Functional abolition of carotid body activity restores insulin action and glucose homeostasis in rats: key roles for visceral adipose tissue and the liver.

AIMS/HYPOTHESIS: We recently described that carotid body (CB) over-activation is involved in the aetiology of insulin resistance and arterial hypertension in animal models of the metabolic syndrome. Additionally, we have demonstrated that CB activity is increased in animal models of insulin resistance, and that carotid sinus nerve (CSN) resection prevents the development of insulin resistance and arterial hypertension induced by high-energy diets. Here, we tested whether the functional abolition of CB by CSN transection would reverse pre-established insulin resistance, dyslipidaemia, obesity, autonomic dysfunction and hypertension in animal models of the metabolic syndrome. The effect of CSN resection on insulin signalling pathways and tissue-specific glucose uptake was evaluated in skeletal muscle, adipose tissue and liver. METHODS: Experiments were performed in male Wistar rats submitted to two high-energy diets: a high-fat diet, representing a model of insulin resistance, hypertension and obesity, and a high-sucrose diet, representing a lean model of insulin resistance and hypertension. Half of each group was submitted to chronic bilateral resection of the CSN. Age-matched control rats were also used. RESULTS: CSN resection normalised systemic sympathetic nervous system activity and reversed weight gain induced by high-energy diets. It also normalised plasma glucose and insulin levels, insulin sensitivity lipid profile, arterial pressure and endothelial function by improving glucose uptake by the liver and perienteric adipose tissue. CONCLUSIONS/INTERPRETATION: We concluded that functional abolition of CB activity restores insulin sensitivity and glucose homeostasis by positively affecting insulin signalling pathways in visceral adipose tissue and liver.

Hybrid Cardiac Telerehabilitation After Acute Coronary Syndrome: Self-selection Predictors and Outcomes.

Aims: To evaluate the effectiveness of a hybrid cardiac telerehabilitation (HCTR) program after acute coronary syndrome (ACS) on patient quality of life (QoL) and physical activity indices throughout phases 2-3 and establish predictors for hybrid program self-selection. Methodology: This single-centre longitudinal retrospective study included patients who attended a cardiac rehabilitation program (CRP) between 2018-2021. Patients self-selected between two groups: Group 1 - conventional CRP (CCRP); Group 2 - HCTR. Baseline characteristics were registered. EuroQol-5D (EQ-5D) and International Physical Activity Questionnaire (IPAQ) were applied at three times: T0 - phase 2 onset; T1 - phase 3 onset; T2 - 3 months after T1. Results: 59 patients participated (Group 1 - 27; Group 2 - 32). We found significant between-group differences regarding occupation (p=0.003). Diabetic patients were less likely to self-select into HCTR (OR=0.21; p<0.05). EQ-5D visual analogue scale and IPAQ result significantly improved between T0-T2 only for HCTR (p=0.001; p=0.021). Conclusions: HCTR was superior to CCRP on physical activity indices and QoL of ACS patients.

Complex network model for COVID-19: Human behavior, pseudo-periodic solutions and multiple epidemic waves.

We propose a mathematical model for the transmission dynamics of SARS-CoV-2 in a homogeneously mixing non constant population, and generalize it to a model where the parameters are given by piecewise constant functions. This allows us to model the human behavior and the impact of public health policies on the dynamics of the curve of active infected individuals during a COVID-19 epidemic outbreak. After proving the existence and global asymptotic stability of the disease-free and endemic equilibrium points of the model with constant parameters, we consider a family of Cauchy problems, with piecewise constant parameters, and prove the existence of pseudo-oscillations between a neighborhood of the disease-free equilibrium and a neighborhood of the endemic equilibrium, in a biologically feasible region. In the context of the COVID-19 pandemic, this pseudo-periodic solutions are related to the emergence of epidemic waves. Then, to capture the impact of mobility in the dynamics of COVID-19 epidemics, we propose a complex network with six distinct regions based on COVID-19 real data from Portugal. We perform numerical simulations for the complex network model, where the objective is to determine a topology that minimizes the level of active infected individuals and the existence of topologies that are likely to worsen the level of infection. We claim that this methodology is a tool with enormous potential in the current pandemic context, and can be applied in the management of outbreaks (in regional terms) but also to manage the opening/closing of borders.

Melanoma classification using light-Fields with morlet scattering transform and CNN: Surface depth as a valuable tool to increase detection rate.

Medical image classification through learning-based approaches has been increasingly used, namely in the discrimination of melanoma. However, for skin lesion classification in general, such methods commonly rely on dermoscopic or other 2D-macro RGB images. This work proposes to exploit beyond conventional 2D image characteristics, by considering a third dimension (depth) that characterises the skin surface rugosity, which can be obtained from light-field images, such as those available in the SKINL2 dataset. To achieve this goal, a processing pipeline was deployed using a morlet scattering transform and a CNN model, allowing to perform a comparison between using 2D information, only 3D information, or both. Results show that discrimination between Melanoma and Nevus reaches an accuracy of 84.00, 74.00 or 94.00% when using only 2D, only 3D, or both, respectively. An increase of 14.29pp in sensitivity and 8.33pp in specificity is achieved when expanding beyond conventional 2D information by also using depth. When discriminating between Melanoma and all other types of lesions (a further imbalanced setting), an increase of 28.57pp in sensitivity and decrease of 1.19pp in specificity is achieved for the same test conditions. Overall the results of this work demonstrate significant improvements over conventional approaches.

CBmeter study: protocol for assessing the predictive value of peripheral chemoreceptor overactivation for metabolic diseases.

INTRODUCTION: Early screening of metabolic diseases is crucial since continued undiagnostic places an ever-increasing burden on healthcare systems. Recent studies suggest a link between overactivated carotid bodies (CB) and the genesis of type 2 diabetes mellitus. The non-invasive assessment of CB activity by measuring ventilatory, cardiac and metabolic responses to challenge tests may have predictive value for metabolic diseases; however, there are no commercially available devices that assess CB activity. The findings of the CBmeter study will clarify the role of the CBs in the genesis of-metabolic diseases and guide the development of new therapeutic approaches for early intervention in metabolic disturbances. Results may also contribute to patient classification and stratification for future CB modulatory interventions. METHODS: This is a non-randomised, multicentric, controlled clinical study. Forty participants (20 control and 20 diabetics) will be recruited from secondary and primary healthcare settings. The primary objective is to establish a new model of early diagnosis of metabolic diseases based on the respiratory and metabolic responses to transient 100% oxygen administration and ingestion of a standardised mixed meal. ANALYSIS: Raw data acquired with the CBmeter will be endorsed against gold standard techniques for heart rate, respiratory rate, oxygen saturation and interstitial glucose quantification and analysed a multivariate analysis software developed specifically for the CBmeter study (CBview). Data will be analysed using clustering analysis and artificial intelligence methods based on unsupervised learning algorithms, to establish the predictive value of diabetes diagnosis. ETHICS: The study was approved by the Ethics Committee of the Leiria Hospital Centre. Patients will be asked for written informed consent and data will be coded to ensure the anonymity of data. DISSEMINATION: Results will be disseminated through publication in peer-reviewed journals and relevant medical and health conferences.

Blood Pressure Regulation by the Carotid Sinus Nerve: Clinical Implications for Carotid Body Neuromodulation.

Chronic carotid sinus nerve (CSN) electrical modulation through kilohertz frequency alternating current improves metabolic control in rat models of type 2 diabetes, underpinning the potential of bioelectronic modulation of the CSN as a therapeutic modality for metabolic diseases in humans. The CSN carries sensory information from the carotid bodies, peripheral chemoreceptor organs that respond to changes in blood biochemical modifications such as hypoxia, hypercapnia, acidosis, and hyperinsulinemia. In addition, the CSN also delivers information from carotid sinus baroreceptors-mechanoreceptor sensory neurons directly involved in the control of blood pressure-to the central nervous system. The interaction between these powerful reflex systems-chemoreflex and baroreflex-whose sensory receptors are in anatomical proximity, may be regarded as a drawback to the development of selective bioelectronic tools to modulate the CSN. Herein we aimed to disclose CSN influence on cardiovascular regulation, particularly under hypoxic conditions, and we tested the hypothesis that neuromodulation of the CSN, either by electrical stimuli or surgical means, does not significantly impact blood pressure. Experiments were performed in Wistar rats aged 10-12 weeks. No significant effects of acute hypoxia were observed in systolic or diastolic blood pressure or heart rate although there was a significant activation of the cardiac sympathetic nervous system. We conclude that chemoreceptor activation by hypoxia leads to an expected increase in sympathetic activity accompanied by compensatory regional mechanisms that assure blood flow to regional beds and maintenance of hemodynamic homeostasis. Upon surgical denervation or electrical block of the CSN, the increase in cardiac sympathetic nervous system activity in response to hypoxia was lost, and there were no significant changes in blood pressure in comparison to control animals. We conclude that the responses to hypoxia and vasomotor control short-term regulation of blood pressure are dissociated in terms of hypoxic response but integrated to generate an effector response to a given change in arterial pressure.

Skin lesion classification using features of 3D border lines.

Machine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2D image features derived from shape, colour or texture, which does not always guarantee the best results. This work presents a contribution to this field, by exploiting the lesions' border line characteristics using a new dimension - depth, which has not been thoroughly investigated so far. A selected group of features is extracted from the depth information of 3D images, which are then used for classification using a quadratic Support Vector Machine. Despite class imbalance often present in medical image datasets, the proposed algorithm achieves a top geometric mean of 94.87%, comprising 100.00% sensitivity and 90.00% specificity, using only depth information for the detection of Melanomas. Such results show that potential gains can be achieved by extracting information from this often overlooked dimension, which provides more balanced results in terms of sensitivity and specificity than other settings.

Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal.

The COVID-19 pandemic has forced policy makers to decree urgent confinements to stop a rapid and massive contagion. However, after that stage, societies are being forced to find an equilibrium between the need to reduce contagion rates and the need to reopen their economies. The experience hitherto lived has provided data on the evolution of the pandemic, in particular the population dynamics as a result of the public health measures enacted. This allows the formulation of forecasting mathematical models to anticipate the consequences of political decisions. Here we propose a model to do so and apply it to the case of Portugal. With a mathematical deterministic model, described by a system of ordinary differential equations, we fit the real evolution of COVID-19 in this country. After identification of the population readiness to follow social restrictions, by analyzing the social media, we incorporate this effect in a version of the model that allow us to check different scenarios. This is realized by considering a Monte Carlo discrete version of the previous model coupled via a complex network. Then, we apply optimal control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations. This work allows testing various scenarios of pandemic management (closure of sectors of the economy, partial/total compliance with protection measures by citizens, number of beds in intensive care units, etc.), ensuring the responsiveness of the health system, thus being a public health decision support tool.

Light Field Image Dataset of Skin Lesions.

Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like dermatology, allowing to find new features and improve classification algorithms, namely those based on machine learning approaches. This paper presents a contribution for the research community, in the form of a publicly available light field image dataset of skin lesions (named SKINL2 v1.0). This dataset contains 250 light fields, captured with a focused plenoptic camera and classified into eight clinical categories, according to the type of lesion. Each light field is comprised of 81 different views of the same lesion. The database also includes the dermatoscopic image of each lesion. A representative subset of 17 central view images of the light fields is further characterised in terms of spatial information (SI), colourfulness (CF) and compressibility. This dataset has high potential for advancing medical imaging research and development of new classification algorithms based on light fields, as well as in clinically-oriented dermatology studies.

Autonomic nervous system monitoring in intensive care as a prognostic tool. Systematic review. / Monitorização do sistema nervoso autônomo em ambiente de cuidados intensivos como ferramenta de prognóstico. Revisão sistemática.

OBJECTIVE: To present a systematic review of the use of autonomic nervous system monitoring as a prognostic tool in intensive care units by assessing heart rate variability. METHODS: Literature review of studies published until July 2016 listed in PubMed/Medline and conducted in intensive care units, on autonomic nervous system monitoring, via analysis of heart rate variability as a prognostic tool (mortality study). The following English terms were entered in the search field: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality"). RESULTS: There was an increased likelihood of death in patients who had a decrease in heart rate variability as analyzed via heart rate variance, cardiac uncoupling, heart rate volatility, integer heart rate variability, standard deviation of NN intervals, root mean square of successive differences, total power, low frequency, very low frequency, low frequency/high frequency ratio, ratio of short-term to long-term fractal exponents, Shannon entropy, multiscale entropy and approximate entropy. CONCLUSION: In patients admitted to intensive care units, regardless of the pathology, heart rate variability varies inversely with clinical severity and prognosis.

OBJETIVO: Apresentar uma revisão sistemática do uso da monitorização do sistema nervoso autônomo como ferramenta de prognóstico, verificando a variabilidade da frequência cardíaca nas unidades de cuidados intensivos. MÉTODOS: Revisão de literatura publicada até julho de 2016 na PubMed/MEDLINE de estudos realizados em unidades de cuidados intensivos, sobre a monitorização do sistema nervoso autônomo, por meio da análise da variabilidade da frequência cardíaca, como ferramenta de prognóstico - estudo da mortalidade. Foram utilizados os seguintes termos em inglês no campo de pesquisa: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality"). RESULTADOS: A probabilidade de morte nos doentes aumentou com a diminuição da variabilidade da frequência cardíaca, estudada por meio da variância da frequência cardíaca, desacoplamento cardíaco, volatilidade da frequência cardíaca, integer heart rate variability, desvio padrão de todos os intervalos RR normais, raiz quadrada da média do quadrado das diferenças entre intervalos RR adjacentes, poder total, componente de baixa frequência, componente de muito baixa frequência, razão entre o componente de baixa frequência e o componente de alta frequência), razão entre expoentes fractais de curto e longo prazo, entropia de Shannon, entropia multiescalar e entropia aproximada. CONCLUSÃO: Nos doentes internados em unidades de cuidados intensivos, independentemente da patologia que motivou o internamento, a variabilidade da frequência cardíaca varia de forma inversa com a gravidade clínica e com o prognóstico.

Classification of reticular pattern and streaks in dermoscopic images based on texture analysis.

The early detection of melanoma is one of the greatest challenges in clinical practice of dermatology, and the reticular pattern is one of the most important dermoscopic structures to improve melanocytic lesion diagnosis. A texture-based approach is developed for the automatic detection of reticular patterns, whose output will assist clinical decision-making. Feature selection was based on the use of two algorithms by means of the classical graylevel co-occurrence matrix and Laws energy masks optimized on a set of 104 dermoscopy images. The AdaBoost (adaptive boosting) approach to machine learning was used within this strategy. Results suggest superiority of LEM for reticular pattern detection in dermoscopic images, achieving a sensitivity of 90.16% and a specificity of 86.67%. The use of automatic classification in dermoscopy to support clinicians is a strong tool to assist diagnosis; however, the use of automatic classification as a complementary tool in clinical routine requires algorithms with high levels of sensitivity and specificity. The results presented in this work will contribute to achieving this goal.

Monitorização do sistema nervoso autônomo em ambiente de cuidados intensivos como ferramenta de prognóstico. Revisão sistemática / Autonomic nervous system monitoring in intensive care as a prognostic tool. Systematic review

RESUMO Objetivo: Apresentar uma revisão sistemática do uso da monitorização do sistema nervoso autônomo como ferramenta de prognóstico, verificando a variabilidade da frequência cardíaca nas unidades de cuidados intensivos. Métodos: Revisão de literatura publicada até julho de 2016 na PubMed/MEDLINE de estudos realizados em unidades de cuidados intensivos, sobre a monitorização do sistema nervoso autônomo, por meio da análise da variabilidade da frequência cardíaca, como ferramenta de prognóstico - estudo da mortalidade. Foram utilizados os seguintes termos em inglês no campo de pesquisa: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality"). Resultados: A probabilidade de morte nos doentes aumentou com a diminuição da variabilidade da frequência cardíaca, estudada por meio da variância da frequência cardíaca, desacoplamento cardíaco, volatilidade da frequência cardíaca, integer heart rate variability, desvio padrão de todos os intervalos RR normais, raiz quadrada da média do quadrado das diferenças entre intervalos RR adjacentes, poder total, componente de baixa frequência, componente de muito baixa frequência, razão entre o componente de baixa frequência e o componente de alta frequência), razão entre expoentes fractais de curto e longo prazo, entropia de Shannon, entropia multiescalar e entropia aproximada. Conclusão: Nos doentes internados em unidades de cuidados intensivos, independentemente da patologia que motivou o internamento, a variabilidade da frequência cardíaca varia de forma inversa com a gravidade clínica e com o prognóstico.

ABSTRACT Objective: To present a systematic review of the use of autonomic nervous system monitoring as a prognostic tool in intensive care units by assessing heart rate variability. Methods: Literature review of studies published until July 2016 listed in PubMed/Medline and conducted in intensive care units, on autonomic nervous system monitoring, via analysis of heart rate variability as a prognostic tool (mortality study). The following English terms were entered in the search field: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality"). Results: There was an increased likelihood of death in patients who had a decrease in heart rate variability as analyzed via heart rate variance, cardiac uncoupling, heart rate volatility, integer heart rate variability, standard deviation of NN intervals, root mean square of successive differences, total power, low frequency, very low frequency, low frequency/high frequency ratio, ratio of short-term to long-term fractal exponents, Shannon entropy, multiscale entropy and approximate entropy. Conclusion: In patients admitted to intensive care units, regardless of the pathology, heart rate variability varies inversely with clinical severity and prognosis.

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

Abstract Introduction Dermoscopy is a non-invasive in vivo imaging technique, used in dermatology in feature identification, among pigmented melanocytic neoplasms, from suspicious skin lesions. Often, in the skin exam is possible to ascertain markers, whose identification and proper characterization is difficult, even when it is used a magnifying lens and a source of light. Dermoscopic images are thus a challenging source of a wide range of digital features, frequently with clinical correlation. Among these markers, one of particular interest to diagnosis in skin evaluation is the reticular pattern. Methods This paper presents a novel approach (avoiding pre-processing, e.g. segmentation and filtering) for reticular pattern detection in dermoscopic images, using texture spectral analysis. The proposed methodology involves a Curvelet Transform procedure to identify features. Results Feature extraction is applied to identify a set of discriminant characteristics in the reticular pattern, and it is also employed in the automatic classification task. The results obtained are encouraging, presenting Sensitivity and Specificity of 82.35% and 76.79%, respectively. Conclusions These results highlight the use of automatic classification, in the context of artificial intelligence, within a computer-aided diagnosis strategy, as a strong tool to help the human decision making task in clinical practice. Moreover, the results were obtained using images from three different sources, without previous lesion segmentation, achieving to a rapid, robust and low complexity methodology. These properties boost the presented approach to be easily used in clinical practice as an aid to the diagnostic process.

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

Abstract Introduction Early detection of suspicious skin lesions is critical to prevent skin malignancies, particularly the melanoma, which is the most dangerous form of human skin cancer. In the last decade, image processing techniques have been an increasingly important tool for early detection and mathematical models play a relevant role in mapping the progression of lesions. Methods This work presents an algorithm to describe the evolution of the border of the skin lesion based on two main measurable markers: the symmetry and the geometric growth path of the lesion. The proposed methodology involves two dermoscopic images of the same melanocytic lesion obtained at different moments in time. By applying a mathematical model based on planar linear transformations, measurable parameters related to symmetry and growth are extracted. Results With this information one may compare the actual evolution in the lesion with the outcomes from the geometric model. First, this method was tested on predefined images whose growth was controlled and the symmetry known which were used for validation. Then the methodology was tested in real dermoscopic melanoma images in which the parameters of the mathematical model revealed symmetry and growth rates consistent with a typical melanoma behavior. Conclusions The method developed proved to show very accurate information about the target growth markers (variation on the growth along the border, the deformation and the symmetry of the lesion trough the time). All the results, validated by the expected phantom outputs, were similar to the ones on the real images.