Search Results (95)

Background/Objectives: Immune checkpoint inhibitors (ICIs) have changed the treatment landscape for several advanced malignancies, but their use is accompanied by immune-related adverse events, including liver injury. Some cases resemble autoimmune hepatitis (AIH), although many are more accurately described as AIH-like immune-mediated hepatitis rather than classical AIH. This distinction matters, as diagnosis is often based on exclusion and management must balance hepatic recovery against interruption of potentially life-prolonging cancer therapy. This systematic review summarised the clinical phenotype, diagnostic assessment, treatment strategies, treatment response, ICI discontinuation, and rechallenge outcomes in patients with ICI-associated AIH-like liver injury. Methods: A systematic PubMed search was performed for English-language human studies reporting autoimmune hepatitis, AIH-like liver injury, or immune-mediated hepatitis following exposure to ICIs. Eligible studies included case reports, case series, retrospective cohorts, prospective cohorts, and pharmacovigilance-type studies with extractable clinical, treatment, or outcome data. Reviews, guidelines, non-original articles, animal studies, non-English publications, and reports without usable liver injury data were excluded. The review followed PRISMA principles. Risk of bias was assessed using Joanna Briggs Institute tools and summarised with ROBVIS. Given the heterogeneity of study design, diagnostic criteria, treatment definitions, and outcome reporting, formal meta-analysis was not appropriate; results were therefore synthesised descriptively. Results: Twenty-two studies were included, comprising 195 patients with ICI-associated AIH-like or immune-mediated hepatitis. Of these, 140 patients received active treatment, and 133/140 achieved clinical or biochemical recovery with varying therapies. Corticosteroids were the most frequently used first-line therapy, with recovery reported in 102/105 patients treated with corticosteroids alone. Mycophenolate mofetil was the main second-line agent for steroid-refractory disease, with response reported in 9/10 treated patients. Other therapies, including tacrolimus, azathioprine, ursodeoxycholic acid, bezafibrate, tocilizumab, basiliximab, infliximab, budesonide, and double plasma molecular adsorption system with or without plasma exchange, were described only in small numbers or isolated cases. Spontaneous recovery without pharmacological treatment was reported in 19 patients. ICI interruption or discontinuation occurred in 141 patients, and rechallenge was reported in 55 patients after recovery, with no recurrent hepatic toxicity documented in the extracted dataset. Conclusions: ICI-associated AIH-like liver injury is an important immune-related toxicity, but the available literature remains fragmented and methodologically heterogeneous. Most reported patients recovered, particularly with corticosteroids, and MMF appears to be the most consistently used escalation therapy in steroid-refractory cases. However, the strength of evidence is limited by uncontrolled designs, variable terminology, inconsistent diagnostic work-up, and non-standardised outcome definitions. Future studies should separate classical AIH from AIH-like immune-mediated hepatitis, use uniform criteria for severity and response, and report treatment denominators clearly, especially for rechallenge and steroid-refractory disease. Full article

Background: Fused deposition modeling (FDM) is one of the most well-known and often published methods for 3D-printed drug delivery systems. In early scientific reports, the active pharmaceutical ingredients were added by soaking, but later, a new milestone was established, after researchers started to manufacture their own filaments by hot-melt extrusion (HME). The number of publications covering this method has multiplied in the last decade, a wide range of natural and synthetic polymers have been tested with versatile active pharmaceutical ingredient components, and various printing parameters and their effects have been investigated. Objectives: In this review, we aim to synthesize how the available quality by design approaches and the scientific results established so far can facilitate the creation of a guideline for appropriate quality production of HME-FDM 3D-printed pharmaceuticals. Methods: Based on PRISMA 2020 guidelines, a systematic search of relevant publications from 2015 to 2025 was carried out using the PubMed database. Twenty-six articles were included, based on number of monitored parameters and methodological description. Reporting of important quality processes and material parameters was assessed. Results: HME, the FDM, and analytical testing experiences were compared and collected into three tables from the selected publications. In two different sections, the pharmacopeial dosage-form tests and the involvement of process analytical technologies (PAT) were also analyzed. We found that reporting of influential parameters is heterogenous, and lack of robust reporting schemes limits the development of QbD approaches. Conclusions: Regarding the data, trends were synthetized, and a guideline was created which is limited by inconsistent parameter reporting. Full article

This study presents a mathematical and kinematic modeling framework for analyzing trajectory behavior in a virtual reality (VR) welding simulator. Twenty novice participants performed repeated welding trials across three sessions, with torch trajectories recorded at 50 Hz in the task space. The proposed framework combines trial-level performance descriptors with derivative-based dynamic features, including spectral arc length (SPARC), log-normalized jerk (LNJ), and the number of velocity peaks (NVP), to characterize movement smoothness, intermittency, and longitudinal trajectory organization in a computer-simulated manual welding task. The results showed that spatial welding error decreased most clearly during the earliest stage of practice, with mean absolute lateral error declining from approximately 2.8 mm in the first trial to approximately 1.7 mm by the third trial. This early improvement was then broadly preserved across subsequent sessions. In contrast, smoothness- and fragmentation-related metrics exhibited more variable temporal patterns, indicating that improvements in task-space accuracy were not necessarily accompanied by uniform reorganization of movement dynamics. Associations between spatial error and kinematic features remained limited, suggesting that geometric task accuracy and dynamic trajectory organization represent complementary aspects of simulated manual performance. Overall, the findings show that high-frequency trajectory analysis in VR provides a useful basis for the mathematical modeling of dynamic behavior in simulated welding systems and supports the use of computer simulation for process-level investigation of manual task execution. Full article

The long-term durability of adhesive anchors in aggressive environments is a critical concern for infrastructure safety, with steel corrosion being one of the most detrimental phenomena. While glass fiber-reinforced polymer (GFRP) anchors offer corrosion-resistant alternatives to steel anchors in harsh marine environments, the bond performance at the anchorage interface progressively deteriorates under wetting–drying (WD) cycles, which may compromise long-term anchorage integrity. However, the bond characteristics of GFRP anchors under WD exposure, particularly the development of predictive models, remain insufficiently understood. This paper presents an experimental investigation into the impact of WD cycles on the bond of GFRP adhesive anchors in concrete. Twenty-four specimens were tested under pull-out loads, considering two key variables: bonded length (40 mm and 80 mm, corresponding to 5 and 10 times the bar diameter) and number of WD cycles (0, 30, 60, and 90). Artificial seawater was prepared via ASTM D1141-98 to simulate marine exposure conditions. The results revealed that both bond strength and bond stiffness decreased significantly with increasing WD cycles, while the failure mode progressively shifted from the bar–adhesive interface to the adhesive–concrete interface. Based on the experimental data, a cycle-dependent bond strength model was developed to predict the bond degradation of the anchor–concrete interface after WD exposure. Requiring only the undegraded concrete strength, the proposed model effectively captures the coupled effects of WD cycles and bonded length on bond strength degradation, presenting a practical tool for the durability design and service life evaluation of GFRP anchorage systems in coastal and marine environments. Full article

To address collision risks in teleoperated mobile robotic vehicles, this study proposes a Human–Machine Interaction-based Advanced Smooth Shared Control (ASSC) system aimed at enhancing obstacle avoidance and achieving smooth shared control between human operators and the automation system. The ASSC system integrates a novel approach using predictive vectors to represent the vehicle’s heading position, automatically adjusting the steering position upon obstacle detection to ensure smooth collision avoidance without changing the driver’s perception. Feedback forces applied to the steering wheel are calculated through an artificial potential field algorithm. Twenty participants were invited to operate the vehicle, providing feedback on the ASSC system’s performance relative to conventional obstacle avoidance methods. Performance metrics such as the effects of communication delays, Time to Complete the Task (TTC), ASSC effectiveness, performance of the delay impact on the ASSC system, and the Number of Obstacle Collisions (NOC) are analyzed. The results demonstrate that the ASSC system significantly outperforms traditional obstacle avoidance methods, providing more precise control in teleoperation. Statistical analysis indicates that the ASSC system improves safety, comfort and operational performance by 12.8%. This research highlights the ASSC system as a promising solution for enhancing automation, safety, and human–machine interaction in teleoperated mobile robotic vehicles. Full article

Background: The mirror neuron system (MNS) has been proposed as a key neural mechanism linking action perception, motor representation, and social cognition. This framework has increasingly been applied to pain research, encompassing pain empathy, observational learning of pain, and rehabilitative interventions such as mirror therapy. However, the literature is conceptually heterogeneous, methodologically diverse, and spans experimental, social, and clinical domains. Objective: This scoping review aims to map the extent, nature, and characteristics of the available evidence on the relationship between the MNS and pain, clarifying how MNS-related mechanisms are defined, investigated, and applied across different contexts. Methods: A scoping review was conducted using the methodological framework proposed by the Joanna Briggs Institute and reported in accordance with PRISMA-ScR guidelines. We searched PubMed/MEDLINE, Scopus, Web of Science, and PsycINFO. Studies were included if they addressed MNS-related mechanisms in pain processing, pain empathy, pain modulation, or pain rehabilitation. Eligible studies were charted and synthesized descriptively and thematically. Results: Twenty-one studies met the inclusion criteria. The evidence was predominantly derived from clinical and rehabilitative settings, with most studies focusing on mirror therapy or mirror visual feedback interventions. The majority of included populations consisting of adults with chronic pain conditions, particularly phantom limb pain and complex regional pain syndrome. Pain intensity, assessed mainly through self-reported clinical scales, was the most frequently reported outcome. A smaller number of studies investigated action observation or motor imagery paradigms, primarily in chronic musculoskeletal pain, showing short-term hypoalgesic effects. Across studies, substantial heterogeneity was observed in the conceptualization of MNS-related constructs, intervention protocols, outcome measures, and follow-up duration. Conclusions: Despite extensive theoretical discussion of the MNS, empirical applications are largely confined to clinical mirror-based interventions, with limited use of direct neurophysiological or neuroimaging markers. Since crucial conceptual and methodological gaps constrain comparability and translation into clinical practice, there is a need for clearer operational definitions and more integrated experimental and clinical research approaches. Full article

This paper proposes a low-cost measurement system that can be used to perform simultaneous blood pressure (BP) and breathing (BR) measurements. Regarding BP measurements, the main parameters that are accessed include systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure blood pressure (MAP), and heartbeat rate (HR). Concerning BR measurements, the main parameters that are accessed include the inspiration period and amplitude (IPA), the expiration period and amplitude (EPA), and the breathing rate (BR), as well as the statistical and standard deviation of all these parameters. The dual measurement capability of the proposed measurement system is very important since blood pressure and breathing parameters are not statistically independent and it is possible to obtain additional and valuable clinical information from the information provided by both biomedical variables when measured simultaneously. The analysis of the correlation between these variables is particularly important after performing intensive physical exercises, since it enables cardiac rehabilitation assessment, pre-surgical risk evaluation, detection of silent ischemia, and monitoring of chronic diseases recovery, among others. Regarding the performance evaluation of the proposed biomedical device, a prototype of the measurement system was developed, tested, and calibrated. Several experimental tests were carried out to evaluate the performance of the proposed measurement system and to obtain the correlation coefficients between different blood pressure and breathing parameters. The tests were based on a statistically significant number of measurements that were performed with a population that integrated twenty students in two groups with different habits of physical exercise practice but subjected to a set of common physical exercises, with graduated intensity levels. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the critical need for scalable, timely, and unbiased methods to monitor disease transmission at the population level. Wastewater-based epidemiology (WBE) provides an effective method for monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission by detecting viral RNA shed into the sewage system. Because it does not rely on individual testing, WBE can offer timely, cost-effective, and community-level insights into infection trends. In this study, we present a hierarchical Beta-Binomial model that integrates SARS-CoV-2 RNA concentration in wastewater with reported COVID-19 case counts to enhance the monitoring of community-level transmission dynamics. The model incorporates wastewater viral loads as a predictor and reported cases as the response, while adjusting for testing volume to account for biases introduced by fluctuations in testing practices. This approach enables reliable estimation of the effective reproduction number ($R_t$), even in the absence of consistent reporting of clinical data. Applied to twenty counties in California, our modeling framework demonstrates the potential of wastewater surveillance to inform public health decision making, particularly in locations with sparse clinical data. Full article

Background: In phenylketonuria (PKU) patients, dried blood spot (DBS) sampling remains the standard method for monitoring phenylalanine (Phe) levels. However, delays in reporting results can hinder timely dietary adjustments. Patients and caregivers have expressed a preference for point-of-care testing (POCT) devices that enable home-based monitoring. Objectives: Our aim was to compare blood Phe measurements in PKU patients and caregiver usability of a POCT system with DBS, which is the standard practice monitoring method. Methods: Twenty participants (eighteen children with PKU and two healthy controls) were recruited. Caregivers of children with PKU were asked to perform blood Phe measurements at home under the supervision of a researcher, using both the POCT device (Egoo Phe system) and DBS sampling. Healthy controls collected the same number of samples using both methods in a hospital setting. The POCT system required 40 µL of blood and used an enzymatic, bioluminescent detection system. DBS samples were analyzed by tandem mass spectrometry (TMS) and required two blood spots (approximately 100 µL of blood). The Egoo Connect App, linked via Bluetooth to the POCT device, displayed results after 29 min. Caregiver usability of the POCT system was assessed using questionnaires at each visit. Results: A total of 100 paired samples were collected. Median values were 274 μmol/L (range: 30–1039) for POCT and 270 μmol/L (range: 20–1190) for DBS. POCT readings were a mean of 4.6% higher than DBS with a noticeable strong correlation observed (y = 1.017x; R² = 0.8450; p < 0.0001). The usability of the POCT system improved with caregiver practice, and all caregivers expressed a preference for POCT over DBS. Conclusions: The POCT system for blood Phe demonstrated strong concordance with DBS and high caregiver acceptance, highlighting its potential to transform PKU care through faster, patient-driven monitoring and more timely clinical decision-making. Full article

The study of the dynamics during droplet breakup is fascinating to engineers. Some industrial applications include fire extinguishing by sprinkler systems, painting of various components, washing processes, and fuel spraying in internal combustion engines, which involve the interaction between liquid droplets, gaseous flow field, and walls. In this work, washing operations effectiveness of civil aviation aircraft engines is analyzed. Periodic washing operations are necessary to slow down the effects of particle deposition, e.g., gas turbine fouling, to reduce the specific fuel consumption and the environmental impact of the gas turbine operation. This analysis describes the dynamics in the primary breakup, related to the breakup of droplets due to aerodynamic forces, which occur when the droplets are set in motion in a fluid domain. The secondary breakup is also considered, which more generally refers to the impact of droplets on surfaces. The latter is studied with particular attention to dry surfaces, investigating the limits for different breakup regimes and how these limits change when the impact occurs with surfaces characterized by different wettability. Surfaces with different roughness are also compared. All the tested cases are referred to surfaces at ambient temperature. Dimensionless numbers generalize the analysis to describe the droplet behavior. The analysis is based on several data reported in the open literature, demonstrating how different washing operations involve different droplet breakup regimes, generating a non-trivial data interpretation. Impact dynamics, droplet characteristics, and erosion issues are analyzed, showing differences and similarities between the literature data proposed in the last twenty years. Washing operation and the effects of gas turbine fouling on the aero-engine performance are still under investigation, demonstrating how experiments and numerical simulations are needed to tackle this detrimental issue. Full article

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy associated with occupational or environmental exposure to asbestos. Effective management of MPM remains challenging due to its prolonged latency period and the typically late onset of clinical symptoms. Accordingly, there is an increasing demand for the implementation of reliable, non-invasive, and data-driven diagnostic strategies within large-scale screening programs. In this context, the chemical profiling of volatile organic compounds (VOCs) in exhaled breath has recently gained recognition as a promising and non-invasive approach for the early detection of cancer, including MPM. Therefore, in this cross-sectional observational study, an overall number of 125 individuals, including 64 MPM patients and 61 healthy controls (HC), were enrolled. End-tidal breath fraction (EXP) was collected directly onto two-bed adsorbent cartridges by an automated sampling system and analyzed by thermal desorption–gas chromatography–mass spectrometry (TD-GC/MS). A machine learning approach based on a random forest (RF) algorithm and trained using a 10-fold cross-validation framework was applied to experimental data, yielding remarkable results (AUC = 86%). Fifteen VOCs reflecting key metabolic alterations characteristic of MPM pathophysiology were found to be able to discriminate between MPM and HC. Moreover, twenty breath samples from asymptomatic former asbestos-exposed (AEx) and eight MPM patients during follow-up (FUMPM) were exploratively analyzed, processed, and tested as blinded samples by the validated statistical method. Good agreement was found between model output and clinical information obtained by CT. These findings underscore the potential of breath VOC analysis as a non-invasive diagnostic approach for MPM and support its feasibility for longitudinal patient and at-risk subjects monitoring. Full article

Background/Objectives: Today, Artificial intelligence (AI) and machine learning (ML) significantly enhance predictive analytics in the healthcare landscape, enabling timely and accurate predictions that lead to proactive interventions, personalized treatment plans, and ultimately improved patient care. As healthcare systems increasingly adopt data-driven approaches, the integration of AI and data analysis has garnered substantial interest, as reflected in the growing number of publications highlighting innovative applications of AI in clinical settings. This review synthesizes recent evidence on application areas, commonly used models, metrics, and challenges. Methods: We conducted a systematic literature review between using Web of Science and Google Scholar databases from 2021–2025 covering a diverse range of AI and ML techniques applied to disease prediction. Results: Twenty-two studies met criteria. The most frequently used machine learning approaches were tree-based ensemble models (e.g., Random Forest, XGBoost, LightGBM) for structured clinical data, and deep learning architectures (e.g., CNN, LSTM) for imaging and time-series tasks. Evaluation most commonly relied on AUROC, F1-score, accuracy, and sensitivity. key challenges remain regarding data privacy, integration with clinical workflows, model interpretability, and the necessity for high-quality representative datasets. Conclusions: Future research should focus on developing interpretable models that clinicians can understand and trust, implementing robust privacy-preserving techniques to safeguard patient data, and establishing standardized evaluation frameworks to effectively assess model performance. Full article

Organic agriculture is a production system based on environmentally friendly practices that promote the conservation of natural resources, biodiversity, and the production of high-quality food. Its tenets are linked to the concept of sustainable development, which integrates environmental, social, and economic goals. In the face of global competition and changes in food systems, studying their competitiveness of organic agriculture is essential. It is key to assessing its potential for long-term development and competition with conventional agriculture. The purpose of this study is to identify and assess the spatial differentiation in the competitiveness of organic agriculture in EU countries. This study assessed the level of input and output competitiveness of organic agriculture in selected EU countries using the author’s synthetic taxonomic indicators consisting of several sub-variables. The competitiveness of organic farming in twenty-three countries (Cyprus, Latvia, Portugal, and Finland were not included due to a lack of statistical data) was analysed using one of the linear ordering methods, i.e., a non-pattern method with a system of fixed weights. The research has shown significant spatial differentiation in both the input competitiveness and the outcome competitiveness of organic agriculture in EU countries. In 2023, Estonia had the highest level of input competitiveness, followed by Austria, the Czech Republic, and Sweden. In 2023, Estonia had the highest synthetic indicator of outcome competitiveness, followed by The Netherlands and Denmark. In addition, an assessment was made of changes in EU organic agriculture in 2014–2023 by analysing the direction and dynamics of changes in selected measures of the development potential of organic agriculture in all member states (27 countries). This sector is characterised by high growth dynamics, including both the area under cultivation and the number of producers and processors of organic food. This study identified several important measures to support the development of organic farming (especially in countries where this type of activity is relatively less competitive) through targeted support mechanisms, such as policy and regulatory measures, financing, agricultural training and advisory services, scientific research, encouraging cooperation, and stimulating demand for organic products. Full article

The APSIM (Agricultural Production Systems Simulator)-Wheat model has been widely used to simulate wheat growth, but the sensitivity characteristics of the model parameters at different soil moisture levels in arid regions are unknown. Based on 2023~2025 winter wheat field data from the Changji Experimental Site, Xinjiang, China, this study conducted a global sensitivity analysis of the APSIM-Wheat model using Morris and EFAST methods. Twenty-one selected parameters were perturbed at ±50% of their baseline values to quantify the sensitivity of the aboveground total dry matter (WAGT) and yield to parameter variations. Parameters exhibiting significant effects on yield were identified. The calibrated APSIM model performance was evaluated against field observations. The results indicated that the order of influential parameters varied slightly across different soil moisture levels. However, the WAGT output was notably sensitive to accumulated temperature from seedling to jointing stage (T1), accumulated temperature from the jointing to the flowering period (T2), accumulated temperature from grain filling to maturity (T4), and crop water demand (E1). Meanwhile, yield output showed greater sensitivity to number of grains per stem (G1), accumulated temperature from flowering to grain filling (T3), potential daily grain filling rate during the grain filling period (P1), extinction coefficient (K), T1, T2, T4, and E1. The sensitivity indices of different soil moisture levels under Morris and EFAST methods showed highly significant consistency. After optimization, the coefficient of determination (R²) was 0.877~0.974, the index of agreement (d-index) was 0.941~0.995, the root mean square error (RMSE) was 319.45~642.69 kg·ha^–1, the mean absolute error (MAE) was 314.69~473.21 kg·ha^–1, the residual standard deviation ratio (RSR) was 0.68~0.93, and the Nash–Sutcliffe efficiency (NSE) was 0.26~0.57, thereby enhancing the performance of the model. This study highlights the need for more careful calibration of these influential parameters to reduce the uncertainty associated with the model. Full article

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and remains a significant global health challenge. Transarterial chemoembolization (TACE) is the treatment of choice for intermediate-stage HCC patients. While TACE induces localized cytotoxic and ischemic tumor necrosis, the resultant hypoxia paradoxically activates pro-angiogenic signaling pathways, which may promote tumor revascularization and recurrence. This study aimed to evaluate the plasma levels of angiogenetic factors pre- and post-TACE to assess their dynamic changes and potential clinical implications. Methods: Twenty-five intermediate-stage HCC patients were included in this monocentric prospective study. Peripheral blood samples were collected at baseline (pre-TACE), 24 h, 3 days, and 1 month post-TACE. Angiogenic factor levels were analyzed using a multiplex bead-based assay. Results: Angiopoietin-2 levels were significantly elevated three days post-TACE, followed by a gradual decline after one month. A similar pattern was observed for hepatocyte growth factor, with a marked increase at 24 h post-TACE and subsequent normalization. Endothelin-1 also exhibited a temporary increase, although it was only detected in four patients. Fibroblast growth factors (1 and 2) and vascular endothelial growth factor A were detected in a limited number of patients, which may indicate low systemic release or the need for a more sensitive detection method. Conclusions: These findings suggest that TACE induces a transient increase in angiogenic factors, likely due to tumor ischemia, tissue injury, or microenvironmental responses. Future studies should explore more sensitive detection methods and evaluate whether these factors could serve as prognostic biomarkers or therapeutic targets in HCC treatment. Full article