Search Results (703)

Background and Objectives: Curcumin is a turmeric-derived polyphenol, and it has shown anticancer potential in various cancers, including breast cancer (BC). Nevertheless, the molecular mechanisms underlying its effects remain incompletely defined. Hsa_circ_0001946 (CDR1as) is a circular RNA (circRNA) that promotes tumor progression by competitively inhibiting microRNA-7-5p (miR-7-5p) in BC. This study investigated whether curcumin regulates the hsa_circ_0001946/miR-7-5p/target gene axis in BC progression. Materials and Methods: BC cell lines (MCF-7 and T47D) and a non-cancerous human mammary epithelial cell line (MCF-10A) were treated with curcumin or transfected with circ_0001946 siRNA or miR-7-5p mimic. Cell proliferation, migration, apoptosis, and protein expression were analyzed by CVDK-8 analysis, a wound healing assay, and flow cytometry, respectively. Also, protein expression levels were quantified via Western blotting. In vitro and in silico findings were further validated by analyzing tumor and adjacent normal tissues from 65 luminal BC patients. Results: Curcumin inhibited the proliferation and migration of MCF-7 and T47D cells in a dose-dependent manner. Knockdown of hsa_circ_0001946 or overexpression of miR-7-5p significantly suppressed proliferation and migration and enhanced apoptosis in BC cells compared to the negative controls. Curcumin treatment led to the knockdown of hsa_circ_0001946, the overexpression of miR-7-5p, and the downregulation of hsa_circ_0001946, CKS2, TOP2A, and PARP1, while it upregulating miR-7-5p. The Western blot confirmed reduced CKS2 protein levels after curcumin treatment. The expression of both hsa_circ_0001946 and CKS2 was significantly upregulated in tumor tissues compared to that of matched adjacent normal tissues, whereas that of miR-7-5p was markedly downregulated. Conclusions: This preliminary study shows that curcumin suppresses BC tumorigenesis by modulating the hsa_circ_0001946/miR-7-5p/target gene axis. While these findings suggest a novel regulatory pathway and potential therapeutic targets, further in vivo validation and clinical trials are required to determine the translational relevance of curcumin in BC therapy. Full article

Eugenol is a compound with promising antimicrobial properties. The rising phenomenon of multidrug resistance of Candida albicans is driving researchers to search for new, alternative therapeutics that would synergize with conventional antifungal drugs. The aim of the present study is to investigate how eugenol and eugenol-based extracts impair C. albicans growth by generation of reactive oxygen species (ROS) and plasma membrane (PM) disruption. The methods that we applied involve structural analysis of eugenol extracts by HPLC, ATR-FTIR, and polyphenol detection. Additionally, determination of ROS level in C. albicans was performed using microscopic and flow cytometry studies and analysis of PM integrity (PI-staining, observation of PM transporter—Cdr1p—localization) and fluidity (fluorometric study). The results indicate that eugenol impacts fungal growth, and this corresponds with increased ROS levels and diminished PM fluidity in the C. albicans WT strain. C. albicans strains deprived of ergosterol (erg11Δ/Δ) exhibited lowered ROS level and no change in PM fluidity in response to the tested eugenol extracts, but they affected its growth and caused PM permeabilization and Cdr1p delocalization. These conclusions indicate that mode of action of eugenol can be related to disruption of PM structure by both ergosterol-dependent and -independent mechanisms. Ergosterol can play a crucial role in maintaining the PM integrity during treatment with lower concentrations of eugenol. Full article

Dysregulation contributes to Alzheimer’s disease (AD) pathophysiology. Zinc therapy promotes enterocyte copper sequestration, potentially reducing systemic copper. Individual biological responses may vary. Methods: ZINCAiD was a 24-week, randomized, double-blind, placebo-controlled phase II trial assessing zinc therapy in individuals with mild cognitive impairment (MCI) due to AD (EudraCT No.: 2019-000604-15; registered on 26 March 2020). Participants were randomized 2:1 to receive elemental zinc (135 mg/day for 12 weeks, then 65 mg/day) or placebo. Ceruloplasmin was measured at predefined intervals for safety monitoring, blinded to the investigators. Post hoc, “Zinc Responders” were defined by ≥20% reduction in ceruloplasmin at week 12. The primary cognitive endpoint was the Cognitive Composite 2 scale (CC2); secondary endpoints included MMSE and CDR-Sob. Findings: Of the 48 participants randomized, 9 discontinued, primarily due to unrelated clinical deterioration; 39 had complete ceruloplasmin data. Two serious adverse events occurred in the Placebo group. Mild gastrointestinal symptoms occurred in eight participants, with only four leading to dropout. In the primary zinc vs. placebo analysis, no significant differences emerged in cognitive outcomes. A post hoc exploratory analysis stratified participants by pharmacodynamic response: 12 individuals with MCI due to AD (31%) met the criteria for “Zinc Responder,” defined by ≥20% reduction in serum ceruloplasmin at week 12. Only Zinc Responders maintained cognitive stability over 24 weeks, whereas the combined group of Zinc Non-Responders and placebo-treated participants showed a significant decline. For the composite cognitive score (CC2), the interaction between visit and response group was significant (p = 0.030), with deterioration observed only in the Non-Responder + Placebo group (Δ = –2.72, p < 0.0001 vs. –0.71, p = 0.35 in Responders). Similar patterns were observed for CDR-Sob (interaction p = 0.017) and MMSE (trend p = 0.09). Interpretation: Zinc therapy stabilized cognition in a pharmacodynamically defined MCI subgroup. These exploratory findings suggest serum ceruloplasmin as a feasible biomarker of target engagement. Larger trials are needed for confirmation. Full article

In alignment with Saudi Arabia’s Vision 2030, the Kingdom aims to achieve one of its main environmental targets: reaching net zero emissions by 2060. This ambitious goal can be realized through Carbon Dioxide Removal (CDR) technologies, particularly Direct Air Capture (DAC), which is among the most promising solutions. DAC offers high potential for extracting Carbon Dioxide (CO₂) directly from the atmosphere and is considered sustainable, especially when powered by renewable energy rather than fossil fuels. However, the technology remains highly cost-intensive. This paper presents a techno-economic assessment of renewable energy configurations to determine the most cost-effective solutions for DAC deployment. The evaluation focuses on Net Present Cost (NPC) and Levelized Cost of Energy (LCOE) across several regions in Saudi Arabia, using the Hybrid Optimization of Multiple Energy Resources (HOMER) Pro software (version 3.18.4). Full article

Background: Monoclonal antibodies play an important role in therapeutic and analytical applications. For recombinant expression, the coding sequences of the variable regions of the heavy and light chains are required. In addition, cloning antibody sequences, including constant regions, reduces the impact of hybridoma cell loss and ensures preservation of the naturally occurring full antibody sequence. Method: We combined amplification of IgG antibody variable regions from hybridoma mRNA with an advanced method for full-length cloning of monoclonal antibodies in a simple two-step workflow. Following Sanger sequencing and evaluation of consensus sequences, the best matching variable, diversity, and joining (V-(D-)J) gene segments were identified according to identity scores from IgBLAST reference sequences. Simultaneously, the mouse IgG subclass was determined at the DNA level based on isotype-specific sequence patterns in the C_H1 domain. Knowing the DNA sequence of V-(D-)J recombination responsible for the complementary determining region 3 (CDR 3), variable region-specific primers were designed and used to amplify the corresponding antibody constant regions. Results: To verify the approach, we applied it to the hybridoma clone BAM-CCMV-29-81 and obtained identical full-length antibody sequences as with RNA Illumina sequencing. Further validation at the protein level using an established MALDI-TOF MS-fingerprinting protocol showed that five out of six genetically encoded CDR domains of the monoclonal antibody BAM-CCMV-29-81 could be efficiently correlated. Conclusion: This simple, streamlined method enables the cost-effective determination of the full-length sequence of monoclonal antibodies from hybridoma cell lines, with the added benefit of obtaining the DNA sequence of the antibody ready for recombinant expression. Full article

Background: Food addiction is a new clinical entity that is beginning to be linked to obesity and eating disorders. The present study aims to investigate the association between the risk of food addiction in children and the presence of obesity. It also explores the relationship between food addiction, the development of eating disorders, and body image dissatisfaction. Material and methods: A matched case-control study was conducted in a Spanish pediatric population (cases have obesity, and controls have normal weight). The main outcome measures were evaluation of food addiction (S-YFAS-C), child feeding attitudes (ChEAT), and evaluation of body image (CDRS). Additionally, sociodemographic and anthropometric data were gathered. Results: A total of 62 children were evaluated (31 cases with age 11 ± 0.7 years and BMI Z-score 2.89 ± 1.33; 31 controls with age 10.7 ± 0.8 years and BMI Z-score −0.05 ± 0.52). For all items on the S-YFAS-C scale, significant differences were observed between the two groups (∧ = 0.252, p = 0.002). Food addiction was diagnosed in 32.3% of cases (2.06 ± 1.7 symptoms) and 22.6% of controls (1.61 ± 1.6 symptoms), although no statistically significant differences were observed between groups. A statistically significant correlation exists between all the scores of the scales studied in the children. Conclusions: Children with obesity have a higher number of food addiction symptoms compared to those with normal weight. In general, as food addiction scores increase, higher scores are observed for the risk of developing eating disorders and body image dissatisfaction. Full article

The acquisition of high-precision spatiotemporal precipitation data with long-term continuity plays an irreplaceable role in supporting agricultural modeling, hydrological forecasting, disaster prevention, and climate research. This study evaluates and corrects daily precipitation from satellite products (GPM, PERSIANN-CDR, CMORPH, GSMaP), merged datasets (GPCP, MSWEP, CHIRPS), and reanalysis products (ERA5, GLDAS) over the Huang-Huai-Hai Plain from 2000 to 2020. The study proposes a two-stage “error correction and residual correction” optimization framework. The error correction stage integrates machine learning with statistical methods (RF-DQDM), while the residual correction stage uses ground observations to dynamically adjust systematic biases. Results show that all corrected products outperform their original versions in spatial patterns and statistical metrics. Original precipitation data exhibit significant systematic errors modulated by topography, with eastern lowlands showing smaller errors. After correction, correlation coefficients rise above 0.8, and RMSE reductions average 60%. And product responses diverge significantly. CHIRPS improves from weakest to top performer, while model limitations constrain GLDAS enhancements. This framework establishes a transferable monsoon region optimization paradigm. This study provides a transferable bias correction framework for monsoon regions and builds a homogenized high-precision precipitation benchmark. It also recommends using CHIRPS or ERA5 for extreme rainfall analysis and MSWEP or GPCP for hydrological applications. Full article

Background/Objectives: Cows produce antibodies with ultralong CDRH3 segments (ulCABs) that contain a disulfide-stabilized knob domain. This domain is connected to the globular core of the antibody by a β-strand stalk. In the crystal structures, the stalk protrudes from the core in an extended conformation and presents the knob at its distal end. However, the rigidity of this topology has been questioned due to the extensive crystal packing present in most ulCAB crystal structures. To gain more insight into the dynamics of ultralong CDRH3s, we performed a comparative molecular dynamics (MD) study of 19 unique ulCABs. Methods: For all 19 systems, one-microsecond MD simulations were performed in explicit solvent. The analyses included an investigation of the systems’ conformational stability and the dynamics of the knob domain as well as an energetic analysis of the intramolecular knob interactions. Results: The simulations show that the extended stalk–knob conformation observed in the crystal structures is not preserved in solution. There are significant differences in the degree of knob dynamics, the orientations of the knobs, the number of flexible stalk residues, and the frequency of the motions. Furthermore, interactions between the knob and the light chain (LC) of the ulCABs were observed in about half of the systems. Conclusions: The study reveals that pronounced knob dynamics is a general feature of ulCABs rather than an exception. The magnitude of knob motions depends on the system, thus reflecting the high sequence diversity of the CDRH3s in ulCABs. The observed knob–LC interactions might play a role in stabilizing distinct knob orientations. The MD simulations of ulCABs could also help to identify suitable knob fragments as mini-antibodies by suggesting appropriate truncation points based on flexible sites in the stalks. Full article

The growing complexity of urban mobility necessitates more adaptive, data-driven approaches to transport demand forecasting. This study incorporates anonymized Call Detail Record (CDR) data—originally collected for mobile network billing—into the conventional four-step travel demand model to more accurately estimate trip behavior. Employing a stay-based method, significant user locations are identified, and individual mobility patterns are reconstructed. These patterns are then aggregated at the zonal level and validated against a large-scale household survey conducted in Sri Lanka. The proposed framework enables the extraction of origin–destination matrices and supports route assignment using CDR data, demonstrating a strong correlation with traditional survey results. This research highlights the potential of repurposed CDR data as a scalable, cost-efficient alternative to conventional travel surveys for estimating travel demand. Full article

Background/Objectives: Solanezumab is a humanized monoclonal antibody designed to bind soluble amyloid-beta (Aβ) and facilitate its clearance from the brain, aiming to slow the progression of Alzheimer’s disease (AD). Methods: A systematic search was applied in four medical databases through October 2024 to identify phase 2 or 3 randomized controlled trials evaluating solanezumab in patients aged ≥50 years with mild AD or in preclinical stages. The primary outcomes were changes in cognitive and functional scales, including ADAS-cog14, MMSE, ADCS-ADL, and CDR-SB. Data were pooled using a random-effects model, and certainty of evidence was assessed using GRADE. Results: Seven trials involving 4181 participants were included. Solanezumab did not significantly reduce cognitive decline based on ADAS-cog14 (MD = −0.75; 95% CI: −2.65 to 1.15; very low certainty) or improve functional scores on ADCS-ADL (MD = 0.85; 95% CI: −1.86 to 3.56; very low certainty) and CDR-SB (MD = −0.15; 95% CI: −0.89 to 0.60; very low certainty). A modest but statistically significant improvement was observed in MMSE scores (MD = 0.59; 95% CI: 0.33 to 0.86; moderate certainty). Conclusions: While solanezumab may offer slight benefits in general cognitive performance, its overall impact on clinically meaningful outcomes remains limited. The results do not support its use as a disease-modifying therapy for Alzheimer’s disease in either preclinical or symptomatic stages. Full article

Artificial Intelligence (AI) and Generative AI are transformative yet double-edged technologies with evolving risks. While research emphasises trustworthy, fair, and responsible AI by focusing on its “what” and “why,” it overlooks practical “how.” To bridge this gap in financial services, an industry at the forefront of AI adoption, this study employs a qualitative approach grounded in existing Responsible AI and Corporate Digital Responsibility (CDR) frameworks. Through thematic analysis of 15 semi-structured interviews conducted with professionals working in finance, we illuminate nine non-technical barriers that practitioners face, such as sustainability challenges, trade-off balancing, stakeholder management, and human interaction, noting that GenAI concerns now eclipse general AI issues. CDR practitioners adopt a more human-centric stance, emphasising consensus-building and “no margin for error.” Our findings offer actionable guidance for more responsible AI strategies and enrich academic debates on Responsible AI and AI-CDR symbiosis. Full article

The immune landscape of tumor-draining lymph nodes (TDLNs) plays a critical role in shaping antitumor responses and influencing prognosis in oral squamous cell carcinoma (OSCC). Among patients with lymph node (LN) metastasis, clinical outcomes vary widely, yet reliable biomarkers for prognostic stratification remain limited. This study aimed to identify immune features in tumors and LNs that differentiate between favorable and poor outcomes in OSCC patients with nodal metastasis. We analyzed T cell receptor (TCR) CDR3 repertoires and the expression of immune-related genes in primary tumors and paired sentinel LNs from OSCC patients who underwent tumor resection and lymphadenectomy. Patients were divided into three groups: Group A (no nodal metastasis), Group B1 (metastasis without recurrence), and Group B2 (metastasis with recurrence). TCR diversity was assessed using the Shannon index. The expression of immune-related genes (e.g., CD3E, CD4, CD8B, FOXP3, CTLA4, IL2, IL4) was measured by quantitative PCR and normalized to GAPDH. TCR diversity was lower in tumors than in non-metastatic LNs, reflecting clonal expansion. Metastatic LNs exhibited tumor-like diversity, suggesting infiltration by tumor-reactive clones. Tumor gene expression did not differ across groups, but LNs from metastatic cases showed the reduced expression of several immune genes. Notably, CD3E, CD8B, CTLA4, IL2, and IL4 distinguished B1 from B2. The immune profiling of LNs offers superior prognostic value over tumor analysis in OSCC patients with LN metastasis. LN-based evaluation may aid in postoperative risk stratification and personalized postoperative management and could inform decisions regarding adjuvant therapy and follow-up strategies. Full article

This study aims to explore how Corporate Digital Responsibility (CDR) influences Jordanian banks’ performance. It focuses on four CDR dimensions—“social, technological, economic, and environmental”—and examines the mediating role of firm size in these relationships. This study is the first to empirically test the mediating effect of firm size in the relationship between CDR and firm performance in the Jordanian banking sector, providing a novel perspective on how digital ethics shape organizational success. Data were collected through a structured survey from 299 bank employees in Jordan. Structural Equation Modeling (SEM) was employed to assess the direct and indirect effects of CDR dimensions on firm performance, with firm size tested as a mediating variable. All four dimensions of CDR significantly and positively affect firm performance. Additionally, firm size plays a partial mediating role in the relationship between CDR and firm performance, indicating that larger banks may better leverage digital responsibility initiatives to enhance performance. The study relies on self-reported data from a single country (Jordan), which may limit generalizability. Future studies could adopt a longitudinal design or expand to other MENA countries for comparative analysis and broader insights. The findings suggest that Jordanian banks should invest in and prioritize CDR strategies, especially in economic and technological domains, to improve their organizational outcomes and stakeholder relationships. Enhancing firm size may amplify the positive impact of CDR. The findings of this study are robust, as validated by further analysis utilizing data from a customer survey. The results derived from customer viewpoints correspond with staff data, substantiating the beneficial influence of Corporate Digital Responsibility (CDR) on banking performance and affirming the substantial mediating effect of company size. Full article

In this paper, we present a new space-time Galerkin-like method, where we treat the discretization of spatial and temporal domains simultaneously. This method utilizes a mixed formulation of the tensor-train (TT) and quantized tensor-train (QTT) (please see Section Tensor-Train Decomposition), designed for the finite element discretization (Q1-FEM) of the time-dependent convection–diffusion–reaction (CDR) equation. We reformulate the assembly process of the finite element discretized CDR to enhance its compatibility with tensor operations and introduce a low-rank tensor structure for the finite element operators. Recognizing the banded structure inherent in the finite element framework’s discrete operators, we further exploit the QTT format of the CDR to achieve greater speed and compression. Additionally, we present a comprehensive approach for integrating variable coefficients of CDR into the global discrete operators within the TT/QTT framework. The effectiveness of the proposed method, in terms of memory efficiency and computational complexity, is demonstrated through a series of numerical experiments, including a semi-linear example. Full article

Call Detail Records (CDRs) from mobile networks offer valuable insights into both network performance and user behavior. With the growing importance of data analytics, analyzing CDRs has become critical for optimizing network resources by forecasting demand across spatial and temporal dimensions. In this study, we examine publicly available CDR data from Telecom Italia to explore the spatiotemporal dynamics of mobile network activity in Milan. This analysis reveals key patterns in traffic distribution highlighting both high- and low-demand regions as well as notable variations in usage over time. To anticipate future network usage, we employ both Automated Machine Learning (AutoML) and the transformer-based TimeGPT model, comparing their performance against traditional forecasting methods such as Long Short-Term Memory (LSTM), ARIMA and SARIMA. Model accuracy is assessed using standard evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R²). Results show that AutoML delivers the most accurate forecasts, with significantly lower RMSE (2.4990 vs. 14.8226) and MAE (1.0284 vs. 7.7789) compared to TimeGPT and a higher R² score (99.96% vs. 98.62%). Our findings underscore the strengths of modern predictive models in capturing complex traffic behaviors and demonstrate their value in resource planning, congestion management and overall network optimization. Importantly, this study is one of the first to Comprehensively assess AutoML and TimeGPT on a high-resolution, real-world CDR dataset from a major European city. By merging machine learning techniques with advanced temporal modeling, this study provides a strong framework for scalable and intelligent mobile traffic prediction. It thus highlights the functionality of AutoML in simplifying model development and the possibility of TimeGPT to extend transformer-based prediction to the telecommunications domain. Full article