Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.

This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.

Discovery of a new eugenol-benznidazole hybrid active against different evolutive stages of Trypanosoma cruzi.

Chagas disease (CD) is a life-threatening illness caused by the protozoan Trypanosoma cruzi and there are only two drugs currently available for pharmacotherapy of this neglected infection (benznidazole and nifurtimox). Their limited efficacy in chronic phase of the disease, problems of toxicity and the growing resistance by the protozoan are directly associated to high rates of drug discontinuation by the patients. In the context of the search for new trypanocidal drug candidates, our group has been working with the chemical manipulation of eugenol to obtain new agents active against T.

Artificial intelligence-based pulmonary embolism classification: Development and validation using real-world data.

This paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V2 and then a recurrent neural network long short-term memory model.

Chemical Composition, Antimicrobial and Antioxidant Activities of Broccoli, Kale, and Cauliflower Extracts.

The brassicas have the potential to prevent chronic non-communicable diseases and it is proposed to evaluate the chemical composition, antioxidant and antimicrobial potential of broccoli, cabbage and extracts. The extracts were prepared and characterized and the antioxidant potential was evaluated against three radicals while the antimicrobial potential was analyzed using three techniques against four bacteria. The extracts have glucosinolates and phenolic compounds in their composition, and effectively inhibit the 2,2-diphenyl-1-picrylhydrazyl radical.

Bioactive aerogels based on native and phosphorylated potato (Solanum tuberosum L.) starches incorporated with star fruit extract (Averrhoa carambola L.).

The aim of this study was to develop a star fruit extract (SFE) and incorporate it into aerogels based on native and phosphorylated potato starches. The phosphorylation of starch enhances its properties by incorporating phosphate groups that increase the spaces between starch molecules, resulting in a more resilient, intact aerogel with enhanced water absorption. The bioactive aerogels based on potato starch and 10, 15, and 20 % (w/w) of SFE were characterized by their morphological and thermogravimetric properties, infrared spectra, water absorption capacity, loading capacity, and antioxidant activity.