La fragilidad en el anciano con enfermedad renal crónica

ResumenEn los últimos años el concepto de fragilidad como «estado de prediscapacidad» se ha extendido de forma amplia en todos los que trabajamos en beneficio de la persona mayor. Su importancia radica no solo en su elevada prevalencia —superior al 25% en mayores de 85 años—, sino a que es considerada un factor de riesgo independiente, que confiere a los ancianos que lo presentan un riesgo elevado de discapacidad, institucionalización y mortalidad.El estudio de la función renal es relevante en pacientes que soportan gran carga de comorbilidad, habiéndose encontrado una importante asociación entre la enfermedad renal crónica y el desarrollo de eventos clínicos adversos como la enfermedad cardiovascular, la insuficiencia cardiaca, la enfermedad renal terminal, el incremento de la susceptibilidad a infecciones y el mayor deterioro funcional.La fragilidad puede ser una situación reversible, por lo que su estudio en el paciente con enfermedad renal crónica es de especial interés. Este artículo tiene por objeto describir las interrelaciones existentes entre envejecimiento, fragilidad y enfermedad renal crónica a la luz de la bibliografía pertinente más relevante y reciente publicada.AbstractIn recent years, the concept of frailty as a “state of pre-disability” has been widely accepted by those involved in the care of the elderly. Its importance lies not only in its high prevalence - more than 25% in people over 85 years of age - but it is also considered an independent risk factor of disability, institutionalisation and mortality amongst the elderly.The study of renal function is relevant in patients with major comorbidities. Studies have shown a significant association between chronic kidney disease and the development of adverse clinical outcomes such as heart disease, heart failure, end-stage renal disease, increased susceptibility to infections and greater functional impairment.Frailty can be reversed, which is why a study of frailty in patients with chronic kidney disease is of particular interest. This article aims to describe the association between ageing, frailty and chronic kidney disease in light of the most recent and relevant scientific publications

LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers

In today's computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and the Edge computing paradigm, benchmarking resource-constrained devices is a critical task to evaluate their suitability and performance. The literature has extensively explored the performance of IoT devices when running high-level benchmarks specialized in particular application scenarios, such as AI or medical applications. However, lower-level benchmarking applications and datasets that analyze the hardware components of each device are needed. This low-level device understanding enables new AI solutions for network, system and service management based on device performance, such as individual device identification, so it is an area worth exploring more in detail. In this paper, we present LwHBench, a low-level hardware benchmarking application for Single-Board Computers that measures the performance of CPU, GPU, Memory and Storage taking into account the component constraints in these types of devices. LwHBench has been implemented for Raspberry Pi devices and run for 100 days on a set of 45 devices to generate an extensive dataset that allows the usage of AI techniques in different application scenarios. Finally, to demonstrate the inter-scenario capability of the created dataset, a series of AI-enabled use cases about device identification and context impact on performance are presented as examples and exploration of the published data

Can Evil IoT Twins Be Identified? Now Yes, a Hardware Behavioral Fingerprinting Methodology

The connectivity and resource-constrained nature of IoT, and in particular single-board devices, opens up to cybersecurity concerns affecting the Industrial Internet of Things (IIoT). One of the most important is the presence of evil IoT twins. Evil IoT twins are malicious devices, with identical hardware and software configurations to authorized ones, that can provoke sensitive information leakages, data poisoning, or privilege escalation in industrial scenarios. Combining behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques is a promising solution to identify evil IoT twins by detecting minor performance differences generated by imperfections in manufacturing. However, existing solutions are not suitable for single-board devices because they do not consider their hardware and software limitations, underestimate critical aspects during the identification performance evaluation, and do not explore the potential of ML/DL techniques. Moreover, there is a dramatic lack of work explaining essential aspects to considering during the identification of identical devices. This work proposes an ML/DL-oriented methodology that uses behavioral fingerprinting to identify identical single-board devices. The methodology leverages the different built-in components of the system, comparing their internal behavior with each other to detect variations that occurred in manufacturing processes. The validation has been performed in a real environment composed of identical Raspberry Pi 4 Model B devices, achieving the identification for all devices by setting a 50% threshold in the evaluation process. Finally, a discussion compares the proposed solution with related work and provides important lessons learned and limitations

From over-stoichiometric to sub-stoichiometric enantioselective protonation with 2-sulfinyl alcohols: a view in perspective

A general study of the enantioselective protonation of prochiral enolates with 2-sulfinyl alcohols is reported. The modification of reaction conditions to reduce drastically the amount of chiral proton source needed to obtain a good enantiomeric excess is reported. The effects of the different factors controlling the stereoselectivity are clearly established. Different protocols for enolate generation are compared.Medio Simon, Mercedes, [email protected] ; Aleman Lopez, Pedro Antonio, [email protected] ; Gil Tomas, Jesus Javier, [email protected] ; Asensio, Aguilar Gregorio, [email protected]

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model, highlighting the potential risks of DFL communications. In response to these identified risks, this work introduces a security module designed for DFL platforms to counter communication-based attacks. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform called Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario has been deployed, involving eight physical devices implementing three security configurations: (i) a baseline with no security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks. The results indicated an average F1 score of 95%, with moderate increases in CPU usage (up to 63.2% +-3.5%) and network traffic (230 MB +-15 MB) under the most secure configuration, mitigating the risks posed by eavesdropping or eclipse attacks

Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges

In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges

Early dysfunction of functional connectivity in healthy elderly with subjective memory complaints

It is still an open question whether subjective memory complaints (SMC) can actually be considered to be clinically relevant predictors for the development of an objective memory impairment and even dementia. There is growing evidence that suggests that SMC are associated with an increased risk of dementia and with the presence of biological correlates of early Alzheimer's disease. In this paper, in order to shed some light on this issue, we try to discern whether subjects with SMC showed a different profile of functional connectivity compared with subjects with mild cognitive impairment (MCI) and healthy elderly subjects. In the present study, we compare the degree of synchronization of brain signals recorded with magnetoencephalography between three groups of subjects (56 in total): 19 with MCI, 12 with SMC and 25 healthy controls during a memory task. Synchronization likelihood, an index based on the theory of nonlinear dynamical systems, was used to measure functional connectivity. Briefly, results show that subjects with SMC have a very similar pattern of connectivity to control group, but on average, they present a lower synchronization value. These results could indicate that SMC are representing an initial stage with a hypo-synchronization (in comparison with the control group) where the brain system is still not compensating for the failing memory networks, but behaving as controls when compared with the MCI subjects