Publications by authors named "David Bedoya"

Chronic rhinosinusitis with nasal polyps (CRSwNP) is an inflammatory condition of the nasal and paranasal tissues, characterized by the presence of bilateral nasal polyps. While etiology and pathogenetic mechanisms are heterogeneous and complex, in most patients, disease is mediated predominantly through type 2 inflammatory processes. Clinical management is challenging, and a multidisciplinary approach is preferred.

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Hyperpolarization chemistry based on reversible exchange of parahydrogen, also known as Signal Amplification By Reversible Exchange (SABRE), is a particularly simple approach to attain high levels of nuclear spin hyperpolarization, which can enhance NMR and MRI signals by many orders of magnitude. SABRE has received significant attention in the scientific community since its inception because of its relative experimental simplicity and its broad applicability to a wide range of molecules, however in vivo detection of molecular probes hyperpolarized by SABRE has remained elusive. Here we describe a first demonstration of SABRE-hyperpolarized contrast detected in vivo, specifically using hyperpolarized [1-C]pyruvate.

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Objectives/hypothesis: To present a modified endoscopic medial maxillectomy (MEMM) approach to control maxillary sinus pathologies.

Methods: MEMM was completed in 13 fresh-frozen specimens. An MEMM includes cutting the nasolacrimal duct, inferior meatus flap, and repositioning the inferior turbinate (IT).

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Background: Idiopathic hyposmia (IH) is a prodromal marker of Parkinson disease (PD). However, IH is common in the general population and only a minority will develop PD. Identification of individuals with IH at prodromal stage of PD would serve to select them to implement neuroprotective agents, when available.

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Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio.

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Advanced computerized methods and models of retrieving knowledge from large multiparameter data bases were used to analyze data on fish and macroinvertebrate composition (metrics), habitat, land use and water quality. The research focused on the north central and northeastern United States and involved thousands of sites monitored by the state agencies. The techniques and methodologies included supervised and unsupervised Artificial Neural Networks (ANN) modeling, Principal Component Analysis, Canonical Component Analysis (both linear and nonlinear), Multiple Regression Analyses, and analyses of variance by ANOVA.

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