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https://www.selleckchem.com/ Validation of the methodology showed that it presented good linearity (R2 > 0.9945), satisfactory accuracy and precision (in the range from 72 ± 16 % to 109 ± 9 %), and limits of quantification (LOQ) and detection (LOD) in the ranges 0.02-1.0 µg g-1 and 0.01-0.2 µg g-1, respectively. The developed method was applied to tobacco samples, proving to be efficient for determination of β-carboline alkaloids. The compounds harmane and norharmane were quantified in samples of fresh tobacco leaves, cured tobacco leaves, twisted tobacco, and cigarettes. Harmine was only not quantified in the cigarettes.Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically dangerous disease that needs to be treated quickly. Thanks to the advancement in machine learning and the computing power of today's microprocessors, deep learning has become an unbelievably valuable tool for detecting diseases, in particular from medical images. In this work, we are interested in differentiating computer tomography (CT) images of healthy brains and ICH using a ResNet-18, a deep residual convolutional neural network. In addition, the gradient-weighted class activation mapping (Grad-CAM) technique was employed to visually explore and understand the network's decisions. The generalizability of the detector was assessed through a 100-iteration Monte Carlo cross-validation (80% of the data for training and 20% for test). In a database with 200 CT images of brains (100 with ICH and 100 without ICH), the detector yielded, on average, 95.93%accuracy, 96.20% specificity, 95.65% sensitivity, 96.40% precision, and 95.91% F1-core, with an average computing time of 165.90 s to train the network (on 160 images) and 1.17 s to test it with 40 CT images. These results are comparable with the state of the

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