Search Results (97,944)

Background/Objectives: Simulation has become an integral part of contemporary surgical training, allowing safe acquisition of technical skills with objective performance assessment. Microsurgery and robotic surgery share several technical features, including fine bimanual coordination, precise instrument control, and stereoscopic vision. This study aimed to evaluate whether a structured microsurgical course is associated with improved performance on a robotic surgical simulator and to explore its potential role within robotic training pathways. Methods: A prospective study was conducted between October 2022 and November 2025 at a single academic center, including 56 participants divided into three groups. Group A consisted of surgical residents attending a 3-day Basic Microsurgery Course; Group B included residents who did not undergo training during the same period; and Group C comprised experienced microsurgeons. Groups A and B performed two robotic simulation tasks at baseline (T0) and after three days (T1). Group C was assessed at T1 only as a reference benchmark. Performance was evaluated using simulator-derived metrics. Statistical analysis was performed using paired and unpaired t-tests. Results: Group A showed significant improvement across several performance parameters following training, whereas no comparable changes were observed in Group B. At T1, Group A demonstrated better performance than Group B in multiple metrics. Group C achieved the highest scores and was considered a reference group. Conclusions: Structured microsurgical training was associated with improved performance in a robotic simulation setting. These findings suggest that microsurgical skills may be transferable to robotic tasks and may contribute to the early phases of robotic skill acquisition. Further studies are required to assess their impact in clinical practice. Full article

Hydropower remains one of the primary sources of power generation. It can be operated as either a base-load or peak-load plant due to its rapid, easy start-up and stop-down capability. However, power plants, old or new, need to be operated and maintained optimally to meet energy demand and maximise economic returns. While the older plants without digital controls such as the Supervisory Control and Data Acquisition (SCADA) system are unable to leverage the evolving technology including big data and Artificial Intelligence (AI), the newer plants or plants that already have some form of data acquisition system have the advantage of leveraging the newer platforms for efficient operation, monitoring and fault diagnosis. Thus, an Artificial Neural Network (ANN), a machine learning (ML) algorithm, was chosen for this case study to predict the generator’s operational stator temperature by selecting six parameters that could potentially affect it. Real data from the 336 MW Chhukha Hydropower Plant (CHP) in Bhutan were used to train the ANN. The prediction of temperature using an ANN in MATLAB^® yielded an R² (correlation coefficient) of 96.8%, which is impressive but can be further improved through various optimisation and tuning methods with increased data volume and complexity. The performance of ANN prediction was validated against other regression models, and the ANN was found to outperform them. This demonstrated its capability to predict and detect generator temperature faults before failures, thereby enhancing hydropower operation and maintenance (O&M) efficiency. The model’s interpretation was also done through Shapley Additive ExPlanations (SHAP). Full article

The Epi2SensA method is a method similar to the validated EpiSensA assay for assessing the skin sensitization potential of chemicals. The Epi2SensA protocol includes adaptation (changes to exposure conditions and the controls) for using an alternative reconstructed human epidermis (RhE) model, the EpiDerm™ model. The interlaboratory validation study evaluated the reliability and predictive capacity of Epi2SensA according to OECD Performance Standards. Four laboratories (Mattek, Now Part of Sartorius, Eurofins Munich, Burleson Research Technologies, Inc., and Food and Drug Safety Center) conducted blinded testing of 20 coded reference substances representing various chemical categories and sensitization potencies. Statistical analysis using modified acceptance criteria (a 60% cell viability threshold) and a modified prediction model (requiring at least two positive gene markers) demonstrated substantially improved performance compared to the original EpiSensA criteria. The between-laboratory reproducibility (BLR) was 85%, the average within-laboratory reproducibility (WLR) was 83.3%, and the average predictivity parameters were 88.1% for sensitivity, 88.9% for specificity, and 88.3% for accuracy. Epi2SensA achieved performance metrics comparable to the validated reference method (EpiSensA), supporting regulatory acceptance of the Epi2SensA assay using the EpiDerm™ model (Mattek Corporation, Now Part of Sartorius, Ashland, MA, USA) as an alternative RhE source for OECD TG 442D skin sensitization testing. Full article

Background/Objectives: High-grade endometrial cancer (EC) is associated with poor outcomes, particularly in populations with a high burden of aggressive histologies. There is a critical need for accessible biomarkers to improve prognostic assessment and guide clinical management. Methods: In this study, we evaluated the feasibility and clinical relevance of monitoring circulating tumor DNA (ctDNA) by tracking somatic TP53 mutations using a routine next-generation sequencing (NGS) assay already implemented in diagnostic practice. Results: Among 21 patients with high-grade EC carrying TP53 mutations in the primary tumor, ctDNA was detectable in over 75% during follow-up. Baseline ctDNA detection strongly correlated with advanced disease: none of the FIGO I tumors were ctDNA-positive at diagnosis, whereas 73% of FIGO > I tumors showed detectable ctDNA. Patients with ctDNA detected at baseline had significantly poorer outcomes, with a 2-year recurrence-free survival (RFS) of 18% versus 60% and a 2-year overall survival (OS) of 40% versus 78%. Longitudinal monitoring revealed that postoperative persistence or reappearance of ctDNA was consistently associated with disease progression, often preceding radiological relapse. Conversely, early ctDNA clearance (at M4–M8) was associated with more favorable clinical trajectories. Conclusions: These findings highlight the potential role of ctDNA as a real-time molecular marker of minimal residual disease and tumor dynamics. Our results demonstrate that TP53-based ctDNA tracking using a standard NGS panel is feasible, sensitive, and clinically informative in high-grade EC. This approach may contribute to improving prognostic stratification and enabling more personalized, responsive clinical management, particularly in high-risk populations. Full article

Accidental crushing by sows is the primary cause of pre-weaning piglet mortality in intensive production, often due to the spatiotemporal lag of manual inspection. While Internet of Things (IoT) solutions exist, they frequently face challenges such as vision occlusion, high hardware costs, and latency. To address these, this study developed a low-cost multi-modal edge computing system based on TinyML. Using an ESP32-S3 microcontroller, the system employs a “Motion-Gated Acoustic Detection” strategy, activating a lightweight 1D-CNN model to identify piglet screams only when an IMU detects high-risk postural transitions of the sow. Results show the quantized model (5.1 KB) achieves 95.56% accuracy and 2 ms inference latency. The total end-to-end response latency is within 179 ms, ensuring intervention within the early “golden rescue window.” The low-power design enables the battery life to cover the entire lactation period. Field tests demonstrated that the system intercepted identified crushing risks within the monitored cohort, supporting its potential for significantly improving piglet survival probability. This research overcomes the limitations of single-modal monitoring and provides a scalable, cost-effective engineering intervention for enhancing animal welfare and achieving intelligent, unattended supervision in precision livestock farming. Full article

Neurodegenerative diseases (NDs) are increasingly considered neurometabolic disorders driven by early mitochondrial dysfunction, neuroinflammation, and synaptic alterations that precede clinical symptoms. This review summarises pre-clinical and experimental evidence suggesting that intermittent fasting (IF) may influence these early pathogenic processes by promoting metabolic switching, enhancing autophagy and mitochondrial quality control, and modulating neuroimmune pathways. We discuss recent advances in biomarker research supporting the early detection of neurodegenerative changes, including ultrasensitive analytical platforms that can identify neuronal, glial, and synaptic injury during preclinical stages. By integrating these biomarker developments with findings from human and experimental intermittent fasting studies, we highlight how high-sensitivity assays provide quantifiable insights into the neurometabolic effects of fasting. Furthermore, we discuss how precision nutrition strategies incorporating multimarker panels, phenotypic and epigenetic signatures, and longitudinal multi-omics profiling may facilitate personalised intermittent fasting protocols and improve monitoring of biological responses. Overall, these findings underscore the relevance of a clinical biochemistry perspective integrating advanced biomarker technologies to evaluate the neurometabolic effects of intermittent fasting as a potential early neuroprotective strategy for individuals at risk of neurodegeneration. Full article

Anticancer peptides (ACPs) offer a promising alternative to conventional chemotherapy but face challenges, including poor selectivity, limited tumor penetration, low cellular uptake, and rapid degradation in serum. To address these barriers, we developed synthetic mRNAs encoding chimeric ACPs designed for enhanced intracellular delivery and activity. mRNAs for constructs SAK6L9AS(1X), SAK6L9AS(4X), and WTAS-K6L9(4X) were transcribed in vitro and tested against 4T1 breast cancer cells. Cytotoxicity was assessed by cell confluence and MTT assays, while apoptosis was evaluated using caspase 3/7 activation, PI staining, and Annexin V flow cytometry. Our results demonstrate that all SAK6L9AS variants induced robust apoptosis and cellular toxicity in 4T1 cells. Importantly, this work provides the first demonstration of intracellular expression of an mRNA-encoded ACP fused to a cell-penetrating peptide, thereby validating a modular platform for RNA-based delivery of anticancer agents. This study highlights the feasibility of mRNA-encoded peptide therapeutics as a scalable and customizable strategy for cancer treatment. By combining the advantages of mRNA delivery with rational peptide design, ACP chimeras can be expressed directly inside tumor cells, overcoming the limitations of exogenous peptide administration. Our findings support further development of synthetic mRNA therapeutics to generate potent, selective anticancer peptides with reduced systemic toxicity and improved translational potential. Full article

Gait analysis study comparing unicompartmental vs. total knee arthroplasty, differences in knee kinematics: a retrospective cohort study. Background and Objectives: Total knee arthroplasty (TKA) is an effective treatment for advanced knee osteoarthritis, although functional outcomes may remain suboptimal in many patients. Unicompartmental knee arthroplasty (UKA) often provides better functional recovery but shows lower long-term implant survival. Recently, personalized TKA approaches have been developed to improve kinematic restoration and patient satisfaction. This study aimed to compare knee kinematics among patients who underwent personalized TKA, medial UKA, and healthy controls. Materials and Methods: This retrospective cohort study included 9 patients treated with robotic-assisted personalized TKA, 9 patients treated with medial UKA, and 9 healthy controls matched for age, sex, and BMI. Inclusion criteria were age 60–80 years, Kellgren–Lawrence grade III–IV, a minimum follow-up of 12 months, deviation from neutral HKA < 15°, healthy contralateral knee, and high postoperative functional scores. Exclusion criteria included valgus knees (HKA > 180°), postoperative complications, and neuromotor disorders. In the TKA group, a Medial Congruent implant was implanted with ROSA robotic assistance using a restricted kinematic alignment (±5° HKA) and asymmetric intercompartmental balancing. In the UKA group, a fixed-bearing medial implant (Physica ZUK) was used. Gait analysis was performed on a markerless instrumented treadmill (WalkerView™; Dalmine, Italy). Differences between groups were analyzed using one-way ANOVA and Tukey’s post-hoc test (p < 0.05). Results: UKA patients walked with a stiffer knee during stance. Knee range of motion during stance increased from UKA (6.3° ± 7.2°) to TKA (13.6° ± 8.8°, p = 0.045) and to controls (16.6° ± 4.5°, p = 0.02). During loading response, UKA patients showed lower flexion (10.2° ± 6.1°) than TKA (19.4° ± 7.9°, p = 0.049) and controls (19.6° ± 2.8°, p = 0.004). Knee flexion during swing was comparable between UKA and TKA. Conclusions: UKA patients demonstrated reduced knee flexion during early stance compared with robotic-assisted TKA and healthy controls. The observed differences may reflect multiple factors, including surgical technique, implant design, and patient-related characteristics. Because preoperative functional data were not available, potential selection bias cannot be excluded. These findings should be interpreted cautiously and warrant confirmation in larger prospective studies. Full article

The electrodeposition of rare earth metal alloys has attracted considerable interest, not only due to the challenges associated with the reduction in metal ions, but also because of their unique material properties and promising technological applications. This review presents a comprehensive analysis of the state-of-the-art in the electrochemical deposition of these alloys, focusing on various electrolytic systems, including aqueous solutions, organic molecular solvents, ionic liquids, and deep eutectic solvents. Despite inherent problematic factors such as low reduction potentials, competing hydrogen evolution reactions, and difficulties in controlling metal formation, recent advancements have enabled improved control over film formation, typically through the induced codeposition of lanthanides with iron-group metals. The influence of key factors, such as electrolyte composition and current/potential modes, on alloy codeposition, elemental and phase composition, structure, and deposition efficiency is discussed. The magnetic properties, electrocatalytic behavior, and corrosion resistance of the deposited films are also shown, highlighting their relevance for high-performance applications. Full article

Background/Objectives: Doxorubicin is an anthracycline chemotherapeutic agent widely used in the treatment of breast cancer. However, its clinical utility is limited by the drug’s resistance development, low oral bioavailability, and dose-dependent side effects. The semi-essential amino acid, L-arginine, has gained attention as a potential adjuvant that could improve the drug distribution and cytotoxic effectiveness of chemotherapeutics. This study aimed to explore the multifunctional effect of L-arginine on the intestinal absorption and anti-breast cancer activity of doxorubicin. Methods: The rabbit in situ intestinal perfusion technique was employed to investigate the membrane transport parameters of doxorubicin both in the absence and presence of L-arginine. Furthermore, the effect of L-arginine on the cytotoxic activity of doxorubicin against breast cancer cells (MCF-7) was assessed using the MTT assay. Results: Co-perfusion of L-arginine with doxorubicin enhanced the fraction of doxorubicin absorbed, with a recorded 4.3-fold enhancement in the jejuno-ileum and a 1.5-fold enhancement in the colon segment. In MCF-7 cells, co-treatment with L-arginine resulted in a significant potentiation of doxorubicin cytotoxicity. At L-arginine concentrations of 10 μM and 50 μM, the recorded IC₅₀ decreased from 41.3 μM to 8.2 μM and to 22.1 μM, respectively. The superior efficacy of 10 μM L-arginine compared to 50 μM reflected a biphasic concentration-dependent response. Conclusions: L-arginine modulated two critical aspects of doxorubicin efficacy, intestinal absorption and cytotoxic activity. The biphasic response emphasizes the importance of L-arginine dose optimization. These findings support the potential of L-arginine as a safe adjuvant for developing oral doxorubicin formulations. This approach can reduce the dose-related toxicity of doxorubicin and improve therapeutic outcomes. Full article

Ultrasound-assisted vacuum drying (USVD) has emerged as an increasingly studied food drying approach to overcome mass and energy transfer limitations associated with conventional vacuum drying. This study aims to clarify the behavior of the USVD process by synthesizing findings from product- and condition-specific studies. This review critically examines 38 core USVD studies published between 2014 and 2025, complemented by related comparative research, to assess the effects of USVD on drying efficiency, product quality, and key process parameters across diverse food matrices. The reviewed literature consistently demonstrates that USVD enhances drying kinetics, with increases in drying rate reaching approximately 94%, depending on product characteristics and operating conditions. Due to shorter drying times, USVD also provides potential economic advantages through reduced energy costs, equipment utilization and overall process costs. Furthermore, research has found that USVD retains quality attributes including color and bioactivity of a wide range of foods. USVD-dried products commonly exhibit improved microstructural integrity and enhanced porosity, which imparts superior rehydration. In conclusion, this study highlights the strong potential of USVD to enhance drying efficiency while preserving product quality. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health burden, yet effective therapeutic options remain limited. This study investigated the protective mechanisms of Astragalus membranous extract (AM) against high-fat diet (HFD)-induced MAFLD in mice using an integrated strategy combining network pharmacology, hepatic metabolomics, and 16S rRNA sequencing. UPLC–Q-Orbitrap–MS/MS identified 37 major constituents in AM, mainly phenolic acids and flavonoids. Iristectorin A, isorhamnetin, ononin, and rhamnocitrin were identified as key candidate compounds due to their relatively high abundance and confirmation as absorbed constituents in vivo. Network pharmacology and molecular docking indicated favorable interactions with hub targets (TNF, EGFR, and AKT1; binding energies < −5.0 kcal/mol) and highlighted the involvement of the AGE–RAGE signaling pathway and inflammation- and lipid metabolism-related processes. In vivo, AM significantly attenuated HFD-induced weight gain, decreased serum ALT and AST levels, and reduced hepatic lipid deposition. AM also alleviated oxidative stress by lowering malondialdehyde (MDA) and increasing superoxide dismutase (SOD) activity, while suppressing hepatic IL-1β and IL-6. Moreover, AM improved gut microbial homeostasis by restoring α-diversity and enriching beneficial genera, including Akkermansia and Bacteroides. Hepatic metabolomics further showed that AM partially normalized lipid metabolic disturbances, particularly glycerophospholipid and sphingolipid metabolism. Collectively, these results suggest that AM mitigates MASLD via a multi-component, multi-target mechanism, potentially through modulation of AGE–RAGE-associated inflammatory signaling and the gut–liver axis, supporting its development as a functional food-derived candidate for metabolic liver disorders. Full article

Background: Adjuvant RT remains a standard therapy for breast cancer, reducing recurrence risk and improving survival; however, it can also induce side effects, including pain. Inflammasome-related biomarkers, such as interleukin-18 (IL-18), play a role in inflammation-mediated pain, and we hypothesize that IL-18 may serve as a potential biomarker for breast cancer RT-induced pain. Methods: The association between IL-18 and pain was assessed among breast cancer patients receiving adjuvant RT. Plasma IL-18 protein concentration was quantified before and after RT using Ella SimplePlex technology (Biotechne). Clinically relevant pain outcomes included pre-RT pain (pain score ≥ 4), post-RT pain (pain score ≥ 4), and RT-related pain (increase in pain from <4 pre-RT to ≥4 post-RT). Multivariable logistic regression assessed the association between IL-18 and pain outcomes, adjusting for demographic and treatment-related factors. The joint effect of IL-18 and obesity on pain were also explored. Results: Patients in the highest pre-RT IL-18 quartile experienced higher odds of both post-RT pain (OR = 2.36, 95% CI: 1.15–4.87) and RT-related pain (OR = 2.73, 95% CI: 1.20–6.26). IL-18 levels increased from pre-RT to post-RT with a mean change of 0.07 (SD = 0.35). In addition, patients with elevated pre-RT IL-18 levels and obesity experienced the highest odds of post-RT pain (OR = 3.97, 95% CI: 1.98–7.98) and RT-related pain (OR = 2.84, 95% CI: 1.32–6.09), suggesting a potential combined effect. Conclusions: Elevated pre-RT IL-18 levels were associated with an increased risk of pain following adjuvant RT, particularly in breast cancer patients with obesity. Thus, IL-18 may serve as a potential biomarker for identifying patients at increased risk for RT-related pain and informing treatment decision. Full article

Historical small towns (HSTs) embody irreplaceable cultural heritage and territorial identity, facing depopulation, economic marginalization, and infrastructure decay. Improving their liveability and attractiveness is essential to reverse these trends and boost sustainable development. In this context, HSTs are potential drivers of circular and sustainable socio-technical systems, where the circular economy (CE) offers a framework for local sustainability. However, HSTs lack adequate sustainable CE implementation tools. This study, the culmination of the H-SMA-CE project, develops a Decision Support System (DSS) to assist local policymakers in planning CE transitions in Italian HSTs. The DSS integrates three building blocks: context analysis (metabolic flows, stakeholder networks), an intervention library with cost–benefit data, and a composite Municipal Circular Economy Index (MCEI). The tool enables users to assess baseline circularity, simulate scenarios, and identify optimal investment portfolios through multi-objective optimization. This approach allows for the simultaneous evaluation of the benefits of each sustainability aspect, i.e., environmental, economic and social. Tested on the municipality of Taurasi (Italy), an HST with a wine-based economy, the results show that balanced intervention strategies yield greater circularity improvements than single-objective approaches. The paper contributes to the discourse on digital tools for sustainability transitions, offering a replicable model for evidence-based CE governance in heritage-rich territorial contexts. Full article

Station-based car-sharing has been shown to reduce resource-intensive private car ownership. However, only a small proportion of the population uses station-based car-sharing, which could be improved by redesigning the service to reduce walking distances and increase availability. We developed a method for designing an efficient and cost-effective station-based car-sharing network for smart cities that emphasizes user comfort and convenience, while reducing the number of needed cars. To quantify the placements, we created a high-resolution synthetic population for Munich, Germany as a case study. The population was based on census and OpenStreetMap data, and each person was assigned to a suitable mobility plan derived from two mobility surveys. Since car ownership and station-based car-sharing are particularly associated with trips for vacations, we supplemented the mobility plans with long-distance travel data from a one-year tracking dataset. This allowed us to perform a spatial and temporal analysis of the theoretical potential of various station placements for station-based car-sharing. The tested station networks varied in user comfort, especially in the distance to the nearest station and the group size of car-sharing users. Our findings indicate that the best trade-off between convenience and efficiency is a station design with a group size of 217–949 people. We further found that the car-sharing fleet size is strongly influenced by long-distance trips, and that a substitution rate of 1:1.25 to 3.3 with private cars is possible. Full article