Origem coronária anómala: da suspeita à revascularizac¸ão cirúrgica

Congenital anomalies of the coronary arteries are uncommon and can present a diagnostic challenge. The authors present the case of a patient with recurrent chest pain during exertion admitted for acute coronary syndrome. Coronary angiography revealed no coronary lesions but showed that the right coronary artery originated from the anterolateral aortic wall, above the sinuses of Valsalva, leading to suspicion of compression by the pulmonary artery, confirmed by CT angiography. The patient underwent surgical revascularization with a good result. The authors highlight the need to consider compression of an anomalous coronary artery by the pulmonary artery in the differential diagnosis of recurrent chest pain on exertion and acute myocardial infarction without significant coronary stenosis

Machine learning and feature selection methods for egfr mutation status prediction in lung cancer

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.This work is financed by the ERDF–European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation–COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT–Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for the free publicly available LIDC-IDRI Database used in this work. They also acknowledge The Cancer Imaging Archive (TCIA) for the open-access NSCLC-Radiogenomics dataset publicly available. This work was supported in part by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalization—COMPETE 2020 Program, and in part by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT), under Project POCI-01-0145-FEDER-030263

Balance training program is highly effective in improving functional status and reducing the risk of falls in elderly women with osteoporosis: a randomized controlled trial

INTRODUCTION: The purpose of this study was to investigate the effect of a 12-month Balance Training Program on balance, mobility and falling frequency in women with osteoporosis. METHODS: Sixty-six consecutive elderly women were selected from the Osteometabolic Disease Outpatient Clinic and randomized into 2 groups: the ‘Intervention’, submitted for balance training; and the ‘Control’, without intervention. Balance, mobility and falling frequency were evaluated before and at the end of the trial, using the Berg Balance Scale (BBS), the Clinical Test Sensory Interaction Balance (CTSIB) and the Timed “Up & Go” Test (TUGT). Intervention used techniques to improve balance consisting of a 1-hour session each week and a home-based exercise program. RESULTS: Sixty women completed the study and were analyzed. The BBS difference was significant higher in the Intervention group compared to Control (5.5 ± 5.67 vs −0.5 ± 4.88 score, p < 0.001). Similarly, the number of patients in the Intervention group presented improvement in two conditions of CTSIB compared to Control (eyes closed and unstable surface condition: 13 vs one patient, p < 0.001 and eyes open, visual conflict and unstable surface condition: 12 vs one patient, p < 0.001). Additionally, the differences between the TUGT were reduced in the Intervention group compared to Control (−3.65 ± 3.61 vs 2.27 ± 7.18 seconds, p< 0.001). Notably, this improvement was paralleled by a reduction in the number of falls/patient in the Intervention group compared to Control (−0.77 ± 1.76 vs 0.33 ± 0.96, p = 0.018). CONCLUSION: This longitudinal prospective study demonstrated that an intervention using balance training is effective in improving functional and static balance, mobility and falling frequency in elderly women with osteoporosis

Beta-alanine (Carnosyn™) supplementation in elderly subjects (60–80 years): effects on muscle carnosine content and physical capacity

The aim of this study was to investigate the effects of beta-alanine supplementation on exercise capacity and the muscle carnosine content in elderly subjects. Eighteen healthy elderly subjects (60–80 years, 10 female and 4 male) were randomly assigned to receive either beta-alanine (BA, n = 12) or placebo (PL, n = 6) for 12 weeks. The BA group received 3.2 g of beta-alanine per day (2 × 800 mg sustained-release Carnosyn™ tablets, given 2 times per day). The PL group received 2 × (2 × 800 mg) of a matched placebo. At baseline (PRE) and after 12 weeks (POST-12) of supplementation, assessments were made of the muscle carnosine content, anaerobic exercise capacity, muscle function, quality of life, physical activity and food intake. A significant increase in the muscle carnosine content of the gastrocnemius muscle was shown in the BA group (+85.4%) when compared with the PL group (+7.2%) (p = 0.004; ES: 1.21). The time-to-exhaustion in the constant-load submaximal test (i.e., TLIM) was significantly improved (p = 0.05; ES: 1.71) in the BA group (+36.5%) versus the PL group (+8.6%). Similarly, time-to-exhaustion in the incremental test was also significantly increased (p = 0.04; ES 1.03) following beta-alanine supplementation (+12.2%) when compared with placebo (+0.1%). Significant positive correlations were also shown between the relative change in the muscle carnosine content and the relative change in the time-to-exhaustion in the TLIM test (r = 0.62; p = 0.01) and in the incremental test (r = 0.48; p = 0.02). In summary, the current data indicate for the first time, that beta-alanine supplementation is effective in increasing the muscle carnosine content in healthy elderly subjects, with subsequent improvement in their exercise capacity

Deep Sequencing of the Oral Microbiome Reveals Signatures of Periodontal Disease

The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (∼2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ∼90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes

Integrated monitoring of mola mola behaviour in space and time

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of finescale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) videorecorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (r(s) = 0.184, p < 0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator's finescale behaviour observed over a two weeks in May 2014

Clinical approach for the classification of congenital uterine malformations

A more objective, accurate and non-invasive estimation of uterine morphology is nowadays feasible based on the use of modern imaging techniques. The validity of the current classification systems in effective categorization of the female genital malformations has been already challenged. A new clinical approach for the classification of uterine anomalies is proposed. Deviation from normal uterine anatomy is the basic characteristic used in analogy to the American Fertility Society classification. The embryological origin of the anomalies is used as a secondary parameter. Uterine anomalies are classified into the following classes: 0, normal uterus; I, dysmorphic uterus; II, septate uterus (absorption defect); III, dysfused uterus (fusion defect); IV, unilateral formed uterus (formation defect); V, aplastic or dysplastic uterus (formation defect); VI, for still unclassified cases. A subdivision of these main classes to further anatomical varieties with clinical significance is also presented. The new proposal has been designed taking into account the experience gained from the use of the currently available classification systems and intending to be as simple as possible, clear enough and accurate as well as open for further development. This proposal could be used as a starting point for a working group of experts in the field