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Simple Summary: Device-assisted enteroscopy is the only diagnostic and therapeutic exam capable of exploring the entire gastrointestinal tract. However, the diagnostic yield of this procedure is not sufficient enough to assure a cost-effective panendoscopy, and there is significant interobserver variability during the exam. Artificial intelligence tools have been proved to be beneficial in several areas of medicine, namely in Gastroenterology, with a strong image component. However, the development of deep learning models for application in device-assisted enteroscopy is still in an embryonic phase. The authors herein aimed to develop a multidevice convolutional neural network based on 338 exams performed in two renowned centers. The present model was able to accurately identify multiple clinically relevant lesions across the entire gastrointestinal tract, with an image processing time that favors its clinical applicability. The first worldwide panendoscopic model showed the potential of artificial intelligence in augmenting the accuracy and cost-effectiveness of device-assisted enteroscopy. Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosis and characterization of biliary strictures is challenging, even after the introduction of digital single-operator cholangioscopy (D-SOC). The endoscopist's visual impression has a suboptimal accuracy and there is a significant interobserver variability. Artificial intelligence tools for image analysis have presented important contributions in several fields of gastroenterology. Convolutional neural networks are highly efficient multi-layered deep neural networks for image analysis, with great results in several fields of medicine. Nevertheless, the role of these deep learning models in digital cholangioscopy is still in a premature phase. With this bicentric international study, the authors aimed to create a deep learning-based algorithm for digital cholangioscopy capable of distinguishing benign from malignant biliary lesions. The present model accurately detected malignant biliary lesions with an image processing rate that favors its clinical applicability. The authors believe that the use of an AI-based model may change the landscape in the digital cholangioscopy diagnostic yield. Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures. [ABSTRACT FROM AUTHOR]

Due to the perishable nature of tourist products, which impacts supply and demand, the possibility of analysing the relationship between customers' satisfaction and service quality can contribute to increased revenues. Machine learning techniques allow the analysis of how these services can be improved or developed and how to reach new markets, and look for the emergence of ideas to innovate and improve interaction with the customer. This paper presents a decision-support system for analysing consumer satisfaction, based on consumer feedback from the customer's experience when transported by a transfer company, in the present case working in the Algarve region, Portugal. The results show how tourists perceive the service and which factors influence their level of satisfaction and sentiment. One of the results revealed that the first impression associated with good news is what creates the most value in the experience, i.e., "first impressions matter". [ABSTRACT FROM AUTHOR]

We revise the Portuguese spiders of the genus Harpactea Bristowe, 1939 (Araneae: Dysderidae). The following seven new species are described: H. adicensis sp. nov., H. crespoi sp. nov., H. dolanskyi sp. nov., H. henriquesi sp. nov., H. korenkoi sp. nov., H. krejcii sp. nov. and H. pekari sp. nov. In three species, H. algarvensis Ferrández, 1990, H. minoccii Ferrández, 1982 and H. tavirensis Wunderlich, 2020 the females are described for the first time. The Portuguese Harpactea spiders belong to two species groups, namely the hombergi group and the corticalis group (sensu Deeleman-Reinhold 1993). The majority of the Portuguese representatives of the corticalis group probably constitute a monophyletic group endemic for the Iberian peninsula, the minoccii subgroup Ferrández, 1990. The synapomorphy of this clade is the unique position of partners during copulation: the male grasps one of the female's chelicerae between his chelicerae. The males' chelicerae are morphologically adapted for this behaviour. Due to this unusual position during copulation the female genitalia are more distant from the male than in other Harpactea species. Therefore, the pedipalps are more elongated . Furthermore, the males of this subgroup possess larger basal part of male copulatory organ (the tegulum), where the ejaculate is stored before copulation. We observed that the males of this subgroup invest significantly more time into single copulation than the other representatives of the corticalis group with smaller tegulum. Therefore, enlargement of the tegulum might reflect different sperm competition strategy, in which males invest more ejaculate in each copulation. In Portugal, Harpactea spiders are frequently found under woody plants that produce slowly decomposing leaf litter, usually Quercus spp., or introduced Eucalyptus sp. These spiders require slightly humid substratum.

Introduction: The COVID-19 pandemic has imposed an unprecedented burden on healthcare systems worldwide, changing the profile of interventional cardiology activity., Objectives: To quantify and compare the number of percutaneous coronary interventions (PCIs) performed for acute and chronic coronary syndromes during the first COVID-19 outbreak with the corresponding period in previous years., Methods: Data on PCI from the prospective multicenter Portuguese Registry on Interventional Cardiology (RNCI) were used to analyze changes in PCI for ST-elevation myocardial infarction (STEMI), non-ST-elevation acute coronary syndromes (NSTE-ACS) and chronic coronary syndromes (CCS). The number of PCIs performed during the initial period of the COVID-19 outbreak in Portugal, from March 1 to May 2, 2020, was compared with the mean frequency of PCIs performed during the corresponding period in the previous three years (2017-2019)., Results: The total number of PCIs procedures was significantly decreased during the initial COVID-19 outbreak in Portugal (-36%, p<0.001). The reduction in PCI procedures for STEMI, NSTE-ACS and CCS was, respectively, -25% (p<0.019), -20% (p<0.068) and -59% (p<0.001)., Conclusions: Compared with the corresponding period in the previous three years, the number of PCI procedures performed for STEMI and CCS decreased markedly during the first wave of the COVID-19 pandemic in Portugal., (Copyright © 2022 Sociedade Portuguesa de Cardiologia. Publicado por Elsevier España, S.L.U. All rights reserved.)