Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 18 de 18
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
AMIA Annu Symp Proc ; 2021: 345-354, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34457149

RESUMO

Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes generalize variably between institutions, but ignored other levels of healthcare organization. We measured SciBERT diagnosis sentiment classifier generalizability between medical specialties using EHR sentences from MIMIC-III. Models trained on one specialty performed better on internal test sets than mixed or external test sets (mean AUCs 0.92, 0.87, and 0.83, respectively; p = 0.016). When models are trained on more specialties, they have better test performances (p < 1e-4). Model performance on new corpora is directly correlated to the similarity between train and test sentence content (p < 1e-4). Future studies should assess additional axes of generalization to ensure deep learning models fulfil their intended purpose across institutions, specialties, and practices.


Assuntos
Aprendizado Profundo , Medicina , Humanos , Idioma , Semântica
2.
PLoS One ; 16(5): e0246165, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33956800

RESUMO

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.


Assuntos
Eletroencefalografia , Aprendizado de Máquina , Processamento de Sinais Assistido por Computador , Inconsciência/fisiopatologia , Anestésicos Intravenosos/farmacologia , Encéfalo/efeitos dos fármacos , Encéfalo/fisiopatologia , Eletroencefalografia/efeitos dos fármacos , Humanos , Masculino , Sevoflurano/efeitos adversos , Inconsciência/induzido quimicamente
3.
ACS Omega ; 5(36): 23289-23298, 2020 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-32954180

RESUMO

Here, we report a nanoparticle-based probe that affords facile cell labeling with cholesterol in cholesterol efflux (CE) assays. This probe, called ezFlux, was optimized through a screening of multiple nanoformulations engineered with a Förster resonance energy transfer (FRET) reporter. The physicochemical- and bio-similarity of ezFlux to standard semi-synthetic acetylated low-density lipoprotein (acLDL) was confirmed by testing uptake in macrophages, the intracellular route of degradation, and performance in CE assays. A single-step fast self-assembly fabrication makes ezFlux an attractive alternative to acLDL. We also show that CE testing using ezFlux is significantly cheaper than that performed using commercial kits or acLDL. Additionally, we analyze clinical trials that measure CE and show that ezFlux has a place in many research and clinical laboratories worldwide that use CE to assess cellular and lipoprotein function.

5.
NPJ Digit Med ; 2: 31, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31304378

RESUMO

Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs, and delayed diagnosis leads to higher cost and worse outcomes. Computer-aided diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep-learning models on 17,587 radiographs to classify fracture, 5 patient traits, and 14 hospital process variables. All 20 variables could be individually predicted from a radiograph, with the best performances on scanner model (AUC = 1.00), scanner brand (AUC = 0.98), and whether the order was marked "priority" (AUC = 0.79). Fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86, DeLong paired AUC comparison, p = 2e-9) or patient data plus hospital process features (AUC = 0.91, p = 1e-21). Fracture prediction on a test set that balanced fracture risk across patient variables was significantly lower than a random test set (AUC = 0.67, DeLong unpaired AUC comparison, p = 0.003); and on a test set with fracture risk balanced across patient and hospital process variables, the model performed randomly (AUC = 0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's fracture predictions. A single model that directly combines image features, patient, and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital process data. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep-learning decision processes so that computers and clinicians can effectively cooperate.

6.
BMC Med Genomics ; 12(Suppl 6): 108, 2019 07 25.
Artigo em Inglês | MEDLINE | ID: mdl-31345219

RESUMO

BACKGROUND: Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene. RESULTS: We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight. CONCLUSION: In sum, by integrating genetic and electronic medical record data, and leveraging one of the world's largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation.


Assuntos
Doenças Cardiovasculares/genética , Genômica , Mutação , Doenças Cardiovasculares/sangue , Colesterol/sangue , Genótipo , Humanos , Triglicerídeos/sangue
7.
Bioinformatics ; 35(21): 4515-4518, 2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-31214700

RESUMO

MOTIVATION: Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge. RESULTS: We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes. AVAILABILITY AND IMPLEMENTATION: PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Registros Eletrônicos de Saúde , Software , Computadores , Bases de Dados Factuais , Humanos , Estudos Observacionais como Assunto
8.
Bioinformatics ; 35(9): 1610-1612, 2019 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-30304439

RESUMO

MOTIVATION: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. RESULTS: We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. AVAILABILITY AND IMPLEMENTATION: Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.


Assuntos
Algoritmos , Software , Aprendizado Profundo , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
9.
PLoS Med ; 15(11): e1002683, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30399157

RESUMO

BACKGROUND: There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. METHODS AND FINDINGS: A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong's test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927-0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745-0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH-NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system-specific biases. CONCLUSION: Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.


Assuntos
Aprendizado Profundo , Diagnóstico por Computador/métodos , Pneumonia/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia Torácica/métodos , Adulto , Idoso , Estudos Transversais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Sistemas de Informação em Radiologia , Reprodutibilidade dos Testes , Estudos Retrospectivos , Estados Unidos
10.
Brief Bioinform ; 19(4): 656-678, 2018 07 20.
Artigo em Inglês | MEDLINE | ID: mdl-28200013

RESUMO

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.


Assuntos
Biologia Computacional/métodos , Bases de Dados Factuais , Doença , Descoberta de Drogas , Reposicionamento de Medicamentos , Proteínas/metabolismo , Humanos , Epidemiologia Molecular , Proteínas/genética
11.
Brief Bioinform ; 18(1): 105-124, 2017 01.
Artigo em Inglês | MEDLINE | ID: mdl-26876889

RESUMO

Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.


Assuntos
Biologia Computacional , Coleta de Dados , Humanos , Software
12.
Bioinformatics ; 32(12): i101-i110, 2016 06 15.
Artigo em Inglês | MEDLINE | ID: mdl-27307606

RESUMO

MOTIVATION: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL). RESULTS: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key 'hub' diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes. CONTACTS: rong.chen@mssm.edu or joel.dudley@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Registros Eletrônicos de Saúde , Afro-Americanos , Bases de Dados Factuais , Grupo com Ancestrais do Continente Europeu , Hispano-Americanos , Humanos
13.
BMJ Open ; 6(3): e010579, 2016 Mar 24.
Artigo em Inglês | MEDLINE | ID: mdl-27013597

RESUMO

OBJECTIVE: To design, develop and prototype clinical dashboards to integrate high-frequency health and wellness data streams using interactive and real-time data visualisation and analytics modalities. MATERIALS AND METHODS: We developed a clinical dashboard development framework called electronic healthcare data visualization (EHDViz) toolkit for generating web-based, real-time clinical dashboards for visualising heterogeneous biomedical, healthcare and wellness data. The EHDViz is an extensible toolkit that uses R packages for data management, normalisation and producing high-quality visualisations over the web using R/Shiny web server architecture. We have developed use cases to illustrate utility of EHDViz in different scenarios of clinical and wellness setting as a visualisation aid for improving healthcare delivery. RESULTS: Using EHDViz, we prototyped clinical dashboards to demonstrate the contextual versatility of EHDViz toolkit. An outpatient cohort was used to visualise population health management tasks (n=14,221), and an inpatient cohort was used to visualise real-time acuity risk in a clinical unit (n=445), and a quantified-self example using wellness data from a fitness activity monitor worn by a single individual was also discussed (n-of-1). The back-end system retrieves relevant data from data source, populates the main panel of the application and integrates user-defined data features in real-time and renders output using modern web browsers. The visualisation elements can be customised using health features, disease names, procedure names or medical codes to populate the visualisations. The source code of EHDViz and various prototypes developed using EHDViz are available in the public domain at http://ehdviz.dudleylab.org. CONCLUSIONS: Collaborative data visualisations, wellness trend predictions, risk estimation, proactive acuity status monitoring and knowledge of complex disease indicators are essential components of implementing data-driven precision medicine. As an open-source visualisation framework capable of integrating health assessment, EHDViz aims to be a valuable toolkit for rapid design, development and implementation of scalable clinical data visualisation dashboards.


Assuntos
Apresentação de Dados , Sistemas de Gerenciamento de Base de Dados , Atenção à Saúde/normas , Eletrônica Médica/métodos , Internet
14.
J Control Release ; 217: 243-55, 2015 Nov 10.
Artigo em Inglês | MEDLINE | ID: mdl-26386437

RESUMO

Macrophages are innate immune cells with great phenotypic plasticity, which allows them to regulate an array of physiological processes such as host defense, tissue repair, and lipid/lipoprotein metabolism. In this proof-of-principle study, we report that macrophages of the M1 inflammatory phenotype can be selectively targeted by model hybrid lipid-latex (LiLa) nanoparticles bearing phagocytic signals. We demonstrate a simple and robust route to fabricate nanoparticles and then show their efficacy through imaging and drug delivery in inflammatory disease models of atherosclerosis and obesity. Self-assembled LiLa nanoparticles can be modified with a variety of hydrophobic entities such as drug cargos, signaling lipids, and imaging reporters resulting in sub-100nm nanoparticles with low polydispersities. The optimized theranostic LiLa formulation with gadolinium, fluorescein and "eat-me" phagocytic signals (Gd-FITC-LiLa) a) demonstrates high relaxivity that improves magnetic resonance imaging (MRI) sensitivity, b) encapsulates hydrophobic drugs at up to 60% by weight, and c) selectively targets inflammatory M1 macrophages concomitant with controlled release of the payload of anti-inflammatory drug. The mechanism and kinetics of the payload discharge appeared to be phospholipase A2 activity-dependent, as determined by means of intracellular Förster resonance energy transfer (FRET). In vivo, LiLa targets M1 macrophages in a mouse model of atherosclerosis, allowing noninvasive imaging of atherosclerotic plaque by MRI. In the context of obesity, LiLa particles were selectively deposited to M1 macrophages within inflamed adipose tissue, as demonstrated by single-photon intravital imaging in mice. Collectively, our results suggest that phagocytic signals can preferentially target inflammatory macrophages in experimental models of atherosclerosis and obesity, thus opening the possibility of future clinical applications that diagnose/treat these conditions. Tunable LiLa nanoparticles reported here can serve as a model theranostic platform with application in various types of imaging of the diseases such as cardiovascular disorders, obesity, and cancer where macrophages play a pathogenic role.


Assuntos
Anti-Inflamatórios/administração & dosagem , Macrófagos/efeitos dos fármacos , Nanopartículas/administração & dosagem , Animais , Anti-Inflamatórios/química , Apolipoproteínas E/genética , Aterosclerose/imunologia , Linhagem Celular , Colesterol/análogos & derivados , Colesterol/química , Citocinas/genética , Fluoresceína-5-Isotiocianato/química , Gadolínio/química , Macrófagos/imunologia , Camundongos Endogâmicos C57BL , Camundongos Knockout , Nanopartículas/química , Obesidade/imunologia , Paclitaxel/administração & dosagem , Paclitaxel/química , Fagocitose , Fosfatidiletanolaminas/química , Fosfatidilserinas/química , Fosfolipases A2/química , Polietilenoglicóis/química , Poliestirenos/química , Rosiglitazona , Tamoxifeno/administração & dosagem , Tamoxifeno/química , Tiazolidinedionas/administração & dosagem , Tiazolidinedionas/química
15.
Bioinformatics ; 31(2): 209-15, 2015 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-25266226

RESUMO

MOTIVATION: Modern molecular technologies allow the collection of large amounts of high-throughput data on the functional attributes of genes. Often multiple technologies and study designs are used to address the same biological question such as which genes are overexpressed in a specific disease state. Consequently, there is considerable interest in methods that can integrate across datasets to present a unified set of predictions. RESULTS: An important aspect of data integration is being able to account for the fact that datasets may differ in how accurately they capture the biological signal of interest. While many methods to address this problem exist, they always rely either on dataset internal statistics, which reflect data structure and not necessarily biological relevance, or external gold standards, which may not always be available. We present a new rank aggregation method for data integration that requires neither external standards nor internal statistics but relies on Bayesian reasoning to assess dataset relevance. We demonstrate that our method outperforms established techniques and significantly improves the predictive power of rank-based aggregations. We show that our method, which does not require an external gold standard, provides reliable estimates of dataset relevance and allows the same set of data to be integrated differently depending on the specific signal of interest. AVAILABILITY: The method is implemented in R and is freely available at http://www.pitt.edu/~mchikina/BIRRA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Algoritmos , Teorema de Bayes , Biomarcadores/análise , Biologia Computacional/métodos , Doença de Parkinson/genética , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Metanálise como Assunto
16.
Inhal Toxicol ; 26(1): 23-9, 2014 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-24417404

RESUMO

CONTEXT: High-density lipoprotein (HDL) particles perform numerous vascular-protective functions. Animal studies demonstrate that exposure to fine or ultrafine particulate matter (PM) can promote HDL dysfunction. However, the impact of PM on humans remains unknown. OBJECTIVE: We aimed to determine the effect of exposure to coarse concentrated ambient particles (CAP) on several metrics of HDL function in healthy humans. METHODS: Thirty-two adults (25.9 ± 6.6 years) were exposed to coarse CAP [76.2 ± 51.5 µg·m(-3)] in a rural location and filtered air (FA) for 2 h in a randomized double-blind crossover study. Venous blood collected 2- and 20-h post-exposures was measured for HDL-mediated efflux of [(3)H]-cholesterol from cells and 20-h exposures for HDL anti-oxidant capacity by a fluorescent assay and paraoxonase activity. The changes [median (first, third quartiles)] between exposures among 29 subjects with available results were compared by matched Wilcoxon tests. RESULTS: HDL-mediated cholesterol efflux capacity did not differ between exposures at either time point [16.60% (15.17, 19.19) 2-h post-CAP versus 17.56% (13.43, 20.98) post-FA, p = 0.768 and 14.90% (12.47, 19.15) 20-h post-CAP versus 17.75% (13.22, 23.95) post-FA, p = 0.216]. HOI [0.26 (0.24, 0.35) versus 0.28 (0.25, 0.40), p = 0.198] and paraoxonase activity [0.54 (0.39, 0.82) versus 0.60 µmol·min(-1 )ml plasma(-1) (0.40, 0.85), p = 0.137] did not differ 20-h post-CAP versus FA, respectively. CONCLUSIONS: Brief inhalation of coarse PM from a rural location did not acutely impair several facets of HDL functionality. Whether coarse PM derived from urban sites, fine particles or longer term PM exposures can promote HDL dysfunction warrant future investigations.


Assuntos
Poluentes Atmosféricos/toxicidade , Lipoproteínas HDL/sangue , Material Particulado/toxicidade , Adolescente , Adulto , Poluição do Ar/efeitos adversos , Animais , Arildialquilfosfatase/sangue , Linhagem Celular Tumoral , Estudos Cross-Over , Método Duplo-Cego , Feminino , Humanos , Lipoproteínas HDL/metabolismo , Macrófagos/metabolismo , Masculino , Camundongos , Pessoa de Meia-Idade , Tamanho da Partícula , População Rural , Adulto Jovem
17.
J Phys Act Health ; 10(2): 160-9, 2013 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-22821941

RESUMO

BACKGROUND: With more than 1.1 million high school athletes playing annually during the 2005-06 to 2009-10 academic years, football is the most popular boys' sport in the United States. METHODS: Using an internet-based data collection tool, RIO, certified athletic trainers (ATs) from 100 nationally representative US high schools reported athletic exposure and football injury data during the 2005-06 to 2009-10 academic years. RESULTS: Participating ATs reported 10,100 football injuries corresponding to an estimated 2,739,187 football-related injuries nationally. The injury rate was 4.08 per 1000 athlete-exposures (AEs) overall. Offensive lineman collectively (center, offensive guard, offensive tackle) sustained 18.3% of all injuries. Running backs (16.3%) sustained more injuries than any other position followed by linebackers (14.9%) and wide receivers (11.9%). The leading mechanism of injury was player-player contact (64.0%) followed by player-surface contact (13.4%). More specifically, injury occurred most commonly when players were being tackled (24.4%) and tackling (21.8%). CONCLUSIONS: Patterns of football injuries vary by position. Identifying such differences is important to drive development of evidence-based, targeted injury prevention efforts.


Assuntos
Traumatismos em Atletas/epidemiologia , Traumatismos em Atletas/etiologia , Futebol Americano/estatística & dados numéricos , Instituições Acadêmicas/estatística & dados numéricos , Humanos
18.
Arterioscler Thromb Vasc Biol ; 32(4): 962-70, 2012 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-22308043

RESUMO

OBJECTIVE: Myeloid-related protein (Mrp) 8/14 complex (is a highly expressed extracellularly secreted protein, implicated in atherosclerosis. In this study, we evaluated the feasibility of targeting Mrp in vivo through synthetic immuno-nanoprobes. METHODS AND RESULTS: Anti-Mrp-14 and nonspecific IgG-conjugated gadolinium nanoprobes (aMrp-) were synthesized and characterized. Pharmacokinetics and vascular targeting via MRI of the formulations were assessed in vivo in high fat-fed apolipoprotein E deficient (ApoE(-/-)), ApoE(-/-)/Mrp14(-/-) (double knockout) and chow-fed wild-type (C57BL/6) mice. Bone marrow-derived myeloid progenitor cells were isolated from both ApoE(-/-) and double knockout mice, differentiated to macrophages, and were treated with LPS, with or without Mrp8, Mrp14, or Mrp8/14; conditioned media was used for in vitro studies. Mrp-activated cells secreted significant amounts of proinflammatory cytokines, which was abolished by pretreatment with aMrp-NP. We show in vitro that aMrp-NP binds endothelial cells previously treated with conditioned media containing Mrp8/14. MRI following intravenous delivery of aMrp-NP revealed prolonged and substantial delineation of plaque in ApoE(-/-) but not double knockout or wild-type animals. Nonspecific IgG-conjugated gadolinium nanoprobe-injected animals in all groups did not show vessel wall enhancement. Flow-cytometric analysis of aortic digesta revealed that aMrp-NP present in Ly-6G(+), CD11b(+), CD11c(+), and CD31(+) cells in ApoE(-/-) but not in double knockout animals. CONCLUSIONS: Targeted imaging with aMrp-NP demonstrates enhancement of plaque with binding to inflammatory cells and reduction in inflammation. This strategy has promise as a theranostic approach for atherosclerosis.


Assuntos
Albuminas/farmacocinética , Anti-Inflamatórios/farmacocinética , Anticorpos/metabolismo , Aterosclerose/metabolismo , Calgranulina A/imunologia , Calgranulina B/imunologia , Meios de Contraste/farmacocinética , Gadolínio DTPA/farmacocinética , Imunoconjugados/farmacocinética , Inflamação/metabolismo , Nanopartículas Metálicas , Albuminas/química , Animais , Anti-Inflamatórios/química , Anti-Inflamatórios/farmacologia , Anticorpos/química , Anticorpos/farmacologia , Apolipoproteínas E/deficiência , Apolipoproteínas E/genética , Aterosclerose/diagnóstico , Aterosclerose/tratamento farmacológico , Aterosclerose/genética , Aterosclerose/imunologia , Calgranulina A/metabolismo , Calgranulina B/genética , Calgranulina B/metabolismo , Células Cultivadas , Meios de Contraste/química , Meios de Cultivo Condicionados/metabolismo , Citocinas/metabolismo , Modelos Animais de Doenças , Estudos de Viabilidade , Citometria de Fluxo , Gadolínio DTPA/química , Células Endoteliais da Veia Umbilical Humana/metabolismo , Humanos , Imunoconjugados/química , Inflamação/diagnóstico , Inflamação/tratamento farmacológico , Inflamação/genética , Inflamação/imunologia , Mediadores da Inflamação/metabolismo , Macrófagos/metabolismo , Imageamento por Ressonância Magnética , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Knockout , Células Progenitoras Mieloides/metabolismo , Distribuição Tecidual
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...