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Background: Fixed-dose combinations have advanced in many therapeutic areas, including otorhinolaryngology, where hearing disorders are increasingly prevalent. Objectives: The present study focuses on developing and evaluating a new capsule combining nicergoline (NIC), piracetam (PIR), and hawthorn extract (HE) for the management of sensorineural hearing loss. Methods: The first phase methodology comprised preformulation studies (DSC, FTIR, and PXRD) to assess compatibility among active substances and excipients. Subsequently, four formulations were prepared and tested for flowability, dissolution behavior in acidic and neutral media, and stability under oxidative, thermal, and photolytic stress. Quantification of the active substances and flavonoids was performed using validated spectrophotometric and HPLC-UV methods. Results: Among the tested variants, the F1 formulation (4.5 mg NIC, 200 mg PIR, 50 mg HE, 2.5 mg magnesium stearate, 2.5 mg sodium starch glycolate, and 240.5 mg monohydrate lactose per capsule) displayed optimal technological properties, superior dissolution in acidic media, and was further selected for evaluation. The antioxidant activity of the formulation was confirmed through the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, Trolox Equivalent Antioxidant Capacity (TEAC), and iron chelation tests, and was primarily attributed to the flavonoid content of the HE. Acute toxicity tests in mice and rats indicated a high safety margin (LD₅₀ > 2500 mg/kg), while ototoxicity assessments showed no adverse effects on auditory function. Conclusions: The developed formulation displayed good stability, safety, and therapeutic potential, while the applied workflow could represent a model for the development of future fixed-dose combinations. Full article

Physics concepts are considered an essential component of STEM (science, technology, engineering, and mathematics) education and fundamental for economic and technological development in the world. However, there can be student academic underperformance, such as the school environment, learning media and infrastructure, student interest and emotions, as well as social and economic development factors in communities. These problems are even more acute in rural areas of developing countries, where poverty is high and teachers often lack the necessary technological skills. The aim of this study was to evaluate the impact of a low-cost STEM tool focused on motivation in learning, in terms of five variables of interest in physics in rural areas, as well as the durability of the tools used to learn 12 physics concepts. A quasi-experimental study was conducted with the participation of 78 high school students, with an average age of 15.82 years, in a rural area of Guerrero, Mexico. The results showed that using the STEM tool significantly increased students’ interest in learning methodology, active participation, and attitude towards physics, facilitating the teacher’s work. In addition, the 3D construction kit used in the experimentation, besides being low-cost, proved to be affordable and durable, making it ideal for use in rural areas. Full article

The resistivity imaging system is specifically designed for the precise measurement of resistivity distributions within drilled columnar core samples. Its coaxial symmetric configuration enables the non-destructive characterization of electrical properties, with broad applications in oil and gas exploration, reservoir evaluation, and geological research. By integrating a ring return electrode and full-circumference electrode arrays, the system can acquire core-scale resistivity data in conductive media environments. The self-developed imaging software employs advanced processing algorithms—including depth correction, amplitude normalization, and image enhancement—to transform raw resistivity measurements into high-resolution surface imaging maps. Experimental results demonstrate that the system can resolve features such as cracks with a minimum width of 0.5 mm and pores with a minimum inner diameter of 0.4 mm in granite core, providing a novel technical approach for the fine-scale characterization of core materials. Full article

Background: The purpose of this study was to evaluate Achilles tendon ruptures (ATR) in NHL players and the effects on return to play and player performance metrics. The incidence, mechanism of injury, management strategy, return to play (RTP), and post-injury were assessed from official online sports databases. Methods: A retrospective review of NHL players who sustained a partial or complete tear of the Achilles tendon from 2008 to 2024 was performed. Data were collected from NHL injury databases and media reports, and included player demographics, injury mechanism, treatment, and post-injury performance metrics. A Wilcoxon signed rank test was used to compare pre-injury and post-injury performance metrics, with significance set at p < 0.05. Results: Here, 15 NHL players with a mean age of 27.8 years were identified, with a prevalence rate of 0.125 injuries per 10,000 athletic exposures. Overall, 73.3% of ATRs were non-contact in nature, with 60.0% of ATRs occurring during off-season training. Fourteen players were managed with non-operative treatment, with no re-ruptures reported. The RTP rate was 93.3%, with players missing a mean number of 45.7 games. However, there was a deterioration in post-injury performance metrics, including games played per season, plus/minus rating, and time on ice per game post-injury. Conclusions: This study found that Achilles tendon ruptures are an uncommon injury in NHL players, with a prevalence rate of 0.125 injuries per 10,000 athletic exposures. A high RTP rate of 93.3% was observed in this cohort. However, there was a deterioration in post-injury performance metrics, including games played per season, plus/minus rating, and time on ice per game post-injury, highlighting the potential devastating sequelae of ATRs in elite NHL athletes. Full article

With the popularization of social media, information related to public health events has seen explosive growth online, making it essential to accurately identify informative tweets with decision-making and management value for public health emergency response and risk monitoring. However, existing methods often suffer performance degradation during cross-event transfer due to differences in data distribution, and research specifically targeting public health events remains limited. To address this, we propose the Label Confusion Domain Adversarial Network (LCDAN), which innovatively integrates label confusion with domain adaptation to enhance the detection of informative tweets across different public health events. First, LCDAN employs an adversarial domain adaptation model to learn cross-domain feature representation. Second, it dynamically evaluates the importance of different source domain samples to the target domain through label confusion to optimize the migration effect. Experiments were conducted on datasets related to COVID-19, Ebola disease, and Middle East Respiratory Syndrome public health events. The results demonstrate that LCDAN significantly outperforms existing methods across all tasks. This research provides an effective tool for information detection during public health emergencies, with substantial theoretical and practical implications. Full article

As augmented reality (AR) advertising becomes increasingly prevalent across digital platforms, understanding how its unique features influence consumer responses is critical for both theory and practice. Based on the elaboration likelihood model (ELM), this study develops and validates a dual-dimension content–dual-route processing model to investigate how different features of AR advertising influence consumer engagement. Specifically, it examines how product-related attributes (attractiveness, informativeness) and technology-related attributes (interactivity, augmentation) shape attitudes toward the ad and purchase intentions through cognitive (information credibility) and affective (enjoyment) pathways. Using data from an online survey (N = 299), the study applies partial least squares structural equation modeling (PLS-SEM) to test the proposed model. The results show that informativeness and augmentation significantly enhance information credibility, while attractiveness primarily influences emotional responses. Interactivity and augmentation positively influence cognitive and affective responses. Mediation analysis confirms the simultaneous activation of central and peripheral processing routes, with flow experience emerging as a significant moderator in selected pathways. By introducing a structured framework for AR advertising content, this study extends the applicability of the ELM in immersive media contexts. It underscores the combined impact of rational evaluation and emotional engagement in shaping consumer behavior and offers practical insights for designing effective AR advertising strategies. Full article

Dye residues from the textile industry constitute a critical wastewater problem. This study aimed to evaluate the removal capacity of methylene blue (MB) in aqueous media, using an adsorbent formulated from activated and sonicated nanoclay (NC) and microatomized Nostoc sphaericum (ANS). NC was obtained by acid treatment, followed by activation with 1 M NaCl and sonication, while ANS was obtained by microatomization in an aqueous medium. NC/ANS was mixed in a 4:1 weight ratio. The NC/ANS synergistic adsorbent was characterized by the point of zero charge (PZC), zeta potential (ζ), particle size, FTIR spectroscopy, and scanning electron microscopy (SEM). NC/ANS exhibited good colloidal stability, as determined by pH_PZC, particle size in the nanometer range, and heterogeneous morphology with functional groups (hydroxyl, carboxyl, and amide), removing between 72.59 and 97.98% from an initial concentration of 10 ppm of MB, for doses of 20 to 30 mg/L of NC/ANS and pH of 5 to 8. Optimal adsorption conditions are achieved at pH 6.8 and 32.9 mg/L of adsorbent NC/ANS. It was observed that the pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetic models best described the adsorption kinetics, indicating a predominance of the physisorption process, with adsorption capacity around 20 mg/g. Isotherm models and thermodynamic parameters of adsorption, ΔS, ΔH, and ΔG, revealed that the adsorption process is spontaneous, favorable, thermodynamically stable, and occurs at the monolayer level, with a regeneration capacity of 90.35 to 37.54% at the fifth cycle. The application of physical activation methods, such as sonication of the clay and microatomization of the algae, allows proposing a novel and alternative synergistic material from organic and inorganic sources that is environmentally friendly and promotes sustainability, with a high capacity to remove cationic dyes in wastewater. Full article

Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection on social media. Key topics include strategies for handling heterogeneous and imbalanced datasets, advanced text preprocessing, robust model evaluation, and the use of appropriate metrics beyond accuracy. Real-world examples illustrate each stage of the process, and an emphasis is placed on transparency, reproducibility, and ethical best practices. While the present work focuses on text-based analysis, we discuss the limitations of this approach—including label inconsistency and a lack of clinical validation—and highlight the need for future research to integrate multimodal signals and gold-standard psychometric assessments. By sharing these frameworks and lessons, this manuscript aims to support the development of more reliable, generalizable, and ethically responsible models for mental health detection and early intervention. Full article

Cassiterite polymetallic sulfide ore exhibits a complex mineral composition and significant variations in mineral properties, which frequently lead to issues such as the over-grinding of cassiterite and under-grinding of sulfide minerals during the grinding process. These issues consequently impair liberation performance in subsequent beneficiation stages. Among these factors, the grinding media ratios stand as one of the critical factors influencing grinding efficiency. Based on these, the paper adopts the single-factor test method to systematically study the influence law of factors such as grinding time, mill rotational rate, and mill filling rate on the particle size composition of ore grinding products and the grinding technology efficiency under different media conditions; in addition, it is compared with the influence law of different conditions of media ratios on the grinding efficiency of ore. The results show that the optimal parameters of the grinding operation are obtained at the grinding time of 4 min, the mill rotational rate of 60%, and the filling rate of 35%. The grinding time and mill filling rate have a relatively more significant effect on the product particle size distribution, while the effect of the mill rotational rate is relatively less significant. When the parameters of grinding operations are optimal, the yield of qualified particle size and grinding technical efficiency are used as the evaluation indices, respectively. Overall, the order of the grinding effect of different media conditions was as follows: steel ball combination of Φ20 mm and Φ25 mm > steel balls of three single sizes > steel ball combination of Φ20 mm and Φ30 mm. The optimal grinding media ratios are Φ20 mm and Φ25 mm (the percentage of the Φ20 mm ball is 90%). The reasonable media ratios will effectively coordinate the optimal grinding effect between different media. The research results can provide the necessary basic data for the subsequent grinding optimization of cassiterite polymetallic sulfide ores. Full article

Streptococcus pyogenes (GAS) is a leading cause of acute otitis media (AOM) and its complications. This study aimed to evaluate the antimicrobial resistance of all isolated bacterial agents recovered from children with AOM and to perform the emm typing of GAS isolates. Antibiotic susceptibility testing was evaluated according to EUCAST criteria. Phenotyping and genotyping were performed for the macrolide-resistant GAS isolates. All GAS isolates were subjected to emm typing. Among the 103 AOM cases considered, we identified GAS isolates (39.4%), Staphylococcus aureus (26.6%), Haemophilus influenzae (13.8%), Streptococcus pneumoniae (11.7%), Moraxella catarrhalis (7.4%), and Serratia marcescens (1.1%). GAS exhibited 32.4% macrolide resistance and 10.8% clindamycin resistance. The M phenotype and mefE gene (18.9%) were the most common, followed by cMLSB (10.8% with ermB), a combination of mefA and ermB (8.1%), and iMLSB (2.7% with ermA). The most prevalent emm types were emm12 (27.0%), emm1 (21.6%), and emm3 (16.2%). The most common GAS emm types identified among AOM patients in this study are found worldwide and are associated with invasive infections in various countries. This may influence the virulence and invasive potential of these strains. Full article

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems. Full article

Fructooligosaccharides (FOS) are short fructans with different degrees of polymerization (DP) and bonds in their structure, generated by the distinct activities of fructosyltransferase enzymes, which produce distinct types of links. FOS are in high demand on the market, mainly because of their prebiotic effects. In recent years, depending on the link type in the FOS structure, prebiotic activity has been shown to be increased. Studies on β-fructanofuranosidases (Ffasa), enzymes with fructosyltransferase activity in yeasts, have reported the production of ¹F-FOS, ⁶F-FOS, and ⁶G-FOS. The aims of this investigation were to evaluate the capability of fifteen different yeasts to grow in Agave sp. juices and to determine the potential of these juices as substrates for FOS production. Additionally, the research aimed to corroborate and analyze the fructosyltransferase activity of enzymatic extracts obtained from agave yeasts by distinct induction media and to identify the role and optimal parameters (time and sucrose and glucose concentrations) for FOS and disaccharides production through Box–Behnken designs. To carry out such a task, different techniques were employed: FT-IR, TLC, and HPAEC-PAD. We found two yeasts with fructosyltransferase activity, P. kudriavzevii ITMLB97 and C. lusitaniae ITMLB85. In addition, within the most relevant results, the production of the FOS 1-kestose, 6-kestose, and neokestose, as well as disaccharides inulobiose, levanobiose, and blastose, molecules with potential applications, was determined. Overall, FOS production requires suitable yeast species, which grow in a medium under optimal conditions, from which microbial enzymes with industrial potential can be obtained. Full article

This study investigates the self-assembly and host–guest complexation behaviour of novel resveratrol-based lipophenols (LipoResv)—resveratrol-4′-linoleate (Resv-4′-LA) and resveratrol-4′-docosahexaenoate (Resv-4′-DHA)—with hydroxypropyl-β-cyclodextrins (HP-β-CDs). These amphiphilic molecules display surfactant-like properties, forming micellar aggregates in aqueous media. Fluorescence spectroscopy was used to determine the critical micelle concentration (CMC), revealing that LipoResv exhibit significantly lower CMC values than their free fatty acids, indicating higher hydrophobicity. The formation of inclusion complexes with HP-β-CDs was evaluated based on changes in CMC values and further confirmed by dynamic light scattering (DLS) and molecular modelling analyses. Resv-4′-LA formed 1:1 complexes (Kc = 720 M⁻¹), while Resv-4′-DHA demonstrated a 1:2 stoichiometry with lower affinity constants (K₁ = 17 M⁻¹, K₂ = 0.18 M⁻¹). Environmental parameters (pH, temperature, and ionic strength) significantly modulated CMC and binding constants. Computational docking and molecular dynamics simulations supported the experimental findings by revealing the key structural determinants of the host–guest affinity and micelle stabilization. Ligand efficiency (LE) analysis further aligned with the experimental data, favouring the unmodified fatty acids. These results highlight the versatile encapsulation capacity of HP-β-CDs for bioactive amphiphile molecules and support their potential applications in drug delivery and functional food systems. Full article

Using solid particulates as a heat transfer medium for concentrated solar power (CSP) systems has many advantages, positioning them as a superior option compared with conventional heat transfer media such as steam, oil, air, and molten salt. However, a critical imperative lies in the comprehensive evaluation of the properties of potential solid particulates intended for utilization under such extreme thermal conditions. This paper undertakes an exhaustive examination of both ambient and high-temperature thermophysical properties of four naturally occurring particulate materials, Riyadh white sand, Riyadh red sand, Saudi olivine sand, and US olivine sand, and one well-known engineered particulate material. The parameters under scrutiny encompass loose bulk density, tapped bulk density, real density, sintering temperature, and thermal conductivity. The results reveal that the theoretical density decreases with the increase in temperature. The bulk density of solid particulates depends strongly on the particulate size distribution, as well as on the compaction. The tapped bulk density was found to be larger than the loose density for all particulates, as expected. The sintering test proved that Riyadh white sand is sintered at the highest temperature and pressure, 1300 °C and 50 MPa, respectively. US olivine sand was solidified at 800 °C and melted at higher temperatures. This proves that US olivine sand is not suitable to be used as a thermal energy storage and heat transfer medium in high-temperature particle-based CSP systems. The experimental results of thermal diffusivity/conductivity reveal that, for all particulates, both properties decrease with the increase in temperature, and results up to 475.5 °C are reported. Full article

In an era where digitalization shapes economic and social landscapes, the intersection of digital skills, ethics, and integrity plays a crucial role in understanding the vulnerability of youth classified as NEET (Not in Education, Employment, or Training). This study explores how risky internet use and digital skill gaps contribute to socio-economic exclusion, integrating a multivariate and spatial approach to assess regional disparities in Europe. This study adopts a systems thinking perspective to explore digital exclusion as an emergent outcome of multiple interrelated subsystems. The research employs logistic regression, Principal Component Analysis (PCA) with Promax rotation, and Geographic Information Systems (GIS) to examine the impact of digital behaviors on NEET status. Using Eurostat data aggregated at the country level for the period (2000–2023) across 28 European countries, this study evaluates 24 digital indicators covering social media usage, instant messaging, daily internet access, data protection awareness, and digital literacy levels. The findings reveal that low digital skills significantly increase the likelihood of being NEET, while excessive social media and internet use show mixed effects depending on socio-economic context. A strong negative correlation between digital security practices and NEET status suggests that youths with a higher awareness of online risks are less prone to socio-economic exclusion. The GIS analysis highlights regional disparities, where countries with limited digital access and lower literacy levels exhibit higher NEET rates. Digital exclusion is not merely a technological issue but a multidimensional socio-economic challenge. To reduce the NEET rate, policies must focus on enhancing digital skills, fostering online security awareness, and addressing regional disparities. Integrating GIS methods allows for the identification of territorial clusters with heightened digital vulnerabilities, guiding targeted interventions for improving youth employability in the digital economy. Full article