Search Results (5,256)

Retrotransposons exhibit increased activity in cancer cells. One possible approach to anticancer therapy is to use this activity to influence the energy balance in cells. Abnormal distribution of retrotransposons in the genome requires additional energy consumption, which can lead to a significant decrease in the total amount of free ATP molecules in the cell. A decrease in ATP levels below a certain threshold can in turn trigger a cell death program. To investigate the possibility of such a scenario, we developed a mathematical model of the cellular energy balance that describes the dynamics of energy consumption by the main cellular processes, including costs of retrotransposon activity. The model considers changes in the concentrations of ATP, active retrotransposons (LINE-1 and SINE) in the human genome, as well as mRNAs and proteins that are expression products of retrotransposon and constitutive genes. We estimated the parameter values in the model based on literature data and numerical optimization. We found a single stable stationary solution, characterized by low retrotransposon activity, and used it as the reference steady state for further analysis. Parametric sensitivity analysis revealed the parameters whose changes had the greatest impact on cellular ATP levels. The LINE-1 deactivation rate constant and the maximum LINE-1 transcription rate were the most sensitive among the transposon-related parameters. Perturbation of these parameters led to a decrease in the number of free ATP to 30% of the reference value and below. Transcription of retrotransposons under perturbed parameters became comparable to the translation of constitutive genes in terms of energy costs. The presented results indicate that cancer cell death can be initiated by increasing the load on the energy balance due to the activation of transposons. Full article

This paper proposes a structured design framework and system architecture for a non-intrusive spatial system aimed at supporting child behavior in everyday domestic environments. Rooted in ethical considerations, our approach defines four core behavior-guided design strategies: routine recovery, emotion-responsive adjustment, behavioral transition induction, and external linkage. Each strategy is meticulously translated into a detailed system logic that outlines input conditions, trigger thresholds, and feedback outputs, designed for implementability with ambient sensing technologies. Through a comparative conceptual analysis of three sensing configurations—low-resolution LiDARs, mmWave radars, and environmental sensors—we evaluate their suitability based on technical feasibility, spatial integration, operationalized privacy metrics, and ethical alignment. Supported by preliminary technical observations from lab-based sensor tests, low-resolution LiDAR emerges as the most balanced option for its ability to offer sufficient behavioral insight while enabling edge-based local processing, robustly protecting privacy, and maintaining compatibility with compact residential settings. Based on this, we present a working three-layered system architecture emphasizing edge processing and minimal-intrusion feedback mechanisms. While this paper primarily focuses on the framework and design aspects, we also outline a concrete pilot implementation plan tailored for small-scale home environments, detailing future empirical validation steps for system effectiveness and user acceptance. This structured design logic and pilot framework lays a crucial foundation for future applications in diverse residential and care contexts, facilitating longitudinal observation of behavioral patterns and iterative refinement through lived feedback. Ultimately, this work contributes to the broader discourse on how technology can ethically and developmentally support children’s autonomy and well-being, moving beyond surveillance to enable subtle, ambient, and socially responsible spatial interactions attuned to children’s everyday lives. Full article

This review aims to provide a comprehensive overview of how oxidative stress drives inflammation, structural remodeling, and clinical expression of childhood asthma, while critically appraising emerging redox-sensitive biomarkers and antioxidant-focused preventive and therapeutic strategies. Oxidative stress arises when reactive oxygen species (ROS) and reactive nitrogen species (RNS) outpace airway defenses. This surplus provokes airway inflammation: ROS/RNS activate nuclear factor kappa-B (NF-κB) and activator protein-1 (AP-1), recruit eosinophils and neutrophils, and amplify type-2 cytokines. Normally, an antioxidant network—glutathione (GSH), enzymes such as catalase (CAT) and superoxide dismutase (SOD), and nuclear factor erythroid 2-related factor 2 (Nrf2)—maintains redox balance. Prenatal and early exposure to fine particulate matter <2.5 micrometers (µm) (PM_2.5), aeroallergens, and tobacco smoke, together with polymorphisms in glutathione S-transferase P1 (GSTP1) and CAT, overwhelm these defenses, driving epithelial damage, airway remodeling, and corticosteroid resistance—the core of childhood asthma pathogenesis. Clinically, biomarkers such as exhaled 8-isoprostane, hydrogen peroxide (H₂O₂), and fractional exhaled nitric oxide (FeNO) surge during exacerbations and predict relapses. Therapeutic avenues include Mediterranean-style diet, regular aerobic exercise, pharmacological Nrf2 activators, GSH precursors, and mitochondria-targeted antioxidants; early trials report improved lung function and fewer attacks. Ongoing translational research remains imperative to substantiate these approaches and to enable the personalization of therapy through individual redox status and genetic susceptibility, ultimately transforming the care and prognosis of pediatric asthma. Full article

Chronic pain is a dynamic, brain-wide condition that eludes effective management by conventional, static treatment approaches. Transcutaneous Electrical Nerve Stimulation (TENS), traditionally perceived as a simple and generic modality, is on the verge of a significant transformation. Guided by advances in brain-state decoding and adaptive algorithms, TENS can evolve into a precision neuromodulation system tailored to individual needs. By integrating multimodal neuroimaging—including the spatial resolution of functional magnetic resonance imaging (fMRI), the temporal sensitivity of an Electroencephalogram (EEG), and the ecological validity of functional near-infrared spectroscopy (fNIRS)—with real-time machine learning, we envision a paradigm shift from fixed stimulation protocols to personalized, closed-loop modulation. This comprehensive review outlines a translational framework to reengineer TENS from an open-loop device into a responsive, intelligent therapeutic platform. We examine the underlying neurophysiological mechanisms, artificial intelligence (AI)-driven infrastructures, and ethical considerations essential for implementing this vision in clinical practice—not only for chronic pain management but also for broader neuroadaptive healthcare applications. Full article

Despite notable progress in cancer therapy, immune evasion remains a major obstacle to effective treatment outcomes. In the context of spaceflight, astronauts are exposed to unique environmental stressors—particularly microgravity and radiation—that profoundly affect cellular and immune homeostasis. Emerging evidence suggests that microgravity alters key cellular processes, including proliferation, apoptosis, adhesion, and oncogenic signaling pathways such as NF-κB and ERK1/2. Concurrently, microgravity (µg) disrupts immune regulation, potentially facilitating both tumor progression and treatment resistance. Of particular concern is the upregulation of human endogenous retroviruses (HERVs), especially HERV-K and HERV-W, under µg conditions, which may exacerbate inflammatory responses and immune system dysregulation. While some studies indicate that µg may impair tumor growth, others reveal enhanced immune evasion and reduced antitumor immunity. Importantly, insights from µg research extend beyond space medicine and provide translational opportunities for terrestrial oncology, including the development of physiologically relevant 3D tumor models for drug screening, the identification of mechano-sensitive pathways (FAK/RhoA, YAP/TAZ) as therapeutic targets, and novel immunotherapeutic strategies involving epigenetic modulation and checkpoint inhibition. This review critically examines the dual role of µg in modulating cancer progression and immune function. We synthesize findings on how µg shapes immune responses, alters tumor–immune system interactions, and impacts the efficacy of immunotherapeutic approaches. Finally, we highlight translational opportunities and challenges for optimizing cancer immunotherapy and precision oncology in both spaceflight and Earth-based environments. Full article

Background/Objectives: Diabetes mellitus (DM) is an increasingly prevalent endocrine disorder affecting humans and companion animals. Type 1 DM (T1DM) and type 2 DM (T2DM) are well characterized in humans, and canine DM most often resembles T1DM, marked by insulin dependence and β-cell destruction. Conversely, feline DM shares key features with human T2DM, including insulin resistance, obesity-related inflammation, and islet amyloidosis. This review provides a comprehensive comparative analysis of antidiabetic therapies in humans, dogs, and cats, focusing on three core areas: disease pathophysiology, pharmacological and delivery strategies, and translational implications. In human medicine, a wide array of insulin analogs, oral hypoglycemic agents, and incretin-based therapies, including glucagon-like peptide-1 receptor agonists (liraglutide) and sodium-glucose cotransporter-2 inhibitors (empagliflozin), are available. Veterinary treatments remain limited to species-adapted insulin formulations and off-label use of human drugs. Interspecies differences in gastrointestinal physiology, drug metabolism, and behavioral compliance influence therapeutic efficacy and pharmacokinetics. Recent innovations, such as microneedle patches for insulin delivery and continuous glucose monitoring systems, show promise in humans and animals. Companion animals with naturally occurring diabetes serve as valuable models for preclinical testing of novel delivery platforms and long-acting formulations under real-world settings. While these technologies show potential, challenges remain in regulatory approval and behavioral adaptation in animals. Conclusions: Future research should prioritize pharmacokinetic bridging studies, veterinary-specific formulation trials, and device validation in animal models. By highlighting shared and species-specific characteristics of DM pathogenesis and treatment, this review advocates a One Health approach toward optimized antidiabetic therapies that benefit human and veterinary medicine. Full article

The integration of organoids with biosensors serves as a miniaturized model of human physiology and diseases, significantly transforming the research frameworks surrounding drug development, toxicity testing, and personalized medicine. This review aims to provide a comprehensive framework for researchers to identify suitable technical approaches and to promote the advancement of organoid sensing towards enhanced biomimicry and intelligence. To this end, several primary methods for technology integration are systematically outlined and compared, which include microfluidic integrated systems, microelectrode array (MEA)-based electrophysiological recording systems, optical sensing systems, mechanical force sensing technologies, field-effect transistor (FET)-based sensing techniques, biohybrid systems based on synthetic biology tools, and label-free technologies, including impedance, surface plasmon resonance (SPR), and mass spectrometry imaging. Through multimodal collaboration such as the combination of MEA for recording electrical signals from cardiac organoids with micropillar arrays for monitoring contractile force, these technologies can overcome the limitations inherent in singular sensing modalities and enable a comprehensive analysis of the dynamic responses of organoids. Furthermore, this review discusses strategies for integrating strategies of multimodal sensing approaches (e.g., the combination of microfluidics with MEA and optical methods) and highlights future challenges related to sensor implantation in vascularized organoids, signal stability during long-term culture, and the standardization of clinical translation. Full article

The study compares the overall sustainability of two organic and two conventional rice farming systems during the 2022 drought. The research aimed to develop an experiment exploring the ability of an integrated methodological approach to identify tradeoffs and provide actionable insights for a sustainable agricultural transition under extreme climate stress. To this aim, the study employed economic analysis, Life Cycle Assessment (LCA) for environmental impact, and the OASIS framework for broader social and resilience indicators. The study revealed tradeoffs between the economic efficiency of conventional rice farming and the ecological resilience of organic systems, a conclusion made possible only through its integrated assessment methodology. By combining different methods, the research suggested that while conventional farms achieved clear financial superiority and greater efficiency per ton of rice, organic systems showcased superior ecological performance per hectare, greater biodiversity, and enhanced resilience. This highlights a crucial research frontier focused on designing hybrid systems or new economic models that can translate the environmental resilience of organic methods into tangible market value, effectively resolving the very tradeoffs this comprehensive assessment suggested. Full article

The construction sector is particularly prone to financial instability, with insolvencies occurring more frequently among micro- and small-scale firms. The current study explores the application of artificial intelligence (AI) and machine learning (ML) models for predicting insolvency within this sector. The research combined a structured literature review with empirical analysis of construction sector-level insolvency data spanning the recent decade. A critical review of studies highlighted a clear shift from traditional statistical methods to AI/ML-driven approaches, with ensemble learning, neural networks, and hybrid learning models demonstrating superior predictive accuracy and robustness. While current predictive models mostly rely on financial ratio-based inputs, this research complements this foundation by introducing additional sector-specific variables. Empirical analysis reveals persistent patterns of distress, with micro- and small-sized construction businesses accounting for approximately 92% to 96% of insolvency cases each year in the Australian construction sector. Key risk signals such as firm size, cash flow risks, governance breaches and capital adequacy issues were translated into practical features that may enhance the predictive sensitivity of the existing models. The study also emphasises the need for digital self-assessment tools to support micro- and small-scale contractors in evaluating their financial health. By transforming predictive insights into accessible, real-time evaluations, such tools can facilitate early interventions and reduce the risk of insolvency among vulnerable construction firms. The current study combines insights from the review of AI/ML insolvency prediction models with sector-specific feature derivation, potentially providing a foundation for future research and practical adaptation in the construction context. Full article

The tumor microenvironment creates strong stress conditions, including hypoxia and nutrient depletion, which cause the blocking of cap-dependent translation. Under stressful conditions, cancer cells exploit the cap-independent translation mechanism mediated by internal ribosome entry site (IRES), which ensures continued protein synthesis. IRES elements located in the 5′ untranslated regions of specific mRNAs allow selective translation of key anti-apoptotic and adaptive proteins. These proteins promote cellular processes that sustain cell survival, among them metabolic reprogramming, redox balance, and epithelial-to-mesenchymal transition, thus facilitating tumor progression and therapy resistance. IRES activity is dynamically regulated by IRES trans-acting factors, such as YB-1, PTB, and hnRNPA1, which respond to cellular stress by enhancing translation of crucial mRNAs. Emerging therapeutic strategies include pharmacological IRES inhibitors, RNA-based approaches targeting ITAF interactions, and IRES-containing vectors for controlled therapeutic gene expression. A deeper understanding of translational reprogramming, IRES structural diversity, and ITAF function is essential to develop targeted interventions to overcome therapeutic resistance and eliminate persistent tumor cell populations. Full article

Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles. Full article

MYC is a key oncogenic driver frequently overexpressed in non-small cell lung carcinoma (NSCLC) and other cancers, where its protein levels often exceed what would be expected from MYC mRNA levels alone, suggesting post-transcriptional regulation. Strategies to inhibit MYC function by targeting mRNA translation hold potential for therapeutics utility in Myc-dependent cancers. We developed TranslationLight, a high-content imaging platform which detects MYC mRNA translation in human cells. Using this system, we conducted a high-throughput screen of ~100,000 compounds to identify small molecules that selectively modulate MYC translation. Candidate compounds were evaluated by immunofluorescence, ribosome profiling, RNA sequencing, cellular thermal shift assays (CETSA), and subcellular localization studies of mRNA and RNA-binding proteins. We identified a lead compound, CMP76, that potently reduces Myc protein without substantially decreasing its mRNA abundance. Mechanistic investigations showed that the compound induces relocalization of MYC mRNA into stress granules, accompanied by translational silencing. CETSA identified hnRNPK as a primary protein target, and compound treatment triggered its cytoplasmic relocalization together with formation of hnRNPK-containing granules colocalizing with MYC mRNA. Analysis across cancer cell lines revealed that sensitivity to CMP76 was significantly associated with RBM42 dependency. This work establishes a novel therapeutic strategy to inhibit MYC translation mediated by hnRNPK, offering a translationally targeted approach to cancer therapy. Full article

Continuous Sign Language Recognition is a task that involves the recognition of complete sequences of Sign Language videos and translating them into spoken or written language. This task is particularly challenging due to the complex temporal structure and variability in signing styles. In this context, temporal video segmentation emerges to distinguish individual signs from transitions in continuous video streams. Despite its importance, temporal segmentation for Sign Language has not yet fully benefited from recent advancements in Machine Learning. Many existing approaches still rely on outdated architecture. This study aims to learn the characteristics that distinguish signs from transitions, which are fundamental elements of Sign Language. To this end, we adapt two current temporal segmentation models, DiffAct and MS-TCN, and apply them to our own precisely annotated datasets for Peruvian Sign Language. We explore three training strategies—baseline, data augmentation, and multi-dataset. Results suggest that they can enhance the scores for both models but at the cost of increased variability across splits. Notably, the diffusion-based model showcased its ability to generalize to unseen sequences through higher scores for sign and transition identification on the test set, reaching a median value of 71.89% for mF1S and 72.68% for mF1B. Full article

Background/Objectives—The personalisation agenda—matching the correct psychological therapy to diverse and comorbid mental disorders—is an unanswered dilemma in the worldwide literature which has far reaching consequences for public health. This hypothesis article addresses the question: can a biomedical approach resolve the personalisation agenda? Methods—Narrative review drawing on clinical psychology, translational psychiatry, and biomedical science literature. Results—Diverse attempts to resolve the personalisation agenda have not yet succeeded. Randomised controlled trials are uniquely biased due to unwanted placebo effects; network meta-analysis cannot address adequately which psychological therapy to use; new methodologies have not yet produced data; and neuroscientific analysis cannot yet explain how trauma-based therapies work. However, a biomedical model which divides psychological therapy into low, medium and high intensity interventions can resolve the personalisation agenda. Conclusions—Combining low intensity (placebo), with medium intensity (cognitive behavioural techniques) and high intensity interventions (trauma-based therapies) are theoretically synergistic if combined with psychosocial treatments/exercise, and used in sequence in the correct order. A biomedical model based on recent advances in placebo studies and neuroplasticity can resolve the personalisation agenda, and improve outcomes for mental disorder. Full article

Medulloblastoma (MB), the most common malignant pediatric brain tumor, has undergone reclassification from a histologically defined disease to a genetically stratified spectrum of distinct subgroups: WNT, SHH, Group 3, and Group 4. Advances in molecular profiling, as captured in the 2021 WHO CNS5 classification, have shown meaningful heterogeneity in terms of tumor biology, prognosis, and therapeutic response. However, translating these insights into precise, less toxic treatments remains an ongoing challenge. This review synthesizes current knowledge on MB subgroup biology, treatment strategies, and emerging therapies such as subgroup-specific inhibitors, immunotherapies, and novel chemotherapeutic regimens. This review also explores risk-adapted approaches while addressing global disparities in access to diagnostics and care. As the field moves toward individualized medicine, closing the gap between molecular understanding and equitable implementation will be crucial to improving outcomes and quality of life for children with medulloblastoma worldwide. Full article