Jose Manuel

We describe the development of a bioink to bioprint human induced pluripotent stem cells (hiPSCs) for possible cardiac tissue engineering using a gelatin methacrylate (GelMA)-based hydrogel. While previous studies have shown that GelMA at a low concentration (5% w/v) allows for the growth of diverse cells, its 3D printability has been found to be limited by its low viscosity. To overcome that drawback, making the hydrogel both compatible with hiPSCs and 3D-printable, we developed an extrudable GelMA-based bioink by adding xanthan gum (XG). The GelMA-XG composite hydrogel had an elastic modulus (~9 kPa) comparable to that of cardiac tissue, and enabled 3D printing with high values of printing accuracy (83%) and printability (0.98). Tests with hiPSCs showed the hydrogel's ability to promote their proliferation within both 2D and 3D cell cultures. The tests also showed that hiPSCs inside hemispheres of the hydrogel were able to differentiate into cardiomyocytes, capable of spontaneous contractions (average frequency of ~0.5 Hz and amplitude of ~2%). Furthermore, bioprinting tests proved the possibility of fabricating 3D constructs of the hiPSC-laden hydrogel, with well-defined line widths (~800 μm).

Cybersecurity threats, particularly those involving lateral movement within networks, pose significant risks to critical infrastructures such as Microsoft Active Directory. This study addresses the need for effective defense mechanisms that minimize network disruption while preventing attackers from reaching key assets. Modeling Active Directory networks as a graph in which the nodes represent the network components and the edges represent the logical interactions between them, we use centrality metrics to derive the impact of hardening nodes in terms of constraining the progression of attacks. We propose using Unsupervised Learning techniques, specifically density-based clustering algorithms, to identify those nodes given the information provided by their metrics. Our approach includes simulating attack paths using a snowball model, enabling us to analytically evaluate the impact of hardening on delaying Domain Administration compromise. We tested our methodology on both real and synthetic Active Directory graphs, demonstrating that it can significantly slow down the propagation of threats from reaching the Domain Administration across the studied scenarios. Additionally, we explore the potential of these techniques to enable flexible selection of the number of nodes to secure. Our findings suggest that the proposed methods significantly enhance the resilience of Active Directory environments against targeted cyber-attacks.

Abstract Multiple myeloma (MM) is linked to chronic NF-κB activity in myeloma cells, but this activity is generally considered a cell-autonomous property of the cancer cells.

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

Graphene quantum dots (GQDs) have garnered significant attention across numerous fields due to their ultrasmall size and exceptional properties. However, their extensive applications may lead to environmental exposure and subsequent uptake by humans. Yet, conflicting reports exist regarding the potential toxicity of GQDs based on experimental investigations. Therefore, a comprehensive understanding of GQD biosafety requires further microscopic and molecular-level investigations. In this study, we employed molecular dynamics (MD) simulations to explore the interactions between GQDs and graphene oxide quantum dots (GOQDs) with a protein model, the human intestinal fatty acid binding protein (HIFABP), that plays a crucial role in mediating the carrier of fatty acids in the intestine. Our MD simulation results reveal that GQDs can be adsorbed on the opening of HIFABP, which serves as an entrance for the fatty acid molecules into the protein’s interior cavity. This adsorption has the potential to obstruct the opening of HIFABP, leading to the loss of its normal biological function and ultimately resulting in toxicity. The adsorption of GQDs is driven by a combination of van der Waals (vdW), π-π stacking, cation-π, and hydrophobic interactions. Similarly, GOQDs also exhibit the ability to block the opening of HIFABP, thereby potentially causing toxicity. The blockage of GOQDs to HIFABP is guided by a combination of vdW, Coulomb, π-π stacking, and hydrophobic interactions. These findings not only highlight the potential harmful effects of GQDs on HIFABP but also elucidate the underlying molecular mechanism, which provides crucial insights into GQD toxicology.

The long-term effects of SARS-CoV-2 infection, and their determinants, are still unknown. This study aimed to assess symptoms one year after admission for COVID-19, according to the organ/system involved, and to identify factors. Cross-sectional study with retrospective data collection from March 2020 to February 2021. Inclusion criteria: aged ≥ 18 years and admitted for COVID-19. Exclusion criteria: death, not localized, refusal to participate, cognitive impairment or language barrier. A telephone survey was conducted on long COVID-related symptoms one year after hospital discharge. n = 486. The most frequent symptom groups were neurological (n = 225; 46.3%) and respiratory (n = 201; 41.4%). Multivariable analysis showed that a history of anxiety was significantly associated with psychiatric symptoms (ORa = 2.04, 95%CI = 1.02–4.06), fibromyalgia/chronic fatigue with general symptoms (ORa = 11.59, 95%CI = 1.47–9.34) and obesity with respiratory (ORa 1.90, 95%CI = 1.27–2.83) and musculoskeletal symptoms (ORa 1.96, 95%CI = 1.30–2.96). Male sex was associated with a significantly lower risk of neurological (ORa 0.64, 95%CI = 0.44–0.93), respiratory (ORa 0.45, 95%CI = 0.31–0.67), general (ORa 0.43, 95%CI = 0.29–0.63), psychiatric (ORa 0.34, 95%CI = 0.22–0.51), musculoskeletal (ORa 0.47, 95%CI = 0.32–0.70), dermatological (ORa 0.24, 95%CI = 0.14–0.42) and digestive (ORa 0.38, 95%CI = 0.20–0.73) symptoms. Advanced age (≥ 71 years) also had a protective effect against general (ORa 0.60, 95%CI = 0.39–0.95), psychiatric (ORa 0.39, 95%CI = 0.23–0.64), and dermatological (ORa 0.47, 95%CI = 0.24–0.92) symptoms. Patients admitted for SARS-CoV-2 infection frequently experience symptoms at one year, especially neurological and respiratory symptoms. Female sex, obesity, a history of anxiety and fibromyalgia/chronic fatigue were independent risk factors for presenting symptoms. Advanced age acted as a protective factor.

Mobile applications, websites and social media networks are now widely used communication tools. With the emergence of communication-related technologies in our lives and, consequently, the rise of social media networks and mobile applications, nutrition-related applications have become popular. Smartphones and other artificial intelligence technologies have become very useful tools for delivering nutrition-related interventions because they are very accessible and cost-effective. Digital interventions are also able to serve a larger number of communities than traditional interventions. Nutrition is not a field that has remained on the sidelines of these technological advances, and numerous mobile applications and technological tools have emerged that are intended to provide dietary advice or guidelines on the process of recovering from a disease. However, many of these applications have limitations and barriers that are important to consider. The aim of this review was to analyze the most current and widely used mobile applications related to nutrition, as well as their complementary tools (activity bracelets and smart scales, among others), highlighting their importance in improving lifestyle habits. In addition, their advantages and disadvantages are discussed and future directions are proposed.

This study addresses an identified literature gap regarding indoor environmental quality in residential buildings, where the primary focus has traditionally been on energy performance rather than comfort optimization. Leveraging the low-cost and easy-to-implement LoRaWAN protocol, this research collects and analyses real-time data on comfort parameters, including temperature, CO2 levels, humidity, lighting, atmospheric pressure, and total volatile organic compounds (TVOC) across various buildings within the INCUBE EU project. The results highlight the dynamic nature of the parameters and emphasize the importance of continuous monitoring to enhance comfort and energy efficiency in smart residential buildings. The findings advocate for integrating technologies like LoRaWAN to optimize indoor environmental quality, ultimately improving residential comfort and occupant well-being.

Research Group CTS1068, Andalusia Research Plan, Junta de Andalucía, 18014 Granada, Spain

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