Search Results (5,265)

Background: Neuroblastoma (NB) presents significant challenges in pediatric oncology, particularly in high-risk cases where local recurrence occurs in ~35% of patients. Cold Atmospheric Plasma (CAP) has emerged as a promising treatment due to its selective cytotoxicity toward cancer cells while sparing normal cells. Methods: This study assessed CAP efficacy using in vitro NB cell lines (SK-N-AS and LAN-5) and in vivo xenograft murine models. In vitro, CAP was applied via a helium jet, and cellular responses were evaluated for viability, reactive oxygen species (ROS), lipid peroxidation, DNA damage, and cell cycle, while apoptosis was measured by Annexin V/PI flow cytometry. In vivo, CAP was applied to unresected tumors and residual tumors after incomplete resection. Tumor regrowth was monitored, and histological analysis was performed. Results: CAP reduced NB cell viability in a dose- and time-dependent manner by increasing intracellular ROS and lipid peroxidation. CAP-treated NB cells showed a 50% rise in oxidative DNA damage, a two-fold increase in apoptosis, and alterations in cell-cycle progression, while normal fibroblasts showed modest effects. CAP predominantly induced apoptosis, though secondary necrosis appeared with prolonged exposures, consistent with caspase-3 and PARP pathways. In xenografts, CAP reduced tumor diameter by 60% and increased caspase-3-positive cells, with minimal effects on normal tissue. Conclusions: CAP demonstrates strong therapeutic potential as a targeted, non-invasive NB treatment, particularly for residual tumors near vascular structures with consistent exposure times (60–300 s). Full article

Background: AMR causes a large global health burden, with approximately 4.95 million deaths linked to bacterial AMR in 2019, 1.27 million due to AMR directly. Although Brazil mandated prescriptions for systemic antibiotics in 2010/2011, self-medication and access without prescriptions continue, with community pharmacists playing a vital part in antimicrobial stewardship (AMS). This study examined antibiotic misuse and AMR in Brazil through community pharmacists’ perspectives, emphasising their dual role as professional actors and frontline observers of public behaviour. Methods: We conducted 20 semi-structured interviews with community pharmacists and performed reflexive thematic analysis of their accounts, repeating five independent analytic cycles to confirm thematic robustness. Results: Six themes were consistently identified as recounted by pharmacists in their practice contexts: Access and Self-Medication; Relationships with Healthcare Professionals; Knowledge and Beliefs about Antibiotics; Use and Adherence; Healthcare System Barriers; and Regulation and Enforcement. Pharmacists mentioned regular requests for antibiotics without prescriptions, drug reuse, and significant impact from community, i.e., from relatives, and peers. The common misunderstanding was that antibiotics treat viral illnesses. Structural issues, for instance GP appointment costs and long waits, made patients seek help from pharmacies. Due to regulation being applied inconsistently, pharmacies struggled to refuse unsuitable requests. Conclusions: Framed through pharmacists’ dual vantage as professionals and frontline observers, the findings highlight intertwined factors underpinning inappropriate antibiotic use in Brazil and support a multi-pronged intervention spanning health system strengthening, professional education, economic considerations, and community engagement. Full article

This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/bin, current-to-best/1/bin, and pbest/1/bin executed within a self-adaptive framework that employs jDE parameter control. To prevent stagnation and premature convergence, TRIDENT-DE incorporates adaptive micro-restart mechanisms, which periodically reinitialize a fraction of the population around the elite solution using Gaussian perturbations, thereby sustaining exploration even in rugged landscapes. Additionally, the algorithm integrates a greedy line-refinement operator that accelerates convergence by projecting candidate solutions along promising base-to-trial directions. These mechanisms are coordinated within a mini-batch update scheme, enabling aggressive iteration cycles while preserving diversity in the population. Experimental results across a diverse set of benchmark problems, including molecular potential energy surfaces and engineering design tasks, show that TRIDENT-DE consistently outperforms or matches state-of-the-art optimizers in terms of both best-found and mean performance. The findings highlight the potential of multi-operator, restart-aware DE frameworks as a powerful approach to advancing the state of the art in global optimization. Full article

Spontaneous rhythmic performance is a fundamental feature of human movement, well established in biomechanical models (EBMs) but less understood in complex physical fitness exercises (PFEs). This study examined the task dependency of spontaneous rhythmic performance across three EBMs (walking, hopping, finger tapping) and seven PFEs (hip abduction, back extension, sit-up, push-up, shoulder abduction, squat, lunge). A total of 15 men and 15 women performed each task at a self-selected pace while wearing inertial sensors. Measures included spontaneous motor tempo (SMT), temporal structure metrics, and their within- and between-trial individual variability (%CV) (ANOVA, SPSS 28.0, p ≤ 0.05). SMT was task-dependent, with EMB tasks being near ~2 Hz (walking: 1.82 ± 0.10 Hz; hopping: 2.08 ± 0.22 Hz; finger tapping: 1.89 ± 0.43 Hz) and PFEs being slower (0.36–0.68 Hz). Temporal structure mirrored these differences with shorter cycle and phase durations in EBM than PFE tasks, with relative phase durations consistently at about a 1:1 ratio. Τhe overall low %CV indicated stable performance (within-trial: 1.4–7.5%; between-trial: 0.5–7.8%). The results highlight the task dependency of SMT and temporal structure, as well as the robustness of an overarching internal timing framework supporting rhythmic motor control across diverse movement contexts. Full article

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here, we establish a leakage-averse, cross-battery evaluation framework encompassing 32 commercial LIBs (B5–B56) spanning diverse cycling histories and temperatures (≈4 °C, 24 °C, 43 °C). Models ranging from classical regressors to ensemble trees and deep sequence architectures were assessed under blocked 5-fold GroupKFold splits using RMSE, MAE, R² with confidence intervals, and inference latency. The results reveal distinct stratification among model families. Sequence-based architectures—CNN–LSTM, GRU, and LSTM—consistently achieved the highest accuracy (mean RMSE ≈ 0.006; per-cell R² up to 0.996), demonstrating strong generalization across regimes. Gradient-boosted ensembles such as LightGBM and CatBoost delivered competitive mid-tier accuracy (RMSE ≈ 0.012–0.015) yet unrivaled computational efficiency (≈0.001–0.003 ms), confirming their suitability for embedded applications. Transformer-based hybrids underperformed, while approximately one-third of cells exhibited elevated errors linked to noise or regime shifts, underscoring the necessity of rigorous evaluation design. Collectively, these findings establish clear deployment guidelines: CNN–LSTM and GRU are recommended where robustness and accuracy are paramount (cloud and edge analytics), while LightGBM and CatBoost offer optimal latency–efficiency trade-offs for embedded controllers. Beyond model choice, the study highlights data curation and leakage-averse validation as critical enablers for transferable and reliable SOH estimation. This benchmarking framework provides a robust foundation for future integration of ML models into real-world battery management systems. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disorder worldwide and a key driver of cardiometabolic complications. Despite its growing burden, the underlying metabolic perturbations remain incompletely understood. The Fatty Liver Index (FLI) provides a validated non-invasive tool for stratifying MASLD in large-scale and clinical studies. Methods: This study utilized data from the Qatar Biobank, applying strict exclusion criteria and propensity score matching, to select 110 adults stratified by FLI into the MASLD group (≥60, n = 55) and the control group (<30, n = 55) with balanced age, sex, and BMI. Untargeted serum metabolomics was performed. Differential metabolite profiles were identified using linear regression adjusted for covariates and validated by multivariate modeling. Functional enrichment analyses were conducted to highlight perturbed metabolic pathways. Results: Metabolomic profiling revealed distinct metabolic signatures: the MASLD group was characterized by elevated glutamate and phospholipids, while the control group showed enrichment of gamma-glutamyl amino acids, plasmalogens, and sphingomyelins. Conclusions: This contrasting pattern reflects disruption of the gamma-glutamyl cycle and consistent depletion of antioxidant plasmalogen species, suggesting impaired redox homeostasis and lipid remodeling as hallmarks of MASLD pathogenesis. These findings provide a foundation for future research into targeted metabolic biomarkers and therapeutic strategies. Longitudinal and mechanistic studies are warranted to determine causal relationships and clinical utility. Full article

Hepatocellular carcinoma continues to be a predominant contributor to oncological fatalities, characterized by restricted treatment alternatives. Although berberine exhibits anti-neoplastic capabilities, the underlying molecular pathways in hepatic malignancy require clarification. A comprehensive computational framework was established, incorporating transcriptomic data analysis, multiple machine learning methodologies, weighted gene co-expression network analysis (WGCNA), and molecular simulation techniques to elucidate berberine’s therapeutic pathways. Transcriptomic datasets from the Cancer Genome Atlas (TCGA) underwent examination to detect differentially expressed genes (DEGs). Ten machine learning methodologies screened critical targets, subsequently validated through molecular docking and 100 ns molecular dynamics simulations. Transcriptomic examination revealed 531 DEGs (341 exhibiting upregulation, 190 demonstrating downregulation) alongside 173 putative berberine interaction targets, yielding 17 intersecting candidates. Machine learning approaches consistently recognized AURKA and CDK1 as principal targets, subsequently confirmed by WGCNA as central genes. Elevated expression of both targets demonstrated correlation with unfavorable survival outcomes (p < 0.05). Computational docking analysis demonstrated robust binding interactions (AURKA: −8.2 kcal/mol; CDK1: −8.4 kcal/mol), with interaction stability validated through molecular dynamics simulations. Functional enrichment analysis unveiled targeting of cell cycle modulation, chromosome segregation, and p53 signaling networks. Berberine manifests anti-hepatocellular carcinoma activities primarily via coordinated targeting of AURKA and CDK1, essential cell cycle modulators. These discoveries provide molecular insights supporting berberine’s potential as adjunctive hepatic cancer therapy. Full article

Electrochemical Impedance Spectroscopy (EIS) measurements are highly sensitive to the fixturing, temperature, and state of charge (SoC) of batteries. For 10 kWh automotive battery modules, we show that variations in SoC and temperature introduce significant errors at low-to-medium frequencies (<100 Hz), while improper fixture wiring affects mainly higher-frequency accuracy, with errors up to 100% in the imaginary part at 1 kHz. In addition, we study repeatability across various tester-module configurations. EIS results remain highly consistent (±100 µΩ) across three different modules. Comparing the same module across two different testers, deviations are even lower (±30 µΩ up to 1 kHz). The EIS evolution is studied with respect to the cycle numbers, where a strong correlation of low-frequency impedance features is demonstrated. A new combined quotient feature is introduced and suggested as a reliable and efficient state of health (SoH) indicator, solely based on a model-free and phenomenological approach. The study demonstrates the potential of EIS as a powerful tool for battery module characterization, provided that its requirements and limitations are carefully addressed through well-defined experimental setups. Accurate and repeatable EIS measurements are particularly important for obtaining accurate electrochemical insights, especially in the low-to-mid frequency domain, where impedance variations are most sensitive to battery states and ageing effects. Full article

This paper introduces a novel BIM-based computational workflow that embeds spatial analysis directly within the Building Information Modelling (BIM) environment to support the evaluation and design of hospital emergency department (ED) layouts. Conventional analyses often depend on external software and repeated data exchange, which limit efficiency and integration with the design process. The proposed method integrates space syntax principles into Revit through Dynamo and custom Python scripts, enabling automated calculation of spatial measures linked to healthcare-specific performance indicators. The workflow was applied to two UK-based ED floor plans in a comparative case study, assessing patient-oriented aspects such as wayfinding, emergency access, and spatial privacy, alongside staff-oriented factors including workstation accessibility and visibility. Results were validated against DepthmapX to ensure consistency and reproducibility. The findings demonstrate that a BIM-native approach can streamline spatial analysis by eliminating import–export cycles, enhancing design iteration, and supporting post-occupancy evaluation. The significance of the study is in providing a decision-support framework for architects and healthcare planners in both designing new and evaluating existing ED layouts, where spatial configuration directly affects efficiency and user experience. Its main contribution is a reproducible workflow that enables real-time evaluation and strengthens the link between spatial analysis and evidence-based healthcare design. Full article

This study evaluated the effects of surface treatments and thermal aging on the surface properties and bonding performance of three CAD/CAM lithium disilicate ceramics: Amber Mill, Vita Suprinity, and IPS e.max CAD. A total of 150 specimens were divided into five groups: control, hydrofluoric acid etching, airborne-particle abrasion, and Er,Cr:YSGG laser irradiation at 2 W and 3 W. Half of the samples underwent thermocycling (10,000 cycles, 5–55 °C). Surface roughness, water contact angle, and microshear bond strength were measured, and failure modes were analyzed. Ceramic type and surface treatment significantly affected outcomes. Amber Mill generally exhibited higher roughness, while IPS e.max CAD achieved the greatest bond strength. Hydrofluoric acid etching consistently enhanced bonding in Vita Suprinity and IPS e.max CAD, whereas 2 W laser irradiation improved bond strength without notably changing roughness. In contrast, 3 W laser treatment increased contact angle, reducing wettability. Thermocycling raised bond strength in control and sandblasted groups but overall increased contact angle. Adhesive failures predominated, though cohesive and mixed failures became more common after aging. Overall, ceramic type, surface treatment, and thermal aging interactively influenced adhesion. Hydrofluoric acid etching remains the most effective treatment, while low-power laser irradiation offers a promising, less invasive alternative. Full article

The mixed metal oxides (MMOs) derived from layered double hydroxides (LDHs) are a typical class of porous materials and have attracted significant attention across various fields due to their high surface area, rich porous structures and various compositions. Regulating the pore structure of MMOs remains an urgent need because of the growing demand for numerous applications including adsorption, catalysis, and energy conversion. Controlling the lateral size of the lamellar crystals in the Co–Al LDH precursor allowed us to engineer the pore structure of Co–Al MMO, an architecture formed by the stacking of these lamellar flakes. The pore size distribution of the Co–Al MMO has been adjusted in the range from several nanometer to hundreds of nanometers. The sample with the optimized pore sizes exhibited a much higher catalytic reaction rate in the aerobic oxidation reaction of benzyl alcohol, about 4.2 times that of the control sample. Further research demonstrated that the high activity was favored by the improved mass transfer rate in the optimized pore architecture. Moreover, sodium silicate was employed as a cross-linking agent to enhance the cohesion within the secondary particles, which consist of stacked lamellar flakes. The resulting silicate-modified Co–Al MMO demonstrated significantly improved catalytic durability, maintaining stable performance over five consecutive reuse cycles—the performance that substantially exceeded that of its un-modified counterpart. Full article

Objectives: Sports-related concussion (SRC) is mostly associated with contact and combat sports. However, emerging evidence suggest that cyclists are also at risk of repeated concussion injury. Moreover, long-term neurophysiological outcomes in cycling cohorts remain underexplored. This novel study investigated the long-term effect of repetitive concussions in cyclists. Road, mountain biking (MTB), and BMX riders with a history of concussions and self-reported persistent symptoms were assess for neurophysiology and cognitive–motor performance compared to previously concussed cyclists with no ongoing symptoms. Both groups were compared to age-matched with controls. Methods: Using a cross-sectional between-group design, 25 cyclists with a history of concussions (15 symptomatic, 10 asymptomatic) and 20 controls completed symptom reporting, cognitive and balance assessments (SCAT5), sensorimotor testing using vibrotactile stimulation, and neurophysiological assessments via transcranial magnetic stimulation (TMS). Results: Symptomatic cyclists reported a higher number of concussions compared to asymptomatic cyclists (p = 0.041). Cognitive testing revealed large effects (d > 1.0), with impaired concentration in symptomatic cyclists compared to controls (p = 0.005). Motor assessments demonstrated large effects (d > 1.0), with slower tandem gait times (p < 0.001) and greater errors (p = 0.02) in the symptomatic group. Sensorimotor testing indicated slowed simple reaction times (p = 0.001) and poorer temporal order judgement (p = 0.038). TMS showed large effects (d > 1.0) in increased cortical inhibition in the symptomatic group, with prolong cortical silent periods (p < 0.05) and large effects (d > 1.0), and reduced short interval intracortical inhibition (p = 0.001) compared to asymptomatic cyclists and controls. Conclusions: Cyclists reporting persistent symptoms showed greater cortical inhibition and impaired cognitive–motor performance, consistent with findings in contact sport athletes. These results suggest that repeated concussions in cycling carry risk of chronic neurophysiological alterations. Cycling disciplines should consider more rigorous concussion identification protocols and stricter management strategies to mitigate persistent and long-term consequences. Full article

Roadway anti-icing requires low-carbon alternatives to chloride salts and electric heating. This study evaluated a seasonal thermal energy storage system that couples a geothermal hydronic heated pavement (HHPS-G) with borehole thermal energy storage (BTES), operated without auxiliary heat. A coupled transient HHPS-G–BTES model was developed and validated against independent experimental data. A continuous cycle was then simulated, consisting of three months of summer pavement heat harvesting and BTES, followed by three months of winter heat discharge. A parametric analysis varied borehole depth (10, 20, and 40 m) and number of units (1, 2, and 4). Results indicated that depth is consistently more effective than unit number. Deeper fields produced larger summer pavement surface cooling with less long-term drift and yielded more persistent winter anti-icing performance. The 40 m 4-unit case lowered the end-of-summer surface temperature by 3.8 °C relative to the no-operation case and kept the surface at or above 0 °C throughout winter. In contrast, the 10 m–1-unit case was near 0 °C by late winter. A depth-first BTES design, supplemented by spacing or edge placement to limit interference, showed practical potential for anti-icing without auxiliary heat. Full article

Metallic glass, as an emerging catalytic material, possesses an atomic structure characterized by long-range disorder and short-range order, which creates abundant and accessible active sites that enhance the adsorption and reactivity toward pollutant molecules, particularly dye compounds. In treating highly colored and recalcitrant Reactive Black 5 (RB5) dye wastewater, Mo₅₁Fe₃₄B₁₅ metallic glass wire demonstrate outstanding catalytic degradation performance within a conventional Fenton-like system. Under acidic conditions (pH = 2), the material exhibits a degradation rate constant of 0.698 min⁻¹ for a 20 ppm RB5 dye solution, achieving a degradation efficiency of 98.8% within 10 min. After 10 consecutive cycles, the efficiency remains at 95%, and throughout 15 cycles, it consistently maintains a performance level above 90%. As the reaction proceeds, the degradation rate gradually decreases, primarily due to the accumulation of corrosion products on the catalyst surface, which are predominantly composed of MoO₃ and Fe₂O₃. During the degradation process, metallic Mo⁰ and Fe⁰ serve as electron donors that facilitate the decomposition of H₂O₂, generating highly reactive hydroxyl radicals (•OH). These radicals attack the chromophoric structure of the dye, leading to its structural disruption and enabling rapid decolorization. Full article

High-grade serous carcinoma (HGSC) of the ovary is characterized by two major histological patterns: a classic papillary/micropapillary architecture and a solid pseudo-endometrioid transitional (SET) variant. We investigated whether the distinct morphologic subtypes are underpinned by transcriptomic differences in the tumor microenvironment (TME). We profiled 21 HGSC tumors (7 SET, 14 classic) using a 770-gene NanoString PanCancer Progression panel. Differential expression analysis revealed ~20 genes with significantly different expression (>4-fold, adjusted p < 0.01) between SET and classic tumors. Unsupervised clustering partially separated SET and classic tumors, suggesting that global gene expression patterns correlate with histologic subtype. SET tumors exhibited upregulation of cell-cycle and epithelial genes (e.g., PTTG1, TRAIL, HER3) and downregulation of genes involved in epithelial–mesenchymal transition (EMT), extracellular matrix (ECM) organization, and angiogenesis (e.g., TWIST2, FGF2, decorin) relative to classic tumors. Notably, PTTG1 and TRAIL were upregulated ~6–9-fold in SET tumors, whereas TWIST2 was ~7-fold downregulated, consistent with reduced EMT in SET tumors. Pathway analysis indicated that SET tumors appear to have an immune-active, stroma-poor microenvironment, in line with an “immunoreactive” phenotype, whereas classic tumors showed a mesenchymal, stroma-rich profile. These molecular distinctions could have diagnostic utility and may inform therapeutic stratification, with key dysregulated genes (e.g., HER3, TRAIL, FGF2) representing potential prognostic or predictive biomarkers. For example, high HER3 expression in SET tumors might predict sensitivity to ERBB3/PI3K inhibitors, whereas stromal factors (e.g., FGF2) enriched in classic HGSC could be targeted with microenvironment-modulating therapies. These preliminary findings require validation before translation into pathology practice via immunohistochemical (IHC) assays (e.g., for HER3 or TRAIL), potentially enabling improved classification and personalized treatment of HGSC. We report effect sizes as log₂ fold change with 95% confidence intervals and emphasize FDR-adjusted q-values. Given the small sample size and the absence of outcome data (OS/PFS/PFI), results are preliminary and hypothesis-generating. Orthogonal protein-level validation and replication in larger, independent cohorts are required before any translational inference. Full article