Publications by authors named "A C Pimentel"

Bulk-fill, monochromatic, and ORMOCER composites were introduced in restorative dentistry with the aim of reducing clinical time and/or alleviating contraction stresses at the interface between the tooth and restoration. While the conversion and immediate properties of these materials are comparable to conventional composites, studies evaluating their long-term properties and the structure of the polymer matrix are lacking. The objective of this study was to evaluate the degree of conversion and, indirectly, the crosslink density of conventional, bulk-fill, monochromatic, and ORMOCER resin composites.

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Cardiac involvement in 2019 coronavirus disease (COVID-19) survivors has been reported frequently. An exacerbated immune response may be the main mechanism of myocardial injury and late cardiac sequelae in this population. We investigated the immune profile in hypertensive and non-hypertensive patients with COVID-19 who developed late cardiac fibrosis and edema, as detected by magnetic resonance imaging (MRI).

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UV light emitting diode (LED) disinfection technologies have advanced over the last decade and expanded the design space for applications in point of use, industrial, and now full-scale water treatment. This literature review examines the progression of UV LED technologies from 2007 to 2023 using key features such as total optical power, price, and wall-plug efficiency. The review found that optical power is increasing while the price per Watt is decreasing; however, the wall plug energy (WPE) is slowly improving over the last decade.

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Liquid biopsy with sequencing of circulating tumor DNA (ctDNA) is a minimally invasive method for sampling body fluids and offers a promising alternative to tissue biopsies that involve greater risks, costs, and time. ctDNA not only identifies actionable targets by revealing unique molecular signatures in cancer, but also may assess treatment response, treatment resistance and progression, and recurrence. Imaging correlates of these applications are already being identified and utilized for various solid tumors.

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This application note explores how to address a challenging problem faced by many academics and publishing professionals in recent years: ensuring the integrity of academic writing in universities and publishing houses due to advances in Artificial Intelligence (AI). It distinguishes AI- and human-generated English manuscripts using classifier models such as decision tree, random forest, extra trees, and AdaBoost. It utilizes Scikit learn libraries to provide statistics (precision, accuracy, recall, F1, MCC, and Cohen's kappa scores) and the confusion matrix to guarantee confidence to the user.

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