Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient’s medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient’s health.

The use of mHealth apps for the self-management of cardiovascular diseases (CVDs) is an increasing trend in patient-centered care. In this research, we conduct a scoping review of mHealth apps for CVD self-management within the period 2014 to 2021. Our review revolves around six main aspects of the current status of mHealth apps for CVD self-management: main CVDs managed, main app functionalities, disease stages managed, common approaches used for data extraction, analysis, management, common wearables used for CVD detection, monitoring and/or identification, and major challenges to overcome and future work remarks. Our review is based on Arksey and O’Malley’s methodological framework for conducting studies. Similarly, we adopted the PRISMA model for reporting systematic reviews and meta-analyses. Of the 442 works initially retrieved, the review comprised 38 primary studies. According to our results, the most common CVDs include arrhythmia (34%), heart failure (32%), and coronary heart disease (18%). Additionally, we found that the majority mHealth apps for CVD self-management can provide medical recommendations, medical appointments, reminders, and notifications for CVD monitoring. Main challenges in the use of mHealth apps for CVD self-management include overcoming patient reluctance to use the technology and achieving the interoperability of mHealth applications with other systems.

Cardiovascular Diseases (CVDs) are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. As an effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc—using the train-test split technique and k-fold cross-validation. Our study identifies the top-two and top-four attributes from CVD datasets analyzing the performance of the accuracy metrics to determine that they are the best for predicting and diagnosing CVD. As our main findings, the ten ML classifiers exhibited appropriate diagnosis in classification and predictive performance with accuracy metric with top-two attributes, identifying three main attributes for diagnosis and prediction of a CVD such as arrhythmia and tachycardia; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking.

Liver diseases are a widespread and severe health concern, affecting millions worldwide. Non-alcoholic fatty liver disease (NAFLD) alone affects one-third of the global population, with some Latin American countries seeing rates exceeding 50%. This alarming trend has prompted researchers to explore new methods for identifying those at risk. One promising approach is using Machine Learning Algorithms (MLAs), which can help predict critical factors contributing to liver disease development. Our study examined nine different MLAs across four datasets to determine their effectiveness in predicting this condition. We analyzed each algorithm’s performance using five important metrics: accuracy, precision, recall, f1-score, and roc_auc. Our results showed that these algorithms were highly effective when used individually and as part of an ensemble modeling technique such as bagging or boosting. We identified alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and albumin as the top four attributes most strongly associated with non-alcoholic fatty liver disease risk across all datasets. Gamma-glutamyl transpeptidase (GGT), hemoglobin, age, and prothrombin time also played significant roles. In conclusion, this research provides valuable insights into how we can better detect and prevent non-alcoholic fatty liver diseases by leveraging advanced machine learning techniques. As such, it represents an exciting opportunity for healthcare professionals seeking more accurate diagnostic tools while improving patient outcomes globally.

Solution blow spinning was used to prepare nonwoven bicomponent fibers constituted by poly (ethylene oxide)-Polysulfone (PEO-PSF). As a new material, deep characterization was carried out to have a database to understand final performance regarding its multiple functions as a potential material for biomedical applications. The morphology was studied by field emission scanning electron and transmission electron microscopy and optical profilometry. Structural characterization was carried out by Fourier transform infrared spectroscopy and thermal degradation by thermogravimetric analysis. Additionally, wettability and mechanical behavior were studied by contact angle measurements and tensile tests, respectively. The bicomponent material was constituted of fibers with a structure mainly described by a core-shell structure, where the PSF phase is located at the center of the fibers, and the PEO phase is mainly located at the outer parts of the fibers, leading to a kind of shell wall. The study of possible interactions between different phases revealed them to be lacking, pointing to the presence of an interface core/shell more than an interphase. The morphology and roughness of the bicomponent material improved its wettability when glycerol was tested. Indeed, its mechanical properties were enhanced due to the PSF core provided as reinforcement material.

Climate change, driven by natural factors and human activity, produces significant environmental changes worldwide. One consequence is increased rainfall, which leads to intense and increasingly frequent storms, sudden increases in river flows, and increased likelihood of emergencies linked to natural disasters. This framework proposes a model based on the System Dynamics (SD) approach that aims to monitor the increase in flow in rapid-response catchments (RRCs). The model evaluates humanitarian logistics strategies to manage supplies during emergency situations and it is based on dynamic simulation, whose advantages are the analysis of causal relationships between variables and their behavior over time, mathematical support during the creation of the simulation model, and the creation of a graphical interface that allows the user to carry out a visual analysis of the variables involved in the model. The results show, through a case study, the implementation of a containment plan based on early decision-making from rapid-response catchment monitoring to generate humanitarian logistics strategies preventing material and human damage. Therefore, the main contribution of this framework is the creation of a simulation model that involves the synergy between two different systems: the analysis of RRC behavior and the humanitarian logistics plan to establish provision policies (food, water and medicine) based on the number of people at risk.