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IntroductionIn the scientific research domain, the Open Science movement stands as a fundamental pillar for advancing knowledge and innovation globally. This article presents the design and implementation of the GITCE Open Data Ecosystem (GITCE-ODE) Research Data Management System (RDMS), developed by the Research Group on Emerging Computational Technologies (GITCE) at the Technological University of Panama, as a platform for the long-term storage, publication, and dissemination of research products.MethodsThe architecture of the GITCE-ODE RDMS encompasses the entire data engineering lifecycle, facilitating information processing stages such as extraction, transformation, loading (ETL), as well as the management and analysis of diverse datasets and metadata.ResultsCompliance with the FAIR principles ensures that published data and products are Findable, Accessible, Interoperable, and Reusable, promoting automation in the discovery and reuse of digital resources. Key considerations of the web portal include file format standardization, data categorization, treatment of semantic context, and organization of resources to ensure efficient management and administration of open research data.DiscussionThrough this platform, GITCE aims to foster collaboration, transparency, and accessibility in scientific research, contributing to the ongoing advancement of knowledge transfer and innovation.

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the intent and entity recognition crucial for dynamic conversational interactions. Two distinct approaches are introduced: a generic GPT-based system (G-GPT) leveraging the pre-trained model with complex prompts for intent and entity detection, and a fine-tuned GPT-based system (FT-GPT) employing customized models for enhanced specificity and efficiency. The evaluation encompassed the systems’ ability to accurately classify intents and recognize named entities, contrasting their adaptability, operational efficiency, and customization capabilities. The results revealed that, while the G-GPT system offers ease of deployment and versatility across various contexts, the FT-GPT system demonstrates superior precision, efficiency, and customization, although it requires initial training and dataset preparation. This research highlights the versatility of LLMs in enriching conversational features for talking assistants, from social robots to interactive chatbots. By tailoring these advanced models, the fluidity and responsiveness of conversational agents can be enhanced, making them more adaptable and effective in a variety of settings, from customer service to interactive learning environments.

Cognitive deficits are very difficult to diagnose during the initial stages; tests typically consist of a patient performing punctual dual-task activities, which are subjectively analyzed to determine the cognitive decline impact on gait. This work supports novel and objective diagnosis methods by stating a baseline on how neurotypical aging affects dual tasks while using a smartphone on the move. With this aim, we propose a twofold research question: Which mobile device tasks performed on the move (dual tasking) have characteristic changes in gait parameters, and which are especially characteristic at older ages? An experiment was conducted with 30 healthy participants where they performed 15 activities (1 single task, 2 traditional dual-tasks and 12 mobile-based dual-tasks) while walking about 50 m. Participants wore a wireless motion tracker (15 sensors) that made the concise analysis of gait possible. The results obtained characterized the gait parameters affected by mobile-based dual-tasking and the impact of normal cognitive decline due to aging. The statistical analysis shows that using smartphone-based dual-tasking produces more significant results than traditional dual-tasking. In the study, 3 out of 10 gait parameters were very significantly affected (p < 0.001) when using the traditional dual tasks, while 5 out of 10 parameters were very significantly affected (p < 0.001) in mobile-based dual-tasking. Moreover, the most characteristic tasks and gait parameters were identified through the obtained results. Future work will focus on applying this knowledge to improve the early diagnosis of MCI.

Background: Following second heart transplantation (HTx), some patients experience graft failure and require third-time heart transplantation. Little data exist to guide decision-making with regard to repeat retransplantation in older patients., Methods: We performed a retrospective cohort analysis of patients receiving a third HTx, as identified in the United Network for Organ Sharing (UNOS) database from 1985 to 2017., Results: The study cohort consisted of N = 60 patients, with an average age of 29 with a standard deviation of ±18 years. Overall survival for the cohort at 1, 5, and 10 years is 83%, 64%, and 44%, respectively. The rate of third-time HTxs has steadily increased in all age groups. Patients older than 50 years now account for 18.3% of all third-time HTxs. Although this group demonstrated longer average previous graft survival, after third HTx they demonstrate significantly poorer survival outcomes compared to third-time HTx recipients younger than 21 (P = 0.05). Age over 50, BMI over 30, and diabetes were all found to be independent risk factors for decreased survival following third HTx., Conclusions: We describe trends in patients undergoing third HTx. We highlight subsets of such recipients who exhibit decreased survival., (© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: Several recent publications suggest an increase in the incidence of Clostridium difficile colitis. However, such studies commonly lack denominators over which to index this rise. There is also concern in the literature that disease virulence is increasing., Methods: Billing, admission, operative, and infection databases at a single tertiary care center identified patients admitted from 1990 to 2006 with a diagnosis of C. difficile infection. Grouped by era, case numbers were indexed against overall hospital, operative, and laboratory volumes. C. difficile colectomy cases were individually examined and analyzed., Results: The number of hospitalized patients diagnosed with C. difficile colitis increased in a linear fashion during the study period (1990, 14 cases; 2006, 927 cases). The colectomy per C. difficile case ratio did not change over the study period (era 1, 0.17%; era 2, 0.20%; era 3, 0.16%). Thirteen patients underwent colectomy with 54% surviving. The increase in patients admitted with a diagnosis of C. difficile was significantly associated with hospital volume (p = 0.04), operative volume (p < 0.001), and lab testing volume (p = 0.008)., Conclusion: The number of C. difficile patients admitted to our hospital is rising at an alarming rate. This reflects national trends and urgent action seems warranted to prevent a C. difficile epidemic.