Calcium identification and scoring based on echocardiography imaging

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient's monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, we developed a simple technique to identify and extract the calcium pixel count from echocardiography imaging, by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, we performed echocardiographic adaptive image binarization. Given that blood maintains the same intensity on echocardiographic images – being always the darker region – we used blood structures in the image to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from our experiments are encouraging. With our technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, we were able to obtain a calcium pixel count, where pixels values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), that correlated well with human expert assessment of calcium area for the same images.Atualmente, é necessário um perito em ecocardiografia para identificar o cálcio na válvula aórtica, e é necessária uma imagem Tomográfica Computorizada (TAC) cardíaca para a quantificação do cálcio. Ao realizar uma TAC, o paciente é sujeito a radiação, pelo que o número de TACs que podem ser realizadas deve ser limitado, restringindo a monitorização do paciente. A Visão por Computador (VC) abriu novas oportunidades para uma maior eficiência na extração de conhecimentos de uma imagem. A aplicação de técnicas de VC na ecocardiografia pode reduzir a carga de trabalho médico para identificar o cálcio e quantificálo, ajudando os médicos a manter um melhor acompanhamento dos seus pacientes. Na nossa abordagem, desenvolvemos uma técnica simples para identificar e extrair o número de pixéis de cálcio da ecocardiografia, através da utilização de VC. Com base em ecocardiografias anónimas de doentes reais, esta abordagem permite a identificação semiautomática do cálcio. Como o brilho das imagens de ecocardiografia (com a intensidade mais elevada corresponde ao cálcio) varia consoante os parâmetros de aquisição, realizámos a binarização das ecocardiografias de forma adaptativa. Dado que o sangue mantém a mesma intensidade nas ecocardiografias - sendo sempre a região mais escura - utilizámos estruturas sanguíneas na imagem para criar um limiar adaptativo para a binarização. Após a binarização, a região de interesse (ROI) com cálcio, foi selecionada interactivamente por um especialista em ecocardiografia e extraída, permitindo-nos calcular o número de pixéis de cálcio, correspondente à quantidade espacial de cálcio. Os resultados obtidos com as nossas experiências são encorajadores. Com a nossa técnica, a partir de ecocardiografias recolhidas para o mesmo paciente com diferentes configurações de aquisição e diferentes brilhos, conseguimos obter uma contagem de pixéis de cálcio, onde os valores de pixéis mostram uma margem de erro absoluta de 3 (numa escala de 0 a 255), que se correlacionou bem com a avaliação humana perita da área de cálcio para as mesmas imagens

Solvent effects on solution enthalpies of adamantyl derivatives

Solution enthalpies of 1-bromoadamantane, 1-adamantanol, and 2-adamantanone in a large set of protic and aprotic solvents are reported at 298.15 K. Solvent effects on the solution processes of these solutes are analyzed in terms of a modified TAKA equation, involving delta(cav) h (s) as the cavity term. The nature and magnitude of the major interactions which influence these processes are assessed and discussed in terms of the solutes' characteristics. New insights on the solution processes under scrutiny are presented

Systematic Review

Funding Information: MP received support from the Portuguese National Funds through FITEC – Programa Interface, with reference CIT INOV – INESC INOVAÇÃO. Funding was also provided from the PhD program in Industrial Management, NOVA Science and Technology Faculty. Funding Information: MP and LVL acknowledge Fundação para a Ciência e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).Background: The digital age, with digital sensors, the Internet of Things (IoT), and big data tools, has opened new opportunities for improving the delivery of health care services, with remote monitoring systems playing a crucial role and improving access to patients. The versatility of these systems has been demonstrated during the current COVID-19 pandemic. Health remote monitoring systems (HRMS) present various advantages such as the reduction in patient load at hospitals and health centers. Patients that would most benefit from HRMS are those with chronic diseases, older adults, and patients that experience less severe symptoms recovering from SARS-CoV-2 viral infection. Objective: This paper aimed to perform a systematic review of the literature of HRMS in primary health care (PHC) settings, identifying the current status of the digitalization of health processes, remote data acquisition, and interactions between health care personnel and patients. Methods: A systematic literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify articles that explored interventions with HRMS in patients with chronic diseases in the PHC setting. Results: The literature review yielded 123 publications, 18 of which met the predefined inclusion criteria. The selected articles highlighted that sensors and wearables are already being used in multiple scenarios related to chronic disease management at the PHC level. The studies focused mostly on patients with diabetes (9/26, 35%) and cardiovascular diseases (7/26, 27%). During the evaluation of the implementation of these interventions, the major difficulty that stood out was the integration of information into already existing systems in the PHC infrastructure and in changing working processes of PHC professionals (83%). Conclusions: The PHC context integrates multidisciplinary teams and patients with often complex, chronic pathologies. Despite the theoretical framework, objective identification of problems, and involvement of stakeholders in the design and implementation processes, these interventions mostly fail to scale up. Despite the inherent limitations of conducting a systematic literature review, the small number of studies in the PHC context is a relevant limitation. This study aimed to demonstrate the importance of matching technological development to the working PHC processes in interventions regarding the use of sensors and wearables for remote monitoring as a source of information for chronic disease management, so that information with clinical value is not lost along the way.publishersversionpublishe

Solution enthalpies of hydroxylic compounds

Solution enthalpies of adamantan-1-ol, 2-methyl- butan-2-ol, and 3-methylbutan-1-ol have been measured at 298.15 K, in a set of 16 protogenic and non-protogenic solvents. The identification and quantification of solvent effects on the solution processes under study were performed using quantitative-structure property relationships. The results are discussed in terms of solute-solvent-solvent interactions and also in terms of the influence of compound's size and position of its hydroxyl group

Georeferenced analysis of urban nightlife and noise based on mobile phone data

Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development.This work was supported by the Fundação para a Ciência e Tecnologia under Grant [UIDB/00315/2020]; and by the project “BLOCKCHAIN.PT (RE-C05-i01.01—Agendas/Alianças Mobilizadoras para a Reindustrialização, Plano de Recuperação e Resiliência de Portugal” in its component 5—Capitalization and Business Innovation and with the Regulation of the Incentive System “Agendas for Business Innovation”, approved by Ordinance No. 43-A/2022 of 19 January 2022)

Mining tourists’ movement patterns in a city

Although tourists generate a large amount of data (known as “big data”) when they visit cities, little is known about their spatial behavior. One of the most significant issues that has recently gained attention is mobile phone usage and user behavior tracking. A spatial and temporal data visualization approach was established with the purpose of finding tourists’ footprints. This work provides a platform for combining multiple data sources into one and transforming information into knowledge. Using Python, we created a method to build visualization dashboards aiming to provide insights about tourists’ movements and concentrations in a city using information from mobile operators. This approach can be replicated to other smart cities with data available. Weather and major events, for instance, have an impact on the movements of tourists. The outputs from this work provide useful information for tourism professionals to understand tourists’ preferences and improve the visitors’ experience. Management authorities may also use these outputs to increase security based on tourists’ concentration and movements. A case study in Lisbon with 4 months data is presented, but the proposed approach can also be used in other cities based on data availability. Results from this case study demonstrate how tourists tend to gather around a set of parishes during a specific time of the day during the months under study, as well as how unusual circumstances, namely international events, impact their overall spatial behavior.This work was supported by EEA Grants Blue Growth Programme (Call #5). Project PT-INNOVATION-0069 – Fish2Fork

Pancreatitis Can Present Like Cancer: Lymphoplasmacytic Sclerosing Pancreatitis in a Patient with a History of Gastric Carcinoma

Autoimmune pancreatitis has been associated with many syndromes in the presence of increased immunoglobulin levels. IgG4 antibodies are elevated in the context of lymphoplasmacytic sclerosing pancreatitis associated with IgG4-related disease. We present the case of a 74-year-old man diagnosed with autoimmune pancreatitis on a cancer background. Awareness of this condition in the cancer patient is crucial for timely diagnosis. Infectious complications might have implications for the choice of immunosuppressant

Solution enthalpies of 1,4-dioxane: study of solvent effects through quantitative structure-property relationships

Solution enthalpies of 1,4-dioxane have been obtained in 15 protic and aprotic solvents at 298.15 K. Breaking the overall process through the use of Solomonov's methodology the cavity term was calculated and interaction enthalpies (Delta H-int) were determined. Main factors involved in the interaction enthalpy have been identified and quantified using a QSPR approach based on the TAKA model equation. The relevant descriptors were found to be pi* and beta, which showed, respectively, exothermic and endothermic contributions. The magnitude of pi* coefficient points toward non-specific solute-solvent interactions playing a major role in the solution process. The positive value of the beta coefficient reflects the endothermic character of the solvents' hydrogen bond acceptor (HBA) basicity contribution, indicating that solvent molecules engaged in hydrogen bonding preferentially interact with each other rather than with 1,4-dioxane. (C) 2013 Elsevier B.V. All rights reserved

A Primary Prevention Clinical Risk Score Model for Patients With Brugada Syndrome (BRUGADA-RISK).

OBJECTIVES: The goal of this study was to develop a risk score model for patients with Brugada syndrome (BrS). BACKGROUND: Risk stratification in BrS is a significant challenge due to the low event rates and conflicting evidence. METHODS: A multicenter international cohort of patients with BrS and no previous cardiac arrest was used to evaluate the role of 16 proposed clinical or electrocardiogram (ECG) markers in predicting ventricular arrhythmias (VAs)/sudden cardiac death (SCD) during follow-up. Predictive markers were incorporated into a risk score model, and this model was validated by using out-of-sample cross-validation. RESULTS: A total of 1,110 patients with BrS from 16 centers in 8 countries were included (mean age 51.8 ± 13.6 years; 71.8% male). Median follow-up was 5.33 years; 114 patients had VA/SCD (10.3%) with an annual event rate of 1.5%. Of the 16 proposed risk factors, probable arrhythmia-related syncope (hazard ratio [HR]: 3.71; p < 0.001), spontaneous type 1 ECG (HR: 3.80; p < 0.001), early repolarization (HR: 3.42; p < 0.001), and a type 1 Brugada ECG pattern in peripheral leads (HR: 2.33; p < 0.001) were associated with a higher risk of VA/SCD. A risk score model incorporating these factors revealed a sensitivity of 71.2% (95% confidence interval: 61.5% to 84.6%) and a specificity of 80.2% (95% confidence interval: 75.7% to 82.3%) in predicting VA/SCD at 5 years. Calibration plots showed a mean prediction error of 1.2%. The model was effectively validated by using out-of-sample cross-validation according to country. CONCLUSIONS: This multicenter study identified 4 risk factors for VA/SCD in a primary prevention BrS population. A risk score model was generated to quantify risk of VA/SCD in BrS and inform implantable cardioverter-defibrillator prescription

Data-Driven Approach for Incident Management in a Smart City

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens&rsquo; quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better