of special interest (AESI) were monitored throughout the study. We included 241 immunocompromised and 100 immunocompetent subjects. Arthralgia, fatigue, myalgia, and nausea were more frequent in the IC group after CoronaVac. Following the first additional dose of mRNA BNT162, pain, induration, and tenderness at injection site, fatigue and myalgia were more frequent in the H group. A heart transplant recipient had a graft rejection temporally associated with the second CoronaVac dose, but there was no literature evidence of causal association. Four cases of AESI were considered related to the vaccine: three erythema multiforme after CoronaVac, all in IC participants, and one paresthesia after mRNA, in a H participant. Our findings were comparable to other studies that evaluated the safety of COVID-19 vaccines in different immunocompromised populations. Both vaccines were safe for immunocompromised participants.
2.
Geography and public health: analysis of the epidemiological dynamics of meningitis in Brazil, between 2010 and 2019
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Silva, Luis Roberto da
; Arruda, Laís Eduarda Silva de
; Barreto, Isabel de Jesus Brandão
; Aragão, João Victor Rodrigues de
; Silva, Maria Luiza Ferreira Imburana da
; Lira, Guilherme
; Teixeira, Camila Maria Barros
; Oliveira, Emília Carolle Azevedo de
.








RESUMO Objetivo: Analisar a dinâmica epidemiológica espaçotemporal das meningites no Brasil, entre os anos de 2010 e 2019. Métodos: Estudo ecológico descritivo com os casos e óbitos por meningites no Brasil (2010–2019) no Sistema de Informações de Agravos de Notificação. Realizaram-se (I) análises de frequências dos casos e óbitos, taxas de prevalência, mortalidade, letalidade, testes de exato de Fisher e qui-quadrado; (II) regressão de Prais-Winsten; e (III) índice de Moran global, local e densidade de Kernel. Resultados: Notificaram-se 182.126 casos de meningites no Brasil, dos quais 16.866 (9,26%) evoluíram para óbito, com taxas de prevalência de 9,03/100.000/habitantes, mortalidade de 0,84/100.000/habitantes e letalidade de 9,26%. Destaca-se a tendência de decrescimento das taxas de prevalência (−9,5%, intervalo de confiança de 95% — IC95% −13,92; −4,96, p<0,01) e mortalidade (−11,74%, IC95% −13,92; −9,48, p<01,01), enquanto a letalidade se manteve estacionária (−2,08%, IC95% −4,9; 0,8; p<0,1941). A maioria dos casos foi de meningites virais (45,7%), entre 1 e 9 anos (32,2%), enquanto a maior parcela dos óbitos foi por meningites bacterianas (68%), entre 40 e 59 anos (26,3%). Nos mapas de Moran e Kernel das taxas de prevalência e mortalidade, destacaram-se com altas taxas os municípios do sul, sudeste e a capital de Pernambuco, no nordeste; já na letalidade, evidenciaram-se o norte, o nordeste e o litoral do sudeste. Conclusão: Encontrou-se decréscimo dos casos e óbitos por meningites neste estudo, entretanto a taxa de letalidade foi maior em áreas com menor prevalência, reforçando a necessidade do aprimoramento das ações de identificação, vigilância e assistência em saúde dos casos, bem como da ampliação da cobertura vacinal.
ABSTRACT Objective: To analyze the spatiotemporal epidemiological dynamics of meningitis in Brazil, between 2010 and 2019. Methods: Descriptive ecological study with cases and deaths due to meningitis in Brazil (2010-2019) in the National Notifiable Diseases Information System (Sistema de Informações de Agravos de Notificação – SINAN). The following analyses were performed: (I) frequency analyses of cases and deaths, prevalence rates, mortality, lethality, Fisher's exact test, and chi-square test; (II) Prais-Winstein regression; and (III) Global, Local Moran's index, and Kernel density. Results: 182,126 cases of meningitis were reported in Brazil, of which 16,866 (9.26%) resulted in death, with prevalence rates of 9.03/100,000 inhabitants, mortality of 0.84/100,000 inhabitants, and lethality of 9.26%. There was a noted trend of decreasing prevalence rates (−9.5%, 95% confidence interval — 95%CI −13.92; −4.96, p<0.01) and mortality (−11.74%, 95%CI −13.92; −9.48, p<0.01), while lethality remained stable (−2.08%, 95%CI −4.9; 0.8; p<0.1941). The majority of cases were viral meningitis (45.7%), among 1-9 years old (32.2%), while the highest proportion of deaths was due to bacterial meningitis (68%), among 40-59 years old (26.3%). In the Moran and Kernel maps of prevalence and mortality rates, municipalities in the South, Southeast, and the capital of Pernambuco in the Northeast stood out with high rates; as for lethality, the North, Northeast, and Southeast coastal areas were highlighted. Conclusion: A decrease in meningitis cases and deaths was found in this study; however, the lethality rate was higher in areas with lower prevalence, emphasizing the need to enhance actions for identifying, monitoring, and providing health care for cases, as well as expanding vaccination coverage.
3.
Catálogo Taxonômico da Fauna do Brasil: Setting the baseline knowledge on the animal diversity in Brazil
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Boeger, Walter A.
; Valim, Michel P.
; Zaher, Hussam
; Rafael, José A.
; Forzza, Rafaela C.
; Percequillo, Alexandre R.
; Serejo, Cristiana S.
; Garraffoni, André R.S.
; Santos, Adalberto J.
Slipinski, Adam
Linzmeier, Adelita M.
Calor, Adolfo R.
Garda, Adrian A.
Kury, Adriano B.
Fernandes, Agatha C.S.
Agudo-Padrón, Aisur I.
Akama, Alberto
Silva Neto, Alberto M. da
Burbano, Alejandro L.
Menezes, Aleksandra
Pereira-Colavite, Alessandre
Anichtchenko, Alexander
Lees, Alexander C.
Bezerra, Alexandra M.R.
Domahovski, Alexandre C.
Pimenta, Alexandre D.
Aleixo, Alexandre L.P.
Marceniuk, Alexandre P.
Paula, Alexandre S. de
Somavilla, Alexandre
Specht, Alexandre
Camargo, Alexssandro
Newton, Alfred F.
Silva, Aline A.S. da
Santos, Aline B. dos
Tassi, Aline D.
Aragão, Allan C.
Santos, Allan P.M.
Migotto, Alvaro E.
Mendes, Amanda C.
Cunha, Amanda
Chagas Júnior, Amazonas
Sousa, Ana A.T. de
Pavan, Ana C.
Almeida, Ana C.S.
Peronti, Ana L.B.G.
Henriques-Oliveira, Ana L.
Prudente, Ana L.
Tourinho, Ana L.
Pes, Ana M.O.
Carmignotto, Ana P.
Wengrat, Ana P.G. da Silva
Dornellas, Ana P.S.
Molin, Anamaria Dal
Puker, Anderson
Morandini, André C.
Ferreira, André da S.
Martins, André L.
Esteves, André M.
Fernandes, André S.
Roza, André S.
Köhler, Andreas
Paladini, Andressa
Andrade, Andrey J. de
Pinto, Ângelo P.
Salles, Anna C. de A.
Gondim, Anne I.
Amaral, Antonia C.Z.
Rondón, Antonio A.A.
Brescovit, Antonio
Lofego, Antônio C.
Marques, Antonio C.
Macedo, Antonio
Andriolo, Artur
Henriques, Augusto L.
Ferreira Júnior, Augusto L.
Lima, Aurino F. de
Barros, Ávyla R. de A.
Brito, Ayrton do R.
Romera, Bárbara L.V.
Vasconcelos, Beatriz M.C. de
Frable, Benjamin W.
Santos, Bernardo F.
Ferraz, Bernardo R.
Rosa, Brunno B.
Sampaio, Brunno H.L.
Bellini, Bruno C.
Clarkson, Bruno
Oliveira, Bruno G. de
Corrêa, Caio C.D.
Martins, Caleb C.
Castro-Guedes, Camila F. de
Souto, Camilla
Bicho, Carla de L.
Cunha, Carlo M.
Barboza, Carlos A. de M.
Lucena, Carlos A.S. de
Barreto, Carlos
Santana, Carlos D.C.M. de
Agne, Carlos E.Q.
Mielke, Carlos G.C.
Caetano, Carlos H.S.
Flechtmann, Carlos H.W.
Lamas, Carlos J.E.
Rocha, Carlos
Mascarenhas, Carolina S.
Margaría, Cecilia B.
Waichert, Cecilia
Digiani, Celina
Haddad, Célio F.B.
Azevedo, Celso O.
Benetti, Cesar J.
Santos, Charles M.D. dos
Bartlett, Charles R.
Bonvicino, Cibele
Ribeiro-Costa, Cibele S.
Santos, Cinthya S.G.
Justino, Cíntia E.L.
Canedo, Clarissa
Bonecker, Claudia C.
Santos, Cláudia P.
Carvalho, Claudio J.B. de
Gonçalves, Clayton C.
Galvão, Cleber
Costa, Cleide
Oliveira, Cléo D.C. de
Schwertner, Cristiano F.
Andrade, Cristiano L.
Pereira, Cristiano M.
Sampaio, Cristiano
Dias, Cristina de O.
Lucena, Daercio A. de A.
Manfio, Daiara
Amorim, Dalton de S.
Queiroz, Dalva L. de
Queiroz, Dalva L. de
Colpani, Daniara
Abbate, Daniel
Aquino, Daniel A.
Burckhardt, Daniel
Cavallari, Daniel C.
Prado, Daniel de C. Schelesky
Praciano, Daniel L.
Basílio, Daniel S.
Bená, Daniela de C.
Toledo, Daniela G.P. de
Takiya, Daniela M.
Fernandes, Daniell R.R.
Ament, Danilo C.
Cordeiro, Danilo P.
Silva, Darliane E.
Pollock, Darren A.
Muniz, David B.
Gibson, David I.
Nogueira, David S.
Marques, Dayse W.A.
Lucatelli, Débora
Garcia, Deivys M.A.
Baêta, Délio
Ferreira, Denise N.M.
Rueda-Ramírez, Diana
Fachin, Diego A.
Souza, Diego de S.
Rodrigues, Diego F.
Pádua, Diego G. de
Barbosa, Diego N.
Dolibaina, Diego R.
Amaral, Diogo C.
Chandler, Donald S.
Maccagnan, Douglas H.B.
Caron, Edilson
Carvalho, Edrielly
Adriano, Edson A.
Abreu Júnior, Edson F. de
Pereira, Edson H.L.
Viegas, Eduarda F.G.
Carneiro, Eduardo
Colley, Eduardo
Eizirik, Eduardo
Santos, Eduardo F. dos
Shimbori, Eduardo M.
Suárez-Morales, Eduardo
Arruda, Eliane P. de
Chiquito, Elisandra A.
Lima, Élison F.B.
Castro, Elizeu B. de
Orlandin, Elton
Nascimento, Elynton A. do
Razzolini, Emanuel
Gama, Emanuel R.R.
Araujo, Enilma M. de
Nishiyama, Eric Y.
Spiessberger, Erich L.
Santos, Érika C.L. dos
Contreras, Eugenia F.
Galati, Eunice A.B.
Oliveira Junior, Evaldo C. de
Gallardo, Fabiana
Hernandes, Fabio A.
Lansac-Tôha, Fábio A.
Pitombo, Fabio B.
Dario, Fabio Di
Santos, Fábio L. dos
Mauro, Fabio
Nascimento, Fabio O. do
Olmos, Fabio
Amaral, Fabio R.
Schunck, Fabio
Godoi, Fábio S. P. de
Machado, Fabrizio M.
Barbo, Fausto E.
Agrain, Federico A.
Ribeiro, Felipe B.
Moreira, Felipe F.F.
Barbosa, Felipe F.
Silva, Fenanda S.
Cavalcanti, Fernanda F.
Straube, Fernando C.
Carbayo, Fernando
Carvalho Filho, Fernando
Zanella, Fernando C.V.
Jacinavicius, Fernando de C.
Farache, Fernando H.A.
Leivas, Fernando
Dias, Fernando M.S.
Mantellato, Fernando
Vaz-de-Mello, Fernando Z.
Gudin, Filipe M.
Albuquerque, Flávio
Molina, Flavio B.
Passos, Flávio D.
Shockley, Floyd W.
Pinheiro, Francielly F.
Mello, Francisco de A.G. de
Nascimento, Francisco E. de L.
Franco, Francisco L.
Oliveira, Francisco L. de
Melo, Francisco T. de V.
Quijano, Freddy R.B.
Salles, Frederico F.
Biffi, Gabriel
Queiroz, Gabriel C.
Bizarro, Gabriel L.
Hrycyna, Gabriela
Leviski, Gabriela
Powell, Gareth S.
Santos, Geane B. dos
Morse, Geoffrey E.
Brown, George
Mattox, George M.T.
Zimbrão, Geraldo
Carvalho, Gervásio S.
Miranda, Gil F.G.
Moraes, Gilberto J. de
Lourido, Gilcélia M.
Neves, Gilmar P.
Moreira, Gilson R.P.
Montingelli, Giovanna G.
Maurício, Giovanni N.
Marconato, Gláucia
Lopez, Guilherme E.L.
Silva, Guilherme L. da
Muricy, Guilherme
Brito, Guilherme R.R.
Garbino, Guilherme S.T.
Flores, Gustavo E.
Graciolli, Gustavo
Libardi, Gustavo S.
Proctor, Heather C.
Gil-Santana, Helcio R.
Varella, Henrique R.
Escalona, Hermes E.
Schmitz, Hermes J.
Rodrigues, Higor D.D.
Galvão Filho, Hilton de C.
Quintino, Hingrid Y.S.
Pinto, Hudson A.
Rainho, Hugo L.
Miyahira, Igor C.
Gonçalves, Igor de S.
Martins, Inês X.
Cardoso, Irene A.
Oliveira, Ismael B. de
Franz, Ismael
Fernandes, Itanna O.
Golfetti, Ivan F.
S. Campos-Filho, Ivanklin
Oliveira, Ivo de S.
Delabie, Jacques H.C.
Oliveira, Jader de
Prando, Jadila S.
Patton, James L.
Bitencourt, Jamille de A.
Silva, Janaina M.
Santos, Jandir C.
Arruda, Janine O.
Valderrama, Jefferson S.
Dalapicolla, Jeronymo
Oliveira, Jéssica P.
Hájek, Jiri
Morselli, João P.
Narita, João P.
Martin, João P.I.
Grazia, Jocélia
McHugh, Joe
Cherem, Jorge J.
Farias Júnior, José A.S.
Fernandes, Jose A.M.
Pacheco, José F.
Birindelli, José L.O.
Rezende, José M.
Avendaño, Jose M.
Duarte, José M. Barbanti
Ribeiro, José R. Inácio
Mermudes, José R.M.
Pujol-Luz, José R.
Santos, Josenilson R. dos
Câmara, Josenir T.
Teixeira, Joyce A.
Prado, Joyce R. do
Botero, Juan P.
Almeida, Julia C.
Kohler, Julia
Gonçalves, Julia P.
Beneti, Julia S.
Donahue, Julian P.
Alvim, Juliana
Almeida, Juliana C.
Segadilha, Juliana L.
Wingert, Juliana M.
Barbosa, Julianna F.
Ferrer, Juliano
Santos, Juliano F. dos
Kuabara, Kamila M.D.
Nascimento, Karine B.
Schoeninger, Karine
Campião, Karla M.
Soares, Karla
Zilch, Kássia
Barão, Kim R.
Teixeira, Larissa
Sousa, Laura D. do N.M. de
Dumas, Leandro L.
Vieira, Leandro M.
Azevedo, Leonardo H.G.
Carvalho, Leonardo S.
Souza, Leonardo S. de
Rocha, Leonardo S.G.
Bernardi, Leopoldo F.O.
Vieira, Letícia M.
Johann, Liana
Salvatierra, Lidianne
Oliveira, Livia de M.
Loureiro, Lourdes M.A. El-moor
Barreto, Luana B.
Barros, Luana M.
Lecci, Lucas
Camargos, Lucas M. de
Lima, Lucas R.C.
Almeida, Lucia M.
Martins, Luciana R.
Marinoni, Luciane
Moura, Luciano de A.
Lima, Luciano
Naka, Luciano N.
Miranda, Lucília S.
Salik, Lucy M.
Bezerra, Luis E.A.
Silveira, Luis F.
Campos, Luiz A.
Castro, Luiz A.S. de
Pinho, Luiz C.
Silveira, Luiz F.L.
Iniesta, Luiz F.M.
Tencatt, Luiz F.C.
Simone, Luiz R.L.
Malabarba, Luiz R.
Cruz, Luiza S. da
Sekerka, Lukas
Barros, Lurdiana D.
Santos, Luziany Q.
Skoracki, Maciej
Correia, Maira A.
Uchoa, Manoel A.
Andrade, Manuella F.G.
Hermes, Marcel G.
Miranda, Marcel S.
Araújo, Marcel S. de
Monné, Marcela L.
Labruna, Marcelo B.
Santis, Marcelo D. de
Duarte, Marcelo
Knoff, Marcelo
Nogueira, Marcelo
Britto, Marcelo R. de
Melo, Marcelo R.S. de
Carvalho, Marcelo R. de
Tavares, Marcelo T.
Kitahara, Marcelo V.
Justo, Marcia C.N.
Botelho, Marcia J.C.
Couri, Márcia S.
Borges-Martins, Márcio
Felix, Márcio
Oliveira, Marcio L. de
Bologna, Marco A.
Gottschalk, Marco S.
Tavares, Marcos D.S.
Lhano, Marcos G.
Bevilaqua, Marcus
Santos, Marcus T.T.
Domingues, Marcus V.
Sallum, Maria A.M.
Digiani, María C.
Santarém, Maria C.A.
Nascimento, Maria C. do
Becerril, María de los A.M.
Santos, Maria E.A. dos
Passos, Maria I. da S. dos
Felippe-Bauer, Maria L.
Cherman, Mariana A.
Terossi, Mariana
Bartz, Marie L.C.
Barbosa, Marina F. de C.
Loeb, Marina V.
Cohn-Haft, Mario
Cupello, Mario
Martins, Marlúcia B.
Christofersen, Martin L.
Bento, Matheus
Rocha, Matheus dos S.
Martins, Maurício L.
Segura, Melissa O.
Cardenas, Melissa Q.
Duarte, Mércia E.
Ivie, Michael A.
Mincarone, Michael M.
Borges, Michela
Monné, Miguel A.
Casagrande, Mirna M.
Fernandez, Monica A.
Piovesan, Mônica
Menezes, Naércio A.
Benaim, Natalia P.
Reategui, Natália S.
Pedro, Natan C.
Pecly, Nathalia H.
Ferreira Júnior, Nelson
Silva Júnior, Nelson J. da
Perioto, Nelson W.
Hamada, Neusa
Degallier, Nicolas
Chao, Ning L.
Ferla, Noeli J.
Mielke, Olaf H.H.
Evangelista, Olivia
Shibatta, Oscar A.
Oliveira, Otto M.P.
Albornoz, Pablo C.L.
Dellapé, Pablo M.
Gonçalves, Pablo R.
Shimabukuro, Paloma H.F.
Grossi, Paschoal
Rodrigues, Patrícia E. da S.
Lima, Patricia O.V.
Velazco, Paul
Santos, Paula B. dos
Araújo, Paula B.
Silva, Paula K.R.
Riccardi, Paula R.
Garcia, Paulo C. de A.
Passos, Paulo G.H.
Corgosinho, Paulo H.C.
Lucinda, Paulo
Costa, Paulo M.S.
Alves, Paulo P.
Roth, Paulo R. de O.
Coelho, Paulo R.S.
Duarte, Paulo R.M.
Carvalho, Pedro F. de
Gnaspini, Pedro
Souza-Dias, Pedro G.B.
Linardi, Pedro M.
Bartholomay, Pedro R.
Demite, Peterson R.
Bulirsch, Petr
Boll, Piter K.
Pereira, Rachel M.M.
Silva, Rafael A.P.F.
Moura, Rafael B. de
Boldrini, Rafael
Silva, Rafaela A. da
Falaschi, Rafaela L.
Cordeiro, Ralf T.S.
Mello, Ramon J.C.L.
Singer, Randal A.
Querino, Ranyse B.
Heleodoro, Raphael A.
Castilho, Raphael de C.
Constantino, Reginaldo
Guedes, Reinaldo C.
Carrenho, Renan
Gomes, Renata S.
Gregorin, Renato
Machado, Renato J.P.
Bérnils, Renato S.
Capellari, Renato S.
Silva, Ricardo B.
Kawada, Ricardo
Dias, Ricardo M.
Siewert, Ricardo
Brugnera, Ricaro
Leschen, Richard A.B.
Constantin, Robert
Robbins, Robert
Pinto, Roberta R.
Reis, Roberto E. dos
Ramos, Robson T. da C.
Cavichioli, Rodney R.
Barros, Rodolfo C. de
Caires, Rodrigo A.
Salvador, Rodrigo B.
Marques, Rodrigo C.
Araújo, Rodrigo C.
Araujo, Rodrigo de O.
Dios, Rodrigo de V.P.
Johnsson, Rodrigo
Feitosa, Rodrigo M.
Hutchings, Roger W.
Lara, Rogéria I.R.
Rossi, Rogério V.
Gerstmeier, Roland
Ochoa, Ronald
Hutchings, Rosa S.G.
Ale-Rocha, Rosaly
Rocha, Rosana M. da
Tidon, Rosana
Brito, Rosangela
Pellens, Roseli
Santos, Sabrina R. dos
Santos, Sandra D. dos
Paiva, Sandra V.
Santos, Sandro
Oliveira, Sarah S. de
Costa, Sávio C.
Gardner, Scott L.
Leal, Sebastián A. Muñoz
Aloquio, Sergio
Bonecker, Sergio L.C.
Bueno, Sergio L. de S.
Almeida, Sérgio M. de
Stampar, Sérgio N.
Andena, Sérgio R.
Posso, Sergio R.
Lima, Sheila P.
Gadelha, Sian de S.
Thiengo, Silvana C.
Cohen, Simone C.
Brandão, Simone N.
Rosa, Simone P.
Ribeiro, Síria L.B.
Letana, Sócrates D.
Santos, Sonia B. dos
Andrade, Sonia C.S.
Dávila, Stephane
Vaz, Stéphanie
Peck, Stewart B.
Christo, Susete W.
Cunha, Suzan B.Z.
Gomes, Suzete R.
Duarte, Tácio
Madeira-Ott, Taís
Marques, Taísa
Roell, Talita
Lima, Tarcilla C. de
Sepulveda, Tatiana A.
Maria, Tatiana F.
Ruschel, Tatiana P.
Rodrigues, Thaiana
Marinho, Thais A.
Almeida, Thaís M. de
Miranda, Thaís P.
Freitas, Thales R.O.
Pereira, Thalles P.L.
Zacca, Thamara
Pacheco, Thaynara L.
Martins, Thiago F.
Alvarenga, Thiago M.
Carvalho, Thiago R. de
Polizei, Thiago T.S.
McElrath, Thomas C.
Henry, Thomas
Pikart, Tiago G.
Porto, Tiago J.
Krolow, Tiago K.
Carvalho, Tiago P.
Lotufo, Tito M. da C.
Caramaschi, Ulisses
Pinheiro, Ulisses dos S.
Pardiñas, Ulyses F.J.
Maia, Valéria C.
Tavares, Valeria
Costa, Valmir A.
Amaral, Vanessa S. do
Silva, Vera C.
Wolff, Vera R. dos S.
Slobodian, Verônica
Silva, Vinícius B. da
Espíndola, Vinicius C.
Costa-Silva, Vinicius da
Bertaco, Vinicius de A.
Padula, Vinícius
Ferreira, Vinicius S.
Silva, Vitor C.P. da
Piacentini, Vítor de Q.
Sandoval-Gómez, Vivian E.
Trevine, Vivian
Sousa, Viviane R.
Sant’Anna, Vivianne B. de
Mathis, Wayne N.
Souza, Wesley de O.
Colombo, Wesley D.
Tomaszewska, Wioletta
Wosiacki, Wolmar B.
Ovando, Ximena M.C.
Leite, Yuri L.R.








ABSTRACT The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others.
4.
Quantile regression for genomic selection of growth curves
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Nascimento, Ana Carolina Campana
; Azevedo, Camila Ferreira
Barreto, Cynthia Aparecida Valiati
Oliveira, Gabriela França
Nascimento, Moysés

ABSTRACT. This study evaluated the efficiency of genome-wide selection (GWS) based on regularized quantile regression (RQR) to obtain genomic growth curves based on genomic estimated breeding values (GEBV) of individuals with different probability distributions. The data were simulated and composed of 2,025 individuals from two generations and 435 markers randomly distributed across five chromosomes. The simulated phenotypes presented symmetrical, skewed, positive, and negative distributions. Data were analyzed using RQR considering nine quantiles (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) and traditional methods of genomic selection (specifically, RR-BLUP, BLASSO, BayesA, and BayesB). In general, RQR-based estimation of the GEBV was efficient-at least for a quantile model, the results obtained were more accurate than those obtained by the other evaluated methodologies. Specifically, in the symmetrical-distribution scenario, the highest accuracy values were obtained for the parameters with the models RQR0.4, RQR0.3, and RQR0.4. For positive skewness, the models RQR0.2, RQR0.3, and RQR0.1 presented higher accuracy values, whereas for negative skewness, the best model was RQR0.9. Finally, the GEBV vectors obtained by RQR facilitated the construction of genomic growth curves at different levels of interest (quantiles), illustrating the weight-age relationship.
5.
Geography and public health: analysis of the epidemiological dynamics of meningitis in Brazil, between 2010 and 2019
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Silva, Luis Roberto da
; Arruda, Laís Eduarda Silva de
; Barreto, Isabel de Jesus Brandão
; Aragão, João Victor Rodrigues de
; Silva, Maria Luiza Ferreira Imburana da
; Lira, Guilherme
; Teixeira, Camila Maria Barros
; Oliveira, Emília Carolle Azevedo de
.








ABSTRACT Objective: To analyze the spatiotemporal epidemiological dynamics of meningitis in Brazil, between 2010 and 2019. Methods: Descriptive ecological study with cases and deaths due to meningitis in Brazil (2010-2019) in the National Notifiable Diseases Information System (Sistema de Informações de Agravos de Notificação – SINAN). The following analyses were performed: (I) frequency analyses of cases and deaths, prevalence rates, mortality, lethality, Fisher's exact test, and chi-square test; (II) Prais-Winstein regression; and (III) Global, Local Moran's index, and Kernel density. Results: 182,126 cases of meningitis were reported in Brazil, of which 16,866 (9.26%) resulted in death, with prevalence rates of 9.03/100,000 inhabitants, mortality of 0.84/100,000 inhabitants, and lethality of 9.26%. There was a noted trend of decreasing prevalence rates (−9.5%, 95% confidence interval — 95%CI −13.92; −4.96, p<0.01) and mortality (−11.74%, 95%CI −13.92; −9.48, p<0.01), while lethality remained stable (−2.08%, 95%CI −4.9; 0.8; p<0.1941). The majority of cases were viral meningitis (45.7%), among 1-9 years old (32.2%), while the highest proportion of deaths was due to bacterial meningitis (68%), among 40-59 years old (26.3%). In the Moran and Kernel maps of prevalence and mortality rates, municipalities in the South, Southeast, and the capital of Pernambuco in the Northeast stood out with high rates; as for lethality, the North, Northeast, and Southeast coastal areas were highlighted. Conclusion: A decrease in meningitis cases and deaths was found in this study; however, the lethality rate was higher in areas with lower prevalence, emphasizing the need to enhance actions for identifying, monitoring, and providing health care for cases, as well as expanding vaccination coverage.
RESUMO Objetivo: Analisar a dinâmica epidemiológica espaçotemporal das meningites no Brasil, entre os anos de 2010 e 2019. Métodos: Estudo ecológico descritivo com os casos e óbitos por meningites no Brasil (2010–2019) no Sistema de Informações de Agravos de Notificação. Realizaram-se (I) análises de frequências dos casos e óbitos, taxas de prevalência, mortalidade, letalidade, testes de exato de Fisher e qui-quadrado; (II) regressão de Prais-Winsten; e (III) índice de Moran global, local e densidade de Kernel. Resultados: Notificaram-se 182.126 casos de meningites no Brasil, dos quais 16.866 (9,26%) evoluíram para óbito, com taxas de prevalência de 9,03/100.000/habitantes, mortalidade de 0,84/100.000/habitantes e letalidade de 9,26%. Destaca-se a tendência de decrescimento das taxas de prevalência (−9,5%, intervalo de confiança de 95% — IC95% −13,92; −4,96, p<0,01) e mortalidade (−11,74%, IC95% −13,92; −9,48, p<01,01), enquanto a letalidade se manteve estacionária (−2,08%, IC95% −4,9; 0,8; p<0,1941). A maioria dos casos foi de meningites virais (45,7%), entre 1 e 9 anos (32,2%), enquanto a maior parcela dos óbitos foi por meningites bacterianas (68%), entre 40 e 59 anos (26,3%). Nos mapas de Moran e Kernel das taxas de prevalência e mortalidade, destacaram-se com altas taxas os municípios do sul, sudeste e a capital de Pernambuco, no nordeste; já na letalidade, evidenciaram-se o norte, o nordeste e o litoral do sudeste. Conclusão: Encontrou-se decréscimo dos casos e óbitos por meningites neste estudo, entretanto a taxa de letalidade foi maior em áreas com menor prevalência, reforçando a necessidade do aprimoramento das ações de identificação, vigilância e assistência em saúde dos casos, bem como da ampliação da cobertura vacinal.
6.
Genetic parameters and selection gain in tropical wheat populations via Bayesian inference
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Mezzomo, Henrique Caletti
; Casagrande, Cleiton Renato
; Azevedo, Camila Ferreira
; Borem, Aluízio
; Barros, Willian Silva
; Nardino, Maicon
.






RESUMO: O processo de desenvolvimento de uma nova cultivar de trigo requer tempo entre a obtenção da população base e a seleção da linhagem mais promissora. Estimar parâmetros genéticos com mais precisão nas primeiras gerações com vistas a antecipar a seleção significa avanços importantes para os programas de melhoramento de trigo. Assim, o presente estudo estima os parâmetros genéticos de populações F2 de trigo tropical e o ganho genético da seleção via abordagem Bayesiana. Para tanto, os autores avaliaram a produtividade de grãos por parcela de 34 populações F2 de trigo tropical. A abordagem Bayesiana proporcionou um ajuste adequado ao modelo, estimando parâmetros genéticos dentro do espaço paramétrico. A herdabilidade (h2) foi de 0,51. Dentre as selecionadas, 11 populações F2 obtiveram desempenho superior às cultivares controle, com ganho genético de seleção de 7,80%. As seguintes populações foram as mais promissoras: Tbio Sossego/CD 1303, CD 1303/Tbio Ponteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton e Tbio Aton/CD 1303. A inferência Bayesiana pode ser usada para melhorar significativamente programas de melhoramento de trigo tropical.
ABSTRACT: The development process of a new wheat cultivar requires time between obtaining the base population and selecting the most promising line. Estimating genetic parameters more accurately in early generations with a view to anticipating selection means important advances for wheat breeding programs. Thus, the present study estimated the genetic parameters of F2 populations of tropical wheat and the genetic gain from selection via the Bayesian approach. To this end, the authors assessed the grain yield per plot of 34 F2 populations of tropical wheat. The Bayesian approach provided an adequate fit to the model, estimating genetic parameters within the parametric space. Heritability (h2) was 0.51. Among those selected, 11 F2 populations performed better than the control cultivars, with genetic gain of 7.80%. The following populations were the most promising: TbioSossego/CD 1303, CD 1303/TbioPonteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton, and Tbio Aton/CD 1303. Bayesian inference can be used to significantly improve tropical wheat breeding programs.
7.
Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice
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Azevedo, Camila Ferreira
; Barreto, Cynthia Aparecida Valiati
; Suela, Matheus Massariol
; Nascimento, Moysés
; Silva Júnior, Antônio Carlos da
; Nascimento, Ana Carolina Campana
; Cruz, Cosme Damião
; Soraes, Plínio César
.








ABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs.
8.
Genome-enabled prediction through quantile random forest for complex traits Genomeenabled Genome enabled
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Valadares, Cristiane Botelho
; Nascimento, Moysés
; Celeri, Maurício de Oliveira
; Nascimento, Ana Carolina Campana
; Barroso, Laís Mayara Azevedo
; Sant’Anna, Isabela de Castro
; Azevedo, Camila Ferreira
.







RESUMO: Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas. RESUMO (QRF paramétrica RF (RF QR. QR . (QR) Especificamente lineares média epistasia dominância. dominância dominância) Adicionalmente GBLUP. GBLUP G BLUP. BLUP F 1000 1 000 1.00 4010 4 010 4.01 SNP disso Quantitative loci 12 03 0 3 0,3 05 5 0, 08 8 0,8 5fold. 5fold fold fold. 5-fold propostos epistático dominante. dominante dominante) fim comparado cenários complexas (QR 100 00 1.0 401 01 4.0 10 1. 40 4.
ABSTRACT: Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits. ABSTRACT (QRF nonparametric non parametric RF (RF QR. QR . (QR) Specifically nonlinear linear functions mean epistasis dominance. dominance dominance) addition GBLUP. GBLUP G BLUP. BLUP F 1000 1 000 1,00 4010 4 010 4,01 markers Besides nonadditive effects Quantitative loci 120 03 0 3 0.3 05 5 0.5 08 8 0.8 validation 5fold fold crossvalidation cross used calculated effect effects. effects) Finally compared scenarios (QR 100 00 1,0 401 01 4,0 12 0. 10 1, 40 4,
9.
Multiple-trait model by Bayesian inference applied to environment efficient Coffea arabica with low-nitrogen nutrient Multipletrait Multiple trait lownitrogen low nitrogen
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Silva Júnior, Antônio Carlos da
; Moura, Waldênia de Melo
; Torres, Lívia Gomes
; Santos, Iara Gonçalves dos
; Silva, Michele Jorge da
; Azevedo, Camila Ferreira
; Cruz, Cosme Damião
.







ABSTRACT Identifying Coffea arabica cultivars that are more efficient in the use of nitrogen is an important strategy and a necessity in the context of environmental and economic impacts attributed to excessive nitrogen fertilization. Although Coffea arabica breeding data have a multi-trait structure, they are often analyzed under a single trait structure. Thus, the objectives of this study were to use a Bayesian multitrait model, to estimate heritability in the broad sense, and to select arabica coffee cultivars with better genetic potential (desirable agronomic traits) in nitrogen-restricted cultivation. The experiment was carried out in a greenhouse with 20 arabica coffee cultivars grown in a nutrient solution with low-nitrogen content (1.5 mM). The experimental design used was in randomized blocks with three replications. Six agromorphological traits of the arabica coffee breeding program and five nutritional efficiency indices were used. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The agromorphological traits were considered highly heritable, with a credibility interval (95% probability): H2 = 0.9538 – 5.89E-01. The Bayesian multitrait model presents an adequate strategy for the genetic improvement of arabica coffee grown in low-nitrogen concentrations. Coffee arabica cultivars Icatu Precoce 3282, Icatu Vermelho IAC 4045, Acaiá Cerrado MG 1474, Tupi IAC 1669-33, Catucaí 785/15, Caturra Vermelho and Obatã IAC 1669/20 demonstrated greater potential for cultivation in low-nitrogen concentration. fertilization multi structure Thus sense desirable nitrogenrestricted restricted 2 lownitrogen low 1.5 15 1 5 (1. mM. mM . mM) replications values heritable 95% 95 (95 probability probability) H 09538 0 9538 0.953 5.89E01. 589E01 E 5.89E 01. 89E 01 5.89E-01 concentrations 3282 4045 1474 166933, 166933 1669 33, 33 1669-33 78515 785 785/15 166920 1669/2 concentration 1. (1 9 (9 0953 953 0.95 89E01 5.89E01 589E0 589E 5.89E-0 328 404 147 16693 166 3 1669-3 7851 78 785/1 16692 1669/ ( 095 0.9 89E0 5.89E0 5.89E- 32 40 14 16 1669- 7 785/ 09 0. 4
10.
Row-col method associated with frequentist and Bayesian statistics in a passion fruit population
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Souza, André Oliveira
; Viana, Alexandre Pio
Silva, Fabyano Fonseca e
Azevedo, Camila Ferreira
Cavalcante, Natan Ramos
Silva, Flavia Alves

Crop Breeding and Applied Biotechnology
- Métricas do periódico
Abstract This study was conducted to test the significance of adding row and column factors in the frequentist and Bayesian models used in the evaluation of a population of Passiflora edulis, as well as selecting promising genotypes to form the next generation. The following parameters were evaluated: number of fruits, yield, fruit weight, transverse fruit diameter, longitudinal fruit diameter, pulp percentage, skin thickness and total soluble solids. For the Bayesian model, two priors were considered, namely, inverse gamma and a priori distribution with extended parameters. The model with a priori distribution with extended parameters showed lower root mean square error and higher correlation coefficient between observed and predicted values than the inverse gamma model. Furthermore, for a selection intensity of 37%, the mixed and Bayesian models selected practically the same progenies in both experiments. The use of the 5-fold cross-validation technique indicated that both tested models were efficient.
11.
IMPACTO-MR: um estudo brasileiro de plataforma nacional para avaliar infecções e multirresistência em unidades de terapia intensiva IMPACTOMR IMPACTO MR IMPACTO-MR
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Tomazini, Bruno M
; Nassar Jr, Antonio Paulo
; Lisboa, Thiago Costa
; Azevedo, Luciano César Pontes de
; Veiga, Viviane Cordeiro
; Catarino, Daniela Ghidetti Mangas
; Fogazzi, Debora Vacaro
; Arns, Beatriz
; Piastrelli, Filipe Teixeira
; Dietrich, Camila
; Negrelli, Karina Leal
Jesuíno, Isabella de Andrade
Reis, Luiz Fernando Lima
Mattos, Renata Rodrigues de
Pinheiro, Carla Cristina Gomes
Luz, Mariane Nascimento
Spadoni, Clayse Carla da Silva
Moro, Elisângela Emilene
Bueno, Flávia Regina
Sampaio, Camila Santana Justo Cintra
Silva, Débora Patrício
Baldassare, Franca Pellison
Silva, Ana Cecilia Alcantara
Veiga, Thabata
Barbante, Leticia
Lambauer, Marianne
Campos, Viviane Bezerra
Santos, Elton
Santos, Renato Hideo Nakawaga
Laranjeiras, Ligia Nasi
Valeis, Nanci
Santucci, Eliana
Miranda, Tamiris Abait
Patrocínio, Ana Cristina Lagoeiro do
Carvalho, Andréa de
Sousa, Eduvirgens Maria Couto de
Sousa, Ancelmo Honorato Ferraz de
Malheiro, Daniel Tavares
Bezerra, Isabella Lott
Rodrigues, Mirian Batista
Malicia, Julliana Chicuta
Silva, Sabrina Souza da
Gimenes, Bruna dos Passos
Sesin, Guilhermo Prates
Zavascki, Alexandre Prehn
Sganzerla, Daniel
Medeiros, Gregory Saraiva
Santos, Rosa da Rosa Minho dos
Silva, Fernanda Kelly Romeiro
Cheno, Maysa Yukari
Abrahão, Carolinne Ferreira
Oliveira Junior, Haliton Alves de
Rocha, Leonardo Lima
Nunes Neto, Pedro Aniceto
Pereira, Valéria Chagas
Paciência, Luis Eduardo Miranda
Bueno, Elaine Silva
Caser, Eliana Bernadete
Ribeiro, Larissa Zuqui
Fernandes, Caio Cesar Ferreira
Garcia, Juliana Mazzei
Silva, Vanildes de Fátima Fernandes
Santos, Alisson Junior dos
Machado, Flávia Ribeiro
Souza, Maria Aparecida de
Ferronato, Bianca Ramos
Urbano, Hugo Corrêa de Andrade
Moreira, Danielle Conceição Aparecida
Souza-Dantas, Vicente Cés de
Duarte, Diego Meireles
Coelho, Juliana
Figueiredo, Rodrigo Cruvinel
Foreque, Fernanda
Romano, Thiago Gomes
Cubos, Daniel
Spirale, Vladimir Miguel
Nogueira, Roberta Schiavon
Maia, Israel Silva
Zandonai, Cassio Luis
Lovato, Wilson José
Cerantola, Rodrigo Barbosa
Toledo, Tatiana Gozzi Pancev
Tomba, Pablo Oscar
Almeida, Joyce Ramos de
Sanches, Luciana Coelho
Pierini, Leticia
Cunha, Mariana
Sousa, Michelle Tereza
Azevedo, Bruna
Dal-Pizzol, Felipe
Damasio, Danusa de Castro
Bainy, Marina Peres
Beduhn, Dagoberta Alves Vieira
Jatobá, Joana D’Arc Vila Nova
Moura, Maria Tereza Farias de
Rego, Leila Rezegue de Moraes
Silva, Adria Vanessa da
Oliveira, Luana Pontes
Sodré Filho, Eliene Sá
Santos, Silvana Soares dos
Neves, Itallo de Lima
Leão, Vanessa Cristina de Aquino
Paes, João Lucidio Lobato
Silva, Marielle Cristina Mendes
Oliveira, Cláudio Dornas de
Santiago, Raquel Caldeira Brant
Paranhos, Jorge Luiz da Rocha
Wiermann, Iany Grinezia da Silva
Pedroso, Durval Ferreira Fonseca
Sawada, Priscilla Yoshiko
Prestes, Rejane Martins
Nascimento, Glícia Cardoso
Grion, Cintia Magalhães Carvalho
Carrilho, Claudia Maria Dantas de Maio
Dantas, Roberta Lacerda Almeida de Miranda
Silva, Eliane Pereira
Silva, Antônio Carlos da
Oliveira, Sheila Mara Bezerra de
Golin, Nicole Alberti
Tregnago, Rogerio
Lima, Valéria Paes
Silva, Kamilla Grasielle Nunes da
Boschi, Emerson
Buffon, Viviane
Machado, André Sant’Ana
Capeletti, Leticia
Foernges, Rafael Botelho
Carvalho, Andréia Schubert de
Oliveira Junior, Lúcio Couto de
Oliveira, Daniela Cunha de
Silva, Everton Macêdo
Ribeiro, Julival
Pereira, Francielle Constantino
Salgado, Fernanda Borges
Deutschendorf, Caroline
Silva, Cristofer Farias da
Gobatto, Andre Luiz Nunes
Oliveira, Carolaine Bomfim de
Dracoulakis, Marianna Deway Andrade
Alvaia, Natália Oliveira Santos
Souza, Roberta Machado de
Araújo, Larissa Liz Cardoso de
Melo, Rodrigo Morel Vieira de
Passos, Luiz Carlos Santana
Vidal, Claudia Fernanda de Lacerda
Rodrigues, Fernanda Lopes de Albuquerque
Kurtz, Pedro
Shinotsuka, Cássia Righy
Tavares, Maria Brandão
Santana, Igor das Virgens
Gavinho, Luciana Macedo da Silva
Nascimento, Alaís Brito
Pereira, Adriano J
Cavalcanti, Alexandre Biasi










Revista Brasileira de Terapia Intensiva
- Métricas do periódico
RESUMO Objetivo: Descrever o IMPACTO-MR, um estudo brasileiro de plataforma nacional em unidades de terapia intensiva focado no impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. Métodos: Descrevemos a plataforma IMPACTO-MR, seu desenvolvimento, critérios para seleção das unidades de terapia intensiva, caracterização da coleta de dados, objetivos e projetos de pesquisa futuros a serem realizados na plataforma. Resultados: Os dados principais foram coletados por meio do Epimed Monitor System® e consistiram em dados demográficos, dados de comorbidades, estado funcional, escores clínicos, diagnóstico de internação e diagnósticos secundários, dados laboratoriais, clínicos e microbiológicos e suporte de órgãos durante a internação na unidade de terapia intensiva, entre outros. De outubro de 2019 a dezembro de 2020, 33.983 pacientes de 51 unidades de terapia intensiva foram incluídos no banco de dados principal. Conclusão: A plataforma IMPACTO-MR é um banco de dados clínico brasileiro de unidades de terapia intensiva focado na pesquisa do impacto das infecções por bactérias multirresistentes relacionadas à assistência à saúde. Essa plataforma fornece dados para o desenvolvimento e pesquisa de unidades de terapia intensiva individuais e ensaios clínicos observacionais e prospectivos multicêntricos. Objetivo IMPACTOMR, IMPACTOMR IMPACTO MR, MR saúde Métodos Resultados System demográficos comorbidades funcional secundários laboratoriais outros 201 2020 33983 33 983 33.98 5 principal Conclusão multicêntricos 20 202 3398 3 98 33.9 2 339 9 33.
ABSTRACT Objective: To describe the IMPACTO-MR, a Brazilian nationwide intensive care unit platform study focused on the impact of health care-associated infections due to multidrug-resistant bacteria. Methods: We described the IMPACTO-MR platform, its development, criteria for intensive care unit selection, characterization of core data collection, objectives, and future research projects to be held within the platform. Results: The core data were collected using the Epimed Monitor System® and consisted of demographic data, comorbidity data, functional status, clinical scores, admission diagnosis and secondary diagnoses, laboratory, clinical, and microbiological data, and organ support during intensive care unit stay, among others. From October 2019 to December 2020, 33,983 patients from 51 intensive care units were included in the core database. Conclusion: The IMPACTO-MR platform is a nationwide Brazilian intensive care unit clinical database focused on researching the impact of health care-associated infections due to multidrug-resistant bacteria. This platform provides data for individual intensive care unit development and research and multicenter observational and prospective trials. Objective IMPACTOMR, IMPACTOMR IMPACTO MR, MR careassociated associated multidrugresistant multidrug resistant bacteria Methods selection collection objectives Results System status scores diagnoses laboratory stay others 201 2020 33983 33 983 33,98 5 Conclusion trials 20 202 3398 3 98 33,9 2 339 9 33,
12.
Analysis of the time series of pertussis in Brazil from 2010 to 2019
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Silva, Luís Roberto da
; Ferreira, Ricardo José
; Arruda, Laís Eduarda Silva de
; Vasconcelos, Alan Dias de
; Freitas, Marcelo Victor de Arruda
; Santos, Isadora Sabrina Ferreira dos
; Silva, José Thiago de Lima
; Silva, Maria Graziele Gonçalves
; Teixeira, Camila Maria Barros
; Lira, Guilherme
; Oliveira, Emília Carolle Azevedo de
.











Revista Brasileira de Saúde Materno Infantil
- Métricas do periódico
Resumo Objetivos: analisar uma década do comportamento espaço-temporal da coqueluche no Brasil e as suas características epidemiológicas. Métodos: estudo ecológico de série temporal dos casos e óbitos por coqueluche do Sistema de Informação de Agravos de Notificação no Brasil (2010-2019). Utilizou-se o método de análise linear generalizada de Prais-Winsten e a análise de Kernel. Resultados: notificaram-se 32.849 casos, desses 466 (1,42%) evoluíram para óbito, com prevalência de 1,63/100.000 habitantes e coeficiente de mortalidade de 0,023/100.000 habitantes. Na análise temporal, evidenciou-se o comportamento cíclico da coqueluche com variações de tendência no período em 2014. A maioria dos casos ocorreu em menores de 1 ano (60,16%, p<0,01), sexo feminino (55,28%, p=0,066) e brancos (48,42%, p=0,14). A maior parcela dos óbitos foi em crianças <1 ano (98,07, p<0,01), sexo feminino (56,01%, p=0,066) e brancos (43,78%, p=0,14). No Kernel da prevalência, destacaram-se as regiões Sul, Sudeste e Nordeste com alta densidade; enquanto para mortalidade, sobressaíram-se Sudeste e Nordeste. Conclusão: observou-se o comportamento cíclico da coqueluche, com tendência de decréscimo nos últimos anos e a concentração de casos no público infantil. O que reforça a importância de fortalecer o processo de imunização da população.
Abstract Objectives: to analyze a decade of spatio-temporal behavior of pertussis in Brazil and its epidemiological characteristics. Methods: ecological time series study of pertussis cases and deaths from the Notifable Diseases Information System in Brazil (2010-2019). The method of generalized linear analysis of Prais-Winsten and the Kernel analysis were used. Results: 32,849 cases were reported, of which 466 (1.42%) evolved to death, with a prevalence of 1.63/100,000 inhabitants and a mortality rate of 0.023/100,000 inhabitants. In the temporal analysis, the cyclical behavior of pertussis was evidenced, with trend variations in the period in 2014. Most cases occurred in children under 1 year of age (60.16%, p<0.01), sex female (55.28%, p=0.066) and white (48.42%, p=0.14). The largest share of deaths was in children aged <1 year (98.07, p<0.01), females (56.01%, p=0.066) and whites (43.78%, p=0.14). In the Kernel of prevalence, the South, Southeast and Northeast regions stood out with high density; while for mortality, the Southeast and Northeast stood out. Conclusions: the cyclical behavior of pertussis was observed, with a decreasing trend in recent years and the concentration of cases in children. This reinforces the importance of strengthening the population’s immunization process.
13.
Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data
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Lima, Leísa Pires
; Azevedo, Camila Ferreira
; Resende, Marcos Deon Vilela de
; Nascimento, Moysés
; Silva, Fabyano Fonseca e
.





ABSTRACT: The development of efficient methods for genome–wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal and plant breeding programs. Bayesian approaches that aim to select regions of single nucleotide polymorphisms (SNPs) proved to be efficient, indicating genes with important effects. Among the selection criteria for SNPs or regions, selection criterion by percentage of variance can be explained by genomic regions (%var), selection of tag SNPs, and selection based on the window posterior probability of association (WPPA). To also detect potentially associated regions, we proposed measuring posterior probability of the interval PPint), which aims to select regions based on the markers of greatest effects. Therefore, the objective of this work was to evaluate these approaches, in terms of efficiency in selecting and identifying markers or regions located within or close to genes associated with traits. This study also aimed to compare these methodologies with single–marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non–overlapping genomic regions. Considering traits with oligogenic inheritance, WPPA criterion followed by %var and PPint criteria were shown to be superior, presenting higher values of detection power, capturing higher percentages of genetic variance and larger areas. For traits with polygenic inheritance, PPint and WPPA criteria were considered superior. Single–marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.
https://doi.org/10.1590/1678-992x-2020-0202
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14.
Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
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Souza, André Oliveira
; Viana, Alexandre Pio
; Silva, Fabyano Fonseca e
; Azevedo, Camila Ferreira
; Silva, Flavia Alves da
; Silva, Fernando Higino Lima e
.






ABSTRACT Methods for genetic improvement of semi–perennial species, such as passion fruit, often involve large areas, unbalanced data, and lack of observations. Some strategies can be applied to solve these problems. In this work, different models and approaches were tested to improve the precision of estimates of genetic evaluation models for several characteristics of the passion fruit. A randomized block design (RBD) model was compared to a posteriori correction, adding two factors to the model (post–hoc blocking Row–Col). These models were also combined with the frequentist and Bayesian approaches to identify which combination yields the most accurate results. These approaches are part of a strategic plan in a perennial plant breeding program to select promising genitors of passion to compose the next selection cycle. For Bayesian, we tested two priors, defining different values for the distribution parameters of effect variances of the model. We also performed a cross–validation test to choose a priori values and compare the frequentist and Bayesian approaches using the root mean square error (RMSE) and the correlation between the predicted and observed values, called Predictive capacity of the model (PC). The model with the post–hoc blocking Row–Col design captured the spatial variability for productivity and number of fruits, directly affecting the experimental precision. Both approaches applied to the models showed a similar performance, with predictive capacity and selective efficiency leading to the selection of the same individuals.
https://doi.org/10.1590/1678-992x-2020-0361
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15.
Bayesian methods for genomic association of chromosomic regions considering the additive-dominance model
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Crop Breeding and Applied Biotechnology
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Abstract Bayesian approaches applied in association studies select regions of single-nucleotide polymorphisms, indicating genes with important effects. The Bayesian methods differ in terms of the distribution assumed for the marker effects. Here, we used the window posterior probability of association to detect potential regions. The present study evaluated the efficiency of these methods in identifying regions located close to genes. Data were simulated in six scenarios. Considering the lack of dominance, BayesA was more efficient in the scenario with three QTLs. For scenarios with 10 or 100 QTLs, BayesCπ and BayesDπ were more efficient according to the false positive rate and detection power. Considering the presence of dominance, all methods were similar in the scenario with three QTLs, except in terms of accuracy. BayesDπ was superior in the scenario with 10 QTLs, while BRR was more efficient in the scenario with 100 QTLs.
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