Search Results (69,648)

Background: Multiple high-powered magnet ingestion is a surgical emergency due to inter-loop attraction leading to ischemia, necrosis, perforation, and fistula formation. While well documented in children, adult cases—particularly those complicated by entero-enteric fistula—remain rare, and management is largely extrapolated from pediatric guidelines. Objective: To present a rare case of adult entero-enteric fistula following multiple neodymium magnet ingestion, we review the literature and propose an adapted management algorithm for adults. Methods: A narrative PubMed review was performed to identify pediatric and adult cases of magnet ingestion complicated by gastrointestinal fistula. Search terms included magnet ingestion, entero-enteric fistula, neodymium, and adult. Reported case characteristics, diagnostic modalities, treatments, and outcomes were analyzed. Results: A 38-year-old male with schizophrenia presented with small bowel obstruction five days after ingesting multiple magnets. Abdominal radiography revealed clustered radiopaque bodies in the distal ileum. Emergency laparotomy identified an entero-enteric fistula caused by pressure necrosis from inter-loop magnetic attraction. Segmental enterectomy with side-to-side anastomosis was performed, with uneventful recovery. The literature review identified only a few adult cases, which showed similar pathophysiology but frequent diagnostic delays and higher complication rates compared with pediatric cases. Conclusions: This case adds to the scarce adult literature on magnet-induced entero-enteric fistula and supports the adaptation of pediatric-based protocols for adults, with attention paid to psychiatric comorbidity and delayed presentation. Early imaging, timely intervention, and multidisciplinary care are essential to prevent severe gastrointestinal injury. Full article

To address environmental pollution issues and optimize the utilization of waste biomass resources, this study proposes a novel eco-friendly sound-absorbing material based on maple leaf waste and tests its sound absorption performance. The fibers were extracted from maple leaf waste through a wet decomposition and grinding process. Metallurgical microscopy was employed to observe the microstructural characteristics of maple leaf fibers to identify the potential synergistic effect. The effects of two key factors—sample thickness and mass density—on sound absorption performance were investigated. The sound absorption coefficients were measured using the transfer function method in a dual-microphone impedance tube to evaluate their sound-absorbing performance. Experimental results demonstrate that the prepared maple leaf fibers, as acoustic materials, exhibit excellent acoustic performance across a wide frequency range, with an average sound absorption coefficient of 0.7. Increasing sample thickness improves the sound absorption coefficient in low- and mid-frequency ranges. Additionally, increased sample mass density was found to enhance acoustic performance in low- and mid-frequency bands. This study developed an eco-friendly material with lightweight and efficient acoustic absorption properties using completely biodegradable maple leaf waste. The results provide high-performance, economical, and ecologically sustainable solutions for controlling building and traffic noise while promoting the development of eco-friendly acoustic materials. Full article

The concept of Multi Q-Fermatean hesitant fuzzy soft sets (MQFHFSS), derived from the integration of multi-Q fuzzy soft sets and Fermatean hesitant fuzzy sets, can be applied in practice to optimise the resolution of complex multi-criteria decision-making problems. The method exceeds traditional approaches such as Fermatean hesitant fuzzy sets, fuzzy soft sets, and Pythagorean fuzzy sets in enhancing the ability to capture higher levels of uncertainty, hesitation, and symmetry in multi-criteria evaluations, thereby supporting more balanced judgments in complex decision-making situations. In this study, we investigate the novel MQFHFSS concept along with the associated operations. The fundamental characteristics of aggregation operators derived from MQFHFSS have been examined to address some complex decision-making issues. Moreover, we discuss some key algebraic features and their different cases, emphasizing the role of symmetry under the influence of MQFHFSS. Finally, we illustrate some numerical examples and solve the real-world decision-making problem by using the proposed technique. Full article

To address the ill-posedness of the Hessian matrix in monocular visual-inertial SLAM (Simultaneous Localization and Mapping) caused by unobservable depth of feature points, which leads to convergence difficulties and reduced robustness, this paper proposes a Pre-Dog-Leg feature optimization method based on an adaptive preconditioner. First, we propose a multi-candidate initialization method with robust characteristics. This method effectively circumvents erroneous depth initialization by introducing multiple depth assumptions and geometric consistency constraints. Second, we address the pathology of the Hessian matrix of the feature points by constructing a hybrid SPAI-Jacobi adaptive preconditioner. This preconditioner is capable of identifying matrix pathology and dynamically enabling preconditioning as a strategy. Finally, we construct a hybrid adaptive preconditioner for the traditional Dog-Leg numerical optimization method. To address the issue of degraded convergence performance when solving pathological problems, we map the pathological optimization problem from the original parameter space to a well-conditioned preconditioned space. The optimization equivalence is maintained by variable recovery. The experiments on the EuRoC dataset show that the method reduces the number of Hessian matrix conditionals by a factor of 7.9, effectively suppresses outliers, and significantly improves the overall convergence time. From the analysis of trajectory error, the absolute trajectory error is reduced by up to 16.48% relative to RVIO2 on the MH_01 sequence, 20.83% relative to VINS-mono on the MH_02 sequence, and up to 14.73% relative to VINS-mono and 34.0% relative to OpenVINS on the highly dynamic MH_05 sequence, indicating that the algorithm achieves higher localization accuracy and stronger system robustness. Full article

Background: Traumatic Brain Injury (TBI) in children is a major cause of morbidity and mortality worldwide. Ocular manifestations are common but often overlooked, despite their potential to cause long-term visual impairment. This study aimed to evaluate the prevalence and characteristics of ocular findings in pediatric TBI patients admitted to the intensive care unit (ICU). Method: We prospectively reviewed records of pediatric patients (≤16 years) with TBI admitted to the Neurosurgery ICU at King Abdullah University Hospital (January 2022–December 2024). TBI was defined using U.S. CDC criteria and confirmed by clinical and radiological findings. Ocular manifestations were identified from ophthalmology consultations, neurosurgical notes, and bedside examinations. Demographics, injury details, and clinical outcomes were recorded. Statistical analyses included Chi-square, Fisher’s exact, and Mann–Whitney U tests, with significance set at p ≤ 0.05. Results: Thirty-eight patients (median age: 8 years; 55.3% male) were included. Ocular findings were present in 20 patients (52.6%). These patients were significantly older (median age 10 vs. 6 years, p = 0.007) and had lower admission GCS scores (11 vs. 14, p = 0.016). Male predominance was higher in the ocular group (75.0% vs. 33.3%, p = 0.030). Ocular findings were significantly associated with surgical intervention (60.0% vs. 22.2%, p = 0.025), orbital fractures (40.0% vs. 5.6%, p = 0.021), basal skull fracture signs (p = 0.036), and extraocular muscle limitation (p = 0.048). On multivariable analysis, orbital fracture remained the only independent predictor of ocular findings (aOR 2.22, 95% CI 1.17–3.57, p = 0.02). Conclusion: Over half of pediatric ICU TBI patients demonstrated ocular manifestations, closely linked to greater injury severity and craniofacial trauma. Routine, comprehensive ophthalmological evaluation should be integrated into the multidisciplinary management of severe pediatric TBI to optimize visual and functional outcomes. Full article

Background: Using natural substances for cancer therapy has attracted considerable interest due to their safety and reduced systemic toxicity. Nigella sativa (NS) oil, a traditional natural oil rich in bioactive compounds, possesses significant therapeutic potential. Brucine (BR), an alkaloid, exhibits potent cytotoxicity against various cancer cell lines; however, its poor selectivity and high systemic toxicity limit its clinical application. Objective: To overcome these challenges, this study aimed to enhance drug delivery and improve therapeutic efficacy. Method: A PEGylated nanoemulsion (NE) incorporating NS and BR was developed and characterized for particle size, size distribution, zeta potential, viscosity, and drug content. The in vitro release of BR was evaluated both with and without serum incubation. A quantitative amount of serum protein associated with the surface of the NE was estimated, and a hemolytic safety assay was carried out. Finally, an in vitro cytotoxicity study was conducted, and the in vivo anti-tumor effect of the developed PEGylated BR-loaded NE was evaluated and compared with its naked counterpart. Result: The developed PEGylated BR-loaded NE possessed favorable characteristics as a nanocarrier for parenteral administration, with a particle size of 188.5 nm, a zeta potential of −1.61, a viscosity of 3.4 cP, and 99% drug content uniformity. It released up to 60.4% of BR over 12 h, while only 18.4 µg/µmol of the total lipids were adsorbed on the surface of the formulation, compared with 54.5 µg/µmol for the naked counterpart. The PEGylated NE was safe, inducing less than 5% of hemolysis, and displayed substantial inhibition of MDA cell growth. Conclusions: The PEGylated NE achieved a significant reduction in tumor volume, suggesting that PEGylated NE may serve as a promising platform for enhancing anti-tumor activity. Full article

Background: Sports vision training improves perceptual–motor skills crucial for performance and injury prevention. Despite proven benefits, little is known about its perception and use among coaches in Portugal. Methods: A cross-sectional online survey was completed by active coaches from various sports, gathering sociodemographic data, awareness of visual training, perceived importance of ten visual skills, and implementation in training plans. Statistical analyses included descriptive tests to summarize sample characteristics, t-tests and two-way ANOVA to compare perceived importance of visual skills across sex and sport modalities, Spearman correlations to assess associations with age, and Firth-corrected logistic regression to identify predictors of incorporating visual training into practice plans. Results: Among 155 participants (88.5% men; mean age 36.9 ± 11.8 years), 73.2% reported incorporating visual training, with no association with self-reported knowledge (p = 0.413). Regarding perceived importance, reaction time was rated highest (1.20 ± 0.44), followed by hand–eye/body coordination (1.61 ± 0.71) and anticipation (1.34 ± 0.55). Age negatively correlated with importance given to visual memory, peripheral vision, concentration, depth perception, coordination, and moving-object recognition (p < 0.05). Multivariable analysis showed age (OR = 1.05; p = 0.0206) and volleyball (OR = 2.45; p = 0.031) positively associated with implementation, while higher perceived importance for visual concentration was negatively associated (OR = 0.54; p = 0.0176). Conclusions: Visual training implementation is high but not always linked to formal knowledge. Adoption is influenced by sport and demographics, and the counterintuitive role of visual concentration underscores the need for tailored educational programs to enhance performance and reduce injury risk. Full article

Coke is an essential raw material in the blast furnace (BF) ironmaking process. Its moisture content significantly impacts BF ironmaking production. This study employs a coupled Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) approach to simulate the drying process of wet coke within a coke silo (CS) dryer. Initially, the model was validated by comparing numerical results with experimental data from the literature. Subsequently, it investigated the gas flow dynamics, heat and mass transfer characteristics, and differences in drying behaviour across distinct dryer zones. Finally, the effects of inlet gas velocity and inlet gas temperature on the drying process were systematically quantified. Simulation results reveal that the bottom of the CS dryer exhibits a low-velocity laminar state, while the middle and upper regions display intense gas flow motion. Consequently, the bottom region exhibits insufficient particle drying in comparison to other zones, with the average particle moisture content decreasing by less than 20%. Under the continuous heat exchange between the hot gas and the particles, the moisture content of the particles decreases rapidly. Based on the drying rate behaviour, the drying process exhibits the following three different stages: the pre-heating period, the constant-rate period, and the falling-rate period. Compared to zones 1 and 3, zone 2 exhibits higher temperatures due to its high heat transfer efficiency, which significantly promotes a reduction in particle moisture content. An increase in inlet gas velocity enhances the particle drying rate and heat flux, accelerates moisture reduction, and raises the temperature. The impact of inlet gas velocity is most pronounced after the constant-rate period, with particle drying uniformity decreasing as the inlet gas velocity increases, consequently leading to a decline in drying quality. Increasing inlet gas temperature significantly increases particle temperature and heat flux throughout the drying period and accelerates the high-rate drying stage. These findings provide fundamental insights for further understanding and studying the coke drying process. Full article

Wireless pipeline robots often suffer from localization drift and position loss due to electromagnetic attenuation and shielding in complex pipeline configurations, which hinders accurate pipeline reconstruction. This paper proposes a trajectory reconstruction method based on Geometric Constraint–Long Short-Term Memory (GC-LSTM). First, a motor control system based on Field-Oriented Control (FOC) was developed for the proposed pipeline robot; second, trajectory errors are mitigated by exploiting pipeline geometric characteristics; third, a Long Short-Term Memory (LSTM) network is used to predict and compensate the robot’s velocity when odometer slip occurs; finally, multi-sensor fusion is employed to obtain the reconstructed trajectory. In straight-pipe tests, the GC-LSTM method reduced the maximum deviation and mean absolute deviation by 69.79% and 72.53%, respectively, compared with the Back Propagation (BP) method, resulting in a maximum deviation of 0.0933 m and a mean absolute deviation of 0.0351 m. In bend-pipe tests, GC-LSTM reduced the maximum deviation and the mean absolute deviation by 60.48% and 69.91%, respectively, compared with BP, yielding a maximum deviation of 0.2519 m and a mean absolute deviation of 0.0850 m. The proposed method significantly improves localization accuracy for wireless pipeline robots and enables more precise reconstruction of pipeline environments, providing a practical reference for accurate localization in pipeline inspection applications. Full article

Background/Objectives: Fragility fractures, particularly in the vertebra and hip, are a significant health concern in the elderly, often associated with osteoporosis. Emerging evidence suggests a link between lipid profiles and bone health, but the characteristics of lipid biomarkers in patients with fragility fractures remain underexplored. Methods: This study analyzed serum lipid biomarkers, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in 10,540 patients aged 50 and older with either vertebral or hip fragility fractures. We compared lipid levels between the two fracture groups and examined the relationship between lipid profiles and baseline characteristics of patients. Results: Patients with vertebral fractures exhibited significantly higher serum levels of TC, TG, HDL-C, and LDL-C compared to those with hip fractures. These differences remained statistically significant after adjusting for confounding variables. Multivariable regression analysis revealed that age was inversely associated with TC, TG, and LDL-C, but positively associated with HDL-C. All lipid levels were significantly higher in women than in men. Time from fracture to admission and BMI were positively associated with TG levels and inversely associated with HDL-C. Vertebral fracture patients had a higher prevalence of abnormally high TC (3.03% vs. 0.78%), TG (9.15% vs. 3.54%), and LDL-C (2.80% vs. 1.04%), but lower prevalence of abnormally low HDL-C (20.53% vs. 26.66%; p < 0.001 for all). Conclusions: Our findings highlight distinct lipid profile characteristics in patients with vertebral and hip fragility fractures, suggesting that physiological or metabolic changes following different fracture types may differentially influence lipid metabolism. These insights may inform targeted prevention and management strategies for fragility fractures. Full article

Background: The aim of the present study is to compare the potential of a high-fat diet, a high-carbohydrate diet, and a high-fat, high-carbohydrate diet to induce liver injury and visceral obesity within a period of five weeks, identify the pattern and degree of hepatic changes at the tissue level, identify the earliest metabolic markers of specific liver changes induced by each type of diet, and to test the possible beneficial effects of steviol glycosides in a rat experimental model. Methods: Wistar rats (n = 56) were divided into seven groups as follows: group BD (before diet), group SD (standard diet), group HFD (high-fat diet), group HCHD (high-carbohydrate diet), group HFHCHD (high-fat high-carbohydrate diet), group SDS (standard diet supplemented with Stevia extract), and group HFDS (high-fat diet supplemented with Stevia extract). Results: Total cholesterol concentrations (2.02 ± 0.22 mmol/L) increased in the HFD group (2.56 ± 0.82 mmol/L) and in the HFDS group (2.89 ± 0.48 mmol/L). The VLDL values before diets were 0.27 ± 0.11 mmol/L and increased most significantly in the HFHCHD group—1.14 ± 0.62 mmol/L. The baseline ALT values (88.4 ± 10.6 U/L) increased in the HFD group (128.13 ± 19.5 U/L) and the HFDS group (127.00 ± 17.74 U/L). Similar increases were registered in the AST/ALT ratio and ALP. Total bilirubin (7.10 ± 1.39 μmol/L) increased in HFD group (27.86 ± 17.01 μmol/L). Serum NO had the lowest values in groups fed diets supplemented with steviol glycosides. All high-calorie diets induced hepatocellular injury. The mass of the perirenal fat depot and cross-sectional area of adipocytes were highest in HFD, HFHCHD, and HFDS groups. Conclusion: High-calorie diets have the potential to induce visceral obesity and hepatocellular injury within a very short period of time, which produces characteristic histological changes and specific biochemical profile. Steviol glycosides may alleviate some aspects of the inflammatory response, but findings about lipid profile parameters and liver enzymes are controversial. Full article

Background: Numerous healthcare services have been affected by the COVID-19 pandemic worldwide. Specialized healthcare services were postponed or canceled, potentially compromising regular services for hemato-oncology patients. The current study aimed to analyze the impact of the COVID-19 pandemic on access to hemato-oncology services in Almaty, the largest city in Kazakhstan. Methods: We retrospectively analyzed the socio-demographic characteristics of patients admitted to two large tertiary centers rendering hemato-oncology services, the City Clinical Hospital 7 (H7) and the Kazakh Institute of Oncology and Radiology (KazIOR). All data were retrieved for the period spanning from 1 March 2019 to 28 February 2022. The retrieved variables included age, gender, type of residence, hospitalization rate, treatment outcomes (discharged/deceased), bed days, diagnoses according to International Classification of Diseases (ICD-10) (acute leukemia and hematopoietic depression, lymphoproliferative diseases, and myeloproliferative diseases), and referral sources (ambulance, another hospital, consultative and diagnostic assistance, primary healthcare, self-referral, and referrals from hematologists’ offices). Results: In the 2019–2022 period, 6763 hemato-oncology hospitalizations were registered: 3583 in H7 and 3180 in KazIOR. The mean age at hospitalization was 55.04 (SD = 16.07) for females and 51.2 (SD = 16.7) for males. The proportion of hospitalized urban and rural patients differed significantly: 6191 (92%) and 571 (8,4%), respectively (χ² = 13.8, p = 0.001). In the 2020–2021 period, fewer patients were discharged (n = 2047) compared to 2019–2020 (n = 2387) and 2021–2022 (n = 2081) (χ² = 20.09, p = 0.003). However, the proportion of deaths in the 2020–2021 period (3.5%) was higher than in the 2019–2020 (3.2%) and 2021–2022 periods (2.6%) (χ² = 20.09, p = 0.003). A total of 403 (19%) hospital admissions were carried out by ambulance (emergency cases) in the 2020–2021 period, 368 (14.8%) in 2019–2020, and 394 (18.3%) in 2021–2022 (χ² = 2231, p < 0.001). The number of patients transferred from other hospitals to H7 and KazIOR increased by 12.4% in the 2020–2021 period. Conclusions: Our findings indicate a negative impact of the COVID-19 pandemic on access to hemato-oncology services, leading to increased mortality. Further studies are warranted to explore factors underlying the trends in hospitalizations and mortality of hemato-oncology patients during healthcare crises. Full article

Managing resource use in cloud and edge environments is crucial for optimizing performance and efficiency. Traditionally, this process is performed with detailed knowledge of the available infrastructure while being application-specific. However, it is common that users cannot accurately specify their applications’ low-level requirements, and they tend to overestimate them—a problem further intensified by their lack of detailed knowledge on the infrastructure’s characteristics. In this context, resource orchestration mechanisms perform allocations based on the provided worst-case assumptions, with a direct impact on the performance of the whole infrastructure. In this work, we propose a resource orchestration mechanism based on intents, in which users provide their high-level workload requirements by specifying their intended preferences for how the workload should be managed, such as prioritizing high capacity, low cost, or other criteria. Building on this, the proposed mechanism dynamically assigns resources to applications through a Reinforcement Learning method leveraging the feedback from the users and infrastructure providers’ monitoring system. We formulate the respective problem as a discrete-time, finite horizon Markov decision process. Initially, we solve the problem using a tabular Q-learning method. However, due to the large state space inherent in real-world scenarios, we also employ Deep Reinforcement Learning, utilizing a neural network for the Q-value approximation. The presented mechanism is capable of continuously adapting the manner in which resources are allocated based on feedback from users and infrastructure providers. A series of simulation experiments were conducted to demonstrate the applicability of the proposed methodologies in intent-based resource allocation, examining various aspects and characteristics and performing comparative analysis. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor with limited therapeutic options despite multimodal treatment. Small interfering RNA (siRNA)-based therapeutics can silence tumor-promoting genes, but achieving efficient and tumor-specific delivery remains challenging. Lipid nanoparticles (LNPs) are promising siRNA carriers; however, conventional antibody conjugation can impair antigen recognition and complicate manufacturing. This study aimed to establish a modular Fc-binding peptide (FcBP)-mediated post-insertion strategy to enable PD-L1-targeted delivery of VEGF-siRNA via LNPs for GBM therapy. Methods: Preformed VEGF-siRNA-loaded LNPs were functionalized with FcBP–lipid conjugates, enabling non-covalent anchoring of anti-PD-L1 antibodies through Fc interactions. Particle characteristics were analyzed using dynamic light scattering and encapsulation efficiency assays. Targeted cellular uptake and VEGF gene silencing were evaluated in PD-L1-positive GL261 glioma cells. Anti-tumor efficacy was assessed in a subcutaneous GL261 tumor model following repeated intratumoral administration using tumor volume and bioluminescence imaging as endpoints. Results: FcBP post-insertion preserved LNP particle size (125.2 ± 1.3 nm), polydispersity, zeta potential, and siRNA encapsulation efficiency. Anti-PD-L1–FcBP-LNPs significantly enhanced cellular uptake (by ~50-fold) and VEGF silencing in PD-L1-expressing GL261 cells compared to controls. In vivo, targeted LNPs reduced tumor volume by 65% and markedly suppressed bioluminescence signals without inducing weight loss. Final tumor weight was reduced by 63% in the anti-PD-L1–FcBP–LNP group (656.9 ± 125.4 mg) compared to the VEGF-siRNA LNP group (1794.1 ± 103.7 mg). The FcBP-modified LNPs maintained antibody orientation and binding activity, enabling rapid functionalization with targeting antibodies. Conclusions: The FcBP-mediated post-insertion strategy enables site-specific, modular antibody functionalization of LNPs without compromising physicochemical integrity or antibody recognition. PD-L1-targeted VEGF-siRNA delivery demonstrated potent, selective anti-tumor effects in GBM murine models. This platform offers a versatile approach for targeted nucleic acid therapeutics and holds translational potential for treating GBM. Full article

The rapid integration of renewable energy sources into modern power systems has exacerbated power quality challenges, particularly broadband oscillation phenomena that threaten grid symmetry and stability. The proposed symplectic geometric mode decomposition (SGMD) method advances the field; however, issues like mode aliasing and over-decomposition are unresolved within the symplectic geometric paradigm. To resolve these limitations in existing methods, this paper proposes a novel time-frequency-coupled symmetry mode decomposition technique. The approach first applies symplectic symmetry geometric mode in the time domain, then iteratively refines the modes using frequency-domain Local Outlier Factor (LOF) detection to suppress aliasing. Final mode integration employs Dynamic Time Warping (DTW) for optimal alignment, enabling accurate extraction of oscillation characteristics. Comparative evaluations demonstrate that the average error of the amplitude and frequency identification of the proposed method are 1.39% and 0.029%, which are lower than the results of SVD at 5.09% and 0.043%. Full article