Search Results (1,707)

Background/Objectives: Radiomics enables extraction of quantitative imaging features to support non-invasive classification of prostate cancer (PCa). Accurate detection of clinically significant PCa (csPCa; Gleason score ≥ 3 + 4) is crucial for guiding treatment decisions. However, many studies explore limited feature selection, classifier, and harmonization combinations, and lack external validation. We aimed to systematically benchmark modeling pipelines and evaluate whether combining radiomics with the lesion-to-normal ADC ratio improves classification robustness and generalizability in multicenter datasets. Methods: Radiomic features were extracted from ADC maps using IBSI-compliant pipelines. Over 100 model configurations were tested, combining eight feature selection methods, fifteen classifiers, and two harmonization strategies across two scenarios: (1) repeated cross-validation on a multicenter dataset and (2) nested cross-validation with external testing on the PROSTATEx dataset. The ADC ratio was defined as the mean lesion ADC divided by contralateral normal tissue ADC, by placing two identical ROIs in each side, enabling patient-specific normalization. Results: In Scenario 1, the best model combined radiomics, ADC ratio, LASSO, and Naïve Bayes (AUC-PR = 0.844 ± 0.040). In Scenario 2, the top-performing configuration used Recursive Feature Elimination (RFE) and Boosted GLM (a generalized linear model trained with boosting), generalizing well to the external set (AUC-PR = 0.722; F1 = 0.741). ComBat harmonization improved calibration but not external discrimination. Frequently selected features were texture-based (GLCM, GLSZM) from wavelet- and LoG-filtered ADC maps. Conclusions: Integrating radiomics with the ADC ratio improves csPCa classification and enhances generalizability, supporting its potential role as a robust, clinically interpretable imaging biomarker in multicenter MRI studies. Full article

The aim of the present study was to test the prediction ability of three different supervised chemometric algorithms, such as linear discriminant analysis (LDA), k-nearest Neighbor (k-NN) and artificial neural networks (ANNs), for fruit juice classification and differentiation, based on isotopic and multielemental content. To accomplish this, a large experimental dataset was analyzed using inductively coupled plasma mass spectrometry (ICP-MS) together with isotope ratio mass spectrometry (IRMS), and a low data fusion approach was applied. Three classifications were tested, namely the following: (i) fruit differentiation of different juice types; (ii) apple and orange juice differentiation; and (iii) distinguishing between processed versus directly pressed apple juices. The results demonstrated that ANNs can offer the most accurate results, compared with LDA and k-NN, for all three cases of classification, highlighting once again the advantages of deep learning models for modeling complex data. The work revealed the higher potential of advanced chemometric methods for accurate classification of fruit juices, compared with traditional approaches. This approach could represent a realistic tool for ensuring the juice’s quality and safety, along with complying with regulations and combating fraud. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

This study uses the CREATE^TM-AV/Kestrel simulation software to model a towed jumper scenario using standard aircraft settings to quantify paratrooper stability and risk of contact during static line airborne operations. The focus areas of this study include a review of the technical build-up, which includes aircraft, paratrooper and static line modeling, plus preliminary functional checkouts executed to verify simulation performance. This research and simulation development effort is driven by the need to meet the analysis demands required to support the US Army Personnel Airdrop with static line length studies and the North Atlantic Treaty Organization (NATO) Joint Airdrop Capability Syndicate (JACS) with airdrop interoperability assessments. Each project requires the use of various aircraft types, static line lengths and exit procedures. To help meet this need and establish a baseline proof of concept (POC) simulation, simulation setups were developed for a towed jumper from both the C-130J and C-17 using a 20-ft static line to support US Army Personnel Airdrop efforts. Concurrently, the JACS is requesting analysis to support interoperability testing to help qualify the T-11 parachute from an Airbus A400M Atlas aircraft, operated by NATO nations. Due to the lack of an available A400M geometry, the C-17 was used to demonstrate the POC, and plans to substitute the geometry are in order when it becomes available. The results of a nominal Computational Fluid Dynamics (CFD) simulation run using a C-17 and C-130J will be reviewed with a sample of the output to help characterize performance differences for the aircraft settings selected. The US Army Combat Capabilities Development Command Soldier Center (DEVCOM-SC) Aerial Delivery Division (ADD) has partnered with the US Air Force Academy (USAFA) High Performance Computing Research Center (HPCRC) to enable Modeling and Simulation (M&S) capabilities that support the Warfighter and NATO airdrop interoperability efforts. Full article

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience. Full article

This systematic literature review addresses the critical challenge of ensuring robustness and adaptability in AI-based machine health monitoring (MHM) systems. While the field has seen a surge in research, a significant gap exists in understanding how to effectively manage data scarcity, unknown fault types, and the integration of diverse data streams for real-world industrial applications. The problem is magnified by the rarity of failure events, which leads to imbalanced datasets and hampers the generalizability of predictive models. To synthesize the current state of research and identify key solutions, we followed a rigorous, modified PRISMA methodology. A comprehensive search across Scopus, IEEE Xplore, Web of Science, and Litmaps initially yielded 3235 records. After a multi-stage screening process, a final corpus of 85 peer-reviewed studies was selected. Data were extracted and synthesized based on a thematic framework of 13 core research questions. A bibliometric analysis was also conducted to quantify publication trends and research focus areas. The analysis reveals a rapid increase in research, with publications growing from 1 in 2018 to 35 in 2025. Key findings highlight the adoption of transfer learning and generative AI to combat data scarcity, with multimodal data fusion emerging as a crucial strategy for enhancing diagnostic accuracy. The most active research themes were found to be Predictive Maintenance and Edge Computing, with 12 and 10 references, respectively, while critical areas like standardization remain under-explored. Overall, this review shows that AI benefits machine health monitoring but still faces challenges in reproducibility, benchmarking, and large-scale validation. Its main limitation is the focus on English peer-reviewed studies, excluding industry reports and non-English work. Future research should develop standardized datasets, energy-efficient edge AI, and socio-technical frameworks for trust and transparency. The study offers a structured overview, a roadmap for future work, and underscores the importance of AI in Industry 4.0. Full article

Age-related impaired wounds often become infected with bacteria, leading to substantial mortality and morbidity in the elderly. The decline in androgen levels with increasing age is believed to exacerbate inflammation during wound infections. Despite its well-documented anti-inflammatory activities in wound repair, little is known about the effect of age-related androgen deprivation on bacterial phagocytosis in impaired chronic wounds. The aim of this study was to investigate the effect of age-related testosterone deprivation on the phagocytic functions of THP-1 monocyte-derived macrophages to eliminate Gram-positive and Gram-negative bacteria in vitro. Host–pathogen interaction experiments were conducted to quantify the macrophage-mediated clearance of two common wound-associated bacteria, methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa, under in vitro environments that model testosterone levels representative of those found in elderly males, healthy young adults and testosterone replacement therapy (TRT). Testosterone and its metabolite 5α-dihydrotestosterone (DHT) significantly dampened the macrophage-mediated phagocytosis of both MRSA and P. aeruginosa in a dose-dependent manner (p < 0.05). Blockade of the androgen receptor (AR) using enzalutamide confirmed that testosterone mediates bacterial clearance through binding to the AR. Blocking the conversion of testosterone to DHT through stimulation of macrophages with the 5-α-reductase inhibitor finasteride reversed the testosterone-mediated effects on bacterial clearance, which confirmed that testosterone could potentially dampen the innate phagocytic responses in macrophages through conversion to DHT. Novel findings in this study suggest that the selective manipulation of the AR and/or blockade of testosterone–DHT conversion may provide effective therapeutic treatments to combat wound infections in the elderly. Full article

As deepfake technology matures, its risks in spreading false information and threatening personal and societal security are escalating. Despite significant accuracy improvements in existing detection models, their inherent opacity limits their practical application in high-risk areas such as forensic investigations and news verification. To address this gap in trust, explainability has become a key research focus. This paper provides a systematic review of explainable deepfake detection methods, categorizing them into three main approaches: forensic analysis, which identifies physical or algorithmic manipulation traces; model-centric methods, which enhance transparency through post hoc explanations or pre-designed processes; and multimodal and natural language explanations, which translate results into human-understandable reports. The paper also examines evaluation frameworks, datasets, and current challenges, underscoring the necessity for trustworthy, reliable, and interpretable detection technologies in combating digital misinformation. Full article

Objectives: In response to the high prevalence of global obesity and associated metabolic diseases, this study aimed to investigate the effects of Pickering emulsions stabilized by cellulase-hydrolyzed pomelo peel insoluble dietary fiber (IDF), namely EPI and its octenyl succinic anhydride (OSA)-modified form (OSA-EPI), on alleviating high-fat diet (HFD)-induced metabolic disorders in mice. Methods: Male C57BL/6J mice were subjected to an HFD-induced obesity model. Biochemical index determination, histopathological examination, gut microbiota analysis, and short-chain fatty acids (SCFAs) analysis were used to study the potential efficacy of pomelo peel IDF-based emulsion (EPI and OSA-EPI) in alleviating obesity and related metabolic diseases. Results: The findings demonstrated that both emulsions effectively mitigated HFD-induced health impairments: reduced weight gain, improved blood glucose and lipid profiles, attenuated tissue steatosis and inflammation, and lowered oxidative stress. Furthermore, both EPI and OSA-EPI restored gut microbiota diversity, promoted the proliferation of beneficial bacterial taxa (e.g., Akkermansia), and inhibited the growth of harmful genera (e.g., Muribaculum, Faecalibaculum). These changes were accompanied by increased production of SCFAs. Conclusions: This study confirms that modified pomelo peel IDF can effectively exert the health intervention effect of IDF on obesity when used as an emulsion stabilizer, providing a robust scientific foundation for the application of emulsified dietary fibers in combating obesity and related metabolic disorders. Full article

The bark of Dillenia indica L. is a rich source of phenolic and triterpenoid compounds, including betulinic acid (BA), known for their antioxidant and anti-aging properties. This study investigated the antioxidant potential of a BA-enriched extract through a multidisciplinary approach combining computational, experimental, and cell-based evaluations. Molecular docking and molecular dynamics simulations revealed that BA binds stably to Kelch-like ECH-associated protein 1 (KEAP1), suggesting activation of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. Extraction conditions were optimized using response surface methodology (RSM) and artificial neural network (ANN) modeling, yielding the maximum total phenolic content (TPC; 85.33 ± 2.26 mg gallic acid equivalents/g) and total flavonoid content (TFC; 75.60 ± 1.66 mg catechin equivalents/g), with ANN demonstrating superior predictive performance compared to RSM. Electrospray ionization tandem mass spectrometry (ESI-MS/MS) confirmed the presence of BA in the optimized extract. Simulated gastrointestinal digestion revealed reductions in TPC, TFC, and radical scavenging activity during the gastric phase. In ultraviolet B (UVB)-irradiated human keratinocyte (HaCaT) cells, the optimized extract significantly reduced intracellular reactive oxygen species (ROS) and upregulated the KEAP1-Nrf2-heme oxygenase-1 (HO-1) pathway, confirming its antioxidant mechanism. These findings highlight the extract’s stability, bioactivity, and mechanistic efficacy, supporting its application as a nutraceutical ingredient for combating oxidative stress and skin aging. Full article

The growing threat of Antimicrobial Resistance (AMR) demands innovative drug discovery, yet conventional 2D cell cultures fail to accurately mimic in vivo conditions, leading to high failure rates in preclinical studies. This review addresses the critical need for more physiologically relevant platforms by exploring recent advancements in bioengineered 3D tissue models for studying bacterial pathogenesis and antimicrobial drug discovery. We conducted a systematic search of peer-reviewed articles from 2015 to 2025 across PubMed, Scopus, and Web of Science, focusing on studies that used 3D models to investigate host–pathogen interactions or antimicrobial screening. Data on model types, biomaterials, fabrication techniques, and key findings were systematically charted to provide a comprehensive overview. Our findings reveal that a diverse range of biomaterials, including biopolymers and synthetic polymers, combined with advanced techniques like 3D bioprinting, are effectively used to create sophisticated tissue scaffolds. While these 3D models demonstrate clear superiority in mimicking biofilm properties and complex host–pathogen dynamics, our analysis identified a significant research gap: very few studies directly integrate these advanced bioengineered 3D models for high-throughput antimicrobial drug discovery. In conclusion, this review highlights the urgent need to bridge this disparity through increased research, standardization, and scalability in this critical interdisciplinary field, with the ultimate goal of accelerating the development of new therapeutics to combat AMR. Full article

Establishing artificial sand-fixing plantations is a key strategy for combating land desertification and enhancing soil carbon sequestration in arid regions. To evaluate the effects of Corethrodendron scoparium (Fisch. & C. A. Mey.) Fisch. & Basiner. plantations on soil carbon storage along the southern edge of the Tengger Desert, a systematic investigation of the 0–100 cm soil profile was conducted, using mobile sand dunes as the control (CK). The study analyzed dynamic changes in soil carbon fractions and their driving factors during the succession of C. scoparium plantations. After 40 years of vegetation restoration, total soil carbon, soil inorganic carbon (SIC), and soil organic carbon (SOC) contents increased by 0.87-, 0.77-, and 1.27-fold, respectively, while the Carbon Pool Management Index improved by 1.40-fold. Following 10 years of restoration, SIC content, as well as the ratios of particulate organic carbon/SOC, inert organic carbon (IOC)/SOC, and heavy-fraction organic carbon/SOC, increased with soil depth. In contrast, SOC content, the absolute amounts of SOC fractions, and the ratios of dissolved organic carbon/SOC, easily oxidizable organic carbon/SOC, light-fraction organic carbon/SOC, and mineral-associated organic carbon (MAOC)/SOC all showed decreasing trends with depth. Overall, C. scoparium plantations enhanced the contents of both labile and stable SOC fractions. The proportions of IOC and MAOC within SOC rose from 52.21% and 34.19% to 60.96% and 45.51%, respectively, indicating greater stability of the soil carbon pool. Structural equation modeling and redundancy analysis revealed that soil pH, bulk density, and soil water content were significantly negatively correlated with carbon fractions, whereas total nitrogen, vegetation cover, C/N ratio, electrical conductivity, available phosphorus, and alkali-hydrolyzable nitrogen were identified as the main drivers of carbon fraction variation. Full article

Background: The increasing prevalence of antibiotic-resistant Mycoplasma genitalium poses a significant challenge to global public health, necessitating the exploration of alternative therapeutic strategies, including vaccine development. Methods: In this study, we employed an immuno-informatics-based reverse vaccinology approach augmented with artificial intelligence-driven tools, to identify and characterize potential B-cell and T-cell epitopes from the hypothetical proteins (HPs) retrieved from the genome of the MG_G37T strain, a previously uncharacterized yet promising vaccine target. Using multiple softwares, a systematic pipeline was utilized to assess the sub-cellular localization, antigenicity, and allergenicity of the selected proteins. Results: Sub-cellular localization analysis identified the presence of several outer membrane and extracellular proteins in the genome of MG_G37T, indicating their surface association and accessibility to immune surveillance. Antigenicity and allergenicity prediction tools led to the identification of two top-scoring hypothetical proteins (fig|2097.71.peg.1 (UniProt ID: P22747) and fig|2097.70.peg.33 (UniProt ID: Q57081)) that demonstrated strong antigenic potential, non-allergenic properties, and suitability as vaccine candidates. Epitope mapping and structural modeling analyses further validated the immunogenic potential of these epitopes, highlighting their ability to interact with host immune components effectively. Comparative analyses with mouse allelic regions indicated the potential translational relevance of these predicted epitopes for preclinical studies. Conclusions: In particular, this study highlights the potential of these two hypothetical proteins as a promising vaccine candidate and provides a strong reason for experimental validation towards the design and development of effective vaccines to combat M. genitalium infections in the era of antimicrobial resistance. Full article

The increasing use of urinary catheters in healthcare, driven by an aging population and escalating antimicrobial resistance, presents both benefits and challenges. While they are essential to managing urinary retention and enabling precise urine output monitoring, their use significantly increases the risk of catheter-associated urinary tract infections (CAUTIs), the most common type of healthcare-associated infection. CAUTI risk is closely linked to the duration of catheterization and the formation of bacterial biofilms on catheter surfaces. These biofilms, often composed of polymicrobial communities encased in an extracellular matrix, promote persistent infections that are highly resistant to conventional antimicrobial therapies. Common CAUTI uropathogens include E. coli, E. faecalis, P. aeruginosa, P. mirabilis, K. pneumoniae, S. aureus, and Candida spp. The complexity and resilience of these biofilm-associated infections underscore the urgent need for innovative treatment strategies. Therefore, dynamic in vitro bladder infection models, which replicate physiological conditions such as urine flow and bladder voiding, have become valuable tools for studying microbial behavior, biofilm development, and therapeutic interventions under real clinical conditions. This review provides an overview of CAUTIs, explores the role of biofilms in sub-optimal responses to antimicrobial treatment and advances in model systems, and presents promising new approaches to combating these infections. Full article

Although orthodontic appliances are widely used in daily practice, they also have their downsides due to the large amount of metal ions released from their surface. In this study, the influence of such released metal ions on the yeast Saccharomyces cerevisiae W303 as a model organism was investigated. Experimental yeast media in which metal ions (iron, aluminum, nickel, chromium, copper, and manganese) were eluted for 3, 7, 14, and 28 days were prepared and then used for yeast cultivation (up to the early stationary growth phase). The growth, increase, and viability of the cells were tested. The mitochondria were isolated from the spheroplasts, and the mitochondrial proteins were obtained and analyzed by liquid chromatography/mass spectrometry. Fortythree significantly altered proteins were identified. They showed significantly reduced expression in all metal-treated groups compared to the control. The metabolic processes for energy supply (glycolysis, gluconeogenesis, tricarboxylic acid cycle, and adenosine triphosphate synthesis) dominated with 50% of the total amount of significantly altered proteins in all samples, but their proportions changed at different time points. The downregulation of mitochondrial proteins such as Atp1, Atp2, and Pet9 under conditions of metal overload suggests a broader impairment of mitochondrial function. Three levels of response to stress can be observed—at relatively low metal ion concentrations in the medium (3 days of elution, approx. 3 mg/L), at medium concentrations (7 days of elution, approx. 5.5 mg/L), and at high concentrations (>8 mg/L, 14 and 28 days of elution), each affecting a specific group of proteins. The results show that mixtures of metal ions in experimental media led to a specific response (in terms of the amount and type of proteins) in each sample type to combat the provoked stress. Full article