PeerJ最新文献

Background: Previous research suggests a possible relationship between physical activity and cognitive functioning in children. However, the findings remain inconsistent, and few studies have examined this link using standardized instruments in preadolescent populations. This study aimed to determine the association between self-reported physical activity and cognitive performance in 10-11-year-old school children.

Methods: A total of 423 children (213 girls and 210 boys; 10.66 ± 0.43 years) participated in this study. The level of physical activity was assessed using the Physical Activity Questionnaire for Children (PAQ-C), while cognitive performance was measured with the d2-R Test of Attention. Data were analyzed using Pearson correlations and a multivariate general linear model (GLM).

Results: Statistically significant correlations were found between PAQ-C scores and two d2-R variables, the total number of items processed (PRZ) and concentration performance (VS), whereas the association with percentage of errors (Ch%) was not significant. The multivariate GLM confirmed these patterns: PAQ-C was significantly associated with PRZ (β = 2.596, p = 0.003) and VS (β = 1.973, p = 0.012), but not with Ch% (p = 0.281). Gender was also a significant predictor of PRZ and VS.

Conclusion: Self-reported physical activity showed small but statistically significant associations with selected attention outcomes, particularly processing speed and concentration. Given the cross-sectional design and reliance on self-report, causal inference is not possible; however, these findings highlight the potential relevance of physical activity for attentional functioning in school settings and underscore the need for longitudinal and intervention-based research.

The growing global demand for effective and safe therapeutics has accelerated advances in biomaterials for drug delivery applications. Biomaterials, including polymers, metals, ceramics, and composites, play a central role in modern medical devices and therapeutic systems by enabling controlled interactions with biological environments. Initially defined as inert materials interfacing with biological systems, biomaterials are now rationally engineered to treat, replace, or evaluate tissue and organ functions. Recent progress in regenerative medicine, nanotechnology, and precision healthcare has expanded their use in drug delivery, where tunable physicochemical properties-such as degradation kinetics, surface chemistry, and mechanical stability-allow controlled release, protection of labile therapeutics, and enhanced accumulation at target sites. Polymer-based biomaterials enable sustained drug release through diffusion-controlled, degradation-mediated, or stimulus-responsive mechanisms, thereby extending therapeutic exposure and reducing systemic dosing frequency compared with conventional formulations. Nanostructured carriers, including liposomes, micelles, and dendrimers, further enhance drug delivery by improving solubility, cellular uptake, and site-specific targeting via size control, surface functionalization, and ligand-mediated interactions. Despite these advances, clinical translation remains limited by challenges related to immune-biomaterial interactions, batch-to-batch variability, long-term biodegradation behavior, and the scalability of manufacturing under regulatory constraints. Future biomaterial development must therefore emphasize precision fabrication, good manufacturing practice-compatible production, and biologically informed design strategies that account for patient-specific variability. This review provides a focused overview of biomaterial-based drug delivery systems, summarizes recent technological advances, and critically discusses mechanistic and translational challenges, including immune compatibility, degradation control, and regulatory compliance, with particular emphasis on their implications for personalized drug delivery.

Background: Slopestyle and snowboard cross are distinct snowboarding disciplines, each characterized by specific technical elements and differing competition dynamics. Examining the corresponding contributions of energy systems and physiological responses in these disciplines will yield essential foundational data to inform both physical conditioning programs and competitive strategies. This study compares the relative contributions of energy metabolism systems and physiological indices between the two disciplines.

Methods: A crossover design was applied to eight elite male athletes registered with the Korea Ski & Snowboard Association; the athletes performed one simulated competition run each for slopestyle and snowboard cross. Oxygen consumption (VO₂), heart rate (HR), and blood lactate concentration (La^-) were measured during the runs, and the contribution of each energy system was calculated based on VO₂ during exercise (oxidative), peak lactate concentration (glycolytic), and excess post-exercise oxygen consumption (phosphagen).

Results: Compared with slopestyle, snowboard cross showed a significantly higher contribution from the glycolytic system (18.6% vs. 12.9%), higher peak lactate concentration (4.17 vs. 3.02 mmol L^-1), and higher peak HR (168 vs. 158 bpm; p < 0.05). The mean run times were 66 s for snowboard cross and 75 s for slopestyle. Conversely, the contributions of the oxidative and phosphagen systems in slopestyle and snowboard cross were around 36.2-27.9% and 50.9-53.6%, respectively, showing no significant difference between the two disciplines; the total energy consumption (W_Total) was also similar between the two disciplines (p > 0.05).

Conclusion: Due to repeated high-intensity movements, snowboard cross is associated with a higher physiological load, as reflected by greater reliance on anaerobic glycolysis and higher lactate concentration and peak heart rate, whereas slopestyle tends to emphasize the use of oxidative metabolism owing to the recovery intervals between obstacles. However, this oxidative contribution did not differ significantly between disciplines. These results highlight the importance of establishing customized fitness training and recovery strategies based on the competition characteristics of each discipline.

Background: Genetic polymorphisms of CYP2C9 significantly influence the efficacy and safety of some drugs, which potentially lead to adverse effects and therapeutic failure. The aim of this study was to investigate the genetic polymorphism of 37 CYP2C9 alleles and evaluate their catalytic activities in agomelatine metabolism in vitro.

Methods: Insect microsomes expressing the 37 recombinant CYP2C9 variants were incubated in a 37 °C water bath with agomelatine. The main active metabolites of agomelatine were detected using a UPLC-MS/MS system. Then, the enzyme kinetic parameters of the 36 variants were calculated and compared with those of the wild-type CYP2C9*1.

Results: Relative to CYP2C9*1, four variants exhibited no significant difference in enzyme activity. Eleven variants exhibited significantly higher intrinsic clearance values, while 13 variants exhibited significantly reduced intrinsic clearance values. The remaining eight variants demonstrated complex metabolic patterns; these variants do not produce the two metabolites equally, and they inhibited the appearance of one metabolite but promoted the production of another metabolite. These results suggest that special attention should be given to subjects carrying poor metabolizers of CYP2C9 alleles when prescribing agomelatine in clinical practice. This study can provide valuable insights for personalized medicine and reduce adverse reactions to some extent.

Urban waterlogging is an escalating challenge under rapid urbanization and climate change, yet accurate spatial prediction remains hindered by nonlinear drivers and spatial heterogeneity. This study proposes a spatially interpretable machine learning framework by integrating remote sensing and geospatial data with hybrid modeling. Using recorded waterlogging locations in Beijing, we constructed a balanced dataset with topographic, hydrological, land cover, and proximity-based predictors. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost)-were evaluated, with XGBoost achieving the best classification performance (area under the curve (AUC) = 0.913 ± 0.055). To enhance spatial interpretability, two hybrid strategies were further developed: (1) XGBoost_MGWR, in which XGBoost serves as the primary predictor and MGWR corrects its spatially structured residuals, thereby improving spatial explanatory power; and (2) MGWR_XGBoost, where MGWR first models spatially varying effects and XGBoost subsequently fits the residuals to refine predictive performance. Results from spatially blocked five-fold cross-validation show that MGWR_XGBoost provides the best probabilistic accuracy (Brier = 0.289 ± 0.039) and the highest area under the precision recall (PR-AUC) (0.576), with substantially higher specificity (0.734) and a spatially stable local R ² pattern; therefore, it was selected for final risk mapping. The proposed framework enables high-resolution, spatially explicit risk mapping and offers practical support for drainage planning, green infrastructure prioritization, and adaptive flood governance. Beyond Beijing, this approach shows strong potential for improving resilience in other data-scarce urban environments facing intensifying flood risks.

The monitoring of the continuous fluctuation of seawater environmental conditions plays a critical role in the management and conservation of marine and coastal ecosystems. Although previous studies in the Santa Marta region (Colombian Caribbean) have examined aspects of seawater quality, the spatiotemporal dynamics of key water quality parameters, impact gradients, and contaminant dispersion remain poorly characterized. This study presents the results of a 14-month monitoring program conducted from 2021 to 2024 at seven sampling stations: four in Santa Marta Bay and three within Tayrona National Natural Park (TNNP). These stations cover areas experiencing varying degrees of anthropogenic impact. The assessment of spatiotemporal dynamics of physicochemical seawater parameters revealed seasonal and anthropogenic influences on water quality. During the rainy periods, increased levels of turbidity, nutrients, pollutants, and oxygen demand were observed, particularly at stations impacted by wastewater discharges. Overall, these parameters exceeded the permissible limit frequency during the rainy periods, where 60% of the samples showed elevated levels of organic load. This revealed an eutrophication gradient extending from the most impacted stations to the sites located within the TNNP. Water quality index (Integrated Coastal Area Management; ICAM) assessments confirmed degraded conditions near sewage, river, and port discharge points, while TNNP stations exhibited acceptable environmental quality. These findings emphasize the protective role of marine protected areas as buffers against pollution, and highlight the need for periodic, long-term monitoring to elucidate links between water quality and ecosystem health, critical for informing conservation and management efforts in regions facing substantial anthropogenic pressures such as Santa Marta Bay and the TNNP.

Background: Emergence Agitation (EA) is a common postoperative complication, particularly prevalent following nasal surgeries. The occurrence of EA not only elevates the risk of self-inflicted injuries or harm to others but may also lead to a series of severe postoperative complications. This study aims to evaluate the effect of remimazolam on EA in patients undergoing nasal surgery and compare its efficacy with that of conventional anesthetic agents.

Materials and methods: This randomized controlled trial included 100 patients undergoing elective nasal surgery under general anesthesia from June to September 2024. Patients were randomly assigned (1:1) to the R group (remimazolam) or the C group (propofol). Agitation levels were measured using Riker Sedation-Agitation Scale (SAS), recording each patient's highest score. The primary outcome was the incidence of EA, with secondary outcomes including hemodynamic parameters, emergence time, and adverse events.

Results: The results indicated that the incidence of EA was significantly lower in the R group compared to the C group (22% vs. 49%; Risk Difference -27% (95% CI [-50% to -0.3%]); Relative Risk 0.45 (95% CI [0.25-0.81]); P = 0.005). Patients in the remimazolam group also demonstrated more stable hemodynamics, with a lower incidence of hypotension (18% vs. 53%, P < 0.001) and significantly reduced occurrence of injection pain (2% vs. 41%, P < 0.001). However, there were no significant differences between the two groups in terms of adverse effects such as bradycardia, postoperative wound bleeding, and postoperative nausea and vomiting.

Conclusions: Remimazolam holds significant clinical application value and potential in surgeries with a high incidence of EA.

Background: This study aimed to evaluate the effectiveness of a k-space learning type deep learning (DL) reconstruction combined with fat-suppressed turbo spin-echo (TSE) T2-weighted imaging (T2WI) in improving image quality and reducing acquisition time for muscle magnetic resonance imaging (MRI).

Methods: In this prospective study, 98 controls (mean age 56.3 years) and 33 patients (mean age 45.2 years) underwent both DL reconstructed TSE (TSE_DL) and standard fat-suppressed TSE T2WI scans of the bilateral thigh using a 3T MRI scanner. Quantitative metrics, including noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were measured. Two radiologists performed qualitative assessments using a 5-point Likert scale to evaluate image quality, anatomical structure visibility, and diagnostic confidence. Inter-reader agreement and statistical comparisons between groups were analyzed.

Results: The acquisition times were 2 min 11s and 1 min 33s for standard TSE and TSE_DL, respectively. TSE_DL demonstrated significantly lower noise and higher SNR and CNR than standard TSE in both healthy volunteers and patients (p < 0.05). Qualitative assessments showed that TSE_DL provided superior overall image quality, better visualization of thigh muscles and femoral bones, sharper edges, improved contrast resolution, and higher diagnostic confidence compared to standard TSE (p < 0.01).

Conclusion: DL during image acquisition reconstruction, combined with fat-suppressed TSE T2WI, significantly enhances image quality and reduces acquisition time in muscle MRI, suggesting its potential for routine clinical use in musculoskeletal imaging.

Background: The role of thyroid hormones and their interaction with sex hormones in adolescent major depressive disorder (MDD), particularly regarding sex differences, remains poorly understood.

Methods: This cross-sectional study included first-episode adolescent depression outpatients. All patients were stratified by depression severity into non-major depression disorder (non-MDD) and MDD groups. Serum levels of thyroid and sex hormones (testosterone, estradiol, progesterone) were measured. Group comparisons, correlation analyses, multivariate logistic regression, and moderation analysis were performed.

Results: The study included 837 patients (597 females and 240 males), comprising 359 non-MDD and 478 MDD cases. The mean age was 15 years (IQR 14-16). Compared to males, females exhibited higher SDS (Z = -7.34, P < 0.001), higher SAS scores (Z = -5.73, P < 0.001), alongside lower T3 (Z = -3.81, P < 0.001), FT4 (Z = -3.54, P < 0.001), and FT3 levels (Z = -6.38, P < 0.001), while sex hormones differed as anticipated: testosterone was higher in males (Z = -21.00, P < 0.001), whereas estradiol and progesterone were higher in females (Z = -13.02, P < 0.001; Z = -5.74, P < 0.001, respectively). SDS scores correlated negatively with T4 (ρ = -0.080, P = 0.021), T3 (ρ = -0.099, P = 0.004), and testosterone (ρ = -0.207, P < 0.001), and positively with estradiol (ρ = 0.144, P < 0.001) and progesterone (ρ = 0.104, P = 0.003). Three multivariate regression models were established, among which Model 3-including age, sex, T3, and testosterone-achieved the highest AUC of 0.80 (95% CI = [0.77-0.83]). Moderation analysis demonstrated that testosterone significantly moderated the T3-depression relationship (interaction β = -0.09, P = 0.04). Furthermore, higher T3 was associated with lower depression severity in high-testosterone subgroups (simple slope = -1.39, P = 0.003).

Conclusions: Thyroid dysfunction is associated with adolescent MDD in a sex-specific manner. A logistic regression model incorporating both thyroid and sex hormones showed improved classification accuracy. Importantly, testosterone was found to moderate the relationship between T3 and depression severity. These findings suggest that assessing thyroid function alongside sex hormone levels may enhance risk stratification in adolescent MDD.

Objective: To develop a high-fidelity three-dimensional finite element model of the whole spine-thorax complex based on high-resolution computed tomography (CT) images of a healthy adult male, and to perform initial validation under representative loading conditions for quantitative analysis of load transmission, coupled motion, and stress distribution. We hypothesized that the model would reproduce published quasi-static segmental moment-rotation behavior and cadaveric thoracic impact responses within acceptable error ranges.

Methods: High-resolution CT data of one healthy adult Chinese male volunteer (25 years; 175 cm; 70 kg) were used to reconstruct detailed anatomical structures, including vertebrae, intervertebral discs, ribs, costal cartilage, sternum, ligaments, respiratory muscles, lungs, and heart. Material properties were assigned based on literature data, and nonlinear contacts were defined among articular and cartilaginous structures. Model validation was carried out using two scenarios: pure-moment loading of the T12-L1 functional spinal unit and a frontal chest impact simulation, with the numerical responses compared against available experimental and cadaveric data.

Results: The T12-L1 moment-rotation curves agreed well with published biomechanical ranges, and the frontal impact simulation produced a peak force (3,270 N) and chest compression (79 mm) closely matching experimental results (3,453 N and 80 mm), with errors of 5.3% and 1.25%, respectively.

Conclusions: The finite element model reproduced static and dynamic responses of the spine-thorax complex within available experimental ranges for the loading conditions examined, providing an initial, non-invasive platform for investigating load transmission, coupled motion, and stress distribution under physiological, pathological, and interventional conditions.