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Background: Fibonacci patterns and tubular forms both arose early in the phylogeny of multicellular organisms. Tubular forms offer the advantage of a regulated internal milieu, and Fibonacci forms may offer packing efficiencies. The underlying mechanisms behind the cellular genesis of Fibonacci and tubular forms remain unknown. Methods: In a multicellular organism, cells adhere to form a macrostructure and to coordinate further replication. We propose and prove simple theorems connecting cell replication and adhesion to Fibonacci forms and simplicial topology. Results: We identify some cellular and molecular properties whereby the contact inhibition of replication by adhered cells may approximate Fibonacci growth patterns. We further identify how a component 2 → 3 cellular multiplication step may generate a multicellular structure with some properties of a two-simplex. Tracking the homotopy of a two-simplex to a circle and to a tube, we identify some molecular and cellular growth properties consistent with the morphogenesis of tubes. We further find that circular and tubular cellular aggregates may be combinatorially favored in multicellular adhesion over flat shapes. Conclusions: We propose a correspondence between the cellular and molecular mechanisms that generate Fibonacci cell counts and those that enable tubular forms. This implies molecular and cellular arrangements that are candidates for experimental testing and may provide guidance for the synthetic biology of hollow morphologies. [ABSTRACT FROM AUTHOR]

Malaria is an infection caused by the Plasmodium parasite that has a major epidemiological, social, and economic impact worldwide. Conventional diagnosis of the disease is based on microscopic examination of thick blood smears. This analysis can be time-consuming, which is key to generate prevention strategies and adequate treatment to avoid the complications associated with the disease. To address this problem, we propose a deep learning-based approach to detect not only malaria parasites but also leukocytes to perform parasite/ μ L blood count. We used positive and negative images with parasites and leukocytes. We performed data augmentation to increase the size of the dataset. The YOLOv8 algorithm was used for model training and using the counting formula the parasites were counted. The results showed the ability of the model to detect parasites and leukocytes with 95% and 98% accuracy, respectively. The time spent by the model to report parasitemia is significantly less than the time spent by malaria experts. This type of system would be supportive for areas with poor access to health care. We recommend validation of such approaches on a large scale in health institutions. [ABSTRACT FROM AUTHOR]

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue. [ABSTRACT FROM AUTHOR]

The development of computational methodologies to support clinical decision-making is of vital importance to reduce morbidity and mortality rates. Specifically, prescriptive analytic is a promising area to support decision-making in the monitoring, treatment and prevention of diseases. These aspects remain a challenge for medical professionals and health authorities. In this study, we propose a methodology for the development of prescriptive models to support decision-making in clinical settings. The prescriptive model requires a predictive model to build the prescriptions. The predictive model is developed using fuzzy cognitive maps and the particle swarm optimization algorithm, while the prescriptive model is developed with an extension of fuzzy cognitive maps that combines them with genetic algorithms. We evaluated the proposed approach in three case studies related to monitoring (warfarin dose estimation), treatment (severe dengue) and prevention (geohelminthiasis) of diseases. The performance of the developed prescriptive models demonstrated the ability to estimate warfarin doses in coagulated patients, prescribe treatment for severe dengue and generate actions aimed at the prevention of geohelminthiasis. Additionally, the predictive models can predict coagulation indices, severe dengue mortality and soil-transmitted helminth infections. The developed models performed well to prescribe actions aimed to monitor, treat and prevent diseases. This type of strategy allows supporting decision-making in clinical settings. However, validations in health institutions are required for their implementation. • A clinical decision-support system based on prescriptive models. • Methodology for the development of clinical prescriptive models. • Fuzzy cognitive maps and particle swarm optimization algorithm for clinical prescriptive models. • Prescriptive clinical models for the treatment of severe dengue and coagulated patients. • Prescriptive clinical models for geohelminthiasis prevention. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodology based on fuzzy cognitive maps (FCMs) and metaheuristic algorithms to generate prescriptive models, called PRescriptiVe FCM (PRV-FCM). FCMs are a set of concepts interrelated that describe the behavior of a system. This kind of modeling has been extensively used to build descriptive and predictive models. We propose an extension of FCMs to develop prescriptive models and support decision-making in different domains. This adaptation characterizes FCMs, using system and prescriptive concepts. After that, it uses a metaheuristic algorithm (in this case, we use a genetic algorithm) to optimize prescriptive concepts based on system concepts and the stability of the FCM. Our proposed prescriptive approach was implemented and tested in four scenarios where it demonstrated its capability to find solutions that lead to desired values for the variables of interest. Specifically, no significant differences were found between the values of the prescriptive variables in the datasets and those generated by PRV-FCM. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning approach developed to guarantee the privacy and security of data stored on local devices. In healthcare, specifically in diseases of public health interest such as dengue, it is necessary to develop strategies that guarantee such data properties. Therefore, the aim of this work was to develop three federated learning approaches for fuzzy cognitive maps for the prediction of mortality and the prescription of treatment of severe dengue. The validation of the approaches was performed on severe dengue datasets from two dengue endemic regions in Colombia. According to the results, the use of federated learning significantly improves the performance of models developed in centralized environments. Additionally, the use of federated learning allows guaranteeing the privacy and security of each client's data due to the local training of the models. Federated learning is a useful tool in healthcare because it guarantees the privacy and security of patient data. Our results demonstrated the ability of aggregated models to predict mortality and prescribe treatment for severe dengue. [ABSTRACT FROM AUTHOR]

Background: Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.Methods: Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified.Results: Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%.Conclusions: We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security. [ABSTRACT FROM AUTHOR]