Peer assessment in medical communication skills training in programmatic assessment: A qualitative study examining faculty and student perceptions

INTRODUCTION

Current literature recommends assessment of communication skills in medical education combining different settings and multiple observers. There is still a gap in understanding about whether and how peers assessment facilitates learning in communication skills training.

METHODS

We designed a qualitative study using focus group interviews and thematic analysis, in a medical course in the Netherlands. We aimed to explore medical students' and teachers' experiences, perceptions, and perspectives about challenges and facilitating factors in PACST (Peer assessment in medical communication skills training).

RESULTS

Most of the participants reported that peer feedback was a valuable experience when learning communication skills. The major challenges for the quality and credibility of PACST reported by the participants are the question whether peer feedback is critical enough for learning and the difficulty of actually engaging students in the assessment process.

CONCLUSION

Teachers reviewing students' peer assessments may improve the quality and their credibility and the reviewed assessments can best be used for learning purposes. We suggest to pay sufficient attention to teachers' roles in PACST, ensuring a safe and trustworthy environment and additionally helping students to internalize the value of being vulnerable during the evaluation process.

Time series causal relationships discovery through feature importance and ensemble models

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.

Neuroinflammation Biomarkers in the AT(N) Framework Across the Alzheimer's Disease Continuum

In the past years, neuroinflammation has been widely investigated in Alzheimer's disease (AD). Evidence from animal, in vivo and post-mortem studies has shown that inflammatory changes are a common feature of the disease, apparently happening in response to amyloid-beta and tau accumulation. Progress in imaging and fluid biomarkers now allows for identifying surrogate markers of neuroinflammation in living individuals, which may offer unprecedented opportunities to better understand AD pathogenesis and progression. In this context, inflammatory mediators and glial proteins (mainly derived from microglial cells and astrocytes) seem to be the most promising biomarkers. Here, we discuss the biological basis of neuroinflammation in AD, revise the proposed neuroinflammation biomarkers, describe what we have learned from anti-inflammatory drug trials, and critically discuss the potential addition of these biomarkers in the AT(N) framework.

Fit-for-Purpose MIP OES Method to Meet the Requirements of the Brazilian Regulations for K and Na in Coconut Water, and Nutritional Assessment

The Brazilian regulations for processed coconut water establish the range concentrations of Na and K, being necessary high-throughput analytical methods, as microwave-induced plasma optical emission spectrometry (MIP OES) for quality control. Samples of natural (green and mature) and processed coconut water were treated by acid decomposition assisted by microwave radiation before their MIP OES analysis. Recoveries for Na and K were 103 and 95%, whereas the relative standard deviation (RSDs) were 7.6 and 1.3%, respectively. The limit of detection (LOD) was 1.77 mg kg-1 for K and 2.31 mg kg-1 for Na. Sodium and K were determined in the ranges of, respectively, 27-490 and 1,600-3,500 mg L-1 in natural samples, whereas in processed samples the ranges were 26-168 and 814-2,054 mg L-1. K-means cluster analysis identified a group of processed samples with compositions similar to green coconut water. All the processed samples were in accordance with the established regulation, except one dehydrated sample for which K content was below the accepted range. Natural coconut water is an excellent source of K in the diet as its daily consumption can supply more than 20% of the recommended daily ingestion of K for adults and 30% for children.

A multi-sensor human gait dataset captured through an optical system and inertial measurement units

Different technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, fitting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant’s legs to capture accelerometer and gyroscope data, and 42 reflexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre-processed in each of two sessions, performed on different days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specific for each system.

Measurement(s)	G Force • Spatial Orientation
Technology Type(s)	Accelerometer • Optical Instrument

Benchmarking Scientific Image Forgery Detectors

The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmark. In light of this, there is no comprehensive understanding on the limitations and capabilities of automatic image analysis tools for scientific images, which might create a false sense of data integrity. To mitigate this issue, we present an extendable open-source algorithm library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. We create a large scientific forgery image benchmark (39,423 images) with enriched ground truth using this library and realistic scientific images. All figures within the benchmark are synthetically doctored using images collected from creative commons sources. While collecting the source images, we ensured that the they did not present any suspicious integrity problems. Because of the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. All evaluated methods had a low performance in this dataset, indicating that scientific images might need a specialized copy-move detector. The dataset and source code are available at https://github.com/phillipecardenuto/rsiil .

Link Prediction Based on Stochastic Information Diffusion

Link prediction (LP) in networks aims at determining future interactions among elements; it is a critical machine-learning tool in different domains, ranging from genomics to social networks to marketing, especially in e-commerce recommender systems. Although many LP techniques have been developed in the prior art, most of them consider only static structures of the underlying networks, rarely incorporating the network's information flow. Exploiting the impact of dynamic streams, such as information diffusion, is still an open research topic for LP. Information diffusion allows nodes to receive information beyond their social circles, which, in turn, can influence the creation of new links. In this work, we analyze the LP effects through two diffusion approaches, susceptible-infected-recovered and independent cascade. As a result, we propose the progressive-diffusion (PD) method for LP based on nodes' propagation dynamics. The proposed model leverages a stochastic discrete-time rumor model centered on each node's propagation dynamics. It presents low-memory and low-processing footprints and is amenable to parallel and distributed processing implementation. Finally, we also introduce an evaluation metric for LP methods considering both the information diffusion capacity and the LP accuracy. Experimental results on a series of benchmarks attest to the proposed method's effectiveness compared with the prior art in both criteria.

How well have CMIP3, CMIP5 and CMIP6 future climate projections portrayed the recently observed warming

Despite the dire conclusions of the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports in terms of global warming and its impacts on Earth’s climate, ecosystems and human society, a skepticism claiming that the projected global warming is alarmist or, at least, overestimated, still persists. Given the years passed since the future climate projections that served as basis for the IPCC 4th, 5th and 6th Assessment Reports were released, it is now possible to answer this fundamental question if the projected global warming has been over or underestimated. This study presents a comparison between CMIP3, CMIP5 and CMIP6 future temperature projections and observations. The results show that the global warming projected by all CMIPs and future climate scenarios here analyzed project a global warming slightly lower than the observed one. The observed warming is closer to the upper level of the projected ones, revealing that CMIPs future climate scenarios with higher GHG emissions appear to be the most realistic ones. These results show that CMIPs future warming projections have been slightly conservative up to 2020, which could suggest a similar cold bias in their warming projections up to the end of the current century. However, given the short future periods here analyzed, inferences about warming at longer timescales cannot be done with confidence, since the models internal variability can play a relevant role on timescales of 20 years and less.

A Novel Approach to Minimising Acute Equine Endometritis That May Help to Prevent the Development of the Chronic State

One of the most commonly encountered challenges in equine breeding is endometritis, which can be difficult to resolve and causes considerable economic losses to the industry. It is a multifactorial condition, developing as an exaggerated form of the normal physiological response to breeding. Seminal plasma proteins, spermatozoa, bacteria and debris initiate an inflammatory response; the resulting fluid and neutrophils are then cleared from the uterus along with the debris. However, in some mares, the response is prolonged or exaggerated, with much fluid formation and neutrophil infiltration leading to acute endometritis. A bacterial cause has been implicated, although in some cases no pathogenic organisms can be isolated on culture. It has been postulated that any one of a variety of bacteria could be involved, or dysbiosis of the uterine microbiome could be responsible. Repeated episodes of acute endometritis may lead to the pathology associated with chronic endometritis, with mucociliary dysfunction, vascular degeneration and plasma cell infiltration. This review examines the information that is currently available about equine endometritis, particularly about the role of the inseminate in the uterus, and its current treatment. There are some promising lines of research into treatment or prevention that may help to resolve the issue.

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates - and to do so with enough efficiency at large scale. We propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

Secondary prevention after acute coronary syndrome - can we achieve dyslipidemia guideline targets?

Funding Acknowledgements

Type of funding sources: None.

Lipid control is one of the most important secondary cardiovascular prevention targets. The 4S trialin 1994 was the first study to demonstrate the benefit of statin therapy in coronary artery disease patients. More recently, the FOURIER trial (2017) and the ODYSSEY Outcomes (2018) demonstrated the cardiovascular benefit of adding a PCSK9 inhibitor to optimized antidyslipidemic therapy.

Owing to the growing number of evidence showing the importance of aggressive lipid control, the European Society of Cardiology (ESC) 2019 Dyslipidemia Guidelines changed the recommendation of LDL targets from below 70 mg/dL to below 55 mg/dL in very high risk patients.

In the light of this new recommendation, we retrospectively analysed all patients who participated in a Coronary Rehabilitation Program (CRP) after an Acute Coronary Syndrome at a tertiary center from May 2008 to June 2019. The CRP consisted in a multi-disciplinary approach to these patients, including 8 to 12 weekly sessions of phase II rehabilitation, exercise prescription, nutrition counselling and life-style intervention, with Cardiology follow-up at the end of the CRP (3 months), 6 months and 12 months after the event. Lipid profile was requested at baseline, 3 months and 12 months after the event. Dyslipidemia was defined using the ESC Guidelines definition.

In total, 989 patients were enrolled: the mean age was 54 ± 10 years and 15% were female. Concerning to cardiovascular risk factors, 56.7% of patients had dyslipidaemia, 41.5% had hypertension, 18.1% were diabetic, 73.1% were smokers or previous smokers, 26.0% had family history of coronary disease and 14.0% had previous coronary disease.

At hospital admission, mean LDL concentration was 121,7 ± 38,8 mg/dL with 2,9% of the patients with values below 55 mg/dL and 7,6% below 70 mg/dL. The vast majority of patients were medicated with statin at hospital discharge and maintained the prescription during the follow-up (97.5% and 97.1%, respectively).

At the end of the CRP, there was a significant decrease of LDL values (p < 0.001) with 18.7% patients with LDL below 55 mg/dL and 46.3% below 70 mg/dL (mean LDL 76.6 ±23.6 mg/dL). At 1-year follow-up, the lipid control was better than at admission, but inferior than at the end of the rehabilitation program, both findings statistically significant (11.0% patients were below 55 mg/dL and 33.1% were below 70 mg/dL, with a mean LDL of 82.7 ± 28.3 mg/dL; p < 0.001).

In conclusion, our real-life observational cohort showed that guideline recommended LDL target is not achieved in most of very high-risk patients even in a structured coronary rehabilitation program. Of note, the number of patients at LDL-target decreased from 3 months to 1-year follow-up. This state the importance of a sustained healthy lifestyle and therapy adherence, with aggressive pharmacologic therapy.

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.

Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function

With the widespread use of biometric authentication comes the exploitation of presentation attacks, possibly undermining the effectiveness of these technologies in real-world setups. One example takes place when an impostor, aiming at unlocking someone else’s smartphone, deceives the built-in face recognition system by presenting a printed image of the user. In this work, we study the problem of automatically detecting presentation attacks against face authentication methods, considering the use-case of fast device unlocking and hardware constraints of mobile devices. To enrich the understanding of how a purely software-based method can be used to tackle the problem, we present a solely data-driven approach trained with multi-resolution patches and a multi-objective loss function crafted specifically to the problem. We provide a careful analysis that considers several user-disjoint and cross-factor protocols, highlighting some of the problems with current datasets and approaches. Such analysis, besides demonstrating the competitive results yielded by the proposed method, provides a better conceptual understanding of the problem. To further enhance efficacy and discriminability, we propose a method that leverages the available gallery of user data in the device and adapts the method decision-making process to the user’s and the device’s own characteristics. Finally, we introduce a new presentation-attack dataset tailored to the mobile-device setup, with real-world variations in lighting, including outdoors and low-light sessions, in contrast to existing public datasets.

Children-menus in restaurants of Portuguese, Hungarian and Croatian shopping centres: A qualitative study

Background

Families to eat out frequently use shopping centres and, there is evidence that children's menus are poor nutritional quality. Obesity rates have increased all over the world, and it has been associated to the observed out of home food consumption. This study aimed to characterise children's menus in shopping center restaurants in Portugal, Hungary and Croatia, as a preliminary study to further develop strategies to intervene.

Methods

An observational cross-sectional study was carried out in shopping centres in Portugal, Hungary and Croatia. Only the ones providing a children's menu were selected. Data collection tool was developed by the research team, consisting of a three-section form to identify and characterise the restaurant and analyses the quality of children's menu.

Results

Researcher visited 295 restaurants, from which only 69 provided a children's menu. From these 12 restaurants offered gifts associated with it. The most frequently featured items on the menu were hamburgers (22%), grilled or fried chicken (19%), nuggets (17%) and pizza (12%). Potato chips were the most frequent side dish option (33%). Only 11 restaurants offered vegetable soup or vegetables. Although water was available, a variety of soft drinks and sugary fruit juices were also options. Sweet dessert is a common part of the menu. Nutritional information is present in only 13% of the menus. Menu prices do not differ much, Portugal having the higher average (x ̅ = €5.35±1.45). Hungary had the lower average (x ̅ = €4.30±1.50) and Croatia the lowest range (min=€3.00, max=€7.40).

Conclusions

Children's menu options have poor quality. Shifting foods offered to children in restaurants has the potential to improve diet quality, reduce excess energy intake and promote healthy eating habits.

Key messages

European strategies are needed to promote children healthy food environment. Following aim is to develop and test balanced menus for children among food chains.

Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses

Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life.IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients' blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients' condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.

AB0968 DIAGNOSIS OF INFLAMMATORY BACK PAIN (IBP) IN A LOW-INCOME POPULATION USING ASAS IBP DEFINITION

Background:

Inflammatory back pain (IBP) is an important entry criterion for identifying patients with axial spondyloarthritis. Due to the very frequent prevalence of chronic non-inflammatory back pain (CBP), IBP is difficult to differentiate and recognize in many clinical practices. CBP is a frequent diagnosis in populations with low socioeconomic status.

Objectives:

To investigate whether IBP ASAS definition help discriminate from CBP in a low-income population.

Methods:

A total of 202 consecutive individuals were directly interviewed in Fortaleza/Brazil, for the prevalence of IBP (ASAS definition), monthly family income (US$), school education [>/≤ 8 school-years(SY)], and smoking habit (present/absent). People from all social levels were included.

Results:

Mean age was 38.9±12.9 years, 100 (49.5%) were male and 36 (17.8%) were smokers; 135 (66.8%) declared less than 730US$ family earnings per month and 71 (35.1%) had less than 8 SY. Although 122 (60.3%) declared lumbar pain, in comparison, 29 (14.3%), 22 (10.9%) and 59 (29.2%) fulfilled ASAS, Berlin, and Calin’s IBP criteria, respectively. There were 39 (58.02%) vs. 83 (61.5%) with vs. without CBP and 9 (13.4%) vs. 20 (14.8%) fulfilling vs. not fulfilling the ASAS IBP definition among those with more or less than 730US$ earnings (P>0.05), respectively.

Conclusion:

This is the first report on the prevalence of IBP in a very low-income population. Over one-third had less than 8SY, revealing very low literacy. Smoking prevalence was also low if compared to 28% smoking prevalence WHO estimates across Europe*. These IBP prevalence data are similar to those reported in wealthier populations, living in higher latitude. Data suggest that ASAS IBP definition may be used to discriminate patients with IBP from those with CBP, regardless of income and literacy.

References:

This is the first report on the prevalence of IBP in a very low-income population. Over one-third had less than 8SY, revealing very low literacy. Smoking prevalence (17.8%) was also low if compared to 28% smoking prevalence WHO estimates across Europe*. These IBP prevalence data are similar to those reported in wealthier populations, living in higher latitude. Data suggest that ASAS IBP definition may be used to discriminate patients with IBP from those with CBP, regardless of income and literacy.

Disclosure of Interests:

Jobson Oliveira: None declared, Rodolfo Nunes: None declared, Guilherme da Silva: None declared, Igor Nogueira: None declared, Artur Azevedo: None declared, Xenofon Baraliakos Grant/research support from: Grant/research support from: AbbVie, BMS, Celgene, Chugai, Merck, Novartis, Pfizer, UCB and Werfen, Consultant of: AbbVie, BMS, Celgene, Chugai, Merck, Novartis, Pfizer, UCB and Werfen, Speakers bureau: AbbVie, BMS, Celgene, Chugai, Merck, Novartis, Pfizer, UCB and Werfen, Airton Rocha Speakers bureau: Not related to the present work

AB0967 OBESITY IMPACTS SURGICAL REPAIR OF ROTATOR CUFF TEAR IN A LOW-INCOME POPULATION

Background:

Rotator cuff tears (RCT) rank high among causes of shoulder pain in primary care. Surgical repair of RCT is indicated when judged refractory to clinical treatment. Comorbidities and environmental issues may impact the surgical result of RCT repair.

Objectives:

to determine the impact of Obesity and Smoking in the outcome of low-income patients subjected to RCT repair.

Methods:

Low-income (<5500,00 US$ annualpib per capta*) patients living in Fortaleza-CE, Brazil were prospectively (2016-2018) evaluated in the Orthopedic and Rheumatology Services of the Hospital Geral de Fortaleza. Demographics and clinical data focusing Obesity (BMI >/≤ 30) and Smoking status (present/absent) were registered. Pain (0 – 10 cm VAS) and functional shoulder score evaluation by the University of California at Los Angeles Shoulder Score (UCLA) and American Shoulder and Elbow Surgeons (ASES) scoring systems following at least 2 years post-operative follow-up were recorded.

Results:

Thirty-nine individuals, 60.9±7.1 years-old, 30 female (68.3%) with 5.1 ± 1.9 years follow-up were evaluated; 21 (44.7%) were Smokers and 12 (25.5%) Obese (BMI >30). Other comorbidities included 16 arterial hypertension, 18 dyslipidemia, 17 osteoarthritis, 4 rheumatoid arthritis, 1 gout. Pain VAS values were 5.1 ± 2.6 vs 3.1 ± 2.8 in Obese vs Non-Obese (p=0.03) and 4 ± 2.8 vs 3.37 ± 2.99 in Smokers vs Non-Smokers (p=0.26), respectively. UCLA was 22.4 ± 8.2 vs. 28.79 ± 5.6 in Obese vs Non-Obese (p=0.004) and 26.2 ± 6.5 vs. 27.75 ± 7.18 (p=0.25) in Smokers vs. Non-smokers, respectively. ASES was 47.89 ± 28.3 vs. 68.1 ± 25.78 (p=0.021) in Obese vs. Non-obese and 58.98 ± 26.69 vs 65.3 ± 28.1 (p=0.243) in Smokers vs Non-Smokers, respectively.

Conclusion:

Using Patient Reported Outcome instruments, these long-term data show that Obesity negatively impacts the outcome of surgical repair of RCT in low-income patients. Smoking status was apparently irrelevant but the number of cigarettes smoked daily was not considered. Emphasis on weight reduction is a practical, affordable though hard to implement measure that could improve surgical results when repairing RCT.

References:

*https://pt.wikipedia.org/wiki/Lista_de_munic%C3%ADpios_do_Brasil_por_PIB

Disclosure of Interests:

Christine Silva: None declared, Natalia Mourão: None declared, Joaquim Landim: None declared, Leila da Rocha: None declared, Marco Lacerda: None declared, Airton Rocha Speakers bureau: Not related to the present work

Relationship of salt added during preparation and salt intake of cooks

Introduction The excessive consumption of sodium is one of the main problems in public health and responsible for the loss of years of a healthy life. The salt added to the meals represents a large part of the salt ingested, and the collaborators/cooks responsible for its addition need careful analysis, and intervention adjusted to the analysed reality, its salt consumption and its perception of saltiness may influence the amount of salt added.

Objectives This study had as main objective to perceive the relationship between the perception of salt of the employees of the Food and Nutrition Unit and the amount of salt added in the meals that were confectioned.

Methodology An observational, analytical, cross-sectional study was conducted, in which 6 selected cooks from the IPSS’s of Águeda participated. In order to estimate the amount of salt added to the meals, 20 samples of meals prepared by the cooks were taken, and a quantitative assessment of average salt per meal was made using Flame Photometry. The cooks were also subjected to two taste sensitivity tests. The data treatment was performed using the statistical software SPSS, and a statistical significance level of 5% was considered.

Results A better perception of the salty taste was observed in the cooks of Institution B. Although there were no differences with statistical significance in the quantity of salt of the meals between the two institutions, the mean and maximum addition of salt in the cooked meals was higher in institution B, regardless of the cook evaluated.

Conclusion The perception of the salty taste of the cooks does not seem to determine the amount of salt added to the meals.

Synthesis of 2-(aminophenyl)adenine derivatives: a simple protocol using the classical iron powder/acetic acid reduction methodology

2-(Nitrophenyl)adenine derivatives were isolated from the reaction of 5-amino-4-amidino-imidazoles with nitrobenzaldehydes. The conversion of the nitro derivatives to 2-(aminophenyl)adenine derivatives was performed using iron/acetic acid as reducing agent, in 70% aqueous ethanol. The products were isolated in good yield and the isolation protocol involves simple filtration and extraction procedures. This methodology is compatible with the presence of functional groups such as amines, ethers, halofluorides, and halochlorides.

Novel ultrastructural findings in bovine oocytes matured in vitro

The ultrastructural morphology of the bovine oocyte at different maturation stages has been previously analyzed but without detailed structural observations at the mature stage. The objective of the present study was thus to establish the ultrastructural characteristics of the mature bovine oocyte in full detail. Oocytes from Bos taurus (Holstein-Friesian) cows were aspirated from ovaries collected after being slaughtered at a local abattoir. After in vitro culture for 24 h, some of them were processed for electron microscopy. We described the ultrastructure of the zona pellucida, which presented three different regions, and novel cytoplasmic findings. There were two types of electron-lucent vesicles (heterogeneous and striated), which were suggested to give rise to lipid droplets, and presence of receptor-mediated endocytosis. In conclusion, our results indicate that although the mature bovine oocyte is devoid of evident yolk, it might be filled with an extensive lipid factory. In addition, even before fertilization, the mature oocyte seemed to absorb nutrients through receptor-mediated endocytosis, indicating active energy use or storage.

A data-driven approach to referable diabetic retinopathy detection

Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize.

OBJECTIVE

We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector.

METHODS

We gradually build the solution based on convolutional neural networks, adding data augmentation, multi-resolution training, robust feature-extraction augmentation, and a patient-basis analysis, testing the effectiveness of each improvement.

RESULTS

The proposed method achieved an area under the ROC curve of 98.2% (95% CI: 97.4-98.9%) under a strict cross-dataset protocol designed to test the ability to generalize - training on the Kaggle competition dataset and testing using the Messidor-2 dataset. With a 5 × 2-fold cross-validation protocol, similar results are achieved for Messidor-2 and DR2 datasets, reducing the classification error by over 44% when compared to most published studies in existing literature.

CONCLUSION

Additional boost strategies can improve performance substantially, but it is important to evaluate whether the additional (computation- and implementation-) complexity of each improvement is worth its benefits. We also corroborate that novel families of data-driven methods are the state of the art for diabetic retinopathy screening.

SIGNIFICANCE

By learning powerful discriminative patterns directly from available training retinal images, it is possible to perform referral diagnostics without detecting individual lesions.

Climate change potential effects on grapevine bioclimatic indices: A case study for the Portuguese demarcated Douro Region (Portugal)

In this work, bioclimatic parameters and indices relevant to the grapevine are estimated for the years 2000 (recent-pat), 2049 (medium-term future) and 2097 (long-term future), based on very high resolution (1 km × 1 km) MPI-WRF RCP8.5 climate simulations. The selected parameters and indices are the mean temperature during the grapevine growing season period (April to October, Tgs), the cumulative rainfall during the grapevine growing season period (Pgs), the Winkler index (WI), the Huglin heliothermic index (HI), the night cold index (CI) and the dryness index (DI). In general, a significant increase in mean temperature during the grapevine growing season period is observed, together with a significant decrease in precipitation. The recent-past WI is associated with the production of high-quality wines; the higher values predicted for the future represent intensive production of wines of intermediate quality. The HI shows the passage of a grapevine growing region considered as temperate-warm to a warm category of higher helio-thermicity. The recent-past CI indicates very cool conditions (associated with quality wines), while in the future there is a tendency for temperate or warmer nights. Finally, DI indicates an increase in water stress considered already high under the recent-past climate conditions. These results point to an increased climatic stress on the Douro region wine production and increased vulnerability of its vine varieties, providing evidence to support strategies aimed to preserve the high-quality wines in the region and their typicality in a sustainable way.

Counteracting the contemporaneous proliferation of digital forgeries and fake news

Fake news has been certainly the expression of the moment: from political round table discussions to newspapers to social and mainstream media. It is everywhere. With such an intense discussion and yet few effective ways to combat it, what can be done? Providing methods to fight back even the least harming hoax is a social responsibility. To look for authenticity in a wide sea of fake news, every detail is a lead. Image appearance and semantic content of text and images are some of the main properties, which can be analyzed to reveal even the slightest lie. In this vein, this work overviews some recent methods applicable to the verification of dubious content in text and images, and discusses how we can put them together as an option to curb away the proliferation of unverified and phony "facts". We briefly present the main idea behind each method, highlighting real situations where they can be applied and discussing expected results. Ultimately, we show how new research areas are working to seamlessly stitch together all these methods so as to provide a unified analysis and to establish the synchronization in space and time - the X-Coherence of heterogeneous sources of information documenting real-world events.

Expanded equine cumulus-oocyte complexes exhibit higher meiotic competence and lower glucose consumption than compact cumulus-oocyte complexes

Equine cumulus-oocyte complexes (COCs) are classified as compact (cCOC) or expanded (eCOC) and vary in their meiotic competence. This difference could be related to divergent glucose metabolism. To test this hypothesis in the present study, eCOCs, cCOCs and expanded or compact mural granulosa cells (EC and CC respectively) were matured in vitro for 30h, at which time maturation rate, glucose metabolism and the expression of genes involved in glucose transport, glycolysis, apoptosis and meiotic competence were determined. There were significant differences between eCOCs and cCOCs in maturation rate (50% vs 21.7% (n=192 and 46) respectively; P<0.001), as well as mean (±s.e.m.) glucose consumption (1.8±0.5 vs 27.9±5.9 nmol per COC respectively) and pyruvate (0.09±0.01 vs 2.4±0.8 nmol per COC respectively) and lactate (4.7±1.3 vs 64.1±20.6 nmol per COC respectively; P<0.05 for all) production. Glucose consumption in EC and CC did not differ significantly. Expression of hyaluronan-binding protein (tumour necrosis factor alpha induced protein 6; TNFAIP6) was increased in eCOCs and EC, and solute carrier family 2 member 1 (SLC2A1) expression was increased in eCOCs, but there were no differences in the expression of glycolysis-related enzymes and solute carrier family 2 member 3 (SLC2A3) between the COC or mural granulosa cell types. The findings of the present study demonstrate that metabolic and genomic differences exist between eCOCs and cCOCs and mural granulosa cells in the horse.

Image Provenance Analysis at Scale

Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the content of individual images to obtain new images. Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images. This is a problem that recently has received the name of image provenance analysis. In these times of public media manipulation (e.g., fake news and meme sharing), obtaining the history of image transformations is relevant for fact checking and authorship verification, among many other applications. This article presents an end-to-end processing pipeline for image provenance analysis, which works at real-world scale. It employs a cutting-edge image filtering solution that is custom-tailored for the problem at hand, as well as novel techniques for obtaining the provenance graph that expresses how the images, as nodes, are ancestrally connected. A comprehensive set of experiments for each stage of the pipeline is provided, comparing the proposed solution with state-of-the-art results, employing previously published datasets. In addition, this work introduces a new dataset of real-world provenance cases from the social media site Reddit, along with baseline results.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.

Glucosensing capacity of rainbow trout telencephalon

To assess the hypothesis of glucosensing systems present in fish telencephalon, we first demonstrated in rainbow trout, by in situ hybridisation, the presence of glucokinase (GK). Then, we assessed the response of glucosensing markers in rainbow trout telencephalon 6 hours after i.c.v. treatment with glucose or 2-deoxyglucose (inducing glucoprivation). We evaluated the response of parameters related to the mechanisms dependent on GK, liver X receptor (LXR), mitochondrial activity, sweet taste receptor and sodium-glucose linked transporter 1 (SGLT-1). We also assessed mRNA abundance of neuropeptides involved in the metabolic control of food intake (agouti-related protein, neuropeptide Y, pro-opiomelanocortin, and cocaine- and amphetamine-related transcript), as well as the abundance and phosphorylation status of proteins possibly involved in linking glucosensing with neuropeptide expression, such as protein kinase B (AkT), AMP-activated protein kinase (AMPK), mechanistic target of rapamycin and cAMP response element-binding protein (CREB). The responses obtained support the presence in the telencephalon of a glucosensing mechanism based on GK and maybe one based on LXR, although they do not support the presence of mechanisms dependent on mitochondrial activity and SGLT-1. The mechanism based on sweet taste receptor responded to glucose but in a converse way to that characterised previously in the hypothalamus. In general, systems responded only to glucose but not to glucoprivation. Neuropeptides did not respond to glucose or glucoprivation. By contrast, the presence of glucose activates Akt and inhibits AMPK, CREB and forkhead box01. This is the first study in any vertebrate species in which the response to glucose of putative glucosensing mechanisms is demonstrated in the telencephalon. Their role might relate to processes other than homeostatic control of food intake, such as the hedonic and reward system.

Immunomodulation of allergic response in children and adolescents: What we can learn from lymphatic filarial infection

BACKGROUND

Although it is well known that allergic diseases involve a strong Th2 immune response, with production of high levels of specific IgE allergen, knowledge on the association between filarial infection and allergies, among paediatric patients is scarce.

OBJECTIVE

To evaluate the allergic response patterns in cases of filarial infection by comparing peripheral eosinophils, total IgE levels, immediate hypersensitivity and cytokine levels in children and adolescents in Brazil.

METHODS

This was an exploratory study with three groups: (I) with filarial infection and without allergic diseases; (II) without filarial infection and with allergic diseases; and (III) without filarial infection and without allergic diseases. The prick test and specific IgE tests for aeroallergens were performed using five antigens. Peripheral eosinophils and total IgE were also evaluated. IL-4 and IL-5 were determined using whole-blood culture stimulated by three antigens.

RESULTS

Eosinophilia and elevated levels of total IgE (≥400IU/dl) were observed in all groups. The prick test was positive in 56.6% of the cases. Group I presented hypersensitive responses similar to the allergic disease groups. In the whole-blood culture stimulated by Dermatophagoides pteronyssinus, average IL-4 production did not differ significantly among the groups, but IL5 production resulting from stimulation was greater in the allergic disease groups (p<0.05).

CONCLUSIONS

The allergic response pattern in group with filarial infection was similar to that of the groups with and without allergic diseases, but the response to IL-5 in the culture stimulated by D. pteronyssinus was an exclusive characteristic of the allergic group.

Effects of androgens on the leptin system in immature male Atlantic salmon parr

Leptin modulates all levels of the reproductive endocrine axis in mammals, and in turn, both leptin and the leptin receptor are regulated by sex steroids. The aim of this study was to investigate if sex steroids regulate the leptin system also in fish. Immature one-year old male Atlantic salmon parr were implanted with Silclear capsules that were either empty or filled with 11-ketoandrostenedione (11KA) or testosterone (T) and the effects of 35-days treatment were investigated on measures of maturation, gene expression of leptin (lepa1, lepa2), leptin receptor (lepra1) and circulating plasma leptin. Both 11-KA and T stimulated the reproductive axis by increasing testes weight and up-regulated pituitary lh-β mRNA levels and for T also fsh-β. T up-regulated transcription levels of lepa1 and lepra1 in the pituitary, while 11-KA had no effect. Leptin receptor expression in the testis was unaltered by either androgen. T up-regulated lepa1 mRNA levels significantly also in the liver, but had no effect on lepa2, and 11KA did not affect hepatic gene expression of either lepa1 or lepa2. Plasma leptin levels did not differ significantly between treatments. The results indicate that androgens regulate gene expression of leptin and the leptin receptor in different tissues in fish and that the effects of leptin might be tissue specific considering plasma levels remained unaltered. Overall, the results suggest a role for leptin in fish reproduction, where sex steroids are able to regulate components of the leptin system differentially in liver and important tissues of the reproductive axis.

History matching through dynamic decision-making

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark.

Quantification and mapping of urban fluxes under climate change: Application of WRF-SUEWS model to Greater Porto area (Portugal)

Climate change and the growth of urban populations are two of the main challenges facing Europe today. These issues are linked as climate change results in serious challenges for cities. Recent attention has focused on how urban surface-atmosphere exchanges of heat and water will be affected by climate change and the implications for urban planning and sustainability. In this study energy fluxes for Greater Porto area, Portugal, were estimated and the influence of the projected climate change evaluated. To accomplish this, the Weather Research and Forecasting Model (WRF) and the Surface Urban Energy and Water Balance Scheme (SUEWS) were applied for two climatological scenarios: a present (or reference, 1986-2005) scenario and a future scenario (2046-2065), in this case the Representative Concentration Pathway RCP8.5, which reflects the worst set of expectations (with the most onerous impacts). The results show that for the future climate conditions, the incoming shortwave radiation will increase by around 10%, the sensible heat flux around 40% and the net storage heat flux around 35%. In contrast, the latent heat flux will decrease about 20%. The changes in the magnitude of the different fluxes result in an increase of the net all-wave radiation by 15%. The implications of the changes of the energy balance on the meteorological variables are discussed, particularly in terms of temperature and precipitation.