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976 results on '"P, Carrieri"'

1. Exploring Sex, Gender, and Gender-Related Sociocultural Factors in Clinical Decision-Making for Older Adults Using a Prescribing Cascade Vignette: A Transnational Qualitative Study: Sex and Gender Considerations in Clinical Decision-Making for Older Adults

Author

Kthupi, Altea, Rochon, Paula A., Santini, Sara, Paoletti, Luca, Mason, Robin, McCarthy, Lisa M., Carrieri, Barbara, Dalton, Kieran, Li, Joyce, Sivayoganathan, Kawsika, Borhani, Parya, Sternberg, Shelley A., Zwas, Donna R., and Savage, Rachel D.

Published
2024
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2. Surgery versus radiation for clinically positive nodal prostate cancer in an other cause mortality risk weighted cohort

Author

Tinsley, Shane A., Stephens, Alex, Finati, Marco, Chiarelli, Giuseppe, Cirulli, Giuseppe Ottone, Morrison, Chase, Richard, Caleb, Hares, Keinnan, Lutchka, Jonathan, Sood, Akshay, Buffi, Nicolò, Lughezzani, Giovanni, Bettocchi, Carlo, Salonia, Andrea, Briganti, Alberto, Montorsi, Francesco, Carrieri, Giuseppe, Rogers, Craig, and Abdollah, Firas

Published
2024
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4. An updated model for predicting side-specific extraprostatic extension in the era of MRI-targeted biopsy

Author

Martini, Alberto, Wever, Lieke, Soeterik, Timo F. W., Rakauskas, Arnas, Fankhauser, Christian Daniel, Grogg, Josias Bastian, Checcucci, Enrico, Amparore, Daniele, Haiquel, Luciano, Rodriguez-sanchez, Lara, Ploussard, Guillaume, Qiang, Peng, Affentranger, Andres, Marquis, Alessandro, Marra, Giancarlo, Ettala, Otto, Zattoni, Fabio, Falagario, Ugo Giovanni, De Angelis, Mario, Kesch, Claudia, Apfelbeck, Maria, Al-Hammouri, Tarek, Kretschmer, Alexander, Kasivisvanathan, Veeru, Preisser, Felix, Lefebvre, Emilie, Olivier, Jonathan, Radtke, Jan Philipp, Carrieri, Giuseppe, Moro, Fabrizio Dal, Boström, Peter, Jambor, Ivan, Gontero, Paolo, Chiu, Peter K., John, Hubert, Macek, Petr, Porpiglia, Francesco, Hermanns, Thomas, van den Bergh, Roderick C. N., van Basten, Jean-Paul A., Gandaglia, Giorgio, and Valerio, Massimo

Published
2024
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5. Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model

Author

Chiarelli, Giuseppe, Stephens, Alex, Finati, Marco, Cirulli, Giuseppe Ottone, Beatrici, Edoardo, Filipas, Dejan K., Arora, Sohrab, Tinsley, Shane, Bhandari, Mahendra, Carrieri, Giuseppe, Trinh, Quoc-Dien, Briganti, Alberto, Montorsi, Francesco, Lughezzani, Giovanni, Buffi, Nicolò, Rogers, Craig, and Abdollah, Firas

Published
2024
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13. Feasibility, safety, efficacy and potential scaling-up of sofosbuvir-based HCV treatment in Central and West Africa: (TAC ANRS 12311 trial)

Author

Lacombe, Karine, Moh, Raoul, Chazallon, Corine, Lemoine, Maud, Sylla, Babacar, Fadiga, Fatoumata, Le Carrou, Jerôme, Marcellin, Fabienne, Kouanfack, Charles, Ciaffi, Laura, Sartre, Michelle Tagni, Sida, Magloire Biwole, Diallo, Alpha, Gozlan, Joel, Seydi, Moussa, Cissé, Viviane, Danel, Christine, Girard, Pierre Marie, Toni, Thomas d’Aquin, Minga, Albert, Boyer, Sylvie, Carrieri, Patrizia, and Attia, Alain

Published
2024
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14. Global survey of stigma among physicians and patients with nonalcoholic fatty liver disease

Author

Younossi, Zobair M, AlQahtani, Saleh A, Alswat, Khalid, Yilmaz, Yusuf, Keklikkiran, Caglayan, Funuyet-Salas, Jesús, Romero-Gómez, Manuel, Fan, Jian-Gao, Zheng, Ming-Hua, El-Kassas, Mohamed, Castera, Laurent, Liu, Chun-Jen, Wong, Vincent Wai-Sun, Zelber-Sagi, Shira, Allen, Alina M, Lam, Brian, Treeprasertsuk, Sombat, Hameed, Saeed, Takahashi, Hirokazu, Kawaguchi, Takumi, Schattenberg, Jörn M, Duseja, Ajay, Newsome, Phil, Francque, Sven, Spearman, C Wendy, Fernández, Marlen I Castellanos, Burra, Patrizia, Roberts, Stuart K, Chan, Wah-Kheong, Arrese, Marco, Silva, Marcelo, Rinella, Mary, Singal, Ashwani K, Gordon, Stuart, Fuchs, Michael, Alkhouri, Naim, Cusi, Kenneth, Loomba, Rohit, Ranagan, Jane, Eskridge, Wayne, Kautz, Achim, Ong, Janus P, Kugelmas, Marcelo, Eguchi, Yuichiro, Diago, Moises, Yu, Ming-Lung, Gerber, Lynn, Fornaresio, Lisa, Nader, Fatema, Henry, Linda, Racila, Andrei, Golabi, Pegah, Stepanova, Maria, Carrieri, Patrizia, Lazarus, Jeffrey V, and Council, the Global NASH

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Liver Disease, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Hepatitis, Health Services, Clinical Research, Prevention, Good Health and Well Being, Global NASH Council, MASH, MASLD, SLD, communication, discrimination, fatty liver, metabolic, patient-reported outcomes, Public Health and Health Services, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background & aimsPatients with fatty liver disease may experience stigma from the disease or comorbidities. In this cross-sectional study, we aimed to understand stigma among patients with nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) and healthcare providers.MethodsMembers of the Global NASH Council created two surveys about experiences/attitudes toward NAFLD and related diagnostic terms: a 68-item patient and a 41-item provider survey.ResultsSurveys were completed by 1,976 patients with NAFLD across 23 countries (51% Middle East/North Africa [MENA], 19% Europe, 17% USA, 8% Southeast Asia, 5% South Asia) and 825 healthcare providers (67% gastroenterologists/hepatologists) across 25 countries (39% MENA, 28% Southeast Asia, 22% USA, 6% South Asia, 3% Europe). Of all patients, 48% ever disclosed having NAFLD/NASH to family/friends; the most commonly used term was "fatty liver" (88% at least sometimes); "metabolic disease" or "MAFLD" were rarely used (never by >84%). Regarding various perceptions of diagnostic terms by patients, there were no substantial differences between "NAFLD", "fatty liver disease (FLD)", "NASH", or "MAFLD". The most popular response was being neither comfortable nor uncomfortable with either term (56%-71%), with slightly greater discomfort with "FLD" among the US and South Asian patients (47-52% uncomfortable). Although 26% of patients reported stigma related to overweight/obesity, only 8% reported a history of stigmatization or discrimination due to NAFLD. Among providers, 38% believed that the term "fatty" was stigmatizing, while 34% believed that "nonalcoholic" was stigmatizing, more commonly in MENA (43%); 42% providers (gastroenterologists/hepatologists 45% vs. 37% other specialties, p = 0.03) believed that the name change to metabolic dysfunction-associated steatotic liver disease (or MASLD) might reduce stigma. Regarding the new nomenclature, the percentage of providers reporting "steatotic liver disease" as stigmatizing was low (14%).ConclusionsThe perception of NAFLD stigma varies among patients, providers, geographic locations and sub-specialties.Impact and implicationsOver the past decades, efforts have been made to change the nomenclature of nonalcoholic fatty liver disease (NAFLD) to better align with its underlying pathogenetic pathways and remove any potential stigma associated with the name. Given the paucity of data related to stigma in NAFLD, we undertook this global comprehensive survey to assess stigma in NAFLD among patients and providers from around the world. We found there is a disconnect between physicians and patients related to stigma and related nomenclature. With this knowledge, educational programs can be developed to better target stigma in NAFLD among all stakeholders and to provide a better opportunity for the new nomenclature to address the issues of stigma.

Published
2023

16. THU-453 Stigma in NAFLD and NASH: a global survey of patients and providers

Author

Younossi, Zobair, Yılmaz, Yusuf, Fan, Jian-Gao, Wong, Vincent Wai-Sun, Kassas, Mohamed El, Zelber-Sagi, Shira, Allen, Alina, Rinella, Mary, Singal, Ashwani, Gordon, Stuart C, Fuchs, Michael, Eskridge, Wayne, Alkhouri, Naim, Alswat, Khalid, Takahashi, Hirokazu, Kawaguchi, Takumi, Ranagan, Jane, Zheng, Ming-Hua, Duseja, Ajay Kumar, Burra, Patrizia, Patrizia, Carrieri, Arrese, Marco, Kautz, Achim, Ong, Janus, Castera, Laurent, Francque, Sven, Kugelmas, Marcelo, Eguchi, Yuichiro, Treeprasertsuk, Sombat, Fernández, Marlen Ivon Castellanos, Gomez, Manuel Romero, Newsome, Philip N, Cusi, Kenneth, Loomba, Rohit, Schattenberg, Jörn, Yu, Ming-Lung, Diago, Moises, Gerber, Lynn, Lam, Brian, Fornaresio, Lisa, Nader, Fatema, Henry, Linda, Racila, Andrei, Golabi, Pegah, Stepanova, Maria, Alqahtani, Saleh, and Lazarus, Jeffrey

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Public Health and Health Services, Gastroenterology & Hepatology, Clinical sciences
Published
2023

17. SAT-129 Sub-optimal global public health policies and strategies to combat hepatocellular carcinoma

Author

Diaz, Luis Antonio, Norero, Blanca, Corsi, Oscar, Ayares, Gustavo, Idalsoaga, Francisco, García, Sergio, Vázquez, Valeria, Lacalle, Lucas, Lazo, Mariana, Ferreccio, Catterina, Mendizabal, Manuel, Piñero, Federico, Martinez, Edmundo, Ijeoma, Ifeorah, Louvet, Alexandre, Piano, Salvatore, Cortez-Pinto, Helena, Wong, Vincent Wai-Sun, Kulkarni, Anand, Cotter, Thomas, Brahmania, Mayur, roblero, Juan Pablo, Dirchwolf, Melisa, Pollarsky, Florencia, George, Jacob, Stauber, Rudolf E, Francque, Sven, Guerra, Patricia, Oliveira, Claudia, Araujo, Roberta, Álvares-da-Silva, Mario, Blaise, Nkegoum, Abraldes, Juan G, Zheng, Ming-Hua, Toro, Luis, Restrepo, Juan Carlos, Ramirez, Wagner, Grgurević, Ivica, Infante, Mirtha, Mbendi, Charles, Carrera, Enrique, Kassas, Mohamed El, Mahmoud, Abdelmajeed, Tesfaye, Yonas Gedamu, Tadesse, Sewale Anagaw, Akalu, Tiruwork Fekadu, Desalegn, Hailemichael, Allaire, Manon, Patrizia, Carrieri, Schattenberg, Jörn, Aguyire, Joan, Micah, Eileen Akonobea, Tachi, Kenneth, Cardona, Katherine Emilia Maldonado, Sanchez, Abel, Sánchez, Marco, Björnsson, Einar S, Iavarone, Massimo, Okamoto, Ryuichi, Some, Fatuma, Hellani, Mohammad Fadel, Gonzalez, Veronica Enith Prado, Chávez-Tapia, Norberto Carlos, Méndez-Sánchez, Nahum, Mucumbi, Sheila Constância Mabote, Ugiagbe, Rose Ashinedu, Akande, Kolawole, Nwoko, Chinenye, Ezenkwa, Uchenna Simon, Okoye, Ifeoma Joy, Hamid, Saeed Sadiq, Quezada, Julissa Lombardo, Girala, Marcos, Padilla, P Martin, Diaz-Ferrer, Javier, Tagle, Martin, Kukla, Michał, Odeghe, Emuobor, wemimo, Rasheed mumini, Reis, Daniela, Mozgovoi, Sergei, Ismail, Mona, Koller, Tomas, Spearman, Wendy, Elhassan, Moawia, Stal, Per, Pazi, Swaleh, Ocanit, Anthony, Masson, Steven, Dunn, Winston, Kamath, Patrick S, Singal, Ashwani, Debes, Jose, Reig, María, Loomba, Rohit, Bataller, Ramon, Lazarus, Jeffrey, Arrese, Marco, and Arab, Juan Pablo

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Public Health and Health Services, Gastroenterology & Hepatology, Clinical sciences
Published
2023

21. Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

Author

Shaffer, Justin P, Nothias, Louis-Félix, Thompson, Luke R, Sanders, Jon G, Salido, Rodolfo A, Couvillion, Sneha P, Brejnrod, Asker D, Lejzerowicz, Franck, Haiminen, Niina, Huang, Shi, Lutz, Holly L, Zhu, Qiyun, Martino, Cameron, Morton, James T, Karthikeyan, Smruthi, Nothias-Esposito, Mélissa, Dührkop, Kai, Böcker, Sebastian, Kim, Hyun Woo, Aksenov, Alexander A, Bittremieux, Wout, Minich, Jeremiah J, Marotz, Clarisse, Bryant, MacKenzie M, Sanders, Karenina, Schwartz, Tara, Humphrey, Greg, Vásquez-Baeza, Yoshiki, Tripathi, Anupriya, Parida, Laxmi, Carrieri, Anna Paola, Beck, Kristen L, Das, Promi, González, Antonio, McDonald, Daniel, Ladau, Joshua, Karst, Søren M, Albertsen, Mads, Ackermann, Gail, DeReus, Jeff, Thomas, Torsten, Petras, Daniel, Shade, Ashley, Stegen, James, Song, Se Jin, Metz, Thomas O, Swafford, Austin D, Dorrestein, Pieter C, Jansson, Janet K, Gilbert, Jack A, and Knight, Rob

Subjects
Microbiology, Biological Sciences, Microbiome, Life Below Water, Animals, Microbiota, Metagenome, Metagenomics, Earth, Planet, Soil, Earth Microbiome Project 500 (EMP500) Consortium, Medical Microbiology
Abstract

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Published
2022

22. Correction: Opioid Agonist Maintenance Treatment Outcomes—The OPTIMUS International Consensus Towards Evidence-Based and Patient-Centred Care, an Interim Report

Author

Wiessing, Lucas, Banka-Cullen, Prakashini, Barbaglia, M. Gabriela, Belackova, Vendula, Belbaisi, Saed A. S., Blanken, Peter, Carrieri, Patrizia, Comiskey, Catherine, Dacosta-Sánchez, Daniel, Dom, Geert, Fabricius, Venus, Faria, Hugo, Ignjatova, Liljana, Inić, Nemanja, Jacobsen, Britta, Javakhishvili, Jana D., Kamendy, Zuzana, Kapitány-Fövény, Máté, Kiss, Anna, Kyprianou, Evi, Marchand, Kirsten, Millar, Tim, Mravcik, Viktor, Mustafa, Naser J. Y., Nordt, Carlos, Partanen, Markus, Pedersen, Mads Uffe, Putkonen, Hanna, Razmadze, Mariam, Roux, Perrine, Schulte, Bernd, Seabra, Paulo, Sordo, Luis, Strada, Lisa, Subata, Emilis, Thoma, Esmeralda, Torrens, Marta, Walley, Alexander Y., and Yiasemi, Ioanna

Published
2024
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23. Opioid Agonist Maintenance Treatment Outcomes—The OPTIMUS International Consensus Towards Evidence-Based and Patient-Centred Care, an Interim Report

Author

Wiessing, Lucas, Banka-Cullen, Prakashini, Barbaglia, M. Gabriela, Belackova, Vendula, Belbaisi, Saed A. S., Blanken, Peter, Carrieri, Patrizia, Comiskey, Catherine, Dacosta-Sánchez, Daniel, Dom, Geert, Fabricius, Venus, Faria, Hugo, Ignjatova, Liljana, Inić, Nemanja, Jacobsen, Britta, Javakhishvili, Jana D., Kamendy, Zuzana, Kapitány-Fövény, Máté, Kiss, Anna, Kyprianou, Evi, Marchand, Kirsten, Millar, Tim, Mravcik, Viktor, Mustafa, Naser J. Y., Nordt, Carlos, Partanen, Markus, Pedersen, Mads Uffe, Putkonen, Hanna, Razmadze, Mariam, Roux, Perrine, Schulte, Bernd, Seabra, Paulo, Sordo, Luis, Strada, Lisa, Subata, Emilis, Thoma, Esmeralda, Torrens, Marta, Walley, Alexander Y., and Yiasemi, Ioanna

Published
2023
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24. No Evidence of Rabies Exposure in Wild Marmosets (Callithrix jacchus) of Northeast Brazil

Author

Benavides, Julio A., Megid, Jane, Castilho, Juliana Galera, Macedo, Carla I., Mourão Fuches, Regina Maria, Frazatti Gallina, Neuza Maria, Boere, Vanner, Zalafon-Silva, Bruna, da Silva, Ramiro Monã, Coutinho, José Flávio Vidal, Arruda, Maria de Fatima, de Oliveira e Silva, Ita, Valença-Montenegro, Mônica Mafra, Cordeiro, Jefferson Farias, Leal, Silvana, Higashi, Cintia de Sousa, Medeiros, Fabíola de Souza, Uchoa de Castro, Alene, Rizzo, Rodrigo, Sena, Fabio Antonio, Gonçalves, Paola de Cassia, Rocha, Silene Manrique, Wada, Marcelo, Vargas, Alexander, Carrieri, Maria Luiza, and Kotait, Ivanete

Published
2023
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26. Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors

Author

Falagario, Ugo G., Lantz, Anna, Jambor, Ivan, Busetto, Gian Maria, Bettocchi, Carlo, Finati, Marco, Ricapito, Anna, Luzzago, Stefano, Ferro, Matteo, Musi, Gennaro, Totaro, Angelo, Racioppi, Marco, Carbonara, Umberto, Checcucci, Enrico, Manfredi, Matteo, D’Aietti, Damiano, Porcaro, Antonio Benito, Nordström, Tobias, Björnebo, Lars, Oderda, Marco, Soria, Francesco, Taimen, Pekka, Aronen, Hannu J., Perez, Ileana Montoya, Ettala, Otto, Marchioni, Michele, Simone, Giuseppe, Ferriero, Mariaconsiglia, Brassetti, Aldo, Napolitano, Luigi, Carmignani, Luca, Signorini, Claudia, Conti, Andrea, Ludovico, Giuseppe, Scarcia, Marcello, Trombetta, Carlo, Claps, Francesco, Traunero, Fabio, Montanari, Emanuele, Boeri, Luca, Maggi, Martina, Del Giudice, Francesco, Bove, Pierluigi, Forte, Valerio, Ficarra, Vincenzo, Rossanese, Marta, Mucciardi, Giuseppe, Pagliarulo, Vincenzo, Tafuri, Alessandro, Mirone, Vincenzo, Schips, Luigi, Antonelli, Alessandro, Gontero, Paolo, Cormio, Luigi, Sciarra, Alessandro, Porpiglia, Francesco, Bassi, PierFrancesco, Ditonno, Pasquale, Boström, Peter J., Messina, Emanuele, Panebianco, Valeria, De Cobelli, Ottavio, and Carrieri, Giuseppe

Published
2023
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27. Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data.

Author

Jiang, Lingjing, Haiminen, Niina, Carrieri, Anna-Paola, Huang, Shi, Vázquez-Baeza, Yoshiki, Parida, Laxmi, Kim, Ho-Cheol, Swafford, Austin D, Knight, Rob, and Natarajan, Loki

Subjects
Reproducibility of Results, Algorithms, Microbiota, classification, feature selection, microbiome, prediction, reproducible, stability, Bioengineering, Statistics, Other Mathematical Sciences, Statistics & Probability
Abstract

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.

Published
2022

31. SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment

Author

Marotz, Clarisse, Belda-Ferre, Pedro, Ali, Farhana, Das, Promi, Huang, Shi, Cantrell, Kalen, Jiang, Lingjing, Martino, Cameron, Diner, Rachel E, Rahman, Gibraan, McDonald, Daniel, Armstrong, George, Kodera, Sho, Donato, Sonya, Ecklu-Mensah, Gertrude, Gottel, Neil, Salas Garcia, Mariana C, Chiang, Leslie Y, Salido, Rodolfo A, Shaffer, Justin P, Bryant, Mac Kenzie, Sanders, Karenina, Humphrey, Greg, Ackermann, Gail, Haiminen, Niina, Beck, Kristen L, Kim, Ho-Cheol, Carrieri, Anna Paola, Parida, Laxmi, Vázquez-Baeza, Yoshiki, Torriani, Francesca J, Knight, Rob, Gilbert, Jack, Sweeney, Daniel A, and Allard, Sarah M

Subjects
Microbiology, Biological Sciences, Emerging Infectious Diseases, Coronaviruses Diagnostics and Prognostics, Infectious Diseases, Genetics, Clinical Research, Coronaviruses, Lung, Coronaviruses Disparities and At-Risk Populations, Infection, Good Health and Well Being, COVID-19, Hospitals, Humans, Pandemics, Phylogeny, RNA, Ribosomal, 16S, RNA, Viral, SARS-CoV-2, Built environment, 16S rRNA, Microbiome, Ecology, Medical Microbiology, Evolutionary biology
Abstract

BackgroundSARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.MethodsWe collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.ResultsSixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.ConclusionsThese results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

Published
2021

32. Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes

Author

Armstrong, George, Cantrell, Kalen, Huang, Shi, McDonald, Daniel, Haiminen, Niina, Carrieri, Anna Paola, Zhu, Qiyun, Gonzalez, Antonio, McGrath, Imran, Beck, Kristen L, Hakim, Daniel, Havulinna, Aki S, Méric, Guillaume, Niiranen, Teemu, Lahti, Leo, Salomaa, Veikko, Jain, Mohit, Inouye, Michael, Swafford, Austin D, Kim, Ho-Cheol, Parida, Laxmi, Vázquez-Baeza, Yoshiki, and Knight, Rob

Subjects
Microbiology, Biological Sciences, Aging, Microbiota, Phylogeny, Medical and Health Sciences, Bioinformatics, Genetics
Abstract

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Published
2021

37. Conservative treatment for high-risk NMIBC failing BCG treatment: who benefits from adding electromotive drug administration (EMDA) of mitomycin C (MMC) to a second BCG induction cycle?

Author

Busetto, Gian Maria, Finati, Marco, Chirico, Marco, Cinelli, Francesco, D’Altilia, Nicola, Falagario, Ugo G., Sanguedolce, Francesca, Del Giudice, Francesco, De Berardinis, Ettore, Ferro, Matteo, Crocetto, Felice, Porreca, Angelo, Di Gianfrancesco, Luca, Calo’, Beppe, Mancini, Vito, Bettocchi, Carlo, Carrieri, Giuseppe, and Cormio, Luigi

Published
2023
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38. Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data

Author

Jiang, Lingjing, Haiminen, Niina, Carrieri, Anna-Paola, Huang, Shi, Vazquez-Baeza, Yoshiki, Parida, Laxmi, Kim, Ho-Cheol, Swafford, Austin D., Knight, Rob, and Natarajan, Loki

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high-dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the training data would lead to large changes in the chosen feature subset, then many of the biological features that an algorithm has found are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metric MSE and proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications. We conclude that Stability is a preferred feature selection criterion over MSE because it better quantifies the reproducibility of the feature selection method.

Published
2020

45. Challenges in benchmarking metagenomic profilers

Author

Sun, Zheng, Huang, Shi, Zhang, Meng, Zhu, Qiyun, Haiminen, Niina, Carrieri, Anna Paola, Vázquez-Baeza, Yoshiki, Parida, Laxmi, Kim, Ho-Cheol, Knight, Rob, and Liu, Yang-Yu

Subjects
Microbiology, Biological Sciences, Genetics, Benchmarking, Computational Biology, Gene Expression Profiling, Metagenomics, Microbiota, Sequence Analysis, DNA, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.

Published
2021

46. EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets

Author

Cantrell, Kalen, Fedarko, Marcus W, Rahman, Gibraan, McDonald, Daniel, Yang, Yimeng, Zaw, Thant, Gonzalez, Antonio, Janssen, Stefan, Estaki, Mehrbod, Haiminen, Niina, Beck, Kristen L, Zhu, Qiyun, Sayyari, Erfan, Morton, James T, Armstrong, George, Tripathi, Anupriya, Gauglitz, Julia M, Marotz, Clarisse, Matteson, Nathaniel L, Martino, Cameron, Sanders, Jon G, Carrieri, Anna Paola, Song, Jin, Swafford, Austin D, Dorrestein, Pieter C, Andersen, Kristian G, Parida, Laxmi, Kim, Ho-Cheol, Vázquez-Baeza, Yoshiki, and Knight, Rob

Subjects
Information and Computing Sciences, Graphics, Augmented Reality and Games, Biological Sciences, Human Genome, Genetics, bioinformatics, microbial ecology
Abstract

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data.IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

Published
2021

47. SelectMDx and Multiparametric Magnetic Resonance Imaging of the Prostate for Men Undergoing Primary Prostate Biopsy: A Prospective Assessment in a Multi-Institutional Study.

Author

Maggi, Martina, Del Giudice, Francesco, Falagario, Ugo G, Cocci, Andrea, Russo, Giorgio Ivan, Di Mauro, Marina, Sepe, Giuseppe Salvatore, Galasso, Fabio, Leonardi, Rosario, Iacona, Gabriele, Carroll, Peter R, Cooperberg, Matthew R, Porreca, Angelo, Ferro, Matteo, Lucarelli, Giuseppe, Terracciano, Daniela, Cormio, Luigi, Carrieri, Giuseppe, De Berardinis, Ettore, Sciarra, Alessandro, and Busetto, Gian Maria

Subjects
PSA, early detection, mpMRI, prostate biopsy, prostate neoplasm, urinary biomarker, Oncology and Carcinogenesis
Abstract

Prostate-specific antigen (PSA) testing as the sole indication for prostate biopsy lacks specificity, resulting in overdiagnosis of indolent prostate cancer (PCa) and missing clinically significant PCa (csPCa). SelectMDx is a biomarker-based risk score to assess urinary HOXC6 and DLX1 mRNA expression combined with traditional clinical risk factors. The aim of this prospective multi-institutional study was to evaluate the diagnostic accuracy of SelectMDx and its association with multiparametric magnetic resonance (mpMRI) when predicting PCa in prostate biopsies. Overall, 310 consecutive subjects were included. All patients underwent mpMRI and SelectMDx prior to prostate biopsy. SelectMDx and mpMRI showed sensitivity and specificity of 86.5% vs. 51.9%, and 73.8% vs. 88.3%, respectively, in predicting PCa at biopsy, and 87.1% vs. 61.3%, and 63.7% vs. 83.9%, respectively, in predicting csPCa at biopsy. SelectMDx was revealed to be a good predictor of PCa, while with regards to csPCa detection, it was demonstrated to be less effective, showing results similar to mpMRI. With analysis of strategies assessed to define the best diagnostic strategy to avoid unnecessary biopsy, SelectMDx appeared to be a reliable pathway after an initial negative mpMRI. Thus, biopsy could be proposed for all cases of mpMRI PI-RADS 4-5 score, and to those with Prostate Imaging-Reporting and Data System (PI-RADS) 1-3 score followed by a positive SelectMDx.

Published
2021

49. Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development

Author

Gardiner, Laura-Jayne, Carrieri, Anna Paola, Wilshaw, Jenny, Checkley, Stephen, Pyzer-Knapp, Edward O, and Krishna, Ritesh

Subjects
Quantitative Biology - Genomics, Quantitative Biology - Quantitative Methods
Abstract

Biomedical data, particularly in the field of genomics, has characteristics which make it challenging for machine learning applications - it can be sparse, high dimensional and noisy. Biomedical applications also present challenges to model selection - whilst powerful, accurate predictions are necessary, they alone are not sufficient for a model to be deemed useful. Due to the nature of the predictions, a model must also be trustworthy and transparent, empowering a practitioner with confidence that its use is appropriate and reliable. In this paper, we propose that this can be achieved through the use of judiciously built feature sets coupled with Bayesian models, specifically Gaussian processes. We apply Gaussian processes to drug discovery, using inexpensive transcriptomic profiles from human cell lines to predict animal kidney and liver toxicity after treatment with specific chemical compounds. This approach has the potential to reduce invasive and expensive animal testing during clinical trials if in vitro human cell line analysis can accurately predict model animal phenotypes. We compare results across a range of feature sets and models, to highlight model importance for medical applications., Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract

Published
2019

50. The oleic/palmitic acid imbalance in exosomes isolated from NAFLD patients induces necroptosis of liver cells via the elongase-6/RIP-1 pathway

Author

Scavo, Maria Principia, Negro, Roberto, Arrè, Valentina, Depalo, Nicoletta, Carrieri, Livianna, Rizzi, Federica, Mastrogiacomo, Rita, Serino, Grazia, Notarnicola, Maria, De Nunzio, Valentina, Lippolis, Tamara, Pesole, Pasqua Letizia, Coletta, Sergio, Armentano, Raffaele, Curri, Maria Lucia, and Giannelli, Gianluigi

Published
2023
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