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IntroductionEarly 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.MethodsThis 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.AnalysisRaw 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.EthicsThe 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.DisseminationResults will be disseminated through publication in peer-reviewed journals and relevant medical and health conferences.

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.

Heart failure (HF) is a prevalent and debilitating condition that significantly affects patients' quality of life and places a substantial burden on healthcare systems. In recent years, digital technologies have been increasingly explored in cardiac rehabilitation (CR), particularly through their integration within Internet of Things (IoT) ecosystems to support remote monitoring and personalized care. This review aimed to provide a focused overview of emerging digital technologies applicable to the rehabilitation of patients with HF, with emphasis on solutions compatible with IoT-based systems. A targeted literature search was conducted in PubMed, Scopus, Cochrane and Web of Science, including studies published between 2019 and 2024. Studies addressing digital technologies in HF rehabilitation. Following a structured selection process, 59 articles were included in the narrative synthesis. The findings indicate a growing body of literature investigating wearable physiological monitoring devices, telehealth-based CR programs, digital platforms, and smart sensors, many of which have been explored for integration within IoT infrastructures. These technologies have been associated with improved remote follow-up, patient engagement, and real-time physiological data collection outside traditional clinical settings. Emerging applications of artificial intelligence within IoT-enabled systems have also been examined to support clinical workflows and adaptive rehabilitation strategies. Despite increasing interest, challenges remain, including heterogeneity in study designs, usability concerns, data privacy and security issues, economic barriers, and limited large-scale clinical validation. Overall, this review suggests that IoT-enabled technologies represent a promising area of research in CR, warranting further investigation to support their sustainable integration into routine HF care.

For preventing health complications and reducing the strain on healthcare systems, early identification of diseases is imperative. In this context, artificial intelligence has become increasingly prominent in the field of medicine, offering essential support for disease diagnosis. This article introduces an algorithm that builds upon an earlier methodology to assess biosignals acquired through cardiopulmonary exercise testing (CPET) for identifying metabolic syndrome (MS), heart failure (HF), and healthy individuals (H). Leveraging support vector machine (SVM) technology, a well-known machine learning classification method, in combination with wavelet transforms for feature extraction, the algorithm takes an innovative approach. The model was trained on CPET data from 45 participants, including 15 with MS, 15 with HF, and 15 healthy controls. For binary classification tasks, the SVM with a polynomial kernel and 5-level wavelet transform (SVM-POL-BW5) outperformed similar methods described in the literature. Moreover, one of the main contributions of this study is the development of a multi-class classification algorithm using the SVM employing a linear kernel and 3-level wavelet transforms (SVM-LIN-MW3), reaching an average accuracy of 95%. In conclusion, the application of SVM-based algorithms combined with wavelet transforms to analyze CPET data shows promise in diagnosing various diseases, highlighting their adaptability and broader potential applications in healthcare.

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 (CB), peripheral chemoreceptor organs that respond to changes in blood biochemical modifications like 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 carotid sinus nerve influence on cardiovascular regulation, particularly under hypoxic conditions, and we tested the hypothesis that neuromodulation of the carotid sinus nerve, either by electrical stimuli or by 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 concluded 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 SNS activity in response to ischemic hypoxia was lost and there were no significant changes in blood pressure in comparison to control animals. We concluded 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.

Sudden cardiac death (SCD) represents a critical public health challenge, emphasizing the need for predictive techniques that model complex physiological dynamics. Studies indicate that the “V-trough” pattern in sympathetic nerve activity (SNA) could act as an early indicator of potentially fatal cardiac events, which can be effectively modeled using a modified version of Chua’s chaotic system, incorporating the variables of heart rate (HR), SNA, and blood pressure (BP). This paper introduces a Chua circuit with delay, and proposes a novel control design technique based on Lurie-type control systems theory combined with mixed-sensitivity H∞ (S/KS/T) methodology. The proposed controller enables precise regulation of HR in Chua’s circuit, both with and without delay, paving the way for the development of advanced devices capable of preventing SCD. Furthermore, the developed theory allows for the project of robust controllers for delayed Lurie systems within the single-input–single-output (SISO) framework. The presented theoretical framework, supported by numerical simulations, demonstrates the effectiveness of the conceptualization, marking a considerable advance in the understanding and early intervention of SCD through robust and nonlinear control systems.

Diabetes is a disease that affects millions of people in the world and its early screening prevents serious health problems, also providing relief in the demand for healthcare services. In the search for methods to support early diagnosis, this article introduces a novel prediabetes risk classification algorithm (PRCA) for type-2 diabetes mellitus (T2DM), utilizing the chemosensitivity of carotid bodies (CB) and K-means clustering technique from the field of machine learning. Heart rate (HR) and respiratory rate (RR) data from eight volunteers with prediabetes and 25 without prediabetes were analyzed. Data were collected in basal conditions and after stimulation of the CBs by inhalation of 100% of oxygen and after ingestion of a standardized meal. During the analysis, a greater variability of groups was observed in people with prediabetes compared to the control group, particularly after inhalation of oxygen. The algorithm developed from these results showed an accuracy of 86% in classifying for prediabetes. This approach, centered on CB chemosensitivity deregulation in early disease stages, offers a nuanced detection method beyond conventional techniques. Moreover, the adaptable algorithm and clustering methodology hold promise as risk classifications for other diseases. Future endeavors aim to validate the algorithm through longitudinal studies tracking disease development among volunteers and expand the study’s scope to include a larger participant pool.

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.

Convolutional neural networks have been effective in several applications, arising as a promising supporting tool in a relevant Dermatology problem: skin cancer diagnosis. However, generalizing well can be difficult when little training data is available. The fine-tuning transfer learning strategy has been employed to differentiate properly malignant from non-malignant lesions in dermoscopic images. Fine-tuning a pre-trained network allows one to classify data in the target domain, occasionally with few images, using knowledge acquired in another domain. This work proposes eight fine-tuning settings based on convolutional networks previously trained on ImageNet that can be employed mainly in limited data samples to reduce overfitting risk. They differ on the architecture, the learning rate and the number of unfrozen layer blocks. We evaluated the settings in two public datasets with 104 and 200 dermoscopic images. By finding competitive configurations in small datasets, this paper illustrates that deep learning can be effective if one has only a few dozen malignant and non-malignant lesion images to study and differentiate in Dermatology. The proposal is also flexible and potentially useful for other domains. In fact, it performed satisfactorily in an assessment conducted in a larger dataset with 746 computerized tomographic images associated with the coronavirus disease.

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.