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Introduction: Capsule endoscopy (CE) is a minimally invasive exam suitable of panendoscopic evaluation of the gastrointestinal (GI) tract. Nevertheless, CE is time-consuming with suboptimal diagnostic yield in the upper GI tract. Convolutional neural networks (CNN) are human brain architecture-based models suitable for image analysis. However, there is no study about their role in capsule panendoscopy. Methods: Our group developed an artificial intelligence (AI) model for panendoscopic automatic detection of pleomorphic lesions (namely vascular lesions, protuberant lesions, hematic residues, ulcers, and erosions). 355,110 images (6,977 esophageal, 12,918 gastric, 258,443 small bowel, 76,772 colonic) from eight different CE and colon CE (CCE) devices were divided into a training and validation dataset in a patient split design. The model classification was compared to three CE experts’ classification. The model’s performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the precision-recall curve. Results: The binary esophagus CNN had a diagnostic accuracy for pleomorphic lesions of 83.6%. The binary gastric CNN identified pleomorphic lesions with a 96.6% accuracy. The undenary small bowel CNN distinguished pleomorphic lesions with different hemorrhagic potentials with 97.6% accuracy. The trinary colonic CNN (detection and differentiation of normal mucosa, pleomorphic lesions, and hematic residues) had 94.9% global accuracy. Discussion/Conclusion: We developed the first AI model for panendoscopic automatic detection of pleomorphic lesions in both CE and CCE from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.

This study examines patterns of physical activity and sedentary behavior in individuals aged 65 years and older in the Beira Baixa region of Portugal with the aim of identifying differences between rural and urban settings. We analyzed data from 40 participants, including 19 rural residents (mean age = 74.7 years, SD = 8.5 years) and 21 urban residents (mean age = 70.1 years, SD = 5.2 years), using a retrospective cross-sectional design complemented by an ecological momentary assessment to capture real-time behavior. The thematic analysis revealed that both cohorts were predominantly sedentary, especially in terms of television consumption. Physical activities were mainly limited to tasks such as housework, gardening and occasional walking, with minimal social interactions. Urban participants, in particular, reported meeting more frequently with family at home and in the neighborhood. Both groups primarily used indoor spaces and nearby streets for physical activity. Our findings highlight the urgent need for tailored weekend physical activity programs to improve sedentary lifestyles among older adults in both rural and urban areas. This study contributes to the literature by highlighting the specific leisure-time preferences and activity patterns of older Portuguese adults, providing a basis for the development of adaptive, age-appropriate interventions. Keywords: Movement behaviors, ageing, observational study, real-time data capture

Background: Capsule endoscopy (CE) improved the digestive tract assessment; yet, its reading burden is substantial. Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. Nonetheless, their application in the esophagus lacks evidence. The study aim was to develop a DL model for esophageal pleomorphic lesion (PL) detection. Methods: A bicentric retrospective study was conducted using 598 CE exams. Three different CE devices provided 7982 esophageal frames, including 2942 PL lesions. The data were divided into the training/validation and test groups, in a patient-split design. Three runs were conducted, each with unique patient sets. The sensitivity, specificity, accuracy, positive and negative predictive value (PPV and NPV), area under the conventional receiver operating characteristic curve (AUC-ROC), and precision–recall curve (AUC-PR) were calculated per run. The model’s diagnostic performance was assessed using the median and range values. Results: The median sensitivity, specificity, PPV, and NPV were 75.8% (63.6–82.1%), 95.8% (93.7–97.9%), 71.9% (50.0–90.1%), and 96.4% (94.2–97.6%), respectively. The median accuracy was 93.5% (91.8–93.8%). The median AUC-ROC and AUC-PR were 0.82 and 0.93. Conclusions: This study focused on the automatic detection of pleomorphic esophageal lesions, potentially enhancing the diagnostic yield of this type of lesion, compared to conventional methods. Specific esophageal DL algorithms may provide a significant contribution and bridge the gap for the implementation of minimally invasive CE-enhanced panendoscopy.

ABSTRACT We report the occurrence of Tapanhuacanga campinorum in the Parque Estadual do Cristalino, in the State of Mato Grosso, Central-West Brazil. This represents the first record of the genus in this State and the species in this region of the country, since T. campinorum was known only from the Amazonian campinas vegetation in the States of Amazonas and Pará, in the northern region of Brazil. We further comment on the distribution, conservation, and taxonomic aspects of this species.

This paper analyses the compliance patterns of the European fiscal rules of the public debt and budget deficit in a group of twelve Member States. The aim is to make a contrafactual analysis to the compliance of those rules from 2020 to 2022 in a hypothetical scenario without the Covid-19 pandemic. Our intention is to discuss the necessity and structure of a future reform of the European fiscal framework. To that effect it is developed a forecasting analysis based on an ARIMA model, from which there will be examined the behaviour of the public finance variables in the context of the Covid-19 pandemic against the scenario without it. Our results point to an improvement in the public finance variables in the absence of the pandemic, as well as better compliance in the respective fiscal rules. Nevertheless, it is recommended a moderate reform of the European fiscal rules by the time of its predicted return in the beginning of 2024. The budget deficit rule should be maintained to prevent dangerous indebtedness dynamics. In contrast, the public debt rule should be partially restructured, due to its inadequacy with and without the Covid-19 pandemic.

Colon capsule endoscopy (CCE) enables a comprehensive, non-invasive, and painless evaluation of the colon, although it still has limited indications. The lengthy reading times hinder its wider implementation, a drawback that could potentially be overcome through the integration of artificial intelligence (AI) models. Studies employing AI, particularly convolutional neural networks (CNNs), demonstrate great promise in using CCE as a viable option for detecting certain diseases and alterations in the colon, compared to other methods like colonoscopy. Additionally, employing AI models in CCE could pave the way for a minimally invasive panenteric or even panendoscopic solution. This review aims to provide a comprehensive summary of the current state-of-the-art of AI in CCE while also addressing the challenges, both technical and ethical, associated with broadening indications for AI-powered CCE. Additionally, it also gives a brief reflection of the potential environmental advantages of using this method compared to alternative ones.

The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management—findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients’ outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn’s disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn’s Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn’s) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0–6006), CECDAI was 6 (0–33), ELIAKIM was 4 (0–38), and SB_AIS was 0.5659 (0–29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman’s r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM ( r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS ( r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores ( r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.

Background: Deltoid muscle rupture is a well-recognized postoperative complication after open rotator cuff repair. The anterior and middle portion of the deltoid muscle are the most common locations of muscular tearing due to the detachment needed to improve surgical access to the rotator cuff in open repair techniques. The risk of deltoid injury is increased when accessory extensive acromioplasty is performed. The literature on treatment options for deltoid muscle rupture in the setting of a failed open rotator cuff is sparse. Case report: The authors report a clinical case of a 55-year-old woman with a deltoid muscle rupture associated with a supraspinatus tendon re-tear after a failed open repair. The patient was submitted to an open supraspinatus tendon re-insertion with a double-row technique and a transosseous deltoid reattachment to its anatomical insertion into the acromion using non-absorbable sutures. Conclusion: In this paper, the authors aim to discuss the clinical and radiological characteristics of this entity and our surgical approach through the review of the current literature.

IntroductionThe inflammatory response after spinal cord injury (SCI) is an important contributor to secondary damage. Infiltrating macrophages can acquire a spectrum of activation states, however, the microenvironment at the SCI site favors macrophage polarization into a pro-inflammatory phenotype, which is one of the reasons why macrophage transplantation has failed.MethodsIn this study, we investigated the therapeutic potential of the macrophage secretome for SCI recovery. We investigated the effect of the secretome in vitro using peripheral and CNS-derived neurons and human neural stem cells. Moreover, we perform a pre-clinical trial using a SCI compression mice model and analyzed the recovery of motor, sensory and autonomic functions. Instead of transplanting the cells, we injected the paracrine factors and extracellular vesicles that they secrete, avoiding the loss of the phenotype of the transplanted cells due to local environmental cues.ResultsWe demonstrated that different macrophage phenotypes have a distinct effect on neuronal growth and survival, namely, the alternative activation with IL-10 and TGF-β1 (M(IL-10+TGF-β1)) promotes significant axonal regeneration. We also observed that systemic injection of soluble factors and extracellular vesicles derived from M(IL-10+TGF-β1) macrophages promotes significant functional recovery after compressive SCI and leads to higher survival of spinal cord neurons. Additionally, the M(IL-10+TGF-β1) secretome supported the recovery of bladder function and decreased microglial activation, astrogliosis and fibrotic scar in the spinal cord. Proteomic analysis of the M(IL-10+TGF-β1)-derived secretome identified clusters of proteins involved in axon extension, dendritic spine maintenance, cell polarity establishment, and regulation of astrocytic activation.DiscussionOverall, our results demonstrated that macrophages-derived soluble factors and extracellular vesicles might be a promising therapy for SCI with possible clinical applications.

ABSTRACT Objective: Characterize the functionality and disability of individuals complaining of low back pain at a physiotherapy service. Method: The study included individuals who complained of low back pain, treated at a physical therapy school clinic, over the age of 18. An online questionnaire was applied, with sociodemographic questions and questions corresponding to the codes of the brief “International Classification of Functioning, Disability, and Health” (ICF) for low back pain. Results: The total sample was 47 individuals, with an average of 65.31 ± 3.8 years, the majority were women (n = 40; 85.1%). The categories: pain functions (b280) and functions related to joint mobility (b710) had a higher prevalence of reports of severe to complete disabilities (89.5% and 55.3%, respectively). The other categories, in general, had a higher prevalence of mild to moderate dysfunction, with a greater report of dysfunction for the categories energy and impulse functions (b130), sleep functions (b134), emotional functions (b152), (76.6%, 66%, 76.6% respectively). Conclusion: The ICF core set for low back pain can be used electronically and allows you to evaluate and observe the various factors that are related to pain, enabling reflection and directing multidisciplinary interventions for the treatment of low back pain. Level of evidence III; Study of nonconsecutive patients; without consistently applied reference “gold” standard.

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

Background and Aims: Capsule endoscopy (CE) revolutionized the study of the small intestine, overcoming the limitations of conventional endoscopy. Nevertheless, reviewing CE images is time-consuming. Convolutional Neural Networks (CNNs) are an artificial intelligence architecture with high performance levels for image analysis. Protruding lesions of the small intestine exhibit enormous morphologic diversity in CE images. We aimed to develop a CNN-based algorithm for automatic detection of varied small-bowel protruding lesions. Methods: A CNN was developed using a pool of CE images containing protruding lesions or normal mucosa/other findings. A total of 2565 patients were included. These images were inserted into a CNN model with transfer learning. We evaluated the performance of the network by calculating its sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results: A CNN was developed based on a total of 21,320 CE images. Training and validation data sets comprising 80% and 20% of the total pool of images, respectively, were constructed for development and testing of the network. The algorithm automatically detected small-bowel protruding lesions with an accuracy of 97.1%. Our CNN had a sensitivity, specificity, positive, and negative predictive values of 95.9%, 97.1%, 83.0%, and 95.7%, respectively. The CNN operated at a rate of approximately 355 frames per second. Conclusion: We developed an accurate CNN for automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

Gastroenterology is increasingly moving towards minimally invasive diagnostic modalities. The diagnostic exploration of the colon via capsule endoscopy, both in specific protocols for colon capsule endoscopy and during panendoscopic evaluations, is increasingly regarded as an appropriate first-line diagnostic approach. Adequate colonic preparation is essential for conclusive examinations as, contrary to a conventional colonoscopy, the capsule moves passively in the colon and does not have the capacity to clean debris. Several scales have been developed for the classification of bowel preparation for colon capsule endoscopy. Nevertheless, their applications are limited by suboptimal interobserver agreement. Our group developed a deep learning algorithm for the automatic classification of colonic bowel preparation, according to an easily applicable classification. Our neural network achieved high performance levels, with a sensitivity of 91%, a specificity of 97% and an overall accuracy of 95%. The algorithm achieved a good discriminating capacity, with areas under the curve ranging between 0.92 and 0.97. The development of these algorithms is essential for the widespread adoption of capsule endoscopy for the exploration of the colon, as well as for the adoption of minimally invasive panendoscopy.

Ingestion of foreign bodies (IFB) and ingestion of caustic agents are frequent non-hemorrhagic causes of endoscopic urgencies, with the potential for severe complications. This study aimed to evaluate the predicting factors of the clinical outcomes of patients hospitalized as a result of IFB or ingestion of caustics (IC). This was a retrospective single-center study of patients admitted for IFB or IC between 2000 and 2019 at a tertiary center. Demographic and clinical data, as well as preliminary exams, were evaluated. Also, variables of the clinical outcomes, including the length of stay (LS) and other inpatient complications, were assessed. Sixty-six patients were included (44 IFB and 22 IC). The median LS was 7 days, with no differences between the groups (p = 0.07). The values of C-reactive protein (CRP) upon admission correlated with the LS in the IFB group (p < 0.01) but not with that of those admitted after IC. In the IFB patients, a diagnosis of perforation on both an endoscopy (p = 0.02) and CT scan (p < 0.01) was correlated with the LS. The Zargar classification was not correlated with the LS in the IC patients (p = 0.36). However, it was correlated with antibiotics, nosocomial pneumonia and an increased need for intensive care treatment. CT assessment of the severity of the caustic lesions did not correlate with the LS. In patients admitted for IFB, CRP values may help stratify the probability of complications. In patients admitted due to IC, the Zargar classification may help to predict inpatient complications, but it does not correlate with the LS.

The evolution of technological tools, namely affordable sensors for data collection, and the growing concerns about maintaining roads in adequate conditions have promoted the development of continuous pavement monitoring systems. This paper presents the installation and use of an innovative pavement monitoring system, which was developed to measure the effects of vehicle loads and temperature on the performance of a pavement structure. The sensors used are based on fibre Bragg grating optical technology, collecting data about the strains imposed in the pavement and the temperature at which those measurements are made. The site selection for the system’s installation and the essential installation details to ensure successful data collection are addressed. A calibration procedure was implemented by performing falling weight deflectometer tests and passing preweighed heavy vehicles over the sensors. In addition to validating the system installation, the results obtained in the calibration confirmed the importance of adequately choosing the distance between sensors. Differences of 50 mm in the position of the load may cause differences of about 20% to 25% in the resulting strains. These results confirmed the importance of increasing the sensor concentration in wheel paths. Furthermore, for loads between 25 kN and 65 kN, raising the temperature by 8 °C caused an increase of about 20% in the horizontal tensile strains measured in the pavement. In summary, it was possible to conclude that this innovative system is capable of capturing the effects of temperature and vehicle speed on the response of the pavement, which may be considered an advantage of this type of monitoring system when compared to those that are only used to determine the loads applied to the pavement or to characterise the type of vehicle.

Introduction: Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. Methods: A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. Results: Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). Discussion/Conclusion: We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

Bilateral luxatio erecta remains a rare condition, with less than 30 cases currently described in the literature. The authors present a case of bilateral inferior glenohumeral dislocation after a fall with low-energy trauma, treated with closed reduction and immobilization for 3 weeks followed by a physiotherapy program for functional rehabilitation. After 1 year of follow-up, the patient presented satisfactory results with a complete recovery of the previous mobility arch of both shoulders.

Atualmente, devido ao advento das mudanças sociais, se evidencia cada vez mais em destaque o avanço tecnológico. Com isso, às denominações religiosas vem utilizando mecanismos de expansão de suas crenças e no Brasil esse fenômeno também vem se enraizando, principalmente nos meios de comunicação. As emissoras de televisão são um dos veículos imediatos e eficazes para a expansão midiática da fé, fenômeno conhecido como Televangelismo. A Igreja Universal do Reino de Deus (IURD) vem se destacando através dos recursos televangélico, entre eles o programa “Fala que Eu Te Escuto” que é um dos responsáveis pelo crescimento de sua denominação. Por fim, a partir de uma abordagem hermenêutica, que contou com análise de quatro programas entre os meses de outubro e novembro de 2017. Entende-se que, com o auxílio do seu programa televangélico, a IURD constitui um elemento indissociável na difusão da sua fé, assim como reforça os dogmas aos já convertidos.

Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.

Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50–90% of the mucosa visible; and unsatisfactory, Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.

Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.

A incapacidade para realizar as atividades rotineiras está positivamente correlacionada com o aumento da idade, com a inatividade física e com o excesso de comportamento sedentário diário. Uma estratégia passível de ser utilizada para incrementar a saúde biopsicossocial da população idosa consiste na sensibilização para a participação em atividades desportivas. O objetivo desta investigação foi sintetizar os estudos científicos primários produzidos no âmbito da prática de desportos coletivos na população idosa. Para tal, foi realizada uma revisão rápida de literatura. As revisões rápidas são uma forma de síntese de evidências científicas, onde as componentes do processo de revisão sistemática são simplificadas de forma a produzir informação relevante de forma atempada. Nesse sentido, foram definidos um conjunto alargado de critérios de elegibilidade, entre os quais foram considerados estudos que, por um lado, incorporassem os motivos que sustentam a prática de desportos coletivos e, por outro lado, transparecessem conclusões relativas aos benefícios dela provenientes. Posteriormente, foi desenvolvida uma estratégia de pesquisa sistemática de literatura em bases eletrónicas de dados científicos (bon, Pubmed e Web of Science). Os estudos foram incluídos na síntese final através de um processo de seleção por fases e os dados foram analisados através de uma análise temática indutiva. A opção pela prática de desportos coletivos deve ser cada vez mais incrementada na medida em que são consideradas atividades muito mais motivadoras e estimulantes. Nesse sentido, os técnicos de intervenção geriátrica devem entender os desportos coletivos,como porexemplo o Walking Football,enquanto versão adaptada daconfiguração tradicional do Futebol, como um mecanismo suscetível de ser concretizado uma vez que denota provocar efeitos benéficos tanto a nível intrapessoal como no âmbito dos vínculos sociais.

Se determinó el fenómeno de agrietamiento que se origina en un acero inoxidable refractario del tipo HK-40 empleado en la fabricación de un eslabón de cadena, defecto que se genera al ser expuesto al ciclo térmico de soldadura con electrodo UTP 65 de 4 mm. La preparación de borde se realizó a tope con bisel de 60o, fueron cortadas muestras para ensayos de dureza, composición química y observación con microscopía óptica. Fueron analizadas la zona fundida, la interfase y zona afectada por el calor, obteniéndose estructuras de austenita, ferrita, ferrita-δ y carburos, así como zona descarburada como consecuencias de las altas temperaturas a las cuales se expone la aleación. La dureza desde un valor inicial de 270 HV en el material en estado en bruto de colada, incrementa hasta 670,2 HV en la zona fundida, propiciado por la formación de la fase sigma y la disminución de la tenacidad y el alargamiento.

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn’s disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision–recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm’s accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.

Objective Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.Design We developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin’s classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Results A total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.Conclusion We developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.

Introduction Caustic substance ingestion is a common cause of esophageal stricture in children. The primary treatment is esophageal dilatation. Although it is known that endoscopic esophageal dilatation is a procedure associated with a high rate of bacteremia, current guidelines do not recommend routine throat swab cultures or antibiotic prophylaxis for the general children population. Case Report We describe a case of a 7-year-old boy presenting with refractory headaches who was diagnosed with cranial abscess after multiple esophageal dilatations due to stenosis caused by caustic soda ingestion. The patient was subjected to neurosurgical intervention and intravenous antibiotic treatment for 6 weeks. Streptococcus viridans culture was positive in purulent abscess content. Conclusion We highlight this condition that, although rare, needs immediate diagnosis and proper treatment. We also recommend routine testing of throat swabs and antibiotics prophylaxis to children undergoing esophageal dilatation.

Esta pesquisa analisa as variações da pluviosidade e suas implicações nos mananciais e no abastecimento público da Região Metropolitana de São Paulo (RMSP). Os dados utilizados provieram da Companhia de Saneamento Básico do Estado de São Paulo (SABESP), responsável pela captação, armazenamento, tratamento e distribuição de água para mais de 21 milhões de pessoas, e da estação "Mirante de Santana" do Instituto Nacional de Meteorologia (INMET). Foram analisadas as chuvas anuais (2003-2017) e mensais (janeiro/2003-junho/2018) em seis sistemas produtores de água: Cantareira, Alto Tietê, Guarapiranga, Alto Cotia, Rio Grande e Rio Claro; e, também, as chuvas diárias durante a "crise hídrica” de 2014-2015. O principal objetivo desta pesquisa foi o de compreender o ritmo de sucessão dos estados atmosféricos associado às chuvas.

Para obtenção de pomares vigorosos e sadios é necessário a produção de mudas com qualidade, e menor custo de formação, observando o mais adequado substrato utilizado, pois o mesmo deve ter características que permitam a boa formação e desenvolvimento da muda. Objetivou-se nesse trabalho avaliar o desenvolvimento de mudas de mamoeiro “Formosa” e “Hawaí” submetidas a diferentes dosagens de esterco bovino, ovino e caprino. O Experimento foi conduzido em casa de vegetação situada na Unidade de Aulas Práticas e Pesquisa (UAPP) do Instituto Federal de Educação, Ciência e Tecnológica do Piauí (Campus Oeiras). As sementes foram semeadas em copos de polietileno com capacidade de 0,5 L, colocando-se duas sementes por recipiente. As mudas foram desbastadas, deixando-se apenas a mais vigorosa por recipiente. A avaliação foi 70 dias após a semeadura, considerando-se as variáveis tais como: porcentagem de germinação, a altura linear das mudas (cm), diâmetro do colo (mm), número de folhas; matéria fresca da parte aérea (g), matéria fresca do sistema radicular (g), matéria seca da parte aérea (g), matéria seca do sistema radicular. Foram obtidos resultados satisfatórios nos tratamentos T5 e T8 composto de esterco ovino (1:1) e (somente esterco ovino) e T12 (somente esterco caprino).

Senegalese sole (Solea senegalensis) has been considered a promising new flatfish species for Mediterranean marine fish farming. Accurate prediction of fillet traits in live animals may allow for more efficient control of muscle deposition in fish. In this sense, this study was undertaken to develop a non-invasive method to predict in vivo fish fillet volume and yield using real-time ultrasonography (RTU). The trial was conducted with 44 market weight Senegalese sole (298.54 ± 87.30 g). Fish were scanned with an Aloka SSD 500V with a 7.5 MHz probe. Ten RTU cross-sectional images were taken from the operculum to the caudal fin at regular intervals. These images were analyzed using Fiji software. These data were then used to estimate the partial volumes of the fillet. Actual fillet volume was determined using Archimedes’ principle. Simple and stepwise multiple regression analyses were then used to develop prediction models of fillet volume and yield. The most cranial RTU sections of the fish fillet were the best single predictors of both fillet volume and fillet yield and were the ones included in the best stepwise models. The best RTU slice area explained 82% of the variation observed in fillet volume, but the other RTU slice areas used as predictors of fillet volume showed poor to moderate accuracy (0.035 ≤ R2 ≤ 0.615). Single RTU partial volumes showed poor to very high accuracy (0.395 ≤ R2 ≤ 0.970) as predictors of fillet volume. The best stepwise model based on the RTU slice areas included three independent variables and explained 88.3% of the observed variation. The best stepwise models based on RTU partial volumes (single volumes and/or combinations of single volumes) explained about 97% of the variation observed in fillet volume. Two RTU volume traits, V1–5 + V6–9, and V1+()+9, showed to be practically direct predictors of the actual fillet volume, explaining, respectively, 97% and 96% of the variation observed in the actual fillet volume. The fillet yields show lower correlations with slice areas (r between 0.044 and 0.601) than with volumes (r between 0.288 and 0.637). While further studies are clearly necessary to better understand the potential of RTU for the estimation of fillet yield in fish in general and Senegalese sole in particular, the present results showed that RTU traits can be very good predictors of Senegalese sole’s fillet volume, either used in regression models or as direct predictors.

Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

This paper presents an analysis of unified systems for traction and battery charging of electric vehicles (EVs), regarding operation modes and implementation cost, when compared to dedicated systems that perform the same functionalities. Concerning the interface of the EV battery charging system with the power grid, four operation modes are analyzed: (1) grid-to-vehicle (G2V); (2) vehicle-to-grid (V2G); (3) vehicle-to-home (V2H); and (4) vehicle-for-grid (V4G), which, byusing a unified system for traction and battery charging, can be linked both to single-phase and three-phase power grids.Moreover, a cost estimation is performed for EV unified systems and for dedicated systems that can perform the samefunctionalities when connected to the power grid, in order to prove the advantages of the system unification. The costestimation is based on two power levels, namely 6 kW (single-phase), related to domestic installations and slow batterycharging, and 50 kW (three-phase), related to industrial installations and fast battery charging. The relevance of unifiedsystems for traction and battery charging of EVs is proven for single-phase and three-phase power grids both in terms ofoperationality and implementation cost.

The huge power requirements of future railways require the usage of energy-efficient strategies towards amore intelligent railway system. The usage of on-board energy storage systems enables better usage of the traction energy with a higher degree of freedom. In this article is proposed a top-level charging controller forthe on-board and wayside railway energy storage systems. Its structure comprehends two processing levels: a real-time fuzzy logic controller for each energy storage system, and a genetic algorithm meta-heuristic, that remotely and automatically tune the fuzzy rules weight. As global results, the reduction of regenerated energy is 22.3% with the fuzzy logic controller. With the optimization strategy, this reduction can be further extendedto 28.7%. The need for a smart railway framework is also discussed towards a realistic implementation of such charging strategy. Thus, with a high degree of flexibility, the efficiency of railway energy systems can be increased with the proposed framework.

This work describes the development and evaluation of two smart home-based Internet of Things (IoT) systems applied to HVAC (Heating, Ventilation and Air Conditioning) monitoring and control, including parameters such as temperature, humidity, air quality, smoke detection, and human presence. These systems are based on a flexible hybrid wireless network architecture combining Bluetooth Low Energy (BLE) and IEEE 802.11/Wi-Fi, in order to adapt to the requirements of different types of sensor and actuator devices. The original implemented network is based on Cypress PSoC 4 BLE boards and HyperText Transfer Protocol (HTTP), whereas the new network uses ESP32 boards and includes Message Queue Telemetry Transport (MQTT), a lightweight messaging protocol suitable for IoT devices which provides additional quality of service (QoS) mechanisms to guarantee the delivery of messages. A smart temperature control system was implemented in the BLE/Wi-Fi gateway (Raspberry Pi) to keep the room temperature inside a user-defined range. An online database was also developed using the Amazon Web Services (AWS) cloud platform, allowing the users to access the HVAC data and control the system parameters, through the Internet, using a mobile app developed for Android devices. Experimental tests were performed to validate the functionalities and performance of the developed systems. The obtained results demonstrate that the new network provides lower delay values compared with the original implementation.

Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.

Background: Colon capsule endoscopy (CCE) is an alternative for patients unwilling or with contraindications for conventional colonoscopy. Colorectal cancer screening may benefit greatly from widespread acceptance of a non-invasive tool such as CCE. However, reviewing CCE exams is a time-consuming process, with risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for automatic detection of colonic protruding lesions in CCE images. An anonymized database of CCE images collected from a total of 124 patients was used. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.

Introdução: A fisioterapia pré-operatória em pacientes oncológicos tem papel fundamental para redução das complicações pós-operatórias. Sabe-se que a avaliação pré-operatória identifica os fatores de risco e e decisiva na redução do desenvolvimento de tais complicações. Objetivo: Verificar o efeito da cinesioterapia pré-operatória e do treino muscular inspiratório nas complicações pulmonares pós-operatórias em pacientes oncológicos. Método: Trinta pacientes foram divididos aleatoriamente em grupo cinesioterapia, grupo treinamento muscular inspiratório e grupo controle, com dez participantes em cada grupo. Foram realizadas avaliação da forca muscular respiratória, teste de caminhada de seis minutos e forca muscular periférica. Foi utilizado o teste de Wilcoxon para comparação pré e pós-tratamento. Também foi realizado o teste Kruskal Wallis para comparação do resultado pós-tratamento e o teste qui-quadrado para variáveis categóricas. Utilizou-se o teste de Cohen (d) para identificação do tamanho do efeito. Resultados: Nove (90%) indivíduos do grupo controle apresentaram complicações, enquanto, nos grupos cinesioterapia e de treinamento muscular inspiratório, as complicações foram observadas em dois (20%) e três (30%) pacientes, respectivamente. As pressões inspiratória máxima (PImáx) e expiratória máxima (PEmáx) no treinamento muscular inspiratório e a PImáx no grupo cinesioterapia demonstraram impacto clinico quando comparados ao grupo controle. Conclusão: A cinesioterapia e o treinamento muscular inspiratório impactaram na redução das complicações pós-operatórias das cirurgias oncológicas.

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

The power supply architecture of most AC electric railway vehicles is constituted by transformers andcatenaries at increasingly high power levels. Inside the vehicles, the interface between the catenary and theDC-bus of the traction motors is based on a transformer and power electronics converters. A large part ofthese are AC-DC four quadrant converters that operate in parallel at relatively small switching frequencies.However, the use of the interleaving principle allows reaching a low harmonic distortion of the catenarycurrent and imposing specific harmonic ranges in this current. Nevertheless, the current is not a puresinusoidal wave and its harmonics can excite unwanted resonances due to the combined effect of the catenarydistributed parameters, the substation equivalent impedance and the current frequency spectrum. This paper analyses this phenomenon and proposes a control strategy capable of minimizing the resonance effects in two different power supply architectures.

Fast Charging Stations (FCS) are a key element for the wide spreading of Electric Vehicles (EVs), by reducing the charging time to a range between 20 to 40 minutes. However, the integration of FCS causes some adverse impacts on the Power Grid (PG), namely the huge increase in the peak demand during short periods of time. This paper addresses the design of powerelectronics converters for an EV DC FCS with local storage capability and easy interface of renewables. In the proposed architecture, the energy storage capability is used to smooth the peak power demand and contributes to stabilize the PG. When integrated in a smart grid, the proposed architecture may even return some of the stored energy back to the PG. Theaccomplishment of the aforementioned objectives requires a set of different power electronics converters, described and discussed along the paper. In order to demonstrate the potentialities of the proposed EV DC FCS architecture, four different case studies were analysed.

This paper presents a performance comparison of a typical nonlinear load when supplied by ac or dc voltages with the same rms value. The performance of the nonlinear load towards its connection to ac and dc power grids is accomplished in terms of the waveforms and efficiency. A simulation model was developed using realistic database models of the powersemiconductors comprising the load, and an experimental setup was assembled, so that the efficiency can be determined and compared for simulation and real operating conditions. Three distinct cases were considered for this study: (1) Loadsupplied by ac voltage; (2) Load supplied by dc voltage; and (3) Load without the input ac-dc converter supplied by dc voltage. The obtained results show that supplying nonlinear loads with dc power grids is advantageous in relation to the ac power grid, and therefore it can be beneficial to adapt nonlinear loads to be powered by dc power grids.

Carcass dissection is a more accurate method for determining the composition of a carcass; however, it is expensive and time-consuming. Techniques like VIA are of great interest once they are objective and able to determine carcass contents accurately. This study aims to evaluate the accuracy of a flexible VIA system to determine the weight and yield of the commercial value of carcass cuts of light lamb. Photos from 55 lamb carcasses are taken and a total of 21 VIA measurements are assessed. The half-carcasses are divided into six primal cuts, grouped according to their commercial value: high-value (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). K-folds cross-validation stepwise regression analyses are used to estimate the weights of the cuts in the groups and their lean meat yields. The models used to estimate the weight of AllC, HVC, MVC and LVC show similar results and a k-fold coefficient of determination (k-fold-R2) of 0.99 is achieved for the HVC and AllC predictions. The precision of the weight and yield of the three prediction models varies from low to moderate, with k-fold-R2 results between 0.186 and 0.530, p < 0.001. The prediction models used to estimate the total lean meat weight are similar and low, with k-fold-R2 results between 0.080 and 0.461, p < 0.001. The results confirm the ability of the VIA system to estimate the weights of parts and their yields. However, more research is needed on estimating lean meat yield.

The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% ≤ CV ≤ 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (−0.50 ≤ r ≤ 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Introduction: Haemoptysis is a common symptom which can sometimes mimic gastrointestinal bleeding. Case description: We describe the case of a 31-year-old man who presented to the emergency department after an episode of sudden nausea and presumed massive haematemesis. The situation was interpreted as gastrointestinal bleeding but clinical evolution and greater attention to the anamnesis resulted in a diagnosis of pulmonary tuberculosis and the provision of appropriate care. Discussion: This report emphasizes the difficulty of differentiating between haemoptysis and haematemesis and the importance of a careful anamnesis and attention to all clinical circumstances for an accurate diagnosis.

A análise da chuva na região Missões, RS feita por Ribeiro, (2012), confirmou a hipótese de que a Região se caracteriza pela variabilidade de episódios chuvosos e de estiagens. Neste artigo, o objetivo é apresentar os resultados da análise têmporo-espacial e do regime das chuvas na área de estudo. Assim, a análise climática é vista a partir dos dados anuais, sazonais e mensais. Para isso procedeu-se a organização dos dados e o software Surfer 8 possibilitou a visualização da chuva através de cartogramas de isoietas, painéis têmporo-espaciais e pluviogramas. Constatou-se que a Região, tanto se destaca nos anos secos e chuvosos perante o entorno, constituindo-se como área seguidamente atingida por extremos no volume acumulado e distribuição da chuva.

JUSTIFICATIVA E OBJETIVOS: A crioterapia é uma modalidade de tratamento com inúmeros efeitos terapêuticos, de baixo custo, de fácil aplicação e acesso. Os objetivos deste estudo foram verificar se existe alteração na sensibilidade de acordo com o tempo de aplicação da crioterapia além do comportamento da sensibilidade cutânea pós-aplicação.MÉTODOS: Vinte e um sujeitos (14 mulheres e 7 homens, idade média 23,8±4 anos) foram divididos aleatoriamente em 3 grupos, com 7 sujeitos cada, tendo recebido aplicação da crioterapia por 10, 20 ou 30 minutos, denominados respectivamente grupos C10, C20 e C30. Todos foram previamente submetidos à avaliação da sensibilidade cutânea com monofilamento de Semmes-Weinstein, e avaliados imediatamente ao término da aplicação da crioterapia, 5, 10 e 15 minutos após a aplicação. Para análise dos dados foi utilizado o teste de Friedman complementado pelo pós-teste de comparação múltipla (post hoc de Dunn).RESULTADOS: Na avaliação imediatamente após a aplicação da crioterapia todos os sujeitos apresentaram hipoestesia. Observou-se diferença significativa no retorno da sensibilidade do grupo C20 em relação aos demais, após 10 minutos da intervenção (p=0,0498). Para os grupos C10 e C30, o retorno da sensibilidade ocorreu após 15 minutos do término da aplicação.CONCLUSÃO: Cinco minutos depois do término da aplicação da crioterapia observou-se ausência de sensibilidade tátil, independentemente do tempo de intervenção. O retorno da sensibilidade normal foi observado depois de 10 minutos quando se aplicou o frio por 20 minutos, e depois de 15 minutos quando aplicado por 10 ou 30 minutos.