Search Results (121)

Background: Uterus transplantation (UTx) is an emerging treatment for absolute uterine factor infertility. However, the use of deceased donors is limited, and donation after circulatory death (DCD) has not yet been utilized. Ischemic injury remains a major barrier, particularly compared with living donor procedures. Hypothermic oxygenated perfusion (HOPE), which has shown protective effects in heart, liver, and kidney transplantation, may offer similar benefits for uterine grafts. Methods: We report the first series applying HOPE to human uteri to improve preservation and enable metabolic injury assessment during perfusion. Six uteri (3 DBD, 3 DCD; median donor age 53 years) underwent 8 h of HOPE following procurement, while paired tissue controls were preserved using static cold storage (SCS). Perfusion was delivered using a pressure-controlled system (15 mmHg, 10 ± 1 °C, VitaSmart^®). Perfusate and tissue samples were analyzed for mitochondrial injury, inflammation, and transcriptional responses. Results: HOPE maintained stable flows (70–150 mL/min), delivered high oxygen levels (pO₂ ≈ 1000 hPa), and increased tissue ATP levels. Stratification based on perfusate flavin mononucleotide (FMN) release identified grafts with greater Complex I/II injury. HOPE was associated with lower levels of mitochondrial injury markers and inflammatory signals, preserved tissue architecture, and promoted gene expression patterns consistent with metabolic recovery compared with paired SCS tissue controls. Conclusions: These findings suggest that HOPE may serve as a preservation approach that enables metabolic and ischemic injury assessment and may facilitate broader use of deceased donor uteri for transplantation. Full article

Background: Aging is associated with increased adiposity, sarcopenia risk, and metabolic vulnerability. Digital tools may enhance adherence to nutritional strategies, but their impact on detailed body composition remains insufficiently explored. Methods: This 6-month prospective controlled longitudinal intervention study included 60 middle-aged and older adults. All participants received a smart watch and smart scale for self-monitoring. The control group attended evaluations only at baseline and study completion, while the intervention group received monthly follow-up and remote feedback. Body composition was assessed using multi-frequency BIA. Statistical analyses included paired tests, effect sizes, correlations, and linear mixed-effects models. Results: Significant reductions were observed in BMI (p < 0.001), fat mass (p = 0.003), and visceral fat (p = 0.003; Cohen’s d = 0.41). The sarcopenic index improved (p = 0.001), while skeletal muscle mass remained stable. ECW increased significantly (p = 0.010). Age was inversely associated with the magnitude of improvement. Mixed-effects modeling confirmed a time-dependent reduction in visceral fat independent of age and sex. Conclusions: A 6-month digitally assisted nutritional monitoring program was associated with favorable changes in adiposity, muscle quality, and hydration status. Multi-frequency BIA provides valuable integrative markers for monitoring nutritional interventions in aging populations. Full article

Swallowing is a critical marker of neurological and emotional health. The ability to monitor it continuously and non-invasively, especially through smart ear-worn devices, holds significant promise for clinical applications. Despite this potential, no public audio datasets currently support reliable swallowing sound detection. Existing datasets focus primarily on speech and breathing, offering limited coverage and lacking detailed annotations for swallowing events. To address this gap, we introduce an in-ear audio dataset specifically designed to capture a wide range of verbal and non-verbal sounds. It includes comprehensive labeling focused on swallowing. The dataset was collected from 34 healthy adults (14 females and 20 males) between the ages of 20 and 29. Each participant performed a series of predefined tasks involving both non-verbal and verbal events. Non-verbal tasks included swallowing, clicking, forceful blinking, touching the scalp, and physical movements such as squatting or walking in place. Verbal tasks consisted of speaking (e.g., describing an image). Recordings were conducted in both quiet and noisy environments to better reflect real-world conditions. Data were captured using a combination of in-/outer-ear microphones, a chest belt to record electrocardiogram (ECG), respiration and acceleration signals, and an ultrasound probe to track tongue movement, which served as a reference for swallowing annotation. All signals were precisely synchronized. To ensure high data quality, the recordings were reviewed using both algorithmic analysis and manual inspection. Swallowing events were identified based on ultrasound signals and validated by an expert to guarantee accurate labeling. As a proof of concept that in-ear audio supports swallow classification, we fine-tune a fully connected neural network on YAMNet embeddings plus zero-crossing rate (ZCR) features. Across the completed folds, the model reaches an F1 score of 0.875 ± 0.013. Full article

“Smart” nanoparticles are often presented as the vanguard of precision cancer therapy, defined by engineered abilities to sense predefined stimuli, enhance targeting, and control therapeutic release. Yet this notion of smartness remains largely material-centric and only partially reflects how nanomedicines behave in vivo. Cells exposed to nanoparticles are not passive recipients of engineered functions; they actively interpret these perturbations through integrated stress-response, metabolic, transcriptional, and innate immune programs. These cell-state trajectories can determine efficacy, tolerance, resistance, or toxicity, and can do so independently of uptake, biodistribution, or triggerable release efficiency. Accordingly, evaluation strategies that prioritize delivery metrics and limited a priori molecular markers may misestimate functional performance and durability. This Perspective proposes a cell-aware reframing in which smartness is defined by biological controllability: the capacity of a nanoparticle system to elicit predictable, mechanistically interpretable, and therapeutically favorable cell-state trajectories across relevant malignant and non-malignant compartments. A practical path forward is to integrate time-resolved functional profiling into benchmarking using compact response signatures that report stress buffering, immune activation or suppression, and the emergence of tolerant states. A practical path forward is to integrate time-resolved functional profiling into benchmarking using compact response signatures that report stress buffering, immune activation or suppression, and the emergence of tolerant states. Here, biological controllability refers to the ability of a nanoparticle system to reproducibly steer integrated cellular stress, metabolic, and immune programs toward predefined therapeutic endpoints while minimizing adaptive escape across heterogeneous compartments. Full article

Tourism 4.0 integrates Industry 4.0 technologies into tourism services to enhance visitor experiences and improve destination management. This study presents the design, implementation, and pilot validation of an integrated IoT–Augmented Reality (IoT–AR) cyber-physical urban node developed for smart tourism infrastructure in Baños de Agua Santa, Ecuador. The system combines distributed environmental sensing, LoRa-based communication, edge-level preprocessing, cloud data management via RESTful services, and immersive visualization through a cross-platform augmented reality mobile interface. The development followed the TDDM4IoTS methodology, adapted into five phases covering requirements analysis, technological design, modeling, validation, and deployment. The architecture supports contextual real-time information delivery while maintaining low power consumption and robustness under heterogeneous connectivity conditions. Field tests confirmed stable communication between sensor nodes and the gateway, as well as reliable AR marker recognition under varying light and distance conditions. Usability evaluation using the System Usability Scale (SUS) yielded a mean score of 84.38, classified as excellent, with high internal consistency (α ≈ 0.89). The results demonstrate technical feasibility and strong user acceptance, providing a scalable and replicable model for interactive IoT–AR urban systems in smart tourism environments. Full article

Tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) is a ubiquitous organophosphate flame retardant posing potential threats to reproductive health. Given that TDCIPP toxicity is often linked to oxidative stress, pyrroloquinoline quinone (PQQ), a potent natural antioxidant and mitochondrial nutrient, was hypothesized to mitigate these adverse effects. This study investigated the impact of TDCIPP exposure on the in vitro maturation of mouse oocytes and evaluated the protective role of PQQ. Using an in vitro maturation model, we assessed the toxic effects of TDCIPP by examining the first polar body extrusion (PBE) rate and cumulus expansion, followed by analyses of oxidative stress (ROS and GSH), mitochondrial integrity (ATP content and distribution), and apoptosis-related markers through transcriptome sequencing (Smart RNA-seq), quantitative real-time PCR, and immunofluorescence. The results demonstrated that TDCIPP significantly suppressed cumulus expansion and reduced the PBE rate. Mechanistically, TDCIPP induced severe oxidative stress, disrupted mitochondrial function, and activated the apoptotic pathway. Furthermore, TDCIPP triggered early apoptotic signaling by downregulating Bcl-2 and upregulating Bax. Notably, supplementation with PQQ effectively reversed these detrimental effects by reducing intracellular ROS levels, maintaining GSH content, preserving mitochondrial density and ATP production, and inhibiting apoptosis. In conclusion, our findings provide new insights into the gamete toxicity of TDCIPP and suggest that PQQ may serve as a potential therapeutic agent to protect oocyte quality against environmental pollutant-induced damage. Full article

This paper presents an experimental study validating the feasibility of Radio Frequency Identification (RFID) marker systems as a complementary solution for autonomous vehicle (AV) localization in Global Navigation Satellite System (GNSS)-degraded urban environments. A novel synchronized dynamic testbed featuring hardware-level integration with wheel revolution tracking enables precise correlation of RFID marker reads with vehicle angular position. Experimental results demonstrate that multi-antenna configurations achieve consistently high read success rates (up to 99.6% at 0.5 m distance), sub-meter localization accuracy (~55 cm marker spacing), and reliable performance at average urban speeds (36 km/h simulated velocity). Spatial diversity from four strategically positioned antennas overcomes multipath interference and orientation challenges inherent to high-speed RFID reading. Processing latency remains well within the 58 ms time budget critical for autonomous navigation. These findings validate RFID’s potential for smart road infrastructure integration and demonstrate a scalable, cost-effective solution for enhancing AV safety and decision-making capabilities through contextual information transmission. Full article

Non-destructive prediction of harvest timing is increasingly important in greenhouse melon cultivation, yet image-based methods alone often fail to reflect environmental factors affecting fruit development. Likewise, environmental or fertigation data alone cannot capture fruit-level variation. This gap calls for a multimodal approach integrating both sources of information. This study presents a fusion model combining RGB images with environmental and fertigation data to predict optimal harvest timing for melons. A YOLOv8n-based model detected fruits and estimated diameters under marker and no-marker conditions, while an LSTM processed time-series variables including temperature, humidity, CO₂, light intensity, irrigation, and electrical conductivity. The extracted features were fused through a late-fusion strategy, followed by an MLP for predicting diameter, biomass, and harvest date. The marker condition improved detection accuracy; however, the no-marker condition also achieved sufficiently high performance for field application. Diameter and weight showed a strong correlation (R² > 0.9), and the fusion model accurately predicted the actual harvest date of 28 August 2025. These results demonstrate the practicality of multimodal fusion for reliable, non-destructive harvest prediction and highlight its potential to bridge the gap between controlled experiments and real-world smart farming environments. Full article

Wearable multi-modal body fluid monitoring enables continuous, non-invasive, and context-aware assessment of human physiology. By integrating biochemical and physical information across multiple modalities, wearable systems overcome the limitations of single-marker sensing and provide a more holistic view of dynamic health states. This review offers a system-level overview of recent advances in multi-modal body fluid monitoring, structured into three hierarchical dimensions. We first examine sensing-combination strategies such as multi-marker analysis within single fluids, coupling biochemical signals with bioelectrical, mechanical, or thermal parameters, and emerging multi-fluid acquisition to improve analytical accuracy and physiological relevance. Next, we discuss platform-integration mechanisms based on biochemical, physical, and hybrid sensing principles, along with monolithic and modular architectures enabled by flexible electronics, microfluidics, microneedles, and smart textiles. Finally, the data-processing patterns are analyzed, involving cross-modal calibration, machine learning inference, and multi-level data fusion to enhance data reliability and support personalized and predictive healthcare. Beyond summarizing technical advances, this review establishes a comprehensive framework that moves beyond isolated signal acquisition or simple metric aggregation toward holistic physiological interpretation. It guides the development of next-generation wearable multi-modal body fluid monitoring systems that overcome the challenges of high integration, miniaturization, and personalized medical applications. Full article

The escalating frequency and severity of drought events pose significant threats to agricultural productivity and food security. Drought stress not only restricts crop growth and yields but also destabilizes agricultural ecosystems. Over evolutionary timescales, plants have developed intricate adaptive strategies, encompassing drought escape (accelerated phenology), avoidance (water-conserving morphology) and tolerance (cellular protection), which involve complex biological mechanisms spanning molecular signaling, metabolic reprogramming and organ morphological remodeling. To mitigate drought risks, breeding drought-tolerant and water-efficient crops is imperative. Currently, drought resistance breeding is undergoing a paradigm shift, transitioning from traditional phenotypic selection toward genomics-assisted selection, molecular design and artificial intelligence (AI)-driven predictive modeling. This review provides a comprehensive analysis of drought stress response mechanisms in crops, integrating three key dimensions: physiological/biochemical adaptations, hormonal signaling networks and morphological/structural modifications. Furthermore, it critically evaluates recent advances in genetic improvement approaches for drought resistance, such as marker-assisted selection, transgenic technology and gene editing. It also explores the integration of multi-omics data and AI to enhance precision molecular breeding and overcome the inherent trade-off between drought resistance and yield potential. By synthesizing advancements in molecular breeding and smart agriculture, this work provides a roadmap for developing climate-resilient crops optimized through synergistic trait engineering and intelligent environmental sensing. Full article

This study aimed to establish a high-performance liquid chromatography coupled with photodiode array detection (HPLC–PDA) method for the simultaneous quantification of 12 ginsenosides in red ginseng sprout (RGS) extract produced from smart-farm-cultivated ginseng sprouts and to evaluate its antioxidant activity as part of a quality assessment framework. Twelve representative major and heat-transformed minor ginsenosides were selected to capture the characteristic protopanaxadiol and protopanaxatriol profiles of RGS. Hydroponically cultivated ginseng sprouts were subjected to nine cycles of steaming and drying, followed by pressurized extraction. The total ginsenoside content was 31.54 mg/g, with Re and Rd as the predominant ginsenosides. The extract exhibited a high total phenolic content (7.98 mg gallic acid equivalents per gram) and flavonoid content (4.65 mg rutin equivalents per gram). Antioxidant activity was evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH IC₅₀ = 7.88 mg/mL) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS IC₅₀ = 24.81 mg/mL) radical scavenging assays. Pearson’s correlation analysis revealed strong positive correlations between ginsenosides (Re, Rg2, Rd, and Rh1), phenolic/flavonoid content, and antioxidant activity (R > 0.84, p < 0.01). This study provides an HPLC–PDA platform that achieves baseline-resolved, simultaneous quantification of 12 ginsenosides in RGS and links this compositional profile to antioxidant markers, supporting the quality control of smart-farm-derived ginseng products. Full article

Background and Objectives: Periodontitis is a common chronic inflammatory disease and a leading cause of tooth loss worldwide. Traditional diagnostic methods, such as probing and radiographic assessment, are retrospective and fail to detect ongoing disease activity. In recent years, salivary biomarkers and oral microbiome profiling have emerged as promising tools for earlier detection and precision-based management. The aim of this review is to synthesize current evidence on salivary and microbiome-derived biomarkers in periodontitis and to evaluate their translational potential in diagnostics and therapy. Materials and Methods: A narrative review was performed using PubMed, Scopus, and Web of Science to identify studies published between 2020 and 2025. Search terms included periodontitis, salivary biomarkers, oral microbiome, dysbiosis, and precision therapy. Priority was given to systematic reviews, meta-analyses, and translational studies that addressed diagnostic or therapeutic applications. Eligible publications included English-language original studies and reviews reporting on the diagnostic or therapeutic relevance of salivary or microbiome biomarkers in periodontitis. Results: Salivary biomarkers such as cytokines, matrix metalloproteinases (MMPs), oxidative stress markers, microRNAs, and extracellular vesicles (EVs) show consistent associations with disease activity and treatment outcomes. Oral microbiome studies reveal that both classical pathogens and community-level dysbiosis contribute to disease risk. Translational advances include chairside immunoassays, biosensors, lab-on-a-chip devices, and artificial intelligence (AI)-driven analyses. Biomarker-guided therapies—such as microbiome modulation, natural bioactive compounds, host-response modulation, and smart biomaterials—are being evaluated with increasing frequency in translational studies. Conclusions: By integrating salivary and microbiome biomarkers with novel diagnostic technologies and emerging therapies, this review complements existing systematic evidence and offers a translational roadmap toward precision periodontology. Full article

(1) Background: Upper limb (UL) function plays a central role in daily life, enabling essential tasks such as reaching, grasping, and eating. While numerous tools exist to evaluate UL kinematics, their application in pediatric populations is often limited by a lack of age-specific validation. This study presents a novel motion analysis protocol featuring a customized marker set, aimed at assessing UL movements in the three anatomical planes across different age groups, with a focus on pediatric applicability. (2) Materials and Methods: A SmartDX motion capture system was used, with 30 markers positioned on the upper body, referencing the trunk as the root of the kinematic chain. Ten healthy participants (mean age: 18.69 ± 12.45 years; range: 8.0–41.4) without UL impairments were recruited. The broad age range was intentionally selected to assess the protocol’s transversal applicability. (3) Results: Results showed excellent intra-operator reliability for shoulder and wrist kinematics (ICC > 0.906) and good reliability for elbow movements (ICC > 0.755). Inter-operator reliability was good to excellent (shoulder ICC > 0.958; elbow ICC > 0.762; wrist ICC > 0.826) Usability, measured via the System Usability Scale, was rated as good (83.25). (4) Conclusions: The proposed protocol demonstrated strong reliability and practical usability, supporting its adoption in clinical and research settings. Its design allows for adaptability across motion capture platforms, promoting wider implementation in pediatric UL functional assessment. Full article

Electrochemical biosensors have emerged as a promising tool for the early detection of diseases in oilseed crops such as rapeseed, soybean, and peanut. These biosensors offer high sensitivity, portability, and cost-effectiveness. Timely diagnosis is critical, as many pathogens exhibit latent infection phases or produce invisible metabolic toxins, leading to substantial yield losses before visible symptoms occur. This review summarises recent advances in the field of nanomaterial-assisted electrochemical sensing for oilseed crop diseases, with a particular focus on sensor mechanisms, interface engineering, and biomolecular recognition strategies. The following innovations are highlighted: nanostructured electrodes, aptamer- and antibody-based probes, and signal amplification techniques. These innovations have enabled the detection of pathogen DNA, enzymes, and toxins at ultra-low concentrations. Notwithstanding these achievements, challenges persist, including signal interference from plant matrices, limitations in device miniaturization, and the absence of standardized detection protocols. Future research should explore the potential of AI-assisted data interpretation, the use of biodegradable sensor materials, and the integration of these technologies with agricultural IoT networks. The aim of this integration is to enable real-time, field-deployable disease surveillance. The integration of laboratory innovations with field applications has been demonstrated to have significant potential in supporting sustainable agriculture and strengthening food security through intelligent crop health monitoring. Full article

This paper presents the novel, smart, commutable, and self-regulated SSF-Roundabout as one of the potential solutions in the environment of smart mobility. The SSF-Roundabout implements traffic counting systems, smart cameras, LED road markers, and Variable Message Signs (VMS) on arms. Based on the instantaneous detection of the traffic demand level, vehicles can be properly channelled or not into right-turn bypass lanes, which the roundabout is equipped with in every arm, to guarantee the requested capacity, Level of Service (LOS), and safety. In total, fifteen very different layout configurations of the SSF-Roundabout are available. Several traffic analyses were performed by using ad hoc traffic engineering closed-form models and case studies based on many origin-destination traffic matrices (M_O/D(t)) and proportions of CAVs in the traffic stream (from 0% to 100%). Simulation results demonstrate the correlation between layout scenarios, traffic intensity, distribution among arms, and composition in terms of CAVs and their impact on entry and total capacity, control delay, and LOS of the SSF-Roundabout. For instance, the right-turn bypass lane activation may produce an entry capacity increase of 48% and a total capacity increase of 50% in the case of 100% of CAVs in traffic streams. Full article