Search Results (39)

Bromoepiandrosterone (BEA), a synthetic analog of the adrenal steroid DHEA, holds promise as a host-directed therapy for both active and latent tuberculosis (TB). Unlike DHEA, BEA lacks hormonal side effects yet retains potent immunomodulatory activity. It promotes a Th1-skewed immune response by enhancing interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α), critical cytokines for macrophage activation and intracellular control of Mycobacterium tuberculosis (Mtb), while suppressing Th2 cytokines such as IL-4. BEA also inhibits 11β-hydroxysteroid dehydrogenase-1, lowering intracellular cortisol levels and reversing the local immunosuppression commonly seen in TB. These features enable BEA to restore immune competency in TB-infected tissues. In murine TB models, BEA halted bacterial growth, reduced pulmonary inflammation, and synergized with standard anti-TB drugs to enhance bacterial clearance. Additionally, DHEA and its analogues have demonstrated direct antimycobacterial activity, likely by interfering with Mtb mycolic acid synthesis, a property BEA is believed to share. For latent TB, BEA’s ability to sustain Th1-mediated immunity and counteract immune suppression could help maintain latency and prevent reactivation, especially in immunocompromised individuals. By boosting immune surveillance and potentially contributing to bacillary clearance, BEA offers a unique adjunctive approach that complements existing TB treatments without contributing to drug resistance. Its dual function, an immune modulator and antimicrobial agent, supports its use across the TB disease spectrum. These properties position BEA as a novel candidate for host-directed therapy aimed at improving outcomes in both drug-sensitive and drug-resistant TB, as well as therapies aimed at enhancing long-term containment of latent infection. Full article

The rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management. Full article

In reinforcement learning (RL), reward function design is critical to the learning efficiency and final performance of agents. However, in complex tasks such as humanoid motion tracking, traditional static weighted reward functions struggle to adapt to shifting learning priorities across training stages, and designing a suitable shaping reward is problematic. To address these challenges, this paper proposes a two-layered reward reinforcement learning framework. The framework decomposes the reward into two layers: an upper-level goal reward that measures task completion, and a lower-level optimizing reward that includes auxiliary objectives such as stability, energy consumption, and motion smoothness. The key innovation lies in the online optimization of the lower-level reward weights via an online meta-heuristic optimization algorithm. This online adaptivity enables goal-conditioned reward shaping, allowing the reward structure to evolve autonomously without requiring expert demonstrations, thereby improving learning robustness and interpretability. The framework is tested on a gymnastic motion tracking problem for the Unitree G1 humanoid robot in the Isaac Gym simulation environment. The experimental results show that, compared to a static reward baseline, the proposed framework achieves 7.58% and 10.30% improvements in upper-body and lower-body link tracking accuracy, respectively. The resulting motions also exhibit better synchronization and reduced latency. The simulation results demonstrate the effectiveness of the framework in promoting efficient exploration, accelerating convergence, and enhancing motion imitation quality. Full article

STEM (Science, Technology, Engineering, and Mathematics) education faces the challenge of incorporating advanced technologies that foster motivation, collaboration, and hands-on learning. This study proposes a portable system capable of transforming ordinary surfaces into interactive learning spaces through gamification and spatial perception. A prototype based on multi-agent architecture was developed on the PANGEA (Platform for automatic coNstruction of orGanizations of intElligent agents) platform, integrating LIDAR (Light Detection and Ranging) sensors for gesture detection, an ultra-short-throw projector for visual interaction and a web platform to manage educational content, organize activities and evaluate student performance. The data from the sensors is processed in real time using ROS (Robot Operating System), generating precise virtual interactions on the projected surface, while the web allows you to configure physical and pedagogical parameters. Preliminary tests show that the system accurately detects gestures, translates them into digital interactions, and maintains low latency in different classroom environments, demonstrating robustness, modularity, and portability. The results suggest that the combination of multi-agent architectures, LIDAR sensors, and gamified platforms offers an effective approach to promote active learning in STEM, facilitate the adoption of advanced technologies in diverse educational settings, and improve student engagement and experience. Full article

The Internet of Things (IoT) has promoted emerging applications that require massive device collaboration, heavy computation, and stringent latency. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems can provide flexible services for user devices (UDs) with wide coverage. The optimization of both latency and energy consumption remains a critical yet challenging task due to the inherent trade-off between them. Joint association, offloading, and computing resource allocation are essential to achieving satisfying system performance. However, these processes are difficult due to the highly dynamic environment and the exponentially increasing complexity of large-scale networks. To address these challenges, we introduce a carefully designed cost function to balance the latency and the energy consumption, formulate the joint problem into a partially observable Markov decision process, and propose two multi-agent deep-reinforcement-learning-based schemes to tackle the long-term problem. Specifically, the multi-agent proximal policy optimization (MAPPO)-based scheme uses centralized learning and decentralized execution, while the closed-form enhanced multi-armed bandit (CF-MAB)-based scheme decouples association from offloading and computing resource allocation. In both schemes, UDs act as independent agents that learn from environmental interactions and historic decisions, make decision to maximize its individual reward function, and achieve implicit collaboration through the reward mechanism. The numerical results validate the effectiveness and show the superiority of our proposed schemes. The MAPPO-based scheme enables collaborative agent decisions for high performance in complex dynamic environments, while the CF-MAB-based scheme supports independent rapid response decisions. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

Background: Breast cancer (BC) is the second most common cancer among women in the United States. It has been estimated that one in eight women will be diagnosed with breast cancer in her lifetime. Various BC risk factors, such as age, physical inactivity, and smoking, play a substantial role in BC occurrence and development. Early life dietary intervention with plant-based bioactive compounds has been studied for its potential role in BC prevention. Sulforaphane (SFN), an isothiocyanate, is an antioxidant and anti-inflammatory agent extracted from broccoli sprouts (BSp) and other plants. Dietary supplementation of SFN suppresses tumor growth by inducing protective epigenetic changes and inhibiting cancer cell proliferation. Inulin, as a dietary fiber, has been studied for alleviating GI discomfort and weight loss by promoting the growth of beneficial bacteria in the gut. Objective: Early-life combinatorial treatment with both phytochemical SFN and potential prebiotic agent inulin at lower and safer dosages may confer more efficacious and beneficial effects in BC prevention. Methods: Transgenic mice representing estrogen receptor-negative BC were fed 26% (w/w) BSp and 2% (w/v) inulin supplemented in food and water, respectively. Results: The combinatorial treatment inhibited tumor growth, increased tumor onset latency, and synergistically reduced tumor weight. Gut microbial composition was analyzed between groups, where Ruminococcus, Muribaculaceae, and Faecalibaculum significantly increased, while Blautia, Turicibacter, and Clostridium sensu stricto 1 significantly decreased in the combinatorial group compared with the control group. Furthermore, combinatorial treatment induced a protective epigenetic effect by inhibiting histone deacetylases (HDACs) and DNA methyltransferases (DNMTs). Intermediates in the AKT/PI3K/MTOR pathway were significantly suppressed by the combinatorial treatment, including PI3K p85, p-AKT, p-PI3K p55, MTOR, and NF-κB. Cell cycle arrest and programmed cell death were induced by the combinatorial treatment via elevating the expression of cleaved-caspase 3 and 7 and inhibiting the expressions of CDK2 and CDK4, respectively. Orally administering F. rodentium attenuated tumor growth and induced apoptosis in a syngeneic triple-negative breast cancer (TNBC) mouse model. Conclusions: Overall, the findings suggest that early-life dietary combinatorial treatment contributed to BC prevention and may be a potential epigenetic therapy that serves as an adjunct to other traditional neoadjuvant therapies. Full article

Insomnia seriously affects people’s health and daily life. There is a growing interest in sleep-promoting agents from natural sources. Nelumbinis Plumula (NP), a traditional Chinese medicine with dual food-medicine homology, has the effects of clearing the heart and calming the mind, showing promising efficacy in treating insomnia. In this study, the effects of NP extract, total alkaloid extract of NP, and crude polysaccharide of NP were measured in para-chlorophenylalanine-induced insomnia rats combined with the pentobarbital sodium experiment. The results indicated both total alkaloid extract and NP total extract could improve insomnia in rats, with the total alkaloid extract demonstrating a stronger effect than NP total extract. Total alkaloid extract significantly prolonged sleep duration and shortened sleep latency. Therefore, total alkaloids in NP appeared to be the main pharmacological substances that exerted sedative effect. Simultaneously, total alkaloid extract could increase the GABA level and reduce the DA level as well as affect the activities of GABRA1, DRD2, 5-HT1A, and AChE proteins. This study can lay an experimental foundation for the further development and application of NP as a remedy for treating insomnia. Full article

Kaposi sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and the etiological agent of several diseases. These include the malignancies Kaposi sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman disease (MCD), as well as the inflammatory disorder KSHV inflammatory cytokine syndrome (KICS). The KSHV lifecycle is characterized by two phases: a default latent phase and a lytic replication cycle. During latency, the virus persists as an episome within host cells, expressing a limited subset of viral genes to evade immune surveillance while promoting cellular transformation. The lytic phase, triggered by various stimuli, results in the expression of the full viral genome, production of infectious virions, and modulation of the tumor microenvironment. Both phases of the KSHV lifecycle play crucial roles in driving viral pathogenesis, influencing oncogenesis and immune evasion. This review dives into the intricate world of the KSHV lifecycle, focusing on the molecular mechanisms that drive its latent and lytic phases, their roles in disease progression, and current therapeutic strategies. Full article

Background: Human immunodeficiency virus (HIV) establishes latent infections in cellular reservoirs, including microglia. HC69 cells, a microglial model of HIV latency, contain an HIV promoter long terminal repeat (LTR)-GFP reporter and were used for testing the efficacy of a two-step magnetoelectric nanoparticle (MENP) and extracellular vesicle (xEV) latency-targeting (MELT) nanotherapeutic. GFP expression in HC69 at rest is low (GFP^Lo), and upon exposure to LTR, transcription-activating agents (i.e., TNF-α) are induced to be high expressing (GFP^Hi). Methods: The first step of MELT utilized ZL0580, an HIV Tat inhibitor loaded into EVs (80%) via incubation. ZL0580-EVs were taken up by GFP^Lo and blocked LTR transcriptional reactivation by 50% and were 90% less toxic than ZL0580 alone. The second step in MELT involved conjugation of monomethyl auristatin E (MMAE) to MENPs. HPLC measurements showed 80% MMAE attachment to MENPs. Flow cytometry-based measurements of the membrane potential indicated that the membranes of GFP^Hi HC69 were 60% more polarized than GFP^Lo HC69 cells. More MMAE–MENPs were internalized by GFP^Lo HC69. Results: Using a mixed-cell blood–brain barrier (BBB) Transwell model, we demonstrated that 20% of MELT crossed the BBB, was taken up by HC69 cells, and reduced LTR reactivation by 10%. Conclusions: Overall, this study demonstrated that MELT can potentially be utilized as a nanotherapeutic to target HIV latency in microglia. Full article

Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination. Full article

Background: Growing evidence suggests that glucose metabolism plays a crucial role in activated immune cells, significantly contributing to the occurrence and development of neuroinflammation and depression-like behaviors. Chronic stress has been reported to induce microglia activation and disturbances in glucose metabolism in the hippocampus. Aims: This study aims to investigate how chronic stress-mediated glycolysis promotes neuroinflammation and to assess the therapeutic potential of the glycolysis inhibitor, 2-deoxy-D-glucose (2-DG), in a model of chronic stress-induced neuroinflammation and depression-like behavior. Methods: In in vitro studies, we first explored the effects of 2-DG on the inflammatory response of microglia cells. The results showed that corticosterone (Cort) induced reactive oxygen species (ROS) production, increased glycolysis, and promoted the release of inflammatory mediators. However, these effects were reversed by intervention with 2-DG. Subsequently, we examined changes in depression-like behavior and hippocampal glycolysis in mice during chronic stress. The results indicated that chronic stress led to prolonged escape latency in the Morris water maze, increased platform-crossing frequency, reduced sucrose preference index, and extended immobility time in the forced swim test, all of which are indicative of depression-like behavior in mice. Additionally, we found that the expression of the key glycolytic enzyme hexokinase 2 (HK2) was upregulated in the hippocampus of stressed mice, along with an increased release of inflammatory factors. Further in vivo experiments investigated the effects of 2-DG on glycolysis and pro-inflammatory mediator production, as well as the therapeutic effects of 2-DG on chronic stress-induced depression-like behavior in mice. The results showed that 2-DG alleviated chronic stress-induced depression-like behaviors, such as improving escape latency and platform-crossing frequency in the Morris water maze, and increasing the time spent in the center of the open field. Additionally, 2-DG intervention reduced the level of glycolysis in the hippocampus and decreased the release of pro-inflammatory mediators. Conclusions: These findings suggest that 2-DG can mitigate neuroinflammation and depressive behaviors by inhibiting glycolysis and inflammatory responses. Overall, our results highlight the potential of 2-DG as a therapeutic agent for alleviating chronic stress-induced neuroinflammation through the regulation of glycolysis. Full article

The Herpesviridae include the Epstein–Barr Virus (EBV) and the Kaposi Sarcoma-associated Herpesvirus (KSHV), both of which are oncogenic gamma-herpesviruses. These viruses manipulate host cellular mechanisms, including through ubiquitin-mediated pathways, to promote viral replication and oncogenesis. Ubiquitin, a regulatory protein which tags substrates for degradation or alters their function, is manipulated by both EBV and KSHV to facilitate viral persistence and cancer development. EBV infects approximately 90% of the global population and is implicated in malignancies including Burkitt lymphoma (BL), Hodgkin lymphoma (HL), post-transplant lymphoproliferative disorder (PTLD), and nasopharyngeal carcinoma. EBV latency proteins, notably LMP1 and EBNA3C, use ubiquitin-mediated mechanisms to inhibit apoptosis, promote cell proliferation, and interfere with DNA repair, contributing to tumorigenesis. EBV’s lytic proteins, including BZLF1 and BPLF1, further disrupt cellular processes to favor oncogenesis. Similarly, KSHV, a causative agent of Kaposi’s Sarcoma and lymphoproliferative disorders, has a latency-associated nuclear antigen (LANA) and other latency proteins that manipulate ubiquitin pathways to degrade tumor suppressors, stabilize oncogenic proteins, and evade immune responses. KSHV’s lytic cycle proteins, such as RTA and Orf64, also use ubiquitin-mediated strategies to impair immune functions and promote oncogenesis. This review explores the ubiquitin-mediated interactions of EBV and KSHV proteins, elucidating their roles in viral oncogenesis. Understanding these mechanisms offers insights into the similarities between the viruses, as well as provoking thought about potential therapeutic targets for herpesvirus-associated cancers. Full article

Serverless computing is a new cloud computing model suitable for providing services in both large cloud and edge clusters. In edge clusters, the autoscaling functions play a key role on serverless platforms as the dynamic scaling of function instances can lead to reduced latency and efficient resource usage, both typical requirements of edge-hosted services. However, a badly configured scaling function can introduce unexpected latency due to so-called “cold start” events or service request losses. In this work, we focus on the optimization of resource-based autoscaling on OpenFaaS, the most-adopted open-source Kubernetes-based serverless platform, leveraging real-world serverless traffic traces. We resort to the reinforcement learning algorithm named Proximal Policy Optimization to dynamically configure the value of the Kubernetes Horizontal Pod Autoscaler, trained on real traffic. This was accomplished via a state space model able to take into account resource consumption, performance values, and time of day. In addition, the reward function definition promotes Service-Level Agreement (SLA) compliance. We evaluate the proposed agent, comparing its performance in terms of average latency, CPU usage, memory usage, and loss percentage with respect to the baseline system. The experimental results show the benefits provided by the proposed agent, obtaining a service time within the SLA while limiting resource consumption and service loss. Full article

Combination antiretroviral therapy (ART) suppresses viral replication to undetectable levels, reduces mortality and morbidity, and improves the quality of life of people living with HIV (PWH). However, ART cannot cure HIV infection because it is unable to eliminate latently infected cells. HIV latency may be regulated by different HIV transcription mechanisms, such as blocks to initiation, elongation, and post-transcriptional processes. Several latency-reversing (LRA) and -promoting agents (LPA) have been investigated in clinical trials aiming to eliminate or reduce the HIV reservoir. However, none of these trials has shown a conclusive impact on the HIV reservoir. Here, we review the cellular and viral factors that regulate HIV-1 transcription, the potential pharmacological targets and genetic and epigenetic editing techniques that have been or might be evaluated to disrupt HIV-1 latency, the role of miRNA in post-transcriptional regulation of HIV-1, and the differences between the mechanisms regulating HIV-1 and HIV-2 expression. Full article