Search Results (3,391)

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021–2026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (κ = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848–0.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL’s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field’s priority research needs. Full article

Alkaline water is increasingly marketed for musculoskeletal and recovery benefits, yet its relevance to healthy aging, sarcopenia prevention, and functional capacity in older adults remains largely unexplored. This systematic evidence map and translational appraisal examined whether the available comparative human evidence on alkaline water is applicable to aging populations and longevity research. Following PRISMA guidance, PubMed and Scopus were searched from January 2005 to September 2025. Eligible studies were controlled or comparative observational human studies reporting muscle strength, physical performance, or recovery outcomes. Risk of bias was assessed using RoB 2, ROBINS-I, and JBI criteria; evidence certainty was judged narratively using GRADE-informed principles. Ten studies met the inclusion criteria. Most enrolled young athletic populations; only two had partial relevance to aging cohorts. Crucially, no study included participants aged 65 years or older or assessed primary sarcopenia-relevant endpoints such as appendicular lean mass, gait speed, or chair-rise performance; this total absence of data in the target demographic represents the central limitation of the current literature. Risk of bias ranged from some concerns to serious. The most consistent signals were short-term improvements in lactate clearance and perceived exertion in young male athletes. Evidence for strength, functional performance, and safety in older adults was absent or indirect. Current evidence, rated low to very low certainty for aging-relevant outcomes, does not support alkaline water as an evidence-based strategy for healthy aging or muscle preservation in older adults. Age-appropriate trials using EWGSOP2-aligned outcomes are urgently needed. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

Background: Polyendocrine metabolic ovarian syndrome (PMOS/PCOS) is the most common hormonal disorder in women of reproductive age and is linked to infertility as well as long-term metabolic and psychological problems. In the Gulf Cooperation Council (GCC) region, rising obesity, dietary changes, and sedentary lifestyles may be increasing its burden. However, prevalence estimates remain highly inconsistent due to differences in diagnostic criteria and measurement methods rather than true variation in disease rates. Objective: This study aimed to describe the situation by systematically pooling available evidence on the prevalence of PMOS among women in GCC countries and by summarizing the range of clinical features reported across included studies. Methods: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. We searched five major bibliographic databases (PubMed, Scopus, Web of Science, Cochrane Library, and Embase) and the Google Scholar search engine for observational studies published up to 1 June 2026. Studies were eligible if they reported PMOS prevalence and related clinical features among women of reproductive age residing in GCC countries. After removing duplicates and screening 570 initially identified records, 25 studies met our inclusion criteria; 24 were included in the quantitative meta-analysis after excluding one high-risk study. Risk of bias was appraised using the Joanna Briggs Institute Checklist for Prevalence Studies. A random-effects meta-analysis using the DerSimonian-Laird method, combined with the Freeman-Tukey double arcsine transformation, was used to estimate the pooled prevalence. Heterogeneity was quantified using the I² statistic and Cochran’s Q test. Subgroup analyses explored differences by country, diagnostic method, study setting, and publication period. Meta-regression was used to identify study-level factors that explained between-study variability. Results: Across 24 studies involving 77,890 women, the pooled prevalence of PMOS was 17.59% (95% CI: 12.98–23.40%). Country-level estimates ranged from 6.56% in Oman to 23.0% in Saudi Arabia. Heterogeneity across all analyses was extremely high (I² = 99.6%), and meta-regression identified the diagnostic tool as the single most important source of variation, explaining 42.7% of between-study variance. Studies using structured clinical criteria (Rotterdam or NIH) yielded prevalence estimates around 13–14%, while those relying on self-report or physician diagnosis without standardized criteria reported considerably higher figures (20–37%). Common clinical features included menstrual irregularity (up to 100% of PMOS cases in clinical cohorts), hirsutism (5–100%), acne and oily skin (17–74%), and obesity (17–73%). Awareness of PMOS among women in the region was highly variable, ranging from under 3% to nearly 100%. Conclusions: PMOS is a significant public health concern across the GCC region. The markedly higher pooled prevalence combined with high rates of obesity and metabolic risk in this population calls for urgent, coordinated action. Standardizing diagnostic practices, investing in population-level screening, and developing culturally tailored awareness programs are essential steps toward reducing the clinical and social burden of PMOS. Full article

Objective diagnosis in psychiatry remains challenging due to the lack of reliable biological markers and the presence of confounding variables in observational data. While EEG-based machine learning models have shown promising classification performance, their validity remains unclear when confounding factors such as age are not explicitly controlled. In this work, we propose a confound-aware mathematical framework for supervised learning, where classification is formulated as a mapping $f:{\mathbb{R}}^{E\times C\times T}\to \mathcal{Y}$ under the presence of a confounding variable A. Within this formulation, model performance is interpreted as a function of both predictive structure and confound dependence. The proposed framework integrates classification, regression, and feature selection into a unified evaluation pipeline. A central contribution is the Cross-Task Explanation Concordance (CTEC) index, a rank-based metric that quantifies the stability of feature importance across models and predictive tasks. Experimental results on a large-scale EEG dataset (N = 670) demonstrate that deep learning models outperform handcrafted approaches under standard evaluation. However, under confound-controlled settings, handcrafted models show a dual response to confound control: age residualization improves classification by removing feature-level noise (+20.3%), while age-matching collapses performance to chance (balanced accuracy, BA = 0.238) by eliminating demographic separability. Deep learning models retain partial robustness under both conditions. These findings highlight that conventional performance metrics may overestimate model validity in the presence of structured bias. The proposed framework provides a general mathematical approach for evaluating supervised learning models under confounding effects and is applicable to a wide range of data-driven systems beyond EEG. Full article

Urban air pollution, especially the problem of PM_2.5, is one of the major health challenges facing the planet today. To provide accurate PM_2.5 predictions despite data noise and missing data, the authors proposed a deep learning model. We constructed a Stacked Autoencoder–Convolutional Neural Network–Bidirectional Long Short-Term Memory–Long Short-Term Memory (SAE-CNN-BiLSTM-LSTM) model that (1) utilises convolutional layers to extract spatial features from the input data, (2) employs bidirectional LSTM layers to capture long-term temporal dependencies, and (3) utilises an autoencoder to learn latent representations of the data to mitigate the effects of missing data. The model was trained on a large dataset of hourly measurements of air quality and meteorological parameters collected between 2018 and 2020 in Klang, Malaysia. The performance of the model on data that were not used during training was evaluated using a range of metrics. The SAE-CNN-BiLSTM-LSTM model achieved a test RMSE of approximately 11.97 µg/m³ and an R² statistic of approximately 0.85 for PM_2.5 concentrations, outperforming the other models tested on the same datasets. The additional metrics of MAE, MAPE, Mean Bias Error, and Index of Agreement confirmed the model’s accuracy and low bias in the prediction of air pollution levels. Statistical tests, such as the Diebold–Mariano test, confirmed the significance of the model’s accuracy over the CNN-LSTM models. These findings indicate that the proposed model effectively captures the dynamics of the air pollution data. The proposed model structure efficiently achieved an accurate and lightweight model for urban air pollution forecasting. Full article

In this work, plasmonic photoconductive antenna (PCA) structures with different grating-width and gap configurations were numerically investigated to evaluate their influence on transient-current generation and terahertz (THz) emission performance. Two geometrical scaling strategies were considered: a fixed-gap configuration with a constant 100 nm photoconductive gap and a proportional-gap configuration in which the gap size was equal to the grating width. Three-dimensional finite element method (FEM) simulations were performed to analyze transient carrier dynamics, THz pulse electric-field behavior, and frequency-domain spectral response under 800 nm optical excitation. The results demonstrate that reducing the inter-grating gap enhances plasmonic near-field confinement and carrier localization near the metal–semiconductor interface, leading to stronger transient-current responses and enhanced THz characteristics. Spatial field and carrier-distribution analyses further confirmed improved electric-field localization and carrier confinement for the fixed-gap structures. In addition, voltage-dependent investigations showed that increasing the applied bias voltage strengthens carrier acceleration and enhances the simulated THz response within the investigated operating range. The results further demonstrate that the observed enhancement is governed not only by grating periodicity but also by the grating-width/gap-size ratio, highlighting the importance of geometrical fill-factor optimization. Polarization-dependent simulations confirmed the plasmonic origin of the enhanced transient-current generation and THz emission. These findings demonstrate that optimal THz performance arises from a balanced interplay between plasmonic field localization, optical absorption, and carrier-transport dynamics, providing design guidelines for the optimization of plasmonic THz PCAs. Full article

Background: Dental ankylosis (DA) in growing patients leads to progressive infraocclusion and alveolar ridge deformities, compromising future implant rehabilitation. Decoronation has been proposed as a biologically driven alternative to extraction for preserving alveolar bone during growth. Objective: We aimed to evaluate the clinical outcomes of decoronation—alveolar ridge preservation, infraocclusion progression, implant site development, and the influence of treatment timing—in growing patients with ankylosed permanent anterior teeth. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines. A comprehensive search of MEDLINE (EBSCO), EMBASE, Scopus, and Web of Science was performed (January 2006–May 2026), supplemented by grey literature screening. Eligible studies included clinical investigations reporting outcomes of decoronation in patients ≤18 years. Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS) and Joanna Briggs Institute (JBI) checklist. Certainty of evidence was evaluated using the GRADE framework. Lastly, an inter-rater agreement was quantified using Cohen’s kappa coefficient. Results: Five studies (two retrospective cohorts and three case series) comprising 140 decoronated teeth with follow-up periods ranging from 1 to 30 years were included. A total of 78 records were identified across four databases; five studies met the eligibility criteria after duplicate removal and screening. Inter-rater agreement at the full-text eligibility stage was good (κ = 0.70). The overall risk of bias was low to moderate, and the certainty of evidence was rated as low using the GRADE framework. Vertical alveolar bone preservation or gain was consistently observed, particularly when decoronation was performed during the prepubertal or pubertal growth phases. The largest cohort (n = 103) reported substantial vertical bone gain when intervention occurred at a mean age of 13.0 years in girls and 14.6 years in boys. Infraocclusion stabilisation or improvement was reported across all studies. In contrast, horizontal ridge reduction persisted, with the only quantitative study reporting a mean bucco-palatal loss of 1.67 ± 1.12 mm (p = 0.004). No included study directly assessed implant placement outcomes. Overall, the certainty of evidence was low due to observational study designs, heterogeneity in outcome assessment, and absence of controlled comparators. Conclusions: Decoronation appears to be a promising strategy for preserving vertical alveolar bone and stabilising infraocclusion in growing patients with ankylosed teeth, particularly when performed before or during the pubertal growth phase. Evidence showed considerable bone height preservation, though horizontal ridge reduction persisted across cases. However, the certainty of evidence remains low because available studies are observational, heterogeneous, and lack direct extraction comparators. Therefore, high-quality prospective studies with standardised outcome measures and controlled comparisons are required to establish definitive clinical protocols. Participants underwent decoronation during childhood or adolescence (≤18 years); reported follow-up periods of up to 30 years reflect monitoring that extended into adulthood. Clinical significance: For clinical decision-making, decoronation should be considered once ankylosis with progressive infraocclusion is confirmed during active growth, ideally before the pubertal spurt; the decision should be guided by growth stage rather than chronological age, and clinicians should anticipate likely horizontal ridge reduction by planning for possible augmentation at implant placement and coordinating multidisciplinary follow-up until skeletal maturity. Full article

Objectives: The aim of the present systematic review and meta-analysis is to compare bracket adhesion failure rates between resin-modified glass ionomer cement (RMGIC) and conventional resin adhesives (CRA) during fixed orthodontic treatment (OT), based on evidence from randomized controlled trials (RCTs). Methods: The research question is “Is there a difference in bracket adhesion failure rates between RMGIC and CRA?” The study was performed in accordance with the PRISMA guidelines. A comprehensive literature search was performed across multiple databases without time or language restrictions through February 2026. Keywords were used in different combinations using Boolean operators. Hand searching was performed and disagreements were resolved via discussion. The risk of bias (RoB) and certainty of evidence (CoE) were assessed using the Cochrane risk of bias tool and Grading of Recommendations Assessment, Development and Evaluation approach, respectively. Quantitative data synthesis was conducted using a random-effects model to calculate pooled odds ratios and 95% confidence intervals. Results: Seven RCTs met the inclusion criteria. Bracket failure rates ranged from 5.95% to 15.0% for RMGIC and 3.4% to 25.0% for CRA. The pooled meta-analysis revealed no statistically significant difference in bracket failure between the two adhesive types (OR = 1.00; 95% CI: 0.60 to 1.67), although substantial statistical heterogeneity was observed (I² = 69.0%, p = 0.0065). One included trial demonstrated significantly improved retention for RMGIC when combined with a specific enamel deproteinization conditioning step prior to bonding. Three studies had a low RoB and the remaining were judged as having “some concerns”. The overall CoE was low. Conclusions: Based on the currently available randomized evidence, no statistically significant difference in bracket adhesion failure rates was observed between RMGIC and CRA during fixed OT. However, given the low CoE, substantial heterogeneity among studies, and relatively short follow-up periods, these findings should be interpreted with caution. Further well-designed randomized controlled trials with longer follow-up are needed to provide more definitive conclusions. Full article

Lithium niobate on insulator (LNOI) has emerged as a promising platform for compact, low-loss phase modulators. The extant LNOI studies evaluate device performance almost exclusively through the Pockels effect, treating piezoelectric–photoelastic strain and thermo-optic drift as decoupled channels. Crucially, both mechanisms directly perturb the phase bias of a fiber-optic gyroscope (FOG), rendering them indispensable in sensing-oriented design. This work establishes a unified multiphysics model of an X-cut TFLN ridge phase modulator that self-consistently couples the electro-optic, piezoelectric–photoelastic, thermo-optic, and pyroelectric channels. The contributions of the four mechanisms are quantitatively decomposed under realistic FOG operating conditions, and the slab thickness, ridge-top width, and electrode gap are systematically optimized to balance modulation efficiency against environmental robustness. The co-optimization of the ridge geometry and electrode gap design maintains the EO overlap factor near 0.55, while reducing the half-wave voltage requirement. This results in a half-wave voltage length of V_πL = 1.65 V·cm at a 4.4 μm electrode gap. The optimized geometry and electrode gap (4.4 μm) are essentially temperature-independent: extracted from the Pockels modulation slope, V_πL remains stable at ≈1.65 V·cm (push–pull single-pass; within ~0.3%) across 25~85 °C. Furthermore, an externally imposed substrate temperature rise of 60 K (the upper end of the 25~85 °C FOG operating range) induces a mode-field-weighted thermal residual corresponding to approximately 27% of the Pockels modulation depth at an applied voltage of 5 V. The present study demonstrates that the DC-coupled operation of TFLN sensor-grade modulators is viable across the full FOG temperature range, without dedicated active temperature stabilization, and the residual thermal-bias offset is absorbed by the FOG’s standard closed-loop servo electronics. The results of the study provide quantitative design guidelines for high-performance, environmentally stable TFLN phase modulators in compact FOG systems. Full article

Skeleton-based human action recognition (HAR) requires models that preserve the local kinematic structure of the human body while capturing long-range spatiotemporal dependencies under noisy or incomplete joint observations. Traditional Graph Convolutional Networks (GCNs) provide topology-aligned inductive bias but are often limited by local information aggregation from neighboring joints. In contrast, attention-based mechanisms capture global interactions, yet they may attend to spurious correlations when skeletal constraints are weakly enforced. This paper proposes Differential Hyperedge Attention-enhanced GCN (DHA-eGCN), a hybrid architecture that couples structure-aware Differential Hyperedge Attention with multi-scale temporal convolution for spatiotemporal skeleton sequence processing. DHA injects skeletal structure into attention via hop-distance relative positional encoding and hyperedge context tokens generated via joint-to-part pooling. It further employs differential attention to suppress shared noisy correlations and enhance interaction selectivity. To strengthen spatial grounding, an explicit GCN branch is added under partial- or full-depth configurations, where the first four or all ten layers are applied with graph convolutions. The model further employs an ensemble strategy that combines predictions from multiple complementary model instances. Our experiments on NTU RGB+D 60 under the X-Sub and X-View protocols, NTU RGB+D 120 under the X-Sub and X-Set protocols, and Northwestern-UCLA demonstrate that DHA-eGCN consistently outperforms or remains competitive with strong graph-based, transformer-based, and hybrid state-of-the-art methods based on the same four-stream architecture. The best configuration achieves 93.7% and 97.0% on NTU RGB+D 60 X-Sub and X-View, respectively; 90.9% and 91.9% on NTU RGB+D 120 X-Sub and X-Set, respectively; and 97.6% on Northwestern-UCLA. Full article

Background: Lepidium meyenii Walp (L. meyenii), traditionally known as maca, is widely recognized for its health-promoting properties, including potential protection against exercise-induced muscle damage (EIMD). However, its precise effect on post-exercise blood biomarkers remains unclear. Objective: This study aimed to qualitatively review research published until April 2026 examining L. meyenii supplementation to reduce blood markers of muscle damage and protein degradation post-exertion in animal studies. Specifically, the effect size (ES) of L. meyenii supplementation on post-exercise levels of creatine kinase (CK), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN) was estimated. Methods: This systematic review and meta-analysis were conducted in accordance with the PRISMA guidelines. The certainty of the evidence was assessed using the GRADE framework. Relevant studies were identified through Web of Science, Scopus, SPORTDiscus, PubMed, and MEDLINE. Eligible studies included in vivo experiments in animals with controlled designs and pre-/post-intervention assessments. Methodological quality and risk of bias were evaluated using the CAMARADES tool. Statistical analysis involved standardized mean differences (SMD) using Hedges’ g with 95% confidence intervals. Results: 15 studies were included in the systematic review, and 14 studies in animals in the meta-analysis. The CAMARADES scores ranged from 5 to 7 points, indicating moderate methodological quality. Supplementation with L. meyenii was not associated with statistically significant changes in LDH (SMD = −1.37; 95% CI −3.34 to 0.59), BUN (SMD = −0.37; 95% CI −2.16 to 1.42) nor CK (SMD = 0.29; 95% CI −5.45 to 6.03), with very high heterogeneity (I² > 97%). Exploratory subgroup analyses and meta-regression analyses by formulation type and dose did not identify any moderators that could robustly explain this heterogeneity. Conclusions: The available evidence does not support a robust overall effect of L. meyenii supplementation on blood biomarkers of muscle damage or protein catabolism in animals subjected to physical stress. The high degree of heterogeneity could not be robustly explained by either the type of formulation or the dose. These findings, which are exploratory and hypothesis-generating in nature, highlight the need for standardized, well-characterized formulations and trials with adequate statistical power. Full article

Remote sensing object detection remains challenging because objects often appear with large scale variation, dense spatial layouts, and strong interference from complex geographical backgrounds. To address these coupled difficulties, we propose EDM-Net, an end-to-end multi-scale detector that organizes feature processing into three coordinated stages: adaptive extraction, intra-scale interaction, and cross-scale fusion. First, an efficient sparse mixture-of-experts (ES-MoE) module is embedded in the backbone to allocate scale-specific convolutional experts according to scene-level feature responses, providing a more adaptive feature basis than a single static extraction path. Second, a dynamic mixing intra-scale feature interaction (DMIFI) module is introduced into the Transformer encoder. This module combines global self-attention with dynamic spatial mixing, thereby preserving long-range context while reintroducing local two-dimensional inductive bias for dense and small objects. Third, a multi-scale synergistic attention fusion (MSAF) module aligns adjacent feature levels through parallel local and global attention branches and structural re-parameterization, reducing semantic dilution during feature aggregation. Comprehensive experiments on three large-scale remote sensing benchmark datasets, DIOR, NWPU VHR-10, and RSOD, demonstrate that EDM-Net consistently improves over the re-trained RT-DETR-R18 baseline under the same experimental protocol, attaining mAP50 scores of 83.7%, 95.6%, and 95.8% respectively. Additional ablation and scale-specific analyses indicate that the three modules contribute complementary gains, especially for small and densely distributed objects. These results suggest that coordinated extraction, interaction, and fusion can improve remote sensing object detection under complex scale and background conditions. Full article

This study investigates an IPS-type Metglas/PMN-PT laminated magnetoelectric composite and its feasibility as a reciprocal mechanical magnetoelectric antenna for low-frequency transmission and reception. Finite-element simulations under quasi-static and frequency-domain conditions reveal strong magnetoelectric coupling under an optimal DC bias field, with both the direct magnetoelectric effect (DME) and converse magnetoelectric effect (CME) exhibiting pronounced resonance near 14.5 kHz, governed by the same longitudinal extensional vibration mode. Five IPS samples were fabricated and experimentally characterized. All devices showed resonant frequencies within 14.1–14.5 kHz, peak DME coefficients of 3.0 × 10⁶ to 3.9 × 10⁶ pC/Oe, and peak CME coefficients of 12.0~15.8 Oe·cm/V, confirming good fabrication consistency, transmit–receive reciprocity, and array-integration potential. The parallel IPS antenna generated a magnetic flux density of 37 nT at 1 m, and exhibited an equivalent magnetic noise of 63 fT/Hz^1/2 at 14.45 kHz. These results demonstrate that the proposed IPS structure combines high-sensitivity reception with efficient low-frequency transmission, showing strong potential for miniaturized, low-power, and long-range magnetic communication and underwater communication applications. Full article

Metanephrine (MN) and normetanephrine (NMN) are critical biomarkers for neuroendocrine tumors (pheochromocytoma and paraganglioma). Following our previous development of a molecularly imprinted solid-phase extraction (MISPE) sorbent for urine analysis, this study evaluated MISPE coupled with HILIC-MS/MS for determining metanephrines in human plasma. Unlike conventional phases, the novel polymer selectively binds analytes as in situ-generated anions via quaternary alkylammonium groups in hydroxide form, ensuring accurate extraction from just 25 µL of plasma. Validated per U.S. FDA guidelines, the assay showed good intra- and interday precision (CV < 10.8%), accuracy (bias < −10.6%) and excellent linearity (R² > 0.99) across pathological ranges (184.3–877.8 ng/L for MN; 174.8–923.0 ng/L for NMN), with low relative standard errors (<6.9%). Excellent selectivity was demonstrated in the presence of structurally close analogs (catecholamines, DOPA and its derivatives). Compared with commercial WCX, the sorbent yielded cleaner extracts, significantly reducing the phospholipid interference. Although lower limits of quantification (92.2 ng/L MN; 87.4 ng/L NMN) slightly exceeded healthy upper thresholds, the method has potential for use in specific clinical scenarios with pronounced biomarker elevations: diagnosis of pheochromocytoma/paraganglioma, monitoring post-treatment metanephrine decline, and tracking tumor-induced hypertensive crises in emergencies. This accessible protocol forms a solid foundation for advanced diagnostics. Full article