Search Results (1,176)

The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies. Full article

The evolution from 4G to 5G—and eventually to the forthcoming 6G networks—has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastructures. However, the unique characteristics of these networks—extensive connectivity, device heterogeneity, and architectural flexibility—impose significant security challenges. This paper introduces a comprehensive framework for enhancing the security of current and emerging wireless networks by integrating state-of-the-art machine learning (ML) techniques into intrusion detection and prevention systems. It also thoroughly explores the key aspects of wireless network security, including architectural vulnerabilities in both 5G and future 6G networks, novel ML algorithms tailored to address evolving threats, privacy-preserving mechanisms, and regulatory compliance with the EU AI Act. Finally, a Wireless Intrusion Detection Algorithm (WIDA) is proposed, demonstrating promising results in improving wireless network security. Full article

Guaranteeing food safety in the European Union (EU) is a continuing issue affected by diverse national traditions, regulatory power, and consumer culture. Despite the presence of a harmonized regulatory context, there continues to be variability in performance among the 27 member states. This study gives an extensive comparative evaluation of EU food safety based on three indicators: Rapid Alert System for Food and Feed (RASFF) alerts, pesticide maximum-residue-limit (MRL) violation, and per capita food loss. Fuzzy TOPSIS, K-means clustering, and scenario-based sensitivity tests are used to give an extensive appraisal of the performance of member states. Alarming differences are quoted as findings of significance. The highest number of RASFF notifications (212) and percentage of pesticide MRL non-compliance (1.5%) were reported in 2022 by Bulgaria, whereas the lowest values were reported by Estonia and Lithuania—15–20 RASFF notifications and less than 0.6% MRL violation rates. A statistically significant correlation (r = 0.72, p < 0.001) between pesticide MRL violation and food safety warnings was confirmed in favor of pesticide regulation as the optimal predictor of food safety warnings. On the other hand, food loss did not significantly affect safety measures but indicated very high variation (from 76 kg/capita per year in Croatia to 142 kg/capita per year in Greece). These findings suggest that while food loss remains an environmental problem, pesticide control is more central to the protection of food safety. Targeted policy is what the research necessitates: intervention and stricter enforcement in low-income countries, and diffusion of best practice from successful states. The composite approach adds to EU food safety policy discourse through the combination of performance indicators and targeted regulatory emphasis. Full article

Background/Objectives: Selinexor is a selective nuclear-export inhibitor approved for hematologic malignancies, characterized by extensive plasma protein binding (>95%). However, a validated analytical method to accurately measure the clinically relevant unbound fraction of selinexor in human plasma has not yet been established. This study aimed to develop a fully validated bioanalytical assay for simultaneous quantification of total and unbound selinexor concentrations in human plasma. Methods: We established and fully validated an analytical method based on liquid chromatography–tandem mass spectrometry (LC-MS/MS) capable of quantifying total and unbound selinexor concentrations in human plasma. Unbound selinexor was separated using ultrafiltration, and selinexor was efficiently extracted from 50 μL of plasma by liquid–liquid extraction. Chromatographic separation was achieved on a C18 column using an isocratic mobile phase (0.1% formic acid:methanol, 12:88 v/v) with a relatively short runtime of 2.5 min. Results: Calibration curves showed excellent linearity over a range of 5–2000 ng/mL for total selinexor (r² ≥ 0.998) and 0.05–20 ng/mL for unbound selinexor (r² ≥ 0.995). The precision (%CV ≤ 10.35%) and accuracy (92.5–104.3%) for both analytes met the regulatory criteria. This method successfully quantified selinexor in plasma samples from renally impaired patients with multiple myeloma, demonstrating potential inter-individual differences in unbound drug concentrations. Conclusions: This validated bioanalytical assay enables precise clinical pharmacokinetic assessments in a short runtime using a small plasma volume and, thus, assists in individualized dosing of selinexor, particularly for renally impaired patients with altered protein binding. Full article

This paper investigates the energy efficiency and positional stability of two types of ultralight unmanned aerial vehicles (UAVs)—bicopter and quadcopter—both with mass below 250 g, under varying flight conditions. The study is motivated by increasing interest in low-weight drones due to their regulatory flexibility and application potential in constrained environments. A comparative methodology was adopted, involving the construction of both UAV types using identical components where possible, including motors, sensors, and power supply, differing only in propulsion configuration. Experimental tests were conducted in wind-free and wind-induced environments to assess power consumption and stability. The data were collected through onboard blackbox logging, and positional deviation was tracked via video analysis. Results show that while the quadcopter consistently demonstrated lower energy consumption (by 6–22%) and higher positional stability, the bicopter offered advantages in simplicity of frame design and reduced component count. However, the bicopter required extensive manual tuning of PID parameters due to the inherent instability introduced by servo-based control. The findings highlight the potential of bicopters in constrained applications, though they emphasize the need for precise control strategies and high-performance servos. The study fills a gap in empirical analysis of energy consumption in lightweight bicopter UAVs. Full article

The development of interoperable and reusable information models is a key challenge for digitalization in manufacturing domains with heterogeneous and complex process chains. Ensuring seamless data exchange requires the standardization of both data syntax and semantics, while maintaining compatibility with existing industry standards. This paper presents a methodology for deriving standardizable and generalizable OPC UA information models tailored to domains with high process variability and interdisciplinary requirements. The methodology integrates system analysis, parameter mapping, and the development of modular submodels, supported by expert input and validation. It emphasizes the reuse and extension of existing OPC UA Companion Specifications to reduce complexity, avoid redundancy, and enable long-term standardization. The approach is exemplified by its application to battery cell production, an emerging manufacturing domain combining process and mechanical engineering with continuous and discrete processes. Its high degree of heterogeneity and lack of domain-specific standards pose significant challenges for model development. Through iterative expert workshops and structured model validation, a dedicated and transferable OPC UA framework is created. The resulting layered model structure combines a cross-industry standard with newly developed, process-aware model elements. This enables both broad applicability and the depth required for complex production environments, while supporting use cases such as traceability, regulatory reporting (e.g., EU Battery Passport), and process optimization. The resulting model improves interoperability, transparency, and data integration, offering a scalable blueprint for other complex manufacturing sectors. Full article

Rana dybowskii, widely distributed and extensively farmed in northeast China, holds significant economic value, particularly for its fallopian tubes, which are used as a traditional Chinese medicinal tonic known as “Oviductus Ranae.” As the light spectrum is a cost-effective regulatory factor in aquaculture, understanding its effects on the tadpole stage of R. dybowskii is critical for optimizing cultivation practices. This study investigated the effects of five light colors (white, red, yellow, blue, and green) on steroid hormone levels and gut microbiota composition in R. dybowskii tadpoles. Steroid hormone levels were measured on days 15, 30, 45, and 60 using high-performance liquid chromatography (HPLC), while gut microbial communities were analyzed through high-throughput 16S rRNA sequencing. Results showed that the testosterone (T) level of frogs in green light (group G) peaked on day 60 (2.62 ± 3.70 ng/g). The estradiol (E2) level in blue light (group B) also peaked on day 60 (2.87 ± 0.71 ng/g). Importantly, sex ratio analysis revealed that the proportion of females was highest under blue light, reaching 61.11%. Meanwhile, the richness and diversity of the gut bacterial community of the tadpoles was highest under yellow light, followed by blue light. These data suggest that hormone levels fluctuated and the composition of the gut flora of R. dybowskii changed under different light colors. Our results advance R. dybowskii physiological knowledge and support aquaculture practices. Full article

The rapid increase in waste electrical and electronic equipment (WEEE) creates major environmental and governance issues in developing countries like Ecuador struggle because they with minimal formal collection and recycling rates. This research presents a potential sustainable management approach that tracks products through their life cycles while following circular economy principles that include product extension and material extraction and waste minimization. A systematic literature review (SLR) using the PRISMA methodology combined with a bibliometric analysis found essential global strategies and technological frameworks and regulatory frameworks. The analysis of articles demonstrates that information management systems (IMSs) together with digital technologies and consistent regulations serve as essential elements for enhancing traceability and material recovery and formal recycling processes. A WEEE management IMS model was developed for the Ecuadorian market through an analysis of the findings; it follows a five-stage development process, starting from the technological infrastructure setup to complete data visualization integration. The proposed model is designed to enable public–private–community partnerships using digital tools that promote sustainable practices. The combination of circular strategies with traceability technologies and strong regulatory frameworks leads to improved WEEE governance, which supports sustainable system transitions in emerging economies. Full article

Deubiquitination plays a pivotal role in regulating plant responses to abiotic stress, growth, and development. Among the deubiquitinase (DUB) families, ubiquitin-specific proteases (UBPs) constitute the largest group. Despite this, limited research has been conducted on the functional characteristics of the UBP gene family in soybean (Glycine max). In this study, we identified 52 UBP gene family members in soybean, all of which harbored UCH (ubiquitin C-terminal hydrolase) domains with short yet evolutionarily conserved Cys-box and His-box. These genes were phylogenetically classified into 14 distinct groups; GmUBP genes within the same group shared analogous patterns of conserved domains and motifs. Moreover, a synteny analysis reveals that the GmUBP family has undergone extensive gene duplication events and shares a close evolutionary relationship with Arabidopsis thaliana. We conducted a focused analysis on GmUBP7, which is a gene exhibiting high expression levels in soybean seeds. Intriguingly, this gene exhibited several haplotypes in natural soybean varieties, with significant differences being observed in relation to seed traits, such as 100-seed weight, total fatty acid content, and protein content among different haplotypes. Collectively, the findings from this study provide a foundation for the functional characterization of GmUBP genes, offering new insights into the regulatory network underlying seed development in soybean. Full article

Long- or post-COVID-19 syndrome, which is also designated by WHO as Post COVID-19 Condition (PCC), is characterized by the persistent symptoms that remain after recovery from SARS-CoV-2 infection. A worldwide consortium of Long COVID-19 Host Genetics Initiative (Long COVID-19 HGI) identified an SNP rs9367106 (G>C; chr6:41,515,652, GRCh38, p = 1.76 × 10⁻¹⁰, OR = 1.63, 95% CI: 1.40–1.89) that is associated with PCC. Unraveling the functional significance of this SNP is of prime importance to understanding the development of the PCC phenotypes and their therapy. Here, in Silico, I explored how the risk allele of this SNP alters the functional mechanisms and molecular pathways leading to the development of PCC phenotypes. Bioinformatic methods include physical interactions using HI-C and Chia-PET analysis, Transcription Factors (TFs) binding ability, RNA structure modeling, epigenetic, and pathway analysis. This SNP resides within two long RNA genes, LINC01276 and FOXP4-AS1, and is located at ~31 kb upstream of a transcription factor FOXP4. This DNA region, including this SNP, physically interacts with FOXP4-AS1 and FOXP4, implying that this regulatory SNP could alter the normal cellular function of FOXP4-AS1 and FOXP4. Furthermore, rs9367106 is in eQTL with the FOXP4 gene in lung tissue. rs9367106 carrying DNA sequences act as distant enhancers and bind with several transcription factors (TFs) including YY1, PPAR-α, IK-1, GR-α, and AP2αA. The G>C transition extensively modifies the RNA structure that may affect the TF bindings and enhancer functions to alter the interactions and functions of these RNA molecules. This SNP also includes an ALU/SINE sequence and alteration of which by the G>C transition may prevent IFIH1/MDA5 activation, leading to suppression of host innate immune responses. LINC01276 targets the MED20 gene that expresses mostly in brain tissues, associated with sleep disorders and basal ganglia abnormalities similar to some of the symptoms of PCC phenotypes. Taken together, G>C transition of rs9367601 may likely alter the function of all three genes to explain the molecular basis of developing the long-term symptomatic abnormalities in the lungs and brain observed after COVID-19 recovery. Full article

Antimicrobial resistance (AMR) poses a growing threat to global health, and its spread through the food chain is gaining increasing attention. While AMR in food of animal origin has been extensively studied, less is known about its prevalence in plant-based foods, particularly fresh and ready-to-eat (RTE) vegetables. This study investigated the occurrence of antimicrobial-resistant bacteria in fresh and RTE vegetables. Isolates were subjected to antimicrobial susceptibility testing and molecular analyses for the characterization of antimicrobial resistance genes (ARGs). A significant proportion of samples were found to harbor antimicrobial-resistant bacteria, including multidrug-resistant strains. Several ARGs, including those encoding extended-spectrum β-lactamases (ESBLs) and resistance to critically important antimicrobials, were detected. The findings point to environmental contamination—potentially originating from wastewater reuse and agricultural practices—as a likely contributor to AMR dissemination in vegetables. The presence of antimicrobial-resistant bacteria and ARGs in fresh produce raises concerns about food safety and public health. The current regulatory framework lacks specific criteria for monitoring AMR in vegetables, highlighting the urgent need for surveillance programs and risk mitigation strategies. This study contributes to a better understanding of AMR in the plant-based food sector and supports the implementation of a One Health approach to address this issue. Full article

Research on structural variations in the field of crop genetics has expanded with the rapid development of genome sequencing technologies. As an important aspect of genomic variations, structural variations have a profound impact on the genetic characteristics of crops and significantly affect their key agronomic traits, such as yield, quality, and disease and stress resistance—by changing the gene arrangement order, copy number, and the positions of regulatory elements. Compared with single-nucleotide polymorphisms, structural variations present a diverse range of types, including deletions, duplications, inversions, and translocations, and their impacts are more extensive and profound. However, research on structural variations in crops still faces many challenges, for example those relating to different ploidy levels, genome repetitiveness, and their associations with phenotypes. Nevertheless, breakthroughs in long-read sequencing technologies and the integration of multi-omics data offer hope for solving these problems. A deep understanding of the impact of structural variations on crops is of great significance for accurately analyzing the evolutionary history of crops and guiding modern crop breeding, and is expected to provide strong support for global food security and the sustainable development of agriculture. Full article

Microbial pigments are gaining acceptance as a green, sustainable substitute for synthetic food pigments due to growing health issues and their adverse health impacts. This review provides an overview of the potential of microbial pigments as natural food colorants and the advantages of microbial pigments over synthetic pigments. Microbial pigments are a natural source of color with medicinal properties like anticancer, antimicrobial, and antioxidant activity. Important pigments covered are astaxanthin, phycocyanin, prodigiosin, riboflavin, β-carotene, violacein, melanin, and lycopene, and their microbial origins and characteristics. The review also covers commercial production of microbial pigments, i.e., strain development and fermentation processes. Microbial pigments also find extensive applications in food industries, including preservatives for food. Also covered are their pharmacological activity and other applications, such as in the formation of nanoparticles. Finally, the challenges and future directions of microbial pigment production are covered, including the need for cost-effective production, regulatory acceptability, and the potential of genetic engineering and fermentation-based technologies to enhance pigment yield and quality. Full article

Background: Developing countries face significant challenges in accessing high-quality pharmaceutical products due to resource constraints, limited regulatory capacity, and market dynamics that often prioritize cost over quality. This review addresses the critical gap in regulatory frameworks that fail to ensure pharmaceutical quality equity between developed and developing nations. Objective: This comprehensive review examines a novel dual-pathway regulatory framework that leverages stringent regulatory authority (SRA) approvals, artificial intelligence-based evaluation systems, and harmonized pricing mechanisms to ensure pharmaceutical quality equity across global markets. Methods: A comprehensive systematic analysis of current regulatory challenges, proposed solutions, and implementation strategies was conducted through an extensive literature review (202 sources, 2019–2025), expert consultation on regulatory science, AI implementation in healthcare, and pharmaceutical policy development. The methodology included an analysis of regulatory precedents, an economic impact assessment, and a feasibility evaluation based on existing technological implementations. Results: The proposed framework addresses key regulatory capacity gaps through two complementary pathways: Pathway 1 enables same-batch distribution from SRA-approved products with pricing parity mechanisms. At the same time, Pathway 2 provides independent evaluation using AI-enhanced systems for differentiated products. Key components include indigenous AI development, which requires systematic implementation over 4–6 years across three distinct stages, outsourced auditing frameworks that reduce costs by 40–50%, and quality-first principles that categorically reject cost-based quality compromises. Implementation analysis demonstrates a potential for achieving a 90–95% quality standardization, accompanied by a 200–300% increase in regulatory evaluation capability. Conclusions: This framework has the potential to significantly improve pharmaceutical quality and access in developing countries while maintaining rigorous safety and efficacy standards through innovative regulatory approaches. The evidence demonstrates substantial public health benefits with projected improvements in population access (85–95% coverage), treatment success rates (90–95% efficacy), and economic benefits (USD 15–30 billion in system efficiencies), providing a compelling case for implementation that aligns with global scientific consensus and Sustainable Development Goal 3.8. Full article

Encouraging discoveries and excellent advances in the fight against cancer have led to innovative therapies such as photothermal therapy (PTT), photodynamic therapy (PDT), drug targeting (DT), gene therapy (GT), immunotherapy (IT), and therapies that combine these treatments with conventional chemotherapy (CT). Furthermore, 2,041,910 new cancer cases and 618,120 cancer deaths have been estimated in the United States for the year 2025. The low survival rate (<50%) and poor prognosis of several cancers, despite aggressive treatments, are due to therapy-induced secondary tumorigenesis and the emergence of drug resistance. Moreover, serious adverse effects and/or great pain usually arise during treatments and/or in survivors, thus lowering the overall effectiveness of these cures. Although prevention is of paramount importance, novel anticancer approaches are urgently needed to address these issues. In the field of anticancer nanomedicine, carbon nanotubes (CNTs) could be of exceptional help due to their intrinsic, unprecedented features, easy functionalization, and large surface area, allowing excellent drug loading. CNTs can serve as drug carriers and as ingredients to engineer multifunctional platforms associated with diverse treatments for both anticancer therapy and diagnosis. The present review debates the most relevant advancements about the adjuvant role that CNTs could have in cancer diagnosis and therapy if associated with PTT, PDT, DT, GT, CT, and IT. Numerous sensing strategies utilising various CNT-based sensors for cancer diagnosis have been discussed in detail, never forgetting the still not fully clarified toxicological aspects that may derive from their extensive use. The unsolved challenges that still hamper the possible translation of CNT-based material in clinics, including regulatory hurdles, have been discussed to push scientists to focus on the development of advanced synthetic and purification work-up procedures, thus achieving more perfect CNTs for their safer real-life clinical use. Full article