142 results on '"Celi A"'
Search Results
2. Agricultural education in Africa using YouTube multilingual animations: A retrospective feasibility study assessing costs to reach language-diverse populations.
- Author
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N Peter Reeves, Victor Giancarlo Sal Y Rosas Celi, Anne N Lutomia, John William Medendorp, Julia Bello-Bravo, and Barry Pittendrigh
- Subjects
Medicine ,Science - Abstract
There is a critical need for widespread information dissemination of agricultural best practices in Africa. Literacy, language and resource barriers often impede such information dissemination. Culturally and linguistically localized, computer-animated training videos placed on YouTube and promoted through paid advertising is a potential tool to help overcome these barriers. The goal of this study is to assess the feasibility of reaching language-diverse populations in Africa using this new type of information dissemination channel. As a case study, cost estimates were obtained for YouTube ad campaigns of a video to prevent post-harvest loss through safe food storage using sanitized jerrycan containers. Seventy-three video variants were created for the most common 16 languages in Ghana, 35 languages in Kenya, and 22 languages in Nigeria. Using these videos, campaigns were deployed country wide or focused on zones of influence that represent economically underdeveloped regions known to produce beans suitable for jerrycan storage. Using data collected from YouTube ad campaigns, language-specific models were created for each country to estimate how many viewers could be reached per US dollar spent. Separate models were created to estimate the number of viewers who watched 25% and 75% of the video (most of video without end credits), reflecting different levels of engagement. For language campaigns with both country wide and zone of influence areas of deployment, separate region-specific models were created. Models showed that the estimated number of viewers per dollar spent varied considerably amongst countries and languages. On average, the expected number of viewers per dollar spent were 1.8 (Range = 0.2-7.3) for 25% watched and 0.8 (Range = 0.1-3.2) for 75% watched in Ghana, 1.2 (0.2-4.8) for 25% watched and 0.5 (Range = 0.1-2.0) for 75% watched in Kenya, and 0.4 (Range = 0.2-1.3) for 25% watched and 0.2 (Range = 0.1-0.5) for 75% watched in Nigeria. English versions of the video were the most cost-effective in reaching viewers in Ghana and Nigeria. In Kenya, English language campaigns ranked 28 (country wide) and 36 (zones of influence) out of 37 analyzed campaigns. Results also showed that many local language campaigns performed well, opening the possibility that targeted knowledge dissemination on topics of importance to local populations, is potentially cost effective. In addition, such targeted information dissemination appears feasible, even during regional and global crises when in-person training may not be possible. In summary, leveraging multilingual computer-animations and digital platforms such as YouTube shows promise for conducting large-scale agricultural education campaigns. The findings of the current study provides the justification to pursue a more rigorous prospective study to verify the efficacy of knowledge exchange and societal impact through this form of information dissemination channel.
- Published
- 2024
- Full Text
- View/download PDF
3. Field-based molecular detection of Batrachochytrium dendrobatidis in critically endangered Atelopus toads and aquatic habitats in Ecuador
- Author
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Riascos-Flores, Lenin R., primary, Bonilla, Julio, additional, Naranjo-Briceño, Leopoldo, additional, Apunte-Ramos, Katherine, additional, Reyes-Ortega, Grace C., additional, Cabrera, Marcela, additional, Cáceres-Andrade, José F., additional, Carrera-Gonzalez, Andrea, additional, Yánez-Galarza, Jomira K., additional, Siavichay Pesántez, Fausto, additional, Oyagata-Cachimuel, Luis A., additional, Goethals, Peter, additional, Celi, Jorge, additional, Van der Heyden, Christine, additional, and Ortega-Andrade, H. Mauricio, additional
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- 2024
- Full Text
- View/download PDF
4. A chemical analysis of the Pelargonium species: P. odoratissimum, P. graveolens, and P. zonale identifies secondary metabolites with activity against gram-positive bacteria with multidrug-resistance.
- Author
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Celi, Diana, Quiroz, Evelyn, Beltrán-Noboa, Andrea, Machado, António, Tejera, Eduardo, and Fernandez-Soto, Paulina
- Subjects
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GRAM-positive bacteria , *ENTEROCOCCUS , *METABOLITES , *ANALYTICAL chemistry , *LINEZOLID , *METHICILLIN-resistant staphylococcus aureus , *MULTIDRUG resistance , *ANTHOCYANINS - Abstract
The Pelargonium genus encompasses around 280 species, most of which are used for medicinal purposes. While P. graveolens, P. odoratissimum, and P. zonale are known to exhibit antimicrobial activity, there is an evident absence of studies evaluating all three species to understand their chemical differences and biological effects. Through the analysis of the hydroalcoholic extracts of P. graveolens, P. odoratissimum, and P. zonale, using HPLC-DAD-MS/MS, quercetin and kaempferol derivatives were identified in these three species. Conversely, gallotannins and anthocyanins were uniquely detected in P. zonale. P. graveolens stood out due to the various types of myricetin derivatives that were not detected in P. odoratissimum and P. zonale extracts. Evaluation of their biological activities revealed that P. zonale displayed superior antibacterial and antibiofilm activities in comparison to the other two species. The antibacterial efficacy of P. zonale was observed towards the clinically relevant strains of Staphylococcus aureus ATCC 25923, Methicillin-resistant Staphylococcus aureus (MRSA) 333, Enterococcus faecalis ATCC 29212, and the Vancomycin-resistant E. faecalis INSPI 032. Fractionation analysis of P. zonale suggested that the antibacterial activity attributed to this plant is due to the presence of quercetin derivatives and kaempferol and its derivatives, alongside their synergistic interaction with gallotannins and anthocyanins. Lastly, the three Pelargonium species exhibited notable antioxidant activity, which may be attributed to their high content of total phenolic compounds. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Performance, carcass characteristics and non-carcass components of Santa Ines and crossbred (Santa Ines x Dorper) lambs finished in different confinement strategies
- Author
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Pereira, Alinne Andrade, primary, Daher, Luciara Celi Chaves, additional, Freitas, Carolina Sarmanho, additional, Monteiro, Samanta do Nascimento, additional, Araújo, Jonas Carneiro, additional, Sousa, Marco Antônio Paula de, additional, Miranda, Andrey de Sousa, additional, Rodrigues, Thomaz Cyro Guimarães de Carvalho, additional, Silva, Jamile Andrea Rodrigues da, additional, Lima, Alyne Cristina Sodré de, additional, Silva, André Guimarães Maciel e, additional, and Lourenço-Júnior, José de Brito, additional
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- 2023
- Full Text
- View/download PDF
6. Agricultural education in Africa using YouTube multilingual animations: A retrospective feasibility study assessing costs to reach language-diverse populations.
- Author
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Reeves, N. Peter, Sal y Rosas Celi, Victor Giancarlo, Lutomia, Anne N., Medendorp, John William, Bello-Bravo, Julia, and Pittendrigh, Barry
- Subjects
- *
AGRICULTURAL education , *INFORMATION dissemination , *NEUROLINGUISTICS , *FEASIBILITY studies , *U.S. dollar , *DIGITAL technology - Abstract
There is a critical need for widespread information dissemination of agricultural best practices in Africa. Literacy, language and resource barriers often impede such information dissemination. Culturally and linguistically localized, computer-animated training videos placed on YouTube and promoted through paid advertising is a potential tool to help overcome these barriers. The goal of this study is to assess the feasibility of reaching language-diverse populations in Africa using this new type of information dissemination channel. As a case study, cost estimates were obtained for YouTube ad campaigns of a video to prevent post-harvest loss through safe food storage using sanitized jerrycan containers. Seventy-three video variants were created for the most common 16 languages in Ghana, 35 languages in Kenya, and 22 languages in Nigeria. Using these videos, campaigns were deployed country wide or focused on zones of influence that represent economically underdeveloped regions known to produce beans suitable for jerrycan storage. Using data collected from YouTube ad campaigns, language-specific models were created for each country to estimate how many viewers could be reached per US dollar spent. Separate models were created to estimate the number of viewers who watched 25% and 75% of the video (most of video without end credits), reflecting different levels of engagement. For language campaigns with both country wide and zone of influence areas of deployment, separate region-specific models were created. Models showed that the estimated number of viewers per dollar spent varied considerably amongst countries and languages. On average, the expected number of viewers per dollar spent were 1.8 (Range = 0.2–7.3) for 25% watched and 0.8 (Range = 0.1–3.2) for 75% watched in Ghana, 1.2 (0.2–4.8) for 25% watched and 0.5 (Range = 0.1–2.0) for 75% watched in Kenya, and 0.4 (Range = 0.2–1.3) for 25% watched and 0.2 (Range = 0.1–0.5) for 75% watched in Nigeria. English versions of the video were the most cost-effective in reaching viewers in Ghana and Nigeria. In Kenya, English language campaigns ranked 28 (country wide) and 36 (zones of influence) out of 37 analyzed campaigns. Results also showed that many local language campaigns performed well, opening the possibility that targeted knowledge dissemination on topics of importance to local populations, is potentially cost effective. In addition, such targeted information dissemination appears feasible, even during regional and global crises when in-person training may not be possible. In summary, leveraging multilingual computer-animations and digital platforms such as YouTube shows promise for conducting large-scale agricultural education campaigns. The findings of the current study provides the justification to pursue a more rigorous prospective study to verify the efficacy of knowledge exchange and societal impact through this form of information dissemination channel. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Machine-supported decision-making to improve agricultural training participation and gender inclusivity
- Author
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Reeves, Norman Peter, primary, Ramadan, Ahmed, additional, Sal y Rosas Celi, Victor Giancarlo, additional, Medendorp, John William, additional, Ar-Rashid, Harun, additional, Krupnik, Timothy Joseph, additional, Lutomia, Anne Namatsi, additional, Bello-Bravo, Julia Maria, additional, and Pittendrigh, Barry Robert, additional
- Published
- 2023
- Full Text
- View/download PDF
8. Machine-supported decision-making to improve agricultural training participation and gender inclusivity
- Author
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Norman Peter Reeves, Ahmed Ramadan, Victor Giancarlo Sal y Rosas Celi, John William Medendorp, Harun Ar-Rashid, Timothy Joseph Krupnik, Anne Namatsi Lutomia, Julia Maria Bello-Bravo, and Barry Robert Pittendrigh
- Subjects
Multidisciplinary - Abstract
Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area.
- Published
- 2023
9. Large-scale rollout of extension training in Bangladesh: Challenges and opportunities for gender-inclusive participation
- Author
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Medendorp, John William, primary, Reeves, N. Peter, additional, Celi, Victor Giancarlo Sal y Rosas, additional, Harun-ar-Rashid, Md., additional, Krupnik, Timothy J., additional, Lutomia, Anne N., additional, Pittendrigh, Barry, additional, and Bello-Bravo, Julia, additional
- Published
- 2022
- Full Text
- View/download PDF
10. Large-scale rollout of extension training in Bangladesh: Challenges and opportunities for gender-inclusive participation
- Author
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John William Medendorp, N. Peter Reeves, Victor Giancarlo Sal y Rosas Celi, Md. Harun-ar-Rashid, Timothy J. Krupnik, Anne N. Lutomia, Barry Pittendrigh, and Julia Bello-Bravo
- Subjects
Male ,Rural Population ,Bangladesh ,Multidisciplinary ,Humans ,Agriculture ,Female ,Women ,Power, Psychological - Abstract
Despite the recognized importance of women’s participation in agricultural extension services, research continues to show inequalities in women’s participation. Emerging capacities for conducting large-scale extension training using information and communication technologies (ICTs) now afford opportunities for generating the rich datasets needed to analyze situational factors that affect women’s participation. Data was recorded from 1,070 video-based agricultural extension training events (131,073 farmers) in four Administrative Divisions of Bangladesh (Rangpur, Dhaka, Khulna, and Rajshahi). The study analyzed the effect of gender of the trainer, time of the day, day of the week, month of the year, Bangladesh Administrative Division, and venue type on (1) the expected number of extension event attendees and (2) the odds of females attending the event conditioned on the total number of attendees. The study revealed strong gender specific training preferences. Several factors that increased total participation, decreased female attendance (e.g., male-led training event held after 3:30 pm in Rangpur). These findings highlight the dilemma faced by extension trainers seeking to maximize attendance at training events while avoiding exacerbating gender inequalities. The study concludes with a discussion of ways to mitigate gender exclusion in extension training by extending data collection processes, incorporating machine learning to understand gender preferences, and applying optimization theory to increase total participation while concurrently improving gender inclusivity.
- Published
- 2022
11. Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation
- Author
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George, Naomi, primary, Moseley, Edward, additional, Eber, Rene, additional, Siu, Jennifer, additional, Samuel, Mathew, additional, Yam, Jonathan, additional, Huang, Kexin, additional, Celi, Leo Anthony, additional, and Lindvall, Charlotta, additional
- Published
- 2021
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12. Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing
- Author
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Rúben Mendes, Stan N. Finkelstein, Marta Fernandes, Steven Horng, Susana M. Vieira, Francisca Leite, Alistair E. W. Johnson, Leo Anthony Celi, and Carlos Palos
- Subjects
Male ,Critical Care and Emergency Medicine ,Physiology ,Social Sciences ,Blood Pressure ,Logistic regression ,computer.software_genre ,Vascular Medicine ,law.invention ,Machine Learning ,0302 clinical medicine ,Patient Admission ,law ,Heart Rate ,Medicine and Health Sciences ,Medicine ,030212 general & internal medicine ,Aged, 80 and over ,Multidisciplinary ,medicine.diagnostic_test ,Respiration ,Middle Aged ,Intensive care unit ,Hospitals ,Stratified sampling ,Emergency Severity Index ,Semantics ,Intensive Care Units ,Female ,Emergency Service, Hospital ,Natural language processing ,Research Article ,Adult ,Computer and Information Sciences ,Science ,Cardiology ,Risk Assessment ,03 medical and health sciences ,Artificial Intelligence ,Humans ,Aged ,Natural Language Processing ,Receiver operating characteristic ,Portugal ,business.industry ,Biology and Life Sciences ,030208 emergency & critical care medicine ,Linguistics ,Emergency department ,Triage ,United States ,Health Care ,Pulse oximetry ,Logistic Models ,Health Care Facilities ,Artificial intelligence ,business ,Physiological Processes ,computer - Abstract
The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage in the stratification of patients and in identifying those with higher risk of ICU admission. Adult patients assigned Manchester Triage System (MTS) or Emergency Severity Index (ESI) 1 to 3 from a Portuguese and a United States Emergency Departments were analyzed. Variables routinely collected at triage were used and natural language processing was applied to the patient chief complaint. Stratified random sampling was applied to split the data in train (70%) and test (30%) sets and 10-fold cross validation was performed for model training. Logistic regression, random forests, and a random undersampling boosting algorithm were used. We compared the performance obtained with the reference model-using only triage priorities-with the models using additional variables. For both hospitals, a logistic regression model achieved higher overall performance, yielding areas under the receiver operating characteristic and precision-recall curves of 0.91 (95% CI 0.90-0.92) and 0.30 (95% CI 0.27-0.33) for the United States hospital and of 0.85 (95% CI 0.83-0.86) and 0.06 (95% CI 0.05-0.07) for the Portuguese hospital. Heart rate, pulse oximetry, respiratory rate and systolic blood pressure were the most important predictors of ICU admission. Compared to the reference models, the models using clinical variables and the chief complaint presented higher recall for patients assigned MTS/ESI 3 and can identify patients assigned MTS/ESI 3 who are at risk for ICU admission.
- Published
- 2020
13. The weekend effect for stroke patients admitted to intensive care: A retrospective cohort analysis
- Author
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Mitchell, William Greig, primary, Pande, Rohit, additional, Robinson, Tom Edward, additional, Jones, Gabriel Davis, additional, Hou, Isabella, additional, and Celi, Leo Anthony, additional
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- 2020
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14. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing
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Fernandes, Marta, primary, Mendes, Rúben, additional, Vieira, Susana M., additional, Leite, Francisca, additional, Palos, Carlos, additional, Johnson, Alistair, additional, Finkelstein, Stan, additional, Horng, Steven, additional, and Celi, Leo Anthony, additional
- Published
- 2020
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15. Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing
- Author
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Fernandes, Marta, primary, Mendes, Rúben, additional, Vieira, Susana M., additional, Leite, Francisca, additional, Palos, Carlos, additional, Johnson, Alistair, additional, Finkelstein, Stan, additional, Horng, Steven, additional, and Celi, Leo Anthony, additional
- Published
- 2020
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16. ITGAM is a risk factor to systemic lupus erythematosus and possibly a protection factor to rheumatoid arthritis in patients from Mexico
- Author
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Ramírez-Bello, Julian, primary, Sun, Celi, additional, Valencia-Pacheco, Guillermo, additional, Singh, Bhupinder, additional, Barbosa-Cobos, Rosa Elda, additional, Saavedra, Miguel A., additional, López-Villanueva, Ricardo F., additional, and Nath, Swapan K., additional
- Published
- 2019
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17. Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation
- Author
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Rene Eber, Edward T. Moseley, Naomi George, Kexin Huang, Charlotta Lindvall, Jennifer Siu, Jonathan Yam, Mathew Samuel, and Leo Anthony Celi
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Male ,Time Factors ,medicine.medical_treatment ,law.invention ,Machine Learning ,Tracheostomy ,Mathematical and Statistical Techniques ,Elderly ,0302 clinical medicine ,law ,Medicine and Health Sciences ,Hospital Mortality ,030212 general & internal medicine ,Respiratory System Procedures ,Simplified Acute Physiology Score ,Multidisciplinary ,Statistics ,Middle Aged ,Prognosis ,Intensive care unit ,Hospitals ,Intensive Care Units ,SAPS II ,Physical Sciences ,Cohort ,Medicine ,Female ,Research Article ,Computer and Information Sciences ,medicine.medical_specialty ,Neural Networks ,Science ,Surgical and Invasive Medical Procedures ,Research and Analysis Methods ,Models, Biological ,03 medical and health sciences ,Deep Learning ,Artificial Intelligence ,Diagnostic Medicine ,Predictive Value of Tests ,Intensive care ,medicine ,Adults ,Humans ,Statistical Methods ,Aged ,Mechanical ventilation ,Receiver operating characteristic ,business.industry ,Biology and Life Sciences ,Retrospective cohort study ,Respiration, Artificial ,Health Care ,030228 respiratory system ,Health Care Facilities ,Age Groups ,People and Places ,Emergency medicine ,Population Groupings ,business ,Mathematics ,Forecasting ,Neuroscience - Abstract
Background Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models. Methods Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model. Results There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality. Discussion We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.
- Published
- 2021
18. Association of hypokalemia with an increased risk for medically treated arrhythmias
- Author
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Zhengbo Zhang, Colin T. Phillips, Junmei Wang, Mengling Feng, and Leo Anthony Celi
- Subjects
Male ,Adenosine ,Lidocaine ,Databases, Factual ,Ibutilide ,Glycobiology ,Myocardial Infarction ,030204 cardiovascular system & hematology ,Amiodarone ,Biochemistry ,0302 clinical medicine ,Risk Factors ,Medicine and Health Sciences ,030212 general & internal medicine ,Longitudinal Studies ,Multidisciplinary ,Pharmaceutics ,Drugs ,Nucleosides ,Middle Aged ,Hypokalemia ,Hospitals ,Glycosylamines ,3. Good health ,Intensive Care Units ,Anesthesia ,Cohort ,Medicine ,Female ,medicine.symptom ,Anti-Arrhythmia Agents ,Arrhythmia ,medicine.drug ,Research Article ,Critical Care ,Science ,Cardiology ,Surgical and Invasive Medical Procedures ,03 medical and health sciences ,Drug Therapy ,Intensive care ,Severity of illness ,medicine ,Humans ,Aged ,Retrospective Studies ,Pharmacology ,business.industry ,Isoproterenol ,Biology and Life Sciences ,Retrospective cohort study ,Arrhythmias, Cardiac ,Health Care ,Health Care Facilities ,Potassium ,business - Abstract
BackgroundPotassium replenishment protocols are often employed across broad patient populations to prevent cardiac arrhythmias. Tailoring potassium thresholds to specific patient populations would reduce unnecessary tasks and cost. The objective of this retrospective cohort study was to determine the threshold at which hypokalemia increases the risk for medically treated arrhythmias in cardiac versus medical and surgical intensive care units.MethodsPatients captured in the publicly available Philips eICU database were assessed for initiation of either intravenous amiodarone, adenosine, ibutilide, isoproterenol, or lidocaine as a surrogate for a clinically significant arrhythmia. A landmark time-to-event analysis was conducted to investigate the association of serum potassium values and time-marked administration of an antiarrhythmic drug. Analysis was adjusted for comorbidities, the use of vasopressor agents, diuretics, as well as age, gender and severity of illness.ResultsAmong 20,665 admissions to cardiac intensive care units, 1,371 (6.6%) were treated with either amiodarone, adenosine, ibutilide, isoproterenol, or lidocaine. For potassium values of ≥3.0ConclusionsSerum potassium levels
- Published
- 2019
19. Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform
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Margot Vander Laenen, Sven Van Poucke, Leo Anthony Celi, Cathy De Deyne, Milan Vukicevic, Zhongheng Zhang, Martin Schmitz, VAN POUCKE, Sven, Zhang, Zhongheng, Schmitz, Martin, Vukicevic, Milan, VANDER LAENEN, Margot, Celi, Leo Anthony, DE DEYNE, Cathy, Institute for Medical Engineering and Science, and Celi, Leo Anthony G.
- Subjects
Critical Care ,Databases, Factual ,020205 medical informatics ,Process (engineering) ,Computer science ,Critical Illness ,Big data ,Information Storage and Retrieval ,lcsh:Medicine ,02 engineering and technology ,computer.software_genre ,0202 electrical engineering, electronic engineering, information engineering ,Data Mining ,Humans ,lcsh:Science ,Multidisciplinary ,business.industry ,Data manipulation language ,lcsh:R ,Reproducibility of Results ,Models, Theoretical ,Predictive analytics ,3. Good health ,Intensive Care Units ,Open data ,Analytics ,Scalability ,Programming Languages ,020201 artificial intelligence & image processing ,lcsh:Q ,Data mining ,business ,computer ,Algorithms ,Research Article - Abstract
With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research., National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.) Grant R01 EB01720501A1)
- Published
- 2016
20. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing
- Author
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Francisca Leite, Carlos Palos, Marta Fernandes, Steven Horng, Rúben Mendes, Alistair E. W. Johnson, Susana M. Vieira, Leo Anthony Celi, and Stan N. Finkelstein
- Subjects
Male ,Critical Care and Emergency Medicine ,Blood Pressure ,computer.software_genre ,Logistic regression ,Vascular Medicine ,Cohort Studies ,Machine Learning ,Risk Factors ,Heart Rate ,Medicine and Health Sciences ,Cardiac Arrest ,Risk of mortality ,Medicine ,Coma ,Multidisciplinary ,Middle Aged ,Hospitals ,Hospitalization ,Neurology ,Cohort ,Female ,Emergency Service, Hospital ,Natural language processing ,Research Article ,Cohort study ,Adult ,Computer and Information Sciences ,Science ,Cardiology ,Risk Assessment ,Artificial Intelligence ,Humans ,Natural Language Processing ,Portugal ,Receiver operating characteristic ,business.industry ,Patient Acuity ,Glasgow Coma Scale ,Emergency department ,Triage ,Heart Arrest ,Health Care ,Logistic Models ,ROC Curve ,Health Care Facilities ,Artificial intelligence ,business ,computer ,Forecasting - Abstract
Emergency department triage is the first point in time when a patient’s acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome—mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients’ chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency–inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Ten-fold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model—a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients’ age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical variables and the chief complaint presented higher recall for patients assigned MTS-3 and can identify those who are at risk of the composite outcome.
- Published
- 2020
21. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
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Joy T. Wu, John Foote, Jonathan Welt, Eric Carlson, Patrick D. Tyler, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi, David W. Grant, Edward T. Moseley, and Yeran Li
- Subjects
0301 basic medicine ,Computer science ,Social Sciences ,lcsh:Medicine ,Cardiovascular Medicine ,computer.software_genre ,Convolutional neural network ,Infographics ,Task (project management) ,0302 clinical medicine ,Mathematical and Statistical Techniques ,Medicine and Health Sciences ,Psychology ,Public and Occupational Health ,030212 general & internal medicine ,lcsh:Science ,Interpretability ,Language ,Multidisciplinary ,Artificial neural network ,Charts ,Alcoholism ,Phenotypes ,Phenotype ,Salient ,Cardiovascular Diseases ,Information Technology ,Natural language processing ,Research Article ,Computer and Information Sciences ,Neural Networks ,Substance-Related Disorders ,Addiction ,Research and Analysis Methods ,03 medical and health sciences ,Text mining ,Mental Health and Psychiatry ,Genetics ,Humans ,Learning ,Representation (mathematics) ,Natural Language Processing ,business.industry ,Deep learning ,Data Visualization ,lcsh:R ,Biology and Life Sciences ,Convolution ,030104 developmental biology ,lcsh:Q ,Artificial intelligence ,business ,computer ,Mathematical Functions ,Neuroscience - Abstract
In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.
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- 2018
22. Feature selection and prediction of treatment failure in tuberculosis
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David Sasson, Ned McCague, Ben Min-Woo Illigens, Christopher Martin Sauer, Iván Sánchez Fernández, Kenneth E. Paik, and Leo Anthony Celi
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0301 basic medicine ,Bacterial Diseases ,Male ,Support Vector Machine ,Extensively Drug-Resistant Tuberculosis ,Antitubercular Agents ,lcsh:Medicine ,Drug resistance ,Diagnostic Radiology ,Machine Learning ,0302 clinical medicine ,Mathematical and Statistical Techniques ,Risk Factors ,Medicine and Health Sciences ,030212 general & internal medicine ,Treatment Failure ,lcsh:Science ,Microscopy ,Multidisciplinary ,Pharmaceutics ,Radiology and Imaging ,Statistics ,Middle Aged ,Pulmonary Imaging ,Infectious Diseases ,Physical Sciences ,Female ,Research Article ,Adult ,medicine.medical_specialty ,Computer and Information Sciences ,Tuberculosis ,Imaging Techniques ,MEDLINE ,Developing country ,Research and Analysis Methods ,Microbiology ,03 medical and health sciences ,Pharmacotherapy ,Drug Therapy ,Artificial Intelligence ,Diagnostic Medicine ,Internal medicine ,Support Vector Machines ,Microbial Control ,medicine ,Humans ,Statistical Methods ,Pharmacology ,business.industry ,lcsh:R ,Extensively drug-resistant tuberculosis ,Biology and Life Sciences ,Stepwise regression ,medicine.disease ,Missing data ,Tropical Diseases ,030104 developmental biology ,lcsh:Q ,Antimicrobial Resistance ,business ,Mathematics ,Forecasting - Abstract
Background Tuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control. Objective To identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure. Methods On a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone. Results The complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30–51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance. Conclusion Machine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.
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- 2018
23. First identification of the benzimidazole resistance-associated F200Y SNP in the beta-tubulin gene in Ascaris lumbricoides
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Furtado, Luis Fernando Viana, primary, Medeiros, Celi da Silva, additional, Zuccherato, Luciana Werneck, additional, Alves, William Pereira, additional, de Oliveira, Valéria Nayara Gomes Mendes, additional, da Silva, Vivian Jordania, additional, Miranda, Guilherme Silva, additional, Fujiwara, Ricardo Toshio, additional, and Rabelo, Élida Mara Leite, additional
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- 2019
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24. Association of hypokalemia with an increased risk for medically treated arrhythmias
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Phillips, Colin T., primary, Wang, Junmei, additional, Celi, Leo Anthony, additional, Zhang, Zhengbo, additional, and Feng, Mengling, additional
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- 2019
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25. Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients
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Ramazzotti, Daniele, primary, Clardy, Peter, additional, Celi, Leo Anthony, additional, Stone, David J., additional, and Rudin, Robert S., additional
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- 2019
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26. The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data
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Celi, Leo A., primary, Citi, Luca, additional, Ghassemi, Marzyeh, additional, and Pollard, Tom J., additional
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- 2019
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27. Feature selection and prediction of treatment failure in tuberculosis
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Sauer, Christopher Martin, primary, Sasson, David, additional, Paik, Kenneth E., additional, McCague, Ned, additional, Celi, Leo Anthony, additional, Sánchez Fernández, Iván, additional, and Illigens, Ben M. W., additional
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- 2018
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28. Improving temporal accuracy of human metabolic chambers for dynamic metabolic studies
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Bin Ni, Erica M. Wohlers, Eric Ruud, Shanshan Chen, Jon K. Moon, and Francesco S. Celi
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0301 basic medicine ,Computer science ,Transfer function ,Biochemistry ,Interval training ,0302 clinical medicine ,Mathematical and Statistical Techniques ,Medicine and Health Sciences ,Public and Occupational Health ,Flow Rate ,Multidisciplinary ,Transfer Functions ,Physics ,Classical Mechanics ,Sports Science ,Curve Fitting ,Signal Filtering ,Physical Sciences ,Medicine ,Engineering and Technology ,Research Article ,Mean squared error ,Biochemical Phenomena ,Noise reduction ,Science ,030209 endocrinology & metabolism ,Fluid Mechanics ,Bioenergetics ,Research and Analysis Methods ,Continuum Mechanics ,03 medical and health sciences ,Control theory ,Humans ,Sports and Exercise Medicine ,Exercise ,Flexibility (engineering) ,Biology and Life Sciences ,Calorimetry, Indirect ,Fluid Dynamics ,Physical Activity ,Noise Reduction ,Term (time) ,Noise ,030104 developmental biology ,Temporal resolution ,Signal Processing ,Energy Metabolism ,Mathematical Functions - Abstract
Metabolic chambers are powerful tools for assessing human energy expenditure, providing flexibility and comfort for the subjects in a near free-living environment. However, the flexibility offered by the large living room size creates challenges in the assessment of dynamic human metabolic signals-such as those generated during high-intensity interval training and short-term involuntary physical activities-with sufficient temporal accuracy. Therefore, this paper presents methods to improve the temporal accuracy of metabolic chambers. The proposed methods include 1) adopting a shortest possible step size, here one minute, to compute the finite derivative terms for the metabolic rate calculation, and 2) applying a robust noise reduction method-total variation denoising-to minimize the large noise generated by the short derivative term whilst preserving the transient edges of the dynamic metabolic signals. Validated against 24-hour gas infusion tests, the proposed method reconstructs dynamic metabolic signals with the best temporal accuracy among state-of-the-art approaches, achieving a root mean square error of 0.27 kcal/min (18.8 J/s), while maintaining a low cumulative error in 24-hour total energy expenditure of less than 45 kcal/day (188280 J/day). When applied to a human exercise session, the proposed methods also show the best performance in terms of recovering the dynamics of exercise energy expenditure. Overall, the proposed methods improve the temporal resolution of the chamber system, enabling metabolic studies involving dynamic signals such as short interval exercises to carry out the metabolic chambers.
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- 2017
29. Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method
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Cristina Aurigemma, Claudio Chiastra, Francesco Burzotta, Eros Montin, Simona Celi, Marco Bologna, Gabriele Dubini, Susanna Migliori, Francesco Migliavacca, and Luca Mainardi
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Cardiovascular Procedures ,Computer science ,medicine.medical_treatment ,lcsh:Medicine ,Computed tomography ,030204 cardiovascular system & hematology ,DISEASE ,Diagnostic Radiology ,030218 nuclear medicine & medical imaging ,Coronary artery disease ,0302 clinical medicine ,Intravascular ultrasound ,Medicine and Health Sciences ,Segmentation ,lcsh:Science ,Tomography ,Coronary Arteries ,Multidisciplinary ,SHEAR-STRESS ,medicine.diagnostic_test ,Radiology and Imaging ,Applied Mathematics ,Simulation and Modeling ,Coronary stenting ,Arteries ,Repeatability ,Coronary Vessels ,Stent placement ,Catheter ,medicine.anatomical_structure ,surgical procedures, operative ,Physical Sciences ,Stents ,ACCURATE ,Radiology ,Anatomy ,INTERVENTION ,Algorithms ,Tomography, Optical Coherence ,Research Article ,Biotechnology ,medicine.medical_specialty ,Coronary Stenting ,Catheters ,Imaging Techniques ,Lumen (anatomy) ,Surgical and Invasive Medical Procedures ,Neuroimaging ,Image processing ,Research and Analysis Methods ,03 medical and health sciences ,STRUTS ,Optical coherence tomography ,Diagnostic Medicine ,INTRAVASCULAR ULTRASOUND ,medicine ,Humans ,Stent implantation ,OCT ,QUANTIFICATION ,ALGORITHM ,cardiovascular diseases ,lcsh:R ,Biology and Life Sciences ,Reproducibility of Results ,Stent ,Percutaneous coronary intervention ,medicine.disease ,equipment and supplies ,Computed Axial Tomography ,Coronary arteries ,Stent Implantation ,Cardiovascular Anatomy ,Blood Vessels ,Feasibility Studies ,Medical Devices and Equipment ,lcsh:Q ,Mathematics ,Neuroscience ,Biomedical engineering - Abstract
Optical coherence tomography (OCT) is an established catheter-based imaging modality for the assessment of coronary artery disease and the guidance of stent placement during percutaneous coronary intervention. Manual analysis of large OCT datasets for vessel contours or stent struts detection is time-consuming and unsuitable for real-time applications. In this study, a fully automatic method was developed for detection of both vessel contours and stent struts. The method was applied to in vitro OCT scans of eight stented silicone bifurcation phantoms for validation purposes. The proposed algorithm comprised four main steps, namely pre-processing, lumen border detection, stent strut detection, and three-dimensional point cloud creation. The algorithm was validated against manual segmentation performed by two independent image readers. Linear regression showed good agreement between automatic and manual segmentations in terms of lumen area (r>0.99). No statistically significant differences in the number of detected struts were found between the segmentations. Mean values of similarity indexes were >95% and >85% for the lumen and stent detection, respectively. Stent point clouds of two selected cases, obtained after OCT image processing, were compared to the centerline points of the corresponding stent reconstructions from micro computed tomography, used as ground-truth. Quantitative comparison between the corresponding stent points resulted in median values of ~150 μm and ~40 μm for the total and radial distances of both cases, respectively. The repeatability of the detection method was investigated by calculating the lumen volume and the mean number of detected struts per frame for seven repeated OCT scans of one selected case. Results showed low deviation of values from the median for both analyzed quantities. In conclusion, this study presents a robust automatic method for detection of lumen contours and stent struts from OCT as supported by focused validation against both manual segmentation and micro computed tomography and by good repeatability.
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- 2017
30. The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data
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Marzyeh Ghassemi, Leo Anthony Celi, Luca Citi, and Tom J. Pollard
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0301 basic medicine ,Science and Technology Workforce ,Biomedical Research ,Computer science ,Careers in Research ,computer.software_genre ,Geographical locations ,Machine Learning ,0302 clinical medicine ,Health care ,Medicine and Health Sciences ,media_common ,Multidisciplinary ,Research Assessment ,Reproducibility ,3. Good health ,Europe ,Professions ,Open data ,Medicine ,Science policy ,Discipline ,Algorithms ,Computer and Information Sciences ,Science Policy ,Science ,Overview ,MEDLINE ,Research Grants ,Research and Analysis Methods ,Machine learning ,Research Funding ,03 medical and health sciences ,Artificial Intelligence ,Humans ,media_common.cataloged_instance ,European Union ,European union ,Biomedicine ,Health Care Policy ,Information Dissemination ,business.industry ,Health Care ,Data sharing ,030104 developmental biology ,030221 ophthalmology & optometry ,Scientists ,Population Groupings ,Artificial intelligence ,People and places ,business ,Delivery of Health Care ,computer - Abstract
Recent years have seen a surge of studies in machine learning in health and biomedicine, driven by digitalization of healthcare environments and increasingly accessible computer systems for conducting analyses. Many of us believe that these developments will lead to significant improvements in patient care. Like many academic disciplines, however, progress is hampered by lack of code and data sharing. In bringing together this PLOS ONE collection on machine learning in health and biomedicine, we sought to focus on the importance of reproducibility, making it a requirement, as far as possible, for authors to share data and code alongside their papers.
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- 2019
31. One-year mortality after recovery from critical illness: A retrospective cohort study
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Lokhandwala, Sharukh, primary, McCague, Ned, additional, Chahin, Abdullah, additional, Escobar, Braiam, additional, Feng, Mengling, additional, Ghassemi, Mohammad M., additional, Stone, David J., additional, and Celi, Leo Anthony, additional
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- 2018
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32. Improving temporal accuracy of human metabolic chambers for dynamic metabolic studies
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Chen, Shanshan, primary, Wohlers, Erica, additional, Ruud, Eric, additional, Moon, Jon, additional, Ni, Bin, additional, and Celi, Francesco S., additional
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- 2018
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33. Behaviour during transportation predicts stress response and lower airway contamination in horses
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Padalino, Barbara, primary, Raidal, Sharanne L., additional, Knight, Peter, additional, Celi, Pietro, additional, Jeffcott, Leo, additional, and Muscatello, Gary, additional
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- 2018
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34. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
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Gehrmann, Sebastian, primary, Dernoncourt, Franck, additional, Li, Yeran, additional, Carlson, Eric T., additional, Wu, Joy T., additional, Welt, Jonathan, additional, Foote, John, additional, Moseley, Edward T., additional, Grant, David W., additional, Tyler, Patrick D., additional, and Celi, Leo A., additional
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- 2018
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35. Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method
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Chiastra, Claudio, primary, Montin, Eros, additional, Bologna, Marco, additional, Migliori, Susanna, additional, Aurigemma, Cristina, additional, Burzotta, Francesco, additional, Celi, Simona, additional, Dubini, Gabriele, additional, Migliavacca, Francesco, additional, and Mainardi, Luca, additional
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- 2017
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36. Physician satisfaction with a multi-platform digital scheduling system
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Deliberato, Rodrigo Octávio, primary, Rocha, Leonardo Lima, additional, Lima, Alex Heitor, additional, Santiago, Caroline Reis Maia, additional, Terra, Jose Cláudio Cyrineu, additional, Dagan, Alon, additional, and Celi, Leo Anthony, additional
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- 2017
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37. Effect of 3'UTR RET Variants on RET mRNA Secondary Structure and Disease Presentation in Medullary Thyroid Carcinoma
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Carla Vaz Ferreira, Tauanne D. Amarante, Rodolfo Vieira Maximiano, Lucieli Ceolin, Jessica Oliboni Scapineli, Ana Luiza Maia, Débora Rodrigues Siqueira, Beatriz Assis-Brazil, Miriam Celi de Souza Nunes, Gerald Weber, and Mirian Romitti
- Subjects
0301 basic medicine ,Male ,Linkage disequilibrium ,3' Untranslated Regions -- genetics ,Heredity ,endocrine system diseases ,Haplotypes -- genetics ,lcsh:Medicine ,Kaplan-Meier Estimate ,Biochemistry ,Linkage Disequilibrium ,Metastasis ,0302 clinical medicine ,Gene Frequency ,Basic Cancer Research ,Medicine and Health Sciences ,Mutation -- genetics ,lcsh:Science ,3' Untranslated Regions ,Thyroid Neoplasms -- genetics -- pathology ,Genetics ,Multidisciplinary ,RNA mensageiro ,Messenger RNA ,Sciences bio-médicales et agricoles ,Proto-Oncogene Proteins c-ret -- genetics ,Middle Aged ,Immunohistochemistry ,Nucleic acids ,Proteínas proto-oncogênicas c-ret ,Proto-Oncogene Proteins c-ret ,Oncology ,030220 oncology & carcinogenesis ,Thermodynamics ,Female ,Regiões 3' não traduzidas ,Anatomy ,RNA, Messenger -- chemistry -- genetics -- metabolism ,Research Article ,Calcitonin ,endocrine system ,congenital, hereditary, and neonatal diseases and abnormalities ,Heterozygote ,Biology ,Gene Frequency -- genetics ,Lymphatic System ,03 medical and health sciences ,Germline mutation ,Predisposição genética para doença ,Neoplasias da glândula tireóide ,Linkage Disequilibrium -- genetics ,Humans ,Computer Simulation ,Genetic Predisposition to Disease ,RNA, Messenger ,Thyroid Neoplasms ,Allele ,Genotyping ,Allele frequency ,Alleles ,Evolutionary Biology ,Population Biology ,lcsh:R ,Haplotype ,Carcinoma neuroendócrino ,Genetic Variation ,Biology and Life Sciences ,Molecular biology ,digestive system diseases ,Hormones ,Carcinoma, Neuroendocrine ,Minor allele frequency ,030104 developmental biology ,Carcinoma, Neuroendocrine -- genetics -- pathology ,Haplotypes ,Genetic Loci ,Mutation ,Nucleic Acid Conformation ,RNA ,Somatic Mutation ,lcsh:Q ,Lymph Nodes ,Frequência do gene ,Population Genetics - Abstract
The RET S836S variant has been associated with early onset and increased risk for metastatic disease in medullary thyroid carcinoma (MTC). However, the mechanism by which this variant modulates MTC pathogenesis is still open to discuss. Of interest, strong linkage disequilibrium (LD) between RET S836S and 3'UTR variants has been reported in Hirschsprung's disease patients., info:eu-repo/semantics/published
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- 2015
38. Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform
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Van Poucke, Sven, Zhang, Zhongheng, Schmitz, Martin, Vukićević, Milan, Vander Laenen, Margot, Celi, Leo Anthony, De Deyne, Cathy, Van Poucke, Sven, Zhang, Zhongheng, Schmitz, Martin, Vukićević, Milan, Vander Laenen, Margot, Celi, Leo Anthony, and De Deyne, Cathy
- Abstract
With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.
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- 2016
39. Procoagulant, Tissue Factor-Bearing Microparticles in Bronchoalveolar Lavage of Interstitial Lung Disease Patients: An Observational Study
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Pierluigi Paggiaro, Federica Martino, C Armani, Laura Tavanti, Antonio Palla, Federica Novelli, Tommaso Neri, Maria Laura Bartoli, Fabio Falaschi, Alessandro Celi, and Concettina Noce
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Male ,Pathology ,Pulmonology ,Pulmonary Fibrosis ,lcsh:Medicine ,Pathology and Laboratory Medicine ,Biochemistry ,Bronchoalveolar Lavage ,Epithelium ,chemistry.chemical_compound ,Cell-Derived Microparticles ,Pulmonary fibrosis ,Molecular Cell Biology ,Medicine and Health Sciences ,Medicine ,lcsh:Science ,Hematopathology ,Multidisciplinary ,medicine.diagnostic_test ,Factor X ,Interstitial lung disease ,Hematology ,respiratory system ,Middle Aged ,medicine.anatomical_structure ,Factor Xa ,Female ,Anatomy ,Cellular Types ,Coagulation Factors ,Research Article ,medicine.medical_specialty ,Clinical Pathology ,Lung injury ,Interstitial Lung Diseases ,Cell Line ,Thromboplastin ,Molecular Genetics ,Tissue factor ,Diagnostic Medicine ,Genetics ,Humans ,Aged ,Lung ,business.industry ,lcsh:R ,Biology and Life Sciences ,Proteins ,Epithelial Cells ,Cell Biology ,medicine.disease ,Bronchoalveolar lavage ,Biological Tissue ,chemistry ,lcsh:Q ,business ,Lung Diseases, Interstitial - Abstract
Coagulation factor Xa appears involved in the pathogenesis of pulmonary fibrosis. Through its interaction with protease activated receptor-1, this protease signals myofibroblast differentiation in lung fibroblasts. Although fibrogenic stimuli induce factor X synthesis by alveolar cells, the mechanisms of local posttranslational factor X activation are not fully understood. Cell-derived microparticles are submicron vesicles involved in different physiological processes, including blood coagulation; they potentially activate factor X due to the exposure on their outer membrane of both phosphatidylserine and tissue factor. We postulated a role for procoagulant microparticles in the pathogenesis of interstitial lung diseases. Nineteen patients with interstitial lung diseases and 11 controls were studied. All subjects underwent bronchoalveolar lavage; interstitial lung disease patients also underwent pulmonary function tests and high resolution CT scan. Microparticles were enumerated in the bronchoalveolar lavage fluid with a solid-phase assay based on thrombin generation. Microparticles were also tested for tissue factor activity. In vitro shedding of microparticles upon incubation with H2O2 was assessed in the human alveolar cell line, A549 and in normal bronchial epithelial cells. Tissue factor synthesis was quantitated by real-time PCR. Total microparticle number and microparticle-associated tissue factor activity were increased in interstitial lung disease patients compared to controls (84±8 vs. 39±3 nM phosphatidylserine; 293±37 vs. 105±21 arbitrary units of tissue factor activity; mean±SEM; p
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- 2014
40. A Survey on Transport Management Practices Associated with Injuries and Health Problems in Horses
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Padalino, Barbara, primary, Raidal, Sharanne L., additional, Hall, Evelyn, additional, Knight, Peter, additional, Celi, Pietro, additional, Jeffcott, Leo, additional, and Muscatello, Gary, additional
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- 2016
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41. Increased Pleiotrophin Concentrations in Papillary Thyroid Cancer
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Jee, Youn Hee, primary, Sadowski, Samira M., additional, Celi, Francesco S., additional, Xi, Liqiang, additional, Raffeld, Mark, additional, Sacks, David B., additional, Remaley, Alan T., additional, Wellstein, Anton, additional, Kebebew, Electron, additional, and Baron, Jeffrey, additional
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- 2016
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42. Effect of 3′UTR RET Variants on RET mRNA Secondary Structure and Disease Presentation in Medullary Thyroid Carcinoma
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Ceolin, Lucieli, primary, Romitti, Mirian, additional, Rodrigues Siqueira, Débora, additional, Vaz Ferreira, Carla, additional, Oliboni Scapineli, Jessica, additional, Assis-Brazil, Beatriz, additional, Vieira Maximiano, Rodolfo, additional, Dias Amarante, Tauanne, additional, de Souza Nunes, Miriam Celi, additional, Weber, Gerald, additional, and Maia, Ana Luiza, additional
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- 2016
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43. Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform
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Poucke, Sven Van, primary, Zhang, Zhongheng, additional, Schmitz, Martin, additional, Vukicevic, Milan, additional, Laenen, Margot Vander, additional, Celi, Leo Anthony, additional, and Deyne, Cathy De, additional
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- 2016
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44. PTPN22 association in systemic lupus erythematosus (SLE) with respect to individual ancestry and clinical sub-phenotypes
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Luis M. Vilá, Juan-Manuel Anaya, Bahram Namjou, Graciela S. Alarcón, John B. Harley, Jeffrey C. Edberg, Marta E. Alarcón-Riquelme, Elizabeth E. Brown, R. H. Scofield, Timothy B. Niewold, Jiyoung Choi, Carl D. Langefeld, Robert P. Kimberly, Barry I. Freedman, Betty P. Tsao, Swapan K. Nath, Jennifer A. Kelly, Bernardo A. Pons-Estel, John D. Reveille, Kenneth M. Kaufman, Joel M. Guthridge, Kathy L. Sivils, Susan A. Boackle, Judith A. James, Timothy J. Vyse, Patrick M. Gaffney, Lindsey A. Criswell, Jaehoon Kim, Sharon A. Chung, Chaim O. Jacob, Michelle Petri, Sang Cheol Bae, Joan T. Merrill, Adam Adler, Stuart B. Glenn, Xana Kim-Howard, Rosalind Ramsey-Goldman, Celi Sun, and Gary S. Gilkeson
- Subjects
Linkage disequilibrium ,US Department of Veterans Affairs Medical Center ,Heredity ,Non-Clinical Medicine ,Oklahoma Medical Research Foundation ,lcsh:Medicine ,Genome-wide association study ,Linkage Disequilibrium ,Cincinnati Children’s Hospital Medical Center ,0302 clinical medicine ,Gene Frequency ,Rosalind Russell Medical Research Center for Arthritis ,Lupus Erythematosus, Systemic ,University of Puerto Rico Medical Sciences Campus ,lcsh:Science ,University of Oklahoma Health Sciences Center ,0303 health sciences ,Multidisciplinary ,Hispanic or Latino ,PTPN22 ,Ethnic Differences ,3. Good health ,Division of Rheumatology ,Phenotype ,Northwestern University Feinberg School of Medicine ,Medicine ,Research Article ,Genotype ,Genotypes ,Inmunología ,Single-nucleotide polymorphism ,Biology ,Department of Internal Medicine ,Polymorphism, Single Nucleotide ,Systemic Lupus Erythematosus ,White People ,Autoimmune Diseases ,03 medical and health sciences ,Johns Hopkins University School of Medicine ,Rheumatology ,Arthritis and Clinical Immunology Research Program ,University of Texas Health Science Center at Houston ,Lupus eritematoso sistémico ,University of Alabama at Birmingham ,medicine ,Genetics ,Humans ,Genetic Predisposition to Disease ,Allele frequency ,030304 developmental biology ,Genetic association ,030203 arthritis & rheumatology ,Lupus erythematosus ,Health Care Policy ,Asian ,Lupus Erythematosus ,lcsh:R ,Autoantibody ,Computational Biology ,Protein Tyrosine Phosphatase, Non-Receptor Type 22 ,medicine.disease ,Department of Medicine ,Enfermedades ,Black or African American ,Logistic Models ,Haplotypes ,Antibodies, Anticardiolipin ,Immunoglobulin G ,Immunology ,Genetic Polymorphism ,lcsh:Q ,Clinical Immunology ,Population Genetics - Abstract
Protein tyrosine phosphatase non-receptor type 22 (PTPN22) is a negative regulator of T-cell activation associated with several autoimmune diseases, including systemic lupus erythematosus (SLE). Missense rs2476601 is associated with SLE in individuals with European ancestry. Since the rs2476601 risk allele frequency differs dramatically across ethnicities, we assessed robustness of PTPN22 association with SLE and its clinical sub-phenotypes across four ethnically diverse populations. Ten SNPs were genotyped in 8220 SLE cases and 7369 controls from in European-Americans (EA), African-Americans (AA), Asians (AS), and Hispanics (HS). We performed imputation-based association followed by conditional analysis to identify independent associations. Significantly associated SNPs were tested for association with SLE clinical sub-phenotypes, including autoantibody profiles. Multiple testing was accounted for by using false discovery rate. We successfully imputed and tested allelic association for 107 SNPs within the PTPN22 region and detected evidence of ethnic-specific associations from EA and HS. In EA, the strongest association was at rs2476601 (P = 4.7 × 10(-9), OR = 1.40 (95% CI = 1.25-1.56)). Independent association with rs1217414 was also observed in EA, and both SNPs are correlated with increased European ancestry. For HS imputed intronic SNP, rs3765598, predicted to be a cis-eQTL, was associated (P = 0.007, OR = 0.79 and 95% CI = 0.67-0.94). No significant associations were observed in AA or AS. Case-only analysis using lupus-related clinical criteria revealed differences between EA SLE patients positive for moderate to high titers of IgG anti-cardiolipin (aCL IgG >20) versus negative aCL IgG at rs2476601 (P = 0.012, OR = 1.65). Association was reinforced when these cases were compared to controls (P = 2.7 × 10(-5), OR = 2.11). Our results validate that rs2476601 is the most significantly associated SNP in individuals with European ancestry. Additionally, rs1217414 and rs3765598 may be associated with SLE. Further studies are required to confirm the involvement of rs2476601 with aCL IgG.
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- 2013
45. Acute histologic chorioamnionitis at term: nearly always noninfectious
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Andrew B. Onderdonk, Ellice Lieberman, Drucilla J. Roberts, Laura E. Riley, Ann C. Celi, Theonia K. Boyd, and Lise C. Johnson
- Subjects
medicine.medical_specialty ,Pathology ,Epidemiology ,medicine.drug_class ,Antibiotics ,Group B Streptococcal Infection ,lcsh:Medicine ,Chorioamnionitis ,Gastroenterology ,Obstetrics and gynaecology ,Pregnancy ,Risk Factors ,Internal medicine ,Placenta ,Humans ,Medicine ,Pregnancy Complications, Infectious ,lcsh:Science ,Multidisciplinary ,business.industry ,lcsh:R ,Obstetrics and Gynecology ,Histology ,Prognosis ,medicine.disease ,Infectious Diseases ,medicine.anatomical_structure ,Acute Disease ,Female ,lcsh:Q ,business ,Research Article - Abstract
Background The link between histologic acute chorioamnionitis and infection is well established in preterm deliveries, but less well-studied in term pregnancies, where infection is much less common. Methodology/Principal Findings We conducted a secondary analysis among 195 low-risk women with term pregnancies enrolled in a randomized trial. Histologic and microbiologic evaluation of placentas included anaerobic and aerobic cultures (including mycoplasma/ureaplasma species) as well as PCR. Infection was defined as ≥1,000 cfu of a single known pathogen or a ≥2 log difference in counts for a known pathogen versus other organisms in a mixed culture. Placental membranes were scored and categorized as: no chorioamnionitis, Grade 1 (subchorionitis and patchy acute chorioamnionitis), or Grade 2 (severe, confluent chorioamnionitis). Grade 1 or grade 2 histologic chorioamnionitis was present in 34% of placentas (67/195), but infection was present in only 4% (8/195). Histologic chorioamnionitis was strongly associated with intrapartum fever >38°C [69% (25/36) fever, 26% (42/159) afebrile, P
- Published
- 2012
46. Procoagulant, Tissue Factor-Bearing Microparticles in Bronchoalveolar Lavage of Interstitial Lung Disease Patients: An Observational Study
- Author
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Novelli, Federica, primary, Neri, Tommaso, additional, Tavanti, Laura, additional, Armani, Chiara, additional, Noce, Concettina, additional, Falaschi, Fabio, additional, Bartoli, Maria Laura, additional, Martino, Federica, additional, Palla, Antonio, additional, Celi, Alessandro, additional, and Paggiaro, Pierluigi, additional
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- 2014
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47. Trends in Severity of Illness on ICU Admission and Mortality among the Elderly
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Fuchs, Lior, primary, Novack, Victor, additional, McLennan, Stuart, additional, Celi, Leo Anthony, additional, Baumfeld, Yael, additional, Park, Shinhyuk, additional, Howell, Michael D., additional, and Talmor, Daniel S., additional
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- 2014
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48. Triphenylmethane Derivatives Have High In Vitro and In Vivo Activity against the Main Causative Agents of Cutaneous Leishmaniasis
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de Souza Pietra, Renata Celi Carvalho, primary, Rodrigues, Lucas Fonseca, additional, Teixeira, Eliane, additional, Fried, Levi, additional, Lefkove, Benjamin, additional, Rabello, Ana, additional, Arbiser, Jack, additional, Ferreira, Lucas Antônio Miranda, additional, and Fernandes, Ana Paula, additional
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- 2013
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49. Acute Histologic Chorioamnionitis at Term: Nearly Always Noninfectious
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Roberts, Drucilla J., primary, Celi, Ann C., additional, Riley, Laura E., additional, Onderdonk, Andrew B., additional, Boyd, Theonia K., additional, Johnson, Lise Carolyn, additional, and Lieberman, Ellice, additional
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- 2012
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50. Inflammatory Adipokines, High Molecular Weight Adiponectin, and Insulin Resistance: A Population-Based Survey in Prepubertal Schoolchildren
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Murdolo, Giuseppe, primary, Nowotny, Bettina, additional, Celi, Federica, additional, Donati, Miranda, additional, Bini, Vittorio, additional, Papi, Francesco, additional, Gornitzka, Gabi, additional, Castellani, Serena, additional, Roden, Michael, additional, Falorni, Adriano, additional, Herder, Christian, additional, and Falorni, Alberto, additional
- Published
- 2011
- Full Text
- View/download PDF
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