Search Results (9,713)

Background/Objectives: The impact of antimicrobial susceptibility testing methodology on the categorization and positioning of cefiderocol and aztreonam-avibactam against metallo-β-lactamase (MBL)-producing Gram-negative bacilli remains unclear. This study aimed to evaluate the in vitro activity of cefiderocol and aztreonam-avibactam against clinical MBL-producing isolates and to assess the agreement between different cefiderocol susceptibility testing methods. Methods: A total of 299 non-duplicate clinical MBL-producing Gram-negative isolates were collected from clinical samples between 2022 and 2025. Antimicrobial susceptibility testing was performed using broth microdilution, disc diffusion, and gradient strip diffusion according to European Committee on Antimicrobial Susceptibility Testing (EUCAST) criteria. Carbapenemase genes were identified by immunochromatography and multiplex PCR. Categorical agreement and error rates between cefiderocol testing methods were analyzed. Results:Klebsiella pneumoniae was the predominant species, mainly producing NDM alone or in combination with OXA-48-like carbapenemases. Aztreonam-avibactam demonstrated complete activity against all Enterobacterales isolates (262/262, 100%) and high activity against Pseudomonas spp. (33/37, 89%). Cefiderocol susceptibility among Enterobacterales varied markedly depending on the testing method. Disc diffusion yielded 14% susceptibility (37/262), which increased to 52% (136/262) after ATU resolution, whereas broth microdilution showed 85% susceptibility (224/262). This resulted in low categorical agreement (42%) and a high rate of major errors (58%), with no very major errors detected. Cefiderocol activity did not differ substantially across carbapenemase types and was highest against VIM-producing Pseudomonas spp. Conclusions: Aztreonam-avibactam showed consistent in vitro activity against MBL-producing Enterobacterales, whereas cefiderocol activity was strongly influenced by the susceptibility testing methodology. Disc diffusion substantially underestimated cefiderocol susceptibility compared with broth microdilution. These findings highlight the critical impact of testing methodology on cefiderocol categorization and support the therapeutic role of last-line agents in the management of MBL-producing Gram-negative infections, with direct implications for clinical microbiology laboratories and antimicrobial stewardship programs. Full article

Histopathological evaluation of tissue sections remains the gold standard for the diagnosis, classification, and grading of breast cancer (BC). The widespread adoption of whole-slide imaging (WSI) has enabled the digitization of histological slides and facilitated the development of artificial intelligence (AI) approaches for computational pathology. In recent years, machine learning and deep learning (DL) algorithms have been increasingly investigated for the analysis of hematoxylin and eosin (H&E)-stained images, with potential applications in tumor detection, histological classification, prognostic stratification, and prediction of treatment response. This narrative review summarizes recent developments in AI-driven models applied to BC histopathology and discusses their potential role in supporting diagnostic and prognostic assessment. Several studies have demonstrated the promising performance of DL algorithms in tasks such as the detection of lymph node metastases, assessment of residual tumor after neoadjuvant therapy, and prediction of clinical outcomes from histopathological images. Emerging research has also explored the possibility of inferring molecular and biomarker information from histology images, although these approaches currently identify statistical associations rather than direct molecular measurements. Despite the rapid expansion of this research field, significant barriers remain before routine clinical implementation can be achieved. Key challenges include dataset bias, variability in staining and image acquisition, limited external validation across institutions, and the need for transparent and reproducible model development. In addition, the translation of AI-based systems into clinical practice requires compliance with regulatory frameworks governing software used for medical purposes, such as those established by the U.S. Food and Drug Administration. Overall, AI represents a promising research direction in computational pathology and may contribute to decision-support tools capable of assisting pathologists in the analysis of digital slides. Continued efforts toward methodological rigor, large multicenter datasets, and prospective validation studies will be essential to determine the future role of AI in BC histopathology. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

Architectural heritage plays a central role in shaping visitors’ emotional experiences within cultural tourism contexts. However, empirical research examining how specific architectural styles evoke emotional responses remains limited, particularly when using objective measurement techniques. This study investigates emotional reactions to architectural heritage by applying the Stimulus–Organism–Response (SOR) theoretical framework. In this model, architectural styles act as environmental stimuli, emotional processing represents the organismic state, and the resulting emotional activation constitutes the response. An experimental protocol was conducted with a sample of 645 participants exposed to a series of standardized architectural heritage images representing different architectural styles and infrastructure types. Emotional reactions were captured in real time through facial emotion recognition technology, enabling the objective measurement of eight basic emotions: neutral, happiness, sadness, surprise, fear, disgust, anger, and contempt. The collected emotional data were statistically analyzed using Analysis of Variance (ANOVA) to identify significant differences in emotional responses across architectural styles, heritage typologies, and gender. When significant differences were detected, Tukey’s HSD post hoc tests were applied to determine specific group contrasts. The findings reveal that different architectural styles generate distinct emotional patterns, highlighting the role of architectural aesthetics as a powerful mediator of affective engagement with heritage environments. From a theoretical perspective, this research contributes to heritage tourism and environmental psychology by integrating the SOR framework with real-time emotion detection technologies, providing a novel methodological approach for analyzing emotional responses to architectural heritage. Full article

Wildlife health surveillance is a vital element of disease prevention, biodiversity conservation, and public health protection, especially as most emerging infectious diseases originate from wildlife. In Slovenia, long-term passive surveillance based on necropsy data has yielded valuable insights into wildlife mortality patterns over the past three decades, despite inherent limitations such as carcass detectability, reporting bias, scavenging, and decomposition. Ongoing cooperation among governmental institutions, veterinary services, hunters, and wildlife management organisations has enabled the effective operation of this system, although passive surveillance remains subject to spatial, temporal, and species-specific biases. Necropsy data show that infectious diseases, particularly parasitic infections, are the main causes of mortality in key species such as roe deer and chamois, reflecting both their population abundance and targeted monitoring. In contrast, carcasses of species such as wild boar, red deer, small mammals, and birds are underrepresented due to ecological factors, biosecurity constraints, or low detectability. Overall, while passive wildlife surveillance does not provide representative population-level mortality estimates, it remains a reliable tool for identifying the presence or absence of significant diseases and for understanding broad mortality patterns when interpreted in the context of known methodological and ecological limitations. Full article

Gallium (Ga) in coal is a nationally emerging strategic mineral resource, yet research on using petrophysical methods to detect the spatial variation in critical metals in coal seams remains limited. Analyzing the distribution characteristics of Ga-rich coal using geophysical well-logging methods is of great significance for the development and utilization of Ga. This study introduces a quantitative method for predicting Ga-rich laminations in ultra-thick bituminous coal seams by integrating: (i) wireline-log-based lithofacies classification, (ii) lithofacies-constrained mineral inversion, and (iii) lithofacies-constrained and laboratory-established Ga–mineral correlations. The coal seam was first classified into four distinct lithofacies types—(i) parting, (ii) medium-ash coal (MA), (iii) low-ash coal (LA), and (iv) extra-low-ash coal (ELA)—through integration of conventional wireline log interpretation, cluster analysis, and XGBoost machine learning. Second, lithofacies-constrained Ga–host mineral associations were established by integrating core sample analysis, correlation analysis, and linear regression modeling. Third, mineral content predictions for each lithofacies were obtained through wireline-log-based mineral inversion, constrained by petrophysical boundaries. Finally, prediction uncertainties were evaluated using Markov Chain Monte Carlo (MCMC) simulation, while Ga-rich laminations were predicted by integrating log-derived mineral inversion results with regressed Ga prediction models. The results demonstrate strong agreement between mineral inversion and XRD analyses within uncertainty ranges, achieving a prediction accuracy of 73.6% for Ga. This validated methodology presents a novel approach for quantifying Ga concentrations in coal, as demonstrated through a case study. Full article

Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr⁻¹, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven structural response. The observed differences are primarily attributed to differences in spatial resolution and methodology between point-based FEM outputs and pixel-averaged satellite observations. The study demonstrates that combining satellite-based monitoring with numerical simulations provides a robust and cost-effective framework for dam safety assessment. This integrated approach supports improved interpretation of deformation behavior and offers practical value in extreme conditions, such as during flood events or climate-driven hydrological changes. Furthermore, continued advances in remote sensing and numerical modeling are expected to enhance the reliability of such approaches, making this methodology a transferable and sustainable solution for dam management worldwide. Full article

Background: Strength training (ST) is essential for enhancing athletic performance and reducing injury risk, yet traditional monitoring relies heavily on subjective assessment, limiting objective and individualized evaluation. Objective: This scoping review critically synthesizes current applications of artificial intelligence (AI) and wearable technologies (WT) in ST, with emphasis on methodological approaches, data characteristics, explainability, and practical readiness. Methods: Searches of PubMed and Scopus identified 13 peer-reviewed studies (2015–2025). Evidence was charted and synthesized to compare AI models, wearable sensor configurations, validation strategies, and translational potential. Results: Studies employed classical machine learning, deep learning, and hybrid approaches alongside inertial, force, strain, and physiological sensors to support exercise classification, load estimation, fatigue detection, and performance monitoring. Deep learning models dominated movement recognition tasks, whereas simpler models often aligned better with small datasets and interpretability requirements. However, most studies relied on limited, homogeneous samples and internal validation, restricting generalizability and real-world applicability. Explainability was inconsistently addressed, particularly in higher-risk applications such as injury prediction. Conclusions: AI-enhanced wearables provide objective and individualized ST monitoring, but current evidence remains largely experimental. To ensure a practical application is implemented, standardized datasets, robust external validation, and greater integration of explainable AI are required to support and deliver trustworthy decision-making. Full article

The integration of emerging technologies and real-world data is transforming the landscape of hematologic clinical trials. Artificial intelligence (AI) offers remarkable capabilities for predictive modeling, patient stratification, and adaptive trial design, while circulating tumor DNA (ctDNA) provides a minimally invasive biomarker for disease monitoring, the early detection of relapse, and treatment response assessment. Concurrently, real-world evidence (RWE) complements traditional clinical trial data by capturing treatment effectiveness, safety, and patient outcomes in broader, heterogeneous populations. This review examines the synergistic potential of AI, ctDNA, and RWE to optimize trial design and decision-making in hematologic malignancies. We discuss methodological innovations, including AI-driven patient selection, ctDNA-guided adaptive interventions, and the incorporation of RWE for external control arms and post-marketing surveillance. Key challenges, such as data standardization, regulatory considerations, and ethical implications, are also addressed. By integrating these advanced tools, clinical trials in hematology can achieve greater efficiency, precision, and translatability, ultimately accelerating the development of personalized therapies and improving patient outcomes. Full article

Background/Objectives: Vision-language models (VLMs) such as GPT (Generative Pre-Trained Transformer) have shown potential for medical image interpretation; however, challenges remain in generating reliable radiological findings in clinical practice, as exemplified by dental pathologies. This study proposes a Self-correction Loop with Structured Output (SLSO) framework as an integrated processing methodology to enhance the accuracy and reliability of AI-generated findings for jaw cysts in dental panoramic radiographs. Methods: Dental panoramic radiographs with jaw cysts were used to implement a 10-step integrated processing framework incorporating image analysis, structured data generation, tooth number extraction, consistency checking, and iterative regeneration. The framework functioned as an external validation mechanism for GPT outputs. Performance was compared against the conventional Chain-of-Thought (CoT) method across seven evaluation items: transparency, internal structure, borders, root resorption, tooth displacement, relationships with other structures, and tooth number. Results: The SLSO framework improved output accuracy for multiple items compared to the CoT method, with the most notable improvements observed in tooth number identification, tooth displacement detection, and root resorption assessment. In successful cases, consistently structured outputs were achieved after up to five regenerations. The framework enforced explicit negative finding descriptions and suppressed hallucinations, although accurate identification of extensive lesions spanning multiple teeth remained limited. Conclusions: This investigation established the feasibility of the proposed integrated processing methodology and provided a foundation for future validation studies with larger, more diverse datasets. Full article

Within the Industry 5.0 paradigm, the management of critical assets requires advanced digital architectures capable of ensuring resilience and operational sustainability. The present systematic review analyzes the state of the art in predictive maintenance (PdM) technologies for turbines and turbomachinery, providing a technical examination of anomaly and fault detection frameworks, extended to remaining useful life (RUL) estimation and root cause analysis (RCA). The work addresses inherent sectoral challenges, ranging from the processing of high-dimensional multivariate time series (MTS) from Supervisory Control and Data Acquisition (SCADA) systems to labeled data scarcity and signal non-stationarity in real-world environments. Both purely data-driven frameworks and hybrid physics-informed models, such as Physics-Informed Neural Networks (PINNs), are critically evaluated against performance indicators. A significant contribution of this study lies in the classification of methodologies based on their readiness for real-time inference, emphasizing the role of Explainable AI (XAI) in providing transparent insights to domain experts, who remain central to decision-making processes. The primary objective of this review is to offer an analytical overview of progress to date against current technological gaps, tracing a clear trajectory for future developments. In this regard, the adoption of Generative AI and Large Language Models (LLMs) is identified as a fundamental step toward evolving into interactive, human-centric decision support systems. Full article

Background/Objectives: While intravitreal administration allows for increased ocular exposure to anti-TNF-α monoclonal antibodies, there is still a need for developing delivery systems able to prolong ocular drug exposure and alleviate patient compliance and safety concerns because of repeated injections. Therefore, the objective of this work was to guide the design of sustained-release formulations of anti-TNF-α monoclonal antibodies for intravitreal administration through a model-based strategy in non-infectious uveitis in the preclinical setting. Methods: Using an in-house-developed anterior uveitis disease model in rats, an intravenous reference dose reducing free TNF-α by 90% at the biophase was established. Intravitreal administrations of sustained-release formulations every 24 weeks were then simulated for adalimumab, golimumab and infliximab to evaluate TNF-α kinetics in the anterior chamber of the eye at different release rates. The selected sustained-release formulation was further evaluated for possible formulation issues causing device emptying before the next administration. Results: Intravitreal administration of sustained-release formulations releasing adalimumab, golimumab or infliximab at 1.802, 0.979 and 1.442 μg/week, respectively, met the predefined criteria of ≥90% reduction in free TNF-α at the biophase. TNF-α levels in aqueous humour were anticipated to be the most sensitive to detect possible formulation issues. Formulation emptying 10, 4 or 8 weeks for adalimumab, golimumab and infliximab, respectively, before next administration triggered TNF-α reaching pathological levels at week 24 post-dose. Conclusions: This work underscores the potential of new approach methodologies in the preclinical drug development of sustained-release formulations for intravitreal administration in ocular inflammatory disorders with less animal testing and without compromising the accuracy of model-informed predictions for human translation. Full article

Background: Almost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In either case, the correlation value will be impacted based on the perspective that the missing data represents no useful information. However, missing values occur in real datasets for a variety of reasons. In metabolomics datasets a major reason for missing values is that a specific measurable phenomenon falls below the detection limits of the analytical instrumentation (left-censored values). These missing data are not missing at random, but represent potentially useful information by virtue of their “missingness” at one end of the data distribution. Methods: To include this information due to left-censored missingness, we propose the information-content-informed Kendall-tau (ICI-Kt) methodology. We develop a statistical test and then show that most missing values in metabolomics datasets are the result of left-censorship. Next, we show how left-censored missing values can be included within the definition of the Kendall-tau correlation coefficient, and how that inclusion leads to an interpretation of information being added to the correlation. We also implement calculations for additional measures of theoretical maxima and pairwise completeness that add further layers of information interpretation in the methodology. Results: Using both simulated and over 700 experimental data sets from the Metabolomics Workbench, we demonstrate that the ICI-Kt methodology allows for the inclusion of left-censored missing data values as interpretable information, enabling both improved determination of outlier samples and improved feature–feature network construction. Conclusions: We provide explicitly parallel implementations in both R and Python that allow fast calculations of all the variables used when applying the ICI-Kt methodology on large numbers of samples. The ICI-Kt methods are available as an R package and Python module on GitHub. Full article

The integration of artificial intelligence (AI) into solar energy systems has emerged as a transformative pathway to enhance efficiency, reliability, and sustainability in renewable energy. This review examines recent advances in AI-driven optimization and integration strategies across photovoltaic and solar thermal technologies with elements of bibliometric analysis to identify trends, methodologies, and research directions. A particular emphasis is placed on machine learning and deep learning techniques applied to solar irradiance forecasting, maximum power point tracking, fault detection, energy management, and predictive maintenance. Unlike earlier reviews that focused on isolated applications, this work highlights the systemic role of AI in enabling smart grids, hybrid systems, and large-scale energy storage integration. The novelty of this contribution lies in mapping the evolution from traditional control methods to intelligent, self-adaptive frameworks that couple physical modeling with data-driven approaches, offering a structured roadmap for future developments. Furthermore, the review identifies challenges such as data scarcity, computational demand, and interpretability of AI models, while outlining opportunities for process intensification, resilience, and techno-economic optimization. By bridging technical progress with implementation prospects, this article provides an updated reference for researchers, policymakers, and industry stakeholders seeking to accelerate the deployment of AI-enhanced solar energy solutions. Full article