116 research outputs found

    DeepAngle: Fast calculation of contact angles in tomography images using deep learning

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    DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle)

    Use of active comparator tirals in dermatology: A repeated cross-sectional analysis

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    Introduction: Spending on medications is expected to grow to $420 billion in 2023, largely driven by introduction of new branded products. While new branded medications can transform how physicians care for patients, others may not offer meaningful benefit over existing less costly alternatives. As additional new products are approved, the need to include active comparators in dermatologic clinical trials is particularly important to guide clinical decision making. Methods: To evaluate the trends in the use of active comparator trials designs, topical medications approved between January 2002 and December 2020 were identified through the 2020 Food and Drug Administration (FDA) Orange Book. For each medication, ClinicalTrials.gov was used to identify associated Phase II, III, and IV clinical trials. The frequency of active comparator was determined based on clinical indication and clinical trial phase. A logistic regression was performed to analyze the prevalence of active comparators between the study interval. Results: 177 trials met the inclusion criteria. Between 2002 and 2020, there was a decrease in the percentage of clinical trials for acne, psoriasis, and eczema that included an active comparator (-2.5% per year; 95% CI 0.9-4.2%). Phase II studies were most likely to include an active comparator (71%), while phase III studies were least likely (32%). Conclusion: Although there is a greater need for comparative effectiveness data in the setting of a growing number of available treatments, our results highlight that use of active comparator trials is decreasing over time, which will hinder comparative effectiveness research
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