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BACKGROUND: The increase in cases of mild cognitive impairment (MCI) underlines the urgency of finding effective methods to slow its progression. Given the limited effectiveness of current pharmacological options to prevent or treat the early stages of this deterioration, non-pharmacological alternatives are especially relevant. OBJECTIVE: To assess the effectiveness of a cognitive-motor intervention based on immersive virtual reality (VR) that simulates an activity of daily living (ADL) on cognitive functions and its impact on depression and the ability to perform such activities in patients with MCI. METHODS: Thirty-four older adults (men, women) with MCI were randomized to the experimental group (n = 17; 75.41 ± 5.76) or control (n = 17; 77.35 ± 6.75) group. Both groups received motor training, through aerobic, balance and resistance activities in group. Subsequently, the experimental group received cognitive training based on VR, while the control group received traditional cognitive training. Cognitive functions, depression, and the ability to perform activities of daily living (ADLs) were assessed using the Spanish versions of the Montreal Cognitive Assessment (MoCA-S), the Short Geriatric Depression Scale (SGDS-S), and the of Instrumental Activities of Daily Living (IADL-S) before and after 6-week intervention (a total of twelve 40-minutes sessions). RESULTS: Between groups comparison did not reveal significant differences in either cognitive function or geriatric depression. The intragroup effect of cognitive function and geriatric depression was significant in both groups (p < 0.001), with large effect sizes. There was no statistically significant improvement in any of the groups when evaluating their performance in ADLs (control, p = 0.28; experimental, p = 0.46) as expected. The completion rate in the experimental group was higher (82.35%) compared to the control group (70.59%). Likewise, participants in the experimental group reached a higher level of difficulty in the application and needed less time to complete the task at each level. CONCLUSIONS: The application of a dual intervention, through motor training prior to a cognitive task based on Immersive VR was shown to be a beneficial non-pharmacological strategy to improve cognitive functions and reduce depression in patients with MCI. Similarly, the control group benefited from such dual intervention with statistically significant improvements. TRIAL REGISTRATION: ClinicalTrials.gov NCT06313931; https://clinicaltrials.gov/study/NCT06313931 .

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Glioblastoma (GBM) is an aggressive form of cancer affecting the Central Nervous System (CNS) of thousands of people every year. Redox alterations have been shown to play a key role in the development and progression of these tumors as Reactive Oxygen Species (ROS) formation is involved in the modulation of several signaling pathways, transcription factors, and cytokine formation. The second-generation oral alkylating agent temozolomide (TMZ) is the first-line chemotherapeutic drug used to treat of GBM, though patients often develop primary and secondary resistance, reducing its efficacy. Antioxidants represent promising and potential coadjutant agents as they can reduce excessive ROS formation derived from chemo- and radiotherapy, while decreasing pharmacological resistance. S-allyl-cysteine (SAC) has been shown to inhibit the proliferation of several types of cancer cells, though its precise antiproliferative mechanisms remain poorly investigated. To date, SAC effects have been poorly explored in GBM cells. Here, we investigated the effects of SAC in vitro, either alone or in combination with TMZ, on several toxic and modulatory endpoints-including oxidative stress markers and transcriptional regulation-in two glioblastoma cell lines from rats, RG2 and C6, to elucidate some of the biochemical and cellular mechanisms underlying its antiproliferative properties. SAC (1-750 µM) decreased cell viability in both cell lines in a concentration-dependent manner, although C6 cells were more resistant to SAC at several of the tested concentrations. TMZ also produced a concentration-dependent effect, decreasing cell viability of both cell lines. In combination, SAC (1 µM or 100 µM) and TMZ (500 µM) enhanced the effects of each other. SAC also augmented the lipoperoxidative effect of TMZ and reduced cell antioxidant resistance in both cell lines by decreasing the TMZ-induced increase in the GSH/GSSG ratio. In RG2 and C6 cells, SAC per se had no effect on Nrf2/ARE binding activity, while in RG2 cells TMZ and the combination of SAC + TMZ decreased this activity. Our results demonstrate that SAC, alone or in combination with TMZ, exerts antitumor effects mediated by regulatory mechanisms of redox activity responses. SAC is also a safe drug for testing in other models as it produces non-toxic effects in primary astrocytes. Combined, these effects suggest that SAC affords antioxidant properties and potential antitumor efficacy against GBM.

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The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.

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This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

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5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection processes. This paper presents the 5G and IoT technologies, explaining common architectures, typical IoT implementations, and recurring problems. This work also presents a detailed and explained overview of interference in general wireless applications, interference unique to 5G and IoT, and possible optimization techniques to overcome these challenges. This manuscript highlights the importance of addressing interference and optimizing network performance in 5G networks to ensure reliable and efficient connectivity for IoT devices, which is essential for adequately functioning business processes. This insight can be helpful for businesses that rely on these technologies to improve their productivity, reduce downtime, and enhance customer satisfaction. We also highlight the potential of the convergence of networks and services in increasing the availability and speed of access to the internet, enabling a range of new and innovative applications and services.

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This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user's point of view.

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The potential treatment of neurodegenerative disorders requires the development of novel pharmacological strategies at the experimental level, such as the endocannabinoid-based therapies. The effects of oleamide (OEA), a fatty acid primary amide with activity on cannabinoid receptors, was tested against mitochondrial toxicity induced by the electron transport chain complex II inhibitor, 3-nitropropionic acid (3-NP), in rat cortical slices. OEA prevented the 3-NP-induced loss of mitochondrial function/cell viability at a concentration range of 5 nM-25 µM, and this protective effect was observed only when the amide was administered as pretreatment, but not as post-treatment. The preservation of mitochondrial function/cell viability induced by OEA in the toxic model induced by 3-NP was lost when the slices were pre-incubated with the cannabinoid receptor 1 (CB1R) selective inhibitor, AM281, or the cannabinoid receptor 2 (CB2R) selective inhibitor, JTE-907. The 3-NP-induced inhibition of succinate dehydrogenase (mitochondrial Complex II) activity was recovered by 25 nM OEA. The amide also prevented the increased lipid peroxidation and the changes in reduced/oxidized glutathione (GSH/GSSG) ratio induced by 3-NP. The cell damage induced by 3-NP, assessed as incorporation of cellular propidium iodide, was mitigated by OEA. Our novel findings suggest that the neuroprotective properties displayed by OEA during the early stages of damage to cortical cells involve the converging activation of CB1R and CB2R and the increase in antioxidant activity, which combined may emerge from the preservation of the functional integrity of mitochondria.

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Over seven million people suffer from an impairment in Mexico; 64.1% are gait-related, and 36.2% are children aged 0 to 14 years. Furthermore, many suffer from neurological disorders, which limits their verbal skills to provide accurate feedback. Robot-assisted gait therapy has shown significant benefits, but the users must make an active effort to accomplish muscular memory, which usually is only around 30% of the time. Moreover, during therapy, the patients' affective state is mostly unsatisfied, wide-awake, and powerless. This paper proposes a method for increasing the efficiency by combining affective data from an Emotiv Insight, an Oculus Go headset displaying an immersive interaction, and a feedback system. Our preliminary study had eight patients during therapy and eight students analyzing the footage using the self-assessment Manikin. It showed that it is possible to use an EEG headset and identify the affective state with a weighted average precision of 97.5%, recall of 87.9%, and F1-score of 92.3% in general. Furthermore, using a VR device could boost efficiency by 16% more. In conclusion, this method allows providing feedback to the therapist in real-time even if the patient is non-verbal and has a limited amount of facial and body expressions.

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Sensor networks are deployed in people's homes to make life easier and more comfortable and secure. They might represent an interesting approach for elderly care as well. This work highlights the benefits of a sensor network implemented in the homes of a group of users between 55 and 75 years old, which encompasses a simple home energy optimization algorithm based on user behavior. We analyze variables related to vital signs to establish users' comfort and tranquility thresholds. We statistically study the perception of security that users exhibit, differentiating between men and women, examining how it affects the person's development at home, as well as the reactivity of the sensor algorithm, to optimize its performance. The proposed algorithm is analyzed under certain performance metrics, showing an improvement of 15% over a sensor network under the same conditions. We look at and quantify the usefulness of accurate alerts on each sensor and how it reflects in the users' perceptions (for men and women separately). This study analyzes a simple, low-cost, and easy-to-implement home-based sensor network optimized with an adaptive energy optimization algorithm to improve the lives of older adults, which is capable of sending alerts of possible accidents or intruders with the highest efficiency.

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In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen's position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation-based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.

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The available research does not allow specific relationships to be established between the applied enzymatic-mechanical treatment, the degree of polymerization, and the characteristics of the cellulose nanofibrils (CNFs) produced. This work aims to establish specific relationships between the intensity of enzymatic treatment, the degree of polymerization of the cellulose, the morphology of CNFs, and the tensile strength of the CNF films. It is determined that the decrease in the degree of polymerization plays an essential role in the fibrillation processes of the cell wall to produce CNFs and that there is a linear relationship between the degree of polymerization and the length of CNFs, which is independent of the type of enzyme, enzyme charge, and intensity of the applied mechanical treatment. In addition, it is determined that the percentage of the decrease in the degree of polymerization of CNFs due to mechanical treatment is irrespective of the applied enzyme charge. Finally, it is shown that the aspect ratio is a good indicator of the efficiency of the fibrillation process, and is directly related to the mechanical properties of CNF films.

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Inter-carrier interference (ICI) in vehicle to vehicle (V2V) orthogonal frequency division multiplexing (OFDM) systems is a common problem that makes the process of detecting data a demanding task. Mitigation of the ICI in V2V systems has been addressed with linear and non-linear iterative receivers in the past; however, the former requires a high number of iterations to achieve good performance, while the latter does not exploit the channel's frequency diversity. In this paper, a transmission and reception scheme for low complexity data detection in doubly selective highly time varying channels is proposed. The technique couples the discrete Fourier transform spreading with non-linear detection in order to collect the available channel frequency diversity and successfully achieving performance close to the optimal maximum likelihood (ML) detector. When compared with the iterative LMMSE detection, the proposed system achieves a higher performance in terms of bit error rate (BER), reducing the computational cost by a third-part when using 48 subcarriers, while in an OFDM system with 512 subcarriers, the computational cost is reduced by two orders of magnitude.

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This paper reports on the progress of a wearable assistive technology (AT) device designed to enhance the independent, safe, and efficient mobility of blind and visually impaired pedestrians in outdoor environments. Such device exploits the smartphone's positioning and computing capabilities to locate and guide users along urban settings. The necessary navigation instructions to reach a destination are encoded as vibrating patterns which are conveyed to the user via a foot-placed tactile interface. To determine the performance of the proposed AT device, two user experiments were conducted. The first one requested a group of 20 voluntary normally sighted subjects to recognize the feedback provided by the tactile-foot interface. The results showed recognition rates over 93%. The second experiment involved two blind voluntary subjects which were assisted to find target destinations along public urban pathways. Results show that the subjects successfully accomplished the task and suggest that blind and visually impaired pedestrians might find the AT device and its concept approach useful, friendly, fast to master, and easy to use.

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Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network's performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols' performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

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The emergence of Industry 4.0 technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), has prompted a reconsideration of methodologies for network security as well as reducing operation and maintenance costs, especially at the physical layer, where the energy consumption plays an important role. This article demonstrates through simulations and experiments that, while the cooperative scheme is more efficient when a WSN is at normal operating conditions, the collaborative scheme offers more enhanced protection against the aggressiveness of jamming in the performance metrics, thus making it safer, reducing operation and maintenance costs and laying the foundations for jamming mitigation. This document additionally offers an algorithm to detect jamming in real time. Firstly, it examines the characteristics and damages caused by the type of aggressor. Secondly, it reflects on the natural immunity of the WSN (which depends on its node density and a cooperative or collaborative configuration). Finally, it considers the performance metrics, especially those that impact energy consumption during transmission.

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Neuroprotective approaches comprising different mechanisms to counteract the noxious effects of excitotoxicity and oxidative stress need validation and detailed characterization. Although S-allylcysteine (SAC) is a natural compound exhibiting a broad spectrum of protective effects characterized by antioxidant, anti-inflammatory, and neuromodulatory actions, the mechanisms underlying its protective role on neuronal cell damage triggered by early excitotoxic insults remain elusive. In this study, we evaluated if the preconditioning or the post-treatment of isolated rat cortical slices with SAC (100 µM) can ameliorate the toxic effects induced by the excitotoxic metabolite quinolinic acid (QUIN, 100 µM), and whether this protective response involves the early display of specific antioxidant and neuroprotective signals. For this purpose, cell viability/mitochondrial reductive capacity, lipid peroxidation, levels of reduced and oxidized glutathione (GSH and GSSG, respectively), the rate of cell damage, the NF-E2-related factor 2/antioxidant response element (Nrf2/ARE) binding activity, heme oxygenase 1 (HO-1) regulation, extracellular signal-regulated kinase (ERK1/2) phosphorylation, and the levels of tumor necrosis factor-alpha (TNF-α) and the neurotrophin brain-derived neurotrophic factor (BDNF) were all estimated in tissue slices exposed to SAC and/or QUIN. The incubation of slices with QUIN augmented all toxic endpoints, whereas the addition of SAC prevented and/or recovered all toxic effects of QUIN, exhibiting better results when administered 60 min before the toxin and demonstrating protective and antioxidant properties. The early stimulation of Nrf2/ARE binding activity, the upregulation of HO-1, the ERK1/2 phosphorylation and the preservation of BDNF tissue levels by SAC demonstrate that this molecule displays a wide range of early protective signals by triggering orchestrated antioxidant responses and neuroprotective strategies. The relevance of the characterization of these mechanisms lies in the confirmation that the protective potential exerted by SAC begins at the early stages of excitotoxicity and neurodegeneration and supports the design of integral prophylactic/therapeutic strategies to reduce the deleterious effects observed in neurodegenerative disorders with inherent excitotoxic events.

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In this work, two new self-tuning collaborative-based mechanisms for jamming detection are proposed. These techniques are named (i) Connected Mechanism and (ii) Extended Mechanism. The first one detects jamming by comparing the performance parameters with respect to directly connected neighbors by interchanging packets with performance metric information, whereas the latter, jamming detection relays comparing defined zones of nodes related with a collector node, and using information of this collector detects a possible affected zone. The effectiveness of these techniques were tested in simulated environment of a quadrangular grid of 7 × 7, each node delivering 10 packets/sec, and defining as collector node, the one in the lower left corner of the grid. The jammer node is sending packets under reactive jamming. The mechanism was implemented and tested in AODV (Ad hoc On Demand Distance Vector), DSR (Dynamic Source Routing), and MPH (Multi-Parent Hierarchical), named AODV-M, DSR-M and MPH-M, respectively. Results reveal that the proposed techniques increase the accurate of the detected zone, reducing the detection of the affected zone up to 15% for AODV-M and DSR-M and up to 4% using the MPH-M protocol.