Search Results (6,367)

In this study, four types of Fe₃O₄-based magnetic nanospheres were functionalized with distinct surface groups to examine how surface chemistry influences their co-transport with tetracycline (TC) in porous media. The functional groups investigated are carboxyl (−COOH), epoxy (−EPOXY), silanol (−SiOH), and amino (−NH₂). Particles bearing −COOH, −EPOXY, or −SiOH are negatively charged, facilitating their transport through porous media, whereas −NH₂-modified particles acquire a positive charge, leading to strong electrostatic attraction to the negatively charged TC and quartz sand, and consequently substantial retention with reduced mobility. Adsorption of TC onto Fe₃O₄-MNPs is predominantly chemisorptive, driven by ligand exchange and the formation of coordination complexes between the ionizable carboxyl and amino groups of TC and the surface hydroxyls of Fe₃O₄-MNPs. Additional contributions arise from electrostatic interactions, hydrogen bonding, hydrophobic effects, and cation–π interactions. Moreover, the carboxylate moiety of TC can coordinate to surface Fe centers via its oxygen atoms. Molecular dynamics simulations reveal a hierarchy of adsorption energies for TC on the differently modified surfaces: Fe₃O₄-NH₂ > Fe₃O₄-EPOXY > Fe₃O₄-COOH > Fe₃O₄-SiOH, consistent with experimental findings. The results underscore that tailoring the surface properties of engineered nanoparticles substantially modulates their environmental fate and interactions, offering insights into the potential ecological risks associated with these nanomaterials. Full article

Objective: Metabolic syndrome (MetS) represents a growing and significant public health burden worldwide. The evidence regarding whether skipping breakfast affects the development of MetS and its components remains inconsistent and uncertain. This study aimed to synthesize the best available evidence regarding the association between skipping breakfast and the risk of MetS and its components. Methods: This systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 guidelines. We systematically searched the PubMed, Embase, Cochrane Library, and Web of Science databases from inception until May 2025. Two reviewers independently screened titles/abstracts and full texts, extracted data, and assessed the risk of bias. This review included cross-sectional and cohort studies on the association between breakfast skipping and the risk of MetS and its components. Results: Nine studies were included after quality evaluation by NOS. Pooled results from the meta-analysis revealed that skipping breakfast was significantly associated with an increased risk of MetS (OR: 1.10, 95% CI: 1.04–1.17) and its components—namely abdominal obesity (OR = 1.17, 95% CI 1.01–1.34), hypertension (OR: 1.21, 95% CI: 1.10–1.32), hyperlipidemia (OR: 1.13, 95% CI: 1.04–1.23), and hyperglycemia (OR = 1.26, 95% CI: 1.16–1.37). Conclusions: The meta-analysis demonstrated that skipping breakfast was significantly associated with an increased risk of MetS and its key components—abdominal obesity, hypertension, hyperlipidemia, and hyperglycemia. These findings highlight regular breakfast consumption as a potential modifiable factor for preventing and managing MetS and related cardiometabolic diseases. Full article

Background: Sodium–glucose cotransporter 2 inhibitors (SGLT2i) have shown cardioprotective effects beyond glucose control. In aortic stenosis, SGLT2 expression is upregulated in myocardium and valve tissue, contributing to inflammation, oxidative stress, thrombogenicity, and calcification. SGLT2 inhibition may counteract these mechanisms, potentially reducing bioprosthetic valve failure after transcatheter aortic valve replacement (TAVR), where the diseased native valve remains in place. Objectives: This study aimed to evaluate whether SGLT2i use is associated with improved clinical outcomes, including all-cause mortality and bioprosthetic valve failure, following TAVR. Methods: We conducted a retrospective cohort study using the TriNetX global health research network. Adults with non-rheumatic aortic stenosis who underwent TAVR were stratified by SGLT2i use. Propensity score matching (1:1) was applied to balance baseline characteristics (n = 2297 per group). Primary outcomes were all-cause mortality and bioprosthetic valve failure during follow-up. Results: Before matching, SGLT2i users had more cardiovascular comorbidities. After matching, SGLT2i use was associated with a significantly lower risk of all-cause mortality (HR: 0.83; 95% CI: 0.71–0.97; p = 0.02) and bioprosthetic valve failure (HR: 0.62; 95% CI: 0.39–0.99; p = 0.04). Conclusions: In a large real-world cohort of TAVR recipients, SGLT2i use was independently associated with reduced mortality and lower risk of bioprosthetic valve failure. These findings support a potential disease-modifying role for SGLT2 inhibitors in this high-risk population and warrant further prospective investigation. Full article

Degradation data plays a crucial role in the reliability assessment and condition monitoring of engineering systems. The stage-wise changes in degradation rates often signal turning points in system performance or potential fault risks. To address the issue of structural changes during the degradation process, this paper constructs a degradation modeling framework based on a two-stage Inverse Gaussian (IG) process and proposes a change-point detection method based on an adjusted CUSUM (cumulative sum) statistic to identify potential stage changes in the degradation path. This method does not rely on complex prior information and constructs statistics by accumulating deviations, utilizing a binary search approach to achieve accurate change-point localization. In simulation experiments, the proposed method demonstrated superior detection performance compared to the classical likelihood ratio method and modified information criterion, verified through a combination of experiments with different change-point positions and degradation rates. Finally, the method was applied to real degradation data of a hydraulic piston pump, successfully identifying two structural change points during the degradation process. Based on these change points, the degradation stages were delineated, thereby enhancing the model’s ability to characterize the true degradation path of the equipment. Full article

Objectives: Neurocognitive disorders (NCDs) refer to a broad spectrum of conditions characterized by declining cognitive functions, such as memory, attention, language, and executive abilities. It is estimated that up to half of patients affected by NCDs remain undiagnosed or are diagnosed at an advanced stage of the disease. This study aimed to analyze the utility of subclinical organ damage markers, which could be used in primary care for the detection and prevention of NCD. Methods: The study participants (n = 137) completed neuropsychological tests (Addenbrooke’s Cognitive Examination/ACE and Mini-Mental State Examination/MMSE), a sociodemographic survey, an interview on past illnesses, and had their ankle-brachial index (ABI) and pulse wave velocity (PWV) values measured. Results: Based on the MMSE test, 26 participants (19.0%) were diagnosed with mild cognitive impairment (MCI) and 8 participants (5.8%) with NCDs. The study found that lower ABI values were associated with worse cognitive performance, suggesting that the ABI may be a useful tool for identifying individuals at increased risk of NCDs, while PWV cannot be used as a predictor for this group of diseases. Conclusions: Lower ABI values were associated with reduced cognitive performance, whereas PWV showed no significant relationship. The secondary findings suggest that physical activity, regular computer use, and better mental well-being were linked to improved cognitive outcomes. A low ABI value could potentially serve as a predictor of cognitive disorders, and as a diagnostic tool that is easily accessible and quick, it may improve diagnostics and the overall health of primary care patients. Health education regarding modifiable risk factors for dementia is also of crucial importance. Full article

Objectives: To systematically review and analyze electronic systems in competitive motorcycles (2020-2025), examining their technical specifications, performance impacts, and technological evolution across MotoGP, World Superbike (WSBK), MotoE, British Superbike (BSB), and Spanish Championship (ESBK) categories. Eligibility criteria: Included studies reporting technical specifications or performance data of electronic systems in professional motorcycle racing, published between January 2020 and December 2025 in English, Spanish, Italian, or Japanese. Excluded: opinion pieces, amateur racing, and studies without quantitative data. Information sources: IEEE Xplore, SAE Technical Papers, Web of Science, Scopus, and specialized motorsport databases were searched through December 15, 2025. Risk of bias: Modified Cochrane Risk of Bias tool for experimental studies and Newcastle-Ottawa Scale for observational studies. Synthesis of results: Synthesis of results: Random-effects meta-analysis using DerSimonian-Laird method for homogeneous outcomes; narrative synthesis for heterogeneous data. The complete PRISMA 2020 checklist is provided in Appendix . Included studies: 87 studies met inclusion criteria (52 experimental, 38 simulation, 23 technical descriptions, 14 comparative analyses). Electronic systems were categorized into six domains: Engine Control Units (ECU, 28 studies, 22%), Vehicle Dynamics (23 studies, 18%), Traction Control (19 studies, 15%), Data Acquisition (21 studies, 17%), Braking Systems (18 studies, 14%), and Emerging Technologies (18 studies, 14%). Note that studies could address multiple domains. Limitations of evidence: Proprietary restrictions limited access to 31% of technical details; 43% lacked cross-category comparisons. Interpretation: Electronic systems are primary performance differentiators, with computational power following Moore’s Law. Future developments point toward distributed architectures and 5G telemetry. Funding: This project has been funded by the R&D programme with reference TEC-2024/TEC-62 and acronym iRoboCity2030-CM, granted by the Comunidad de Madrid through the Dirección General de Investigación e Innovación Tecnológica, Orden 5696/2024. Full article

Introduction/Background: Maternal depression is a significant risk factor for infant emotion regulation (ER), often linked to detrimental mother–infant interactions. Individual effects of maternal depression and maternal sensitivity are known, but their combined influence on infant ER across different emotional contexts remains underexplored. This study investigates concurrent relations among maternal depression, maternal sensitivity, and infant ER in low- and high-arousal contexts in a matched sample of primarily White educated mothers. Methods: We examined 5-month-old infants of clinically depressed and nondepressed mothers. Maternal sensitivity was coded from home observations; infant ER behaviors (e.g., gaze aversion, object-attend, self-soothing) were assessed through observation during modified Still-Face Paradigm (SFP) and fear-eliciting tasks. Results: Clinically depressed mothers exhibited lower maternal sensitivity than nondepressed mothers. Infants of depressed mothers used adaptive ER strategies less—specifically, lower monitoring and gaze aversion in the SFP, and lower gaze aversion and object-attend in the Fear task. Maternal sensitivity moderated the association between maternal depression and infant gaze aversion during the SFP and both gaze avert and object-attend during the Fear task. There was a context-specific regulatory difference for self-soothing; only infants of depressed mothers used self-soothing significantly more during the high-arousal Fear task. Conclusions: These findings underscore the interplay between maternal clinical depression and sensitivity in affecting infant ER. Maternal sensitivity acts as a crucial buffer against the adverse effects of maternal depression on infant ER. The results also indicate that infant emotion regulation varies in different contexts of low and high arousal. Interventions that target maternal sensitivity could significantly improve emotion regulation in infants of depressed mothers. Full article

Background and Objectives: To date, several machine learning (ML) prognostic prediction models have been investigated for patients with acute myocardial infarction (AMI). However, few studies have compared the prognostic performance of ML techniques in AMI patients who underwent percutaneous coronary intervention (PCI). We sought to compare the prognostic performance among various machine learning techniques to determine which one showed the best prediction ability. Materials and Methods: Using data from the large, multicenter COREA-AMI registry, this study analyzed 10,172 patients to predict major adverse cardiac events (MACEs) at 1 and 5 years. MACE was defined as a composite of cardiac death, myocardial infarction, or cerebrovascular accident. Results: Compared with the four other ML techniques and traditional logistic regression, the random forest (RF) model consistently demonstrated the highest predictive performance. At 5 years, the RF model achieved a superior area under the curve (AUC) of 0.822, an accuracy of 0.804, and an F1 score of 0.870. To ensure clinical interpretability, a SHapley Additive exPlanations analysis was performed on the RF model. It identified key independent predictors for MACEs. The top nonmodifiable predictors included age, renal function, and left ventricular ejection fraction, whereas modifiable risk factors included dual antiplatelet therapy, statin therapy, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker therapy, and adherence to these optimal medical therapy. Conclusions: In this real-world patient cohort, the RF model provided modest improvements in long-term risk stratification, and our findings highlight the continuing importance of guideline-directed medical therapy in determining patient prognosis. Full article

We propose an edge-enhanced federated learning framework for real-time itinerary optimization in elderly oriented adventure tourism, addressing the critical need for adaptive scheduling that balances activity intensity with health constraints. The system integrates lightweight convolutional neural networks with a priority-based scheduling algorithm, processing participant profiles and real-time biometric data through a decentralized computation model to enable dynamic adjustments. A modified Hungarian algorithm incorporates physical exertion scores, temporal proximity weights, and health risk factors, then optimizes activity assignments while respecting physiological recovery requirements. The federated learning architecture operates across distributed edge nodes, preserving data privacy through localized model training and periodic global aggregation. Furthermore, the framework interfaces with transportation systems and medical monitoring infrastructure, automatically triggering itinerary modifications when vital sign anomalies exceed adaptive thresholds. Implemented on NVIDIA Jetson AGX Orin modules, the system achieves 300 ms end-to-end latency for real-time schedule updates, meeting stringent safety requirements for elderly participants. The proposed method demonstrates significant improvements over conventional itinerary planners through its edge computing efficiency and personalized adaptation capabilities, particularly in handling the latency-sensitive demands of intensive tourism scenarios. Experimental results show robust performance across diverse participant profiles and activity types, confirming the system’s practical viability for real-world deployment in elderly adventure tourism operations. Full article

Background: Despite advances in surgical techniques and perioperative care, pancreatic resections such as pancreaticoduodenectomy (PD) and distal pancreatectomy (DP) remain high-risk procedures. Postoperative complications significantly impact morbidity, mortality, and patient quality of life. Methods: This narrative review summarizes recent literature on major complications following pancreatic surgery, including postoperative pancreatic fistula (POPF), delayed gastric emptying (DGE), and post-pancreatectomy hemorrhage (PPH), with an emphasis on incidence, risk factors, outcomes, and current preventive strategies. Results: POPF is a leading complication, occurring in 5–22% of cases and often linked with sepsis and hemorrhage. Key risk factors include high BMI, soft pancreatic texture, and small duct size. Preventive measures like Pasireotide, modified anastomosis techniques, and neoadjuvant therapy show variable success. DGE affects up to 57% of PD patients and is associated with prolonged recovery; antecolic reconstruction and erythromycin may reduce incidence. PPH, though less frequent (3–13%), can be life-threatening, particularly when secondary to POPF. Endovascular approaches are now favored for late arterial bleeding. Other complications include wound infections, abscesses, bile leaks, and pulmonary issues, all contributing to extended hospital stays and diminished quality of life. Conclusions: Pancreatic surgery continues to carry significant risks, with POPF, DGE, and PPH being the most impactful complications. While multiple interventions have shown promise, standardized protocols and predictive tools are still needed. Surgery should be performed in high-volume centers with experienced multidisciplinary teams to optimize outcomes. Full article

Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, including antimicrobial resistance and toxic effects. Reliable, sensitive, and selective analytical methods are essential to ensure food safety and enable accurate monitoring of antibiotic contamination in honey. This study aimed to validate a multi-analyte procedure in accordance with the parameters established in Commission Implementing Regulation (EU) 2021/808 for the identification and quantification of antibiotics, including tetracyclines, lincosamides, quinolones, macrolides, β-lactams, sulfonamides, and diaminopyrimidines. Methods: An extraction protocol was developed using 0.1% formic acid in ACN:H₂O (80:20, v/v), followed by a modified QuEChERS with the addition of 1 g NaCl and 2 g MgSO₄. The extracts were analyzed by UHPLC-TOF-MS. Results: The method, validated under CIR (EU) 2021/808, demonstrated robust performance, with recoveries ranging from 80.1% to 117.6%, repeatability between 0.5% and 32.2%, reproducibility between 2.3% and 31.6%, and determination coefficients (R²) ranging from 0.9429 to 0.9982. Validation was achieved for 15 antibiotic residues, with CCβ from 3 to 15 μg·kg⁻¹, LODs between 0.09 and 6.19 μg·kg⁻¹, and LOQs between 0.29 and 18.77 μg·kg⁻¹. Application to 10 commercial Portuguese honey revealed no detectable levels of the target antibiotics. Conclusions: The combination of a simplified extraction with UHPLC-TOF-MS provides a reliable approach for the determination of antibiotics in honey. This validated method represents a valuable tool for food safety monitoring and risk assessment of apiculture practices. Full article

Background/Objectives: Hearing loss is a disorder that develops because of being exposed to high noise levels affecting the quality of life among affected individuals. A review of the literature was conducted to explore the prevalence of hearing loss and its associated factors among workers in the metal industry. Methods: The literature search was conducted on ScienceDirect, Google Scholar, Pub Med, ResearchGate and African Journals Online databases to identify articles according to the Preferred Reporting Items for Systematic Reviews and Meta analyses (PRISMA) guidelines. The studies published in scientific journals between January 2014 and December 2024 describing hearing loss and its associated factors among workers in the metal industry were considered for inclusion in the review. The articles were screened by the author. The Critical Appraisal Skills Programme (CASP) quality assessment tool with modified checklist questions was used to evaluate the quality of studies. Results: Following the literature search and using the relevant inclusion criteria, a total of 127 articles were identified, and 8 articles with a total of 2605 participants were included in the review. The sample sizes ranged from 93 to 606. The participants’ age ranged from 19 to 65 years. A review of studies showed varying prevalence of hearing loss ranging from 13.8% to 59%. Furthermore, the studies have found working experience, advanced age, cigarette smoking, tinnitus, working in areas of high noise levels and not using hearing protective devices to be associated with a risk of developing hearing loss. Conclusions: The review found that workers in the metal industry are at risk of developing hearing loss and, therefore, implementation of control measures to prevent the occurrence of hearing loss is necessary. Full article

Introduction: 3D-printing is an emerging technology in the field of prosthetics, offering advantages such as cost-effectiveness, ease of customization, and improved accessibility. While previous reviews have focused on limited aspects, the aim of this systematic review is to provide a comprehensive evaluation of the clinical outcomes of 3D-printed prostheses for both upper and lower limbs. Methods: A search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across six databases (PubMed, Web of Science, EBSCO, Scopus, Cochrane Library, and Sage). Studies on 3D-printed prostheses in human rehabilitation that focused on the clinical outcomes of the device were included, while studies lacking clinical data, 3D printing details, or focusing on traditional manufacturing methods were excluded. Finally, the risk of bias was assessed using the modified Downs & Black Checklist. Results: A total of 1420 studies were identified, with 11 meeting the inclusion criteria. The included studies assessed different 3D-printed prosthetic types and upper and lower limb prostheses. The main clinical outcomes analyzed were functional performance, design and material integrity, and overall effectiveness of 3D-printed prostheses. Studies on upper limb prostheses reported improved dexterity, range of motion (ROM), and user satisfaction, despite some durability limitations. Lower limb prostheses showed enhancements in comfort, gait parameters, and customization, particularly in amphibious and partial foot designs. Conclusions: 3D-printed prostheses show potential to improve functional performance, patient satisfaction, fit, and implementation feasibility compared to conventional methods. However, limitations such as small sample sizes, variability in assessment tools, and limited high-quality evidence highlight the need for further research to support broader clinical adoption. Full article

Objectives: Anterior petrosectomy is a challenging neurosurgical procedure requiring precise identification and preservation of multiple critical structures. This technical note explores the feasibility of using real-time near-infrared indocyanine green (NIR-ICG) fluorescence with white light overlay to enhance visualization of the petrous internal carotid artery (ICA) during transpetrosal drilling. We aimed to assess its utility for planning and performing modified Dolenc–Kawase drilling. Methods: We integrated NIR-ICG and white light overlay using a robotic microscope with simultaneous visualization capabilities. This technique was applied to improve neurovascular preservation and skull base landmark identification. Intraoperative video frames and images were captured during an anterior transpetrosal approach for a petroclival meningioma, with technical details, surgical time, and feedback documented. Results: Real-time NIR-ICG with white light overlay successfully identified the posterior genu, horizontal petrosal segment, anterior genu, and superior petrosal sinus. It facilitated precise localization of cochlear landmarks, enabling tailored drilling of the Dolenc–Kawase rhomboid according to patient anatomy and accommodating potential anatomical variants. Conclusions: This approach could enhance intraoperative safety and improve exposure, possibly reducing neurovascular risks without extending operative time. It may serve as a valuable adjunct for complex skull base surgeries. Full article

Background: Self-medication (SM) among women during pregnancy poses a critical risk to maternal health, and SM is still commonly practiced in Saudi Arabia. Therefore, this study aims to assess the factors, attitudes, and prevalence of SM among pregnant women in the Ministry of Health (MOH) First Health Cluster. Methods: A cross-sectional design was used among 400 pregnant women who received care at primary, secondary, and tertiary healthcare levels. A structured tool was adopted and modified based on the literature review, expert and focus group interviews, and the experiences of the target participants. The dependent variables included history of illness, access to healthcare services, medication usage, and perception of SM, while the independent variables included socioeconomic status. Statistical Package for the Social Sciences (SPSS), Version 25 was used for analysis. Results: The prevalence of SM was 36.5%. Cough syrup and antipyretics were the most commonly used medications, while hair problems and weight loss were the most frequently reported reasons for SM. The primary sources of information guiding SM behavior were prior prescriptions and previous experience. Education level, occupation, age, number of pregnancies, and miscarriage history were all significantly associated with SM (p < 0.05). Conclusions: SM was found to be frequent among pregnant women in the First Health Cluster, highlighting the need for educational interventions and regulatory measures to reduce unsafe practices and improve maternal health. Full article