Search Results (24,619)

Background/Objective: The most effective strategy for addressing users’ prosthetic needs is a comprehensive clinical assessment that provides a holistic understanding of the individual’s symptoms, health, function, and environmental barriers and facilitators. A standardized evaluation form would provide guidance for a structured approach to comprehensive clinical assessments of people with LLA. The objective of this study was to determine a list of relevant elements to be included in prosthetic evaluation for adults with lower limb amputation. Methods: Three independent focus group discussions were conducted with prosthetists (n = 15), prosthesis users (n = 11), and decision makers (n = 4) to identify all relevant elements that should be included in the clinical assessment of prosthetic services. The final content was then determined using the Delphi technique, with 35 panelists (18 prosthetists and decision makers, and 17 prosthesis users) voting in each round. Results: A total of 91 elements were identified through the focus group, of which 78 were included through the Delphi process. The identified elements are mostly related to the physical health of the prosthesis user (e.g., mobility, pain, and medical information), while others address personal or psychosocial aspects (e.g., activities of daily living, goals, and motivation) or technical aspects (prosthesis-related). Conclusions: Through a Delphi consensus, a list of relevant elements to be included in a prosthetic evaluation was generated. These results will inform the development of a standardized clinical prosthetic assessment form. This form has the potential to improve the quality of clinical evaluations, guide interventions, and enhance the well-being of prosthetic users. Full article

This work investigated the ion exchange technique for selective separation of rare earth elements (REE) from acid mine drainage (AMD), using different column systems, pH values, and eluent concentrations. Systematic analysis of pH and eluent concentration showed that an initial pH of 6.0 and 0.02 mol L⁻¹ NH₄EDTA are the optimal conditions, achieving 98.4% heavy REE purity in the initial stage (0 to 10 bed volumes). This represents a 32-fold increase compared to the original AMD (6.7% heavy REE). The speciation of REE and impurities was determined by Visual Minteq 4.0 software using pH 2.0, which corresponds to the pH at the inlet of the fractionation column. Under this condition, La and Nd and the impurities (Ca, Mg, and Mn) remained in the fractionation column, while Al was partially retained. In addition, the heavy REE (Y and Dy) were mainly in the form of REE-EDTA complexes and not as free cations, which made fractionation more feasible. The fractionation column minimized impurities, retaining 100% of Ca and 67% of Al, generating a liquor concentrated in heavy REE. This sustainable approach adopted herein meets the critical needs for scalable recovery of REE from diluted effluents, representing a circular economy strategy for critical metals. Full article

Enhancing the specificity and applicability of PCR-based genome-walking methods is highly desirable. A new and universal genome-walking tool, called center degenerated walking-primer PCR (CDWP-PCR), is presented in this study. CDWP-PCR involves adopting a center degenerated walking primer (cdWP) in the secondary/tertiary round of amplification. This cdWP is generated by degenerating the seven central nucleotides of the normal walking primer (nWP) used in primary PCR to NNNNNNN (where N includes the bases A, T, C, and G). Clearly, a partially complementary structure is formed between the two primers. Accordingly, the primary CDWP-PCR non-target products defined by the nWP are diluted in secondary/tertiary CDWP-PCR, as these non-targets have difficulty in annealing with the cdWP; conversely, the primary target product can still be efficiently amplified. The working performance of the proposed CDWP-PCR is verified through cloning of the unknown flanks of three known genes. All the clear DNA bands in the tertiary CDWP-PCRs are confirmed to be correct, and the largest DNA band is 8.0 kb. Overall, CDWP-PCR can be considered as a reliable supplement to existing genome-walking methods. Full article

Social trust has been linked to the development and severity of depression, but trust is a complex, multidimensional construct. This study examines the extent to which two distinct forms of trust, general trust and specific trust, predict depressive symptoms. Drawing on longitudinal data from the Directorate of Health’s national surveys conducted in 2007 and 2009, the analysis includes responses from 3211 Icelanders selected through a stratified random sample. Depressive symptoms were assessed using the Depression, Anxiety, and Stress Scale (DASS), while specific trust captured trust in close relationships, and general trust measured broader perceptions of trustworthiness in others. The two forms of trust together explained 7.6% of the variance in depressive symptoms, with specific trust contributing a substantially greater share. Both remained significant predictors after controlling for prior depression and physical health. These findings highlight the protective role of specific trust and suggest that general trust, an indicator of broader social capital, may also help buffer against depression. The results underscore the relevance of trust as a public health resource and support continued research into social determinants of mental health in Iceland. Full article

Background: Non-communicable diseases (NCDs) are a leading cause of mortality globally, contributing significantly to the burden on healthcare systems. Understanding the spatiotemporal patterns of NCD mortality is crucial for identifying vulnerable populations and regions at high risk. Objectives: Here, we evaluated the spatiotemporal patterns of NCD mortality in the Metropolitan Area of the Valley of Mexico (MAVM) from 2000 to 2019 for five International Classification of Diseases chapters (4, 5, 6, 9, and 10) at two spatial scales: the municipal level and metropolitan region. Methods: Mortality rates were calculated for the total population and stratified by sex and age groups at both spatial scales. In addition, the relative risk (RR) of mortality was estimated to identify vulnerable population groups and regions with a high risk of mortality, using women and the 25–34 age group as reference categories for population-level analysis, and the overall MAVM mortality rate as the reference for municipal-level analysis. Results: Mortality trends showed that circulatory-system diseases (Chapter 9) are emerging as a concerning health issue, with 45 municipalities showing increasing mortality trends, especially among older adults. Respiratory-system diseases (Chapter 10), mental and behavioral disorders (Chapter 5) and nervous-system diseases (Chapter 6) predominantly did not exhibit a consistent general mortality trend. However, upon disaggregating by sex and age groups, specific negative or positive trends emerged at the municipal level for some of these chapters or subgroups. Endocrine, nutritional, and metabolic diseases (Chapter 4) showed a complex pattern, with some age groups presenting increasing mortality trends, and 52 municipalities showing increasing trends overall. The RR showed men and older age groups (≥35 years) exhibiting higher mortality risks. The temporal trend of RR allowed us to identify spatial mortality hotspots mainly in chapters related to circulatory, endocrine, and respiratory diseases, forming four geographical clusters in Mexico City that show persistent high risk of mortality. Conclusions: The spatiotemporal analysis highlights municipalities and vulnerable populations with a consistently elevated mortality risk. These findings emphasize the need for monitoring NCD mortality patterns at both the municipal and metropolitan levels to address disparities and guide the implementation of health policies aimed at reducing mortality risk in vulnerable populations. Full article

Rapid screening of foodborne pathogens is critical for food safety, yet current detection techniques often suffer from low efficiency and complexity. In this study, we developed a sliding microfluidic colorimetric biosensor for the fast, sensitive, and multiplex detection of Salmonella. First, the target bacteria were specifically captured by antibody-functionalized magnetic nanoparticles in the microfluidic chip, forming magnetic bead–bacteria complexes. Then, through motor-assisted sliding of the chip, manganese dioxide (MnO₂) nanoflowers conjugated with secondary antibodies were introduced to bind the captured bacteria, generating a dual-antibody sandwich structure. Finally, a second sliding step brought the complexes into contact with a chromogenic substrate, where the MnO₂ nanoflowers catalyzed a colorimetric reaction, and the resulting signal was used to quantify the Salmonella concentration. Under optimized conditions, the biosensor achieved a detection limit of 10 CFU/mL within 20 min. In spiked pork samples, the average recovery rate of Salmonella ranged from 94.9% to 125.4%, with a coefficient of variation between 4.0% and 6.8%. By integrating mixing, separation, washing, catalysis, and detection into a single chip, this microfluidic biosensor offers a user-friendly, time-efficient, and highly sensitive platform, showing great potential for the on-site detection of foodborne pathogens. Full article

The Korteweg–de Vries (KdV) equation is a nonlinear evolution equation that reflects a wide variety of dispersive wave occurrences with limited amplitude. It has also been used to describe a range of major physical phenomena, such as shallow water waves that interact weakly and nonlinearly, acoustic waves on a crystal lattice, lengthy internal waves in density-graded oceans, and ion acoustic waves in plasma. The KdV equation is one of the most well-known soliton models, and it provides a good platform for further research into other equations. The KdV equation has several forms. The aim of this study is to introduce and investigate a (2+1)-dimensional generalized fifth-order KdV equation with power law nonlinearity (gFKdVp). The research methodology employed is the Lie group analysis. Using the point symmetries of the gFKdVp equation, we transform this equation into several nonlinear ordinary differential equations (ODEs), which we solve by employing different strategies that include Kudryashov’s method, the $(G^{\prime }/G)$ expansion method, and the power series expansion method. To demonstrate the physical behavior of the equation, 3D, density, and 2D graphs of the obtained solutions are presented. Finally, utilizing the multiplier technique and Ibragimov’s method, we derive conserved vectors of the gFKdVp equation. These include the conservation of energy and momentum. Thus, the major conclusion of the study is that analytic solutions and conservation laws of the gFKdVp equation are determined. Full article

Normal and anomalous diffusion processes are characterized by the time evolution of the mean square displacement of a diffusing molecule ${\sigma }^2\left(t\right)$. When ${\sigma }^2\left(t\right)$ is a power function of time, the process is described by a fractional subdiffusion, fractional superdiffusion or normal diffusion equation. However, for other forms of ${\sigma }^2\left(t\right)$, diffusion equations are often not defined. We show that to describe diffusion characterized by ${\sigma }^2\left(t\right)$, the g-subdiffusion equation with the fractional Caputo derivative with respect to a function g can be used. Choosing an appropriate function g, we obtain Green’s function for this equation, which generates the assumed ${\sigma }^2\left(t\right)$. A method for solving such an equation, based on the Laplace transform with respect to the function g, is also described. Full article

This study aimed to examine how downward load applied during instrumentation affects the stress generation and shaping properties in thermally treated and non-treated NiTi rotary instruments. ProTaper Universal (PTU; non-thermally treated) and ProTaper Gold (PTG; thermally treated) were used to prepare J-shaped canals in resin blocks. Load-controlled automated instrumentation and torque/force sensing devices were employed with preset downward loads of 1, 2, or 3 N (n = 10 each). The torque/force, instrumentation time, and canal-centering ratio were measured and analyzed using two-way or one-way analysis of variance with Tukey’s test (α = 0.05). In the PTU-1N group, instrumentation was not completed because a ledge was formed in all canals. The PTU-3N group showed significantly greater upward force (screw-in force) and clockwise torque, along with a significantly smaller canal-centering ratio (less deviation) at the apical 0 mm level, than the PTU-2N group (p < 0.05). The downward load did not influence the instrumentation time (p > 0.05). In the PTG groups, these effects of downward load on the force generation and canal-centering ratio were not significant (p > 0.05). In the non-thermally treated PTU instruments, greater downward loads enhanced screw-in force while decreasing apical canal deviation; however, these effects were abolished in the thermally treated PTG instruments. This study highlights the importance of adapting the instrumentation technique with instrument characteristics: thermally treated flexible instruments facilitate smoother use, while stiffer, non-thermally treated ones may require precise control of downward loads. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

The aim of this study was to improve the efficiency of in situ uranium leaching by developing a specialized methodology for selecting rational parameters for the chemical treatment of production wells. This approach was designed to enhance the filtration properties of ores and extend the uninterrupted operation period of wells, considering the clay content of the productive horizon, the geological characteristics of the ore-bearing layer, and the composition of precipitation-forming materials. The mineralogical characteristics of ore and precipitate samples formed during the in situ leaching of uranium under various mining and geological conditions at a uranium deposit in the Syrdarya depression were identified using an X-ray diffraction analysis. It was established that ores of the Santonian stage are relatively homogeneous and consist mainly of quartz. During well operation, the precipitates formed are predominantly gypsum, which has little impact on the filtration properties of the ore. Ores of the Maastrichtian stage are less homogeneous and mainly composed of quartz and smectite, with minor amounts of potassium feldspar and kaolinite. The leaching of these ores results in the formation of gypsum with quartz impurities, which gradually reduces the filtration properties of the ore. Ores of the Campanian stage are heterogeneous, consisting mainly of quartz with varying proportions of clay minerals and gypsum. The leaching of these ores generates a variety of precipitates that significantly reduce the filtration properties of the productive horizon. Effective compositions and concentrations of decolmatant (clog removal) solutions were selected under laboratory conditions using a specially developed methodology and a TESCAN MIRA scanning electron microscope. Based on a scanning electron microscope analysis of the samples, the effectiveness of a decolmatizing solution based on hydrochloric and hydrofluoric acids (taking into account the concentration of the acids in the solution) was established for the destruction of precipitate formation during the in situ leaching of uranium. Geological blocks were ranked by their clay content to select rational parameters of decolmatant solutions for the efficient enhancement of ore filtration properties and the prevention of precipitation formation. Pilot-scale testing of the selected decolmatant parameters under various mining and geological conditions allowed the optimal chemical treatment parameters to be determined based on the clay content and the composition of precipitates in the productive horizon. An analysis of pilot well trials using the new approach showed an increase in the uninterrupted operational period of wells by 30%–40% under average mineral acid concentrations and by 25%–45% under maximum concentrations with surfactant additives in complex geological settings. As a result, an effective methodology for ranking geological blocks based on their ore clay content and precipitate composition was developed to determine the rational parameters of decolmatant solutions, enabling a maximized filtration performance and an extended well service life. This makes it possible to reduce the operating costs of extraction, control the geotechnological parameters of uranium well mining, and improve the efficiency of the in situ leaching of uranium under complex mining and geological conditions. Additionally, the approach increases the environmental and operational safety during uranium ore leaching intensification. Full article

The development of big data analytics in healthcare has created a demand for privacy-conscious and scalable machine learning algorithms that can allow the use of patient information across different healthcare organizations. In this study, the difficulties that come with traditional federated learning frameworks in healthcare sectors, such as scalability, computational effectiveness, and preserving patient privacy for numerous healthcare systems, are discussed. In this work, a new conceptual model known as Hierarchical Federated Learning (HFL) for large, integrated healthcare organizations that include several institutions is proposed. The first level of aggregation forms regional centers where local updates are first collected and then sent to the second level of aggregation to form the global update, thus reducing the message-passing traffic and improving the scalability of the HFL architecture. Furthermore, the HFL framework leveraged more robust privacy characteristics such as Local Differential Privacy (LDP), Gaussian Differential Privacy (GDP), Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE). In addition, a Novel Aggregated Gradient Perturbation Mechanism is presented to alleviate noise in model updates and maintain privacy and utility. The performance of the proposed HFL framework is evaluated on real-life healthcare datasets and an artificial dataset created using Generative Adversarial Networks (GANs), showing that the proposed HFL framework is better than other methods. Our approach provided an accuracy of around 97% and 30% less privacy leakage compared to the existing models of FLBM-IoT and PPFLB. The proposed HFL approach can help to find the optimal balance between privacy and model performance, which is crucial for healthcare applications and scalable and secure solutions. Full article

Identifying predictors of pro-environmental behaviors (PEBs) can not only figure out who concerns about the environment most but also inform possible pathways that advance or inhabit such prosocial actions. Most past studies and theories focus on factors that reside within personal characteristics or sociopsychological mechanisms rather than taking a holistic view that integrates these two elements into a framework. This study investigates how socioeconomic status (SES) correlates with PEBs, integrating both structural and psychological mechanisms. Drawing on the Stimulus–Organism–Response (SOR) theoretical framework, this paper examines the paradox whereby individuals with higher SES exhibit more frequent environmental actions yet demonstrate lower willingness to pay (WTP)—a form of economic sacrifice. Using nationally representative data from the 2021 Chinese General Social Survey (CGSS), our structural equation modeling reveals that adulthood SES positively correlates with environmental values and behaviors but negatively correlates with WTP. This challenges the traditional linear assumption that greater willingness necessarily leads to greater action. Additionally, while childhood SES predicts adult SES, it shows no direct effect on environmental engagement. These findings highlight multidimensional pathways by which SES shape environmental actions, necessitating differentiated policy approaches to build a sustainable world. Full article

Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious. The study of their quantitative structure–activity relationships through machine learning algorithms can shed light on a rational and effective design. Methods: Information on the antimicrobial activity of peptides was collected, and their structures were characterized by molecular descriptors generation to design regression and classification models based on machine learning algorithms. The contribution of each descriptor in the generated models was evaluated by determining its relative importance and, finally, the antimicrobial activity of new peptides was estimated. Results: A structured database of antimicrobial peptides and their descriptors was obtained, with which 56 machine learning models were generated. Random Forest-based models showed better performance, and of these, regression models showed variable performance (R² = 0.339–0.574), while classification models showed good performance (MCC = 0.662–0.755 and ACC = 0.831–0.877). Those models based on bacterial groups showed better performance than those based on the entire dataset. The properties of the new peptides generated are related to important descriptors that encode physicochemical properties such as lower molecular weight, higher charge, propensity to form alpha-helical structures, lower hydrophobicity, and higher frequency of amino acids such as lysine and serine. Conclusions: Machine learning models allowed to establish the structure–activity relationships of antimicrobial peptides. Classification models performed better than regression models. These models allowed us to make predictions and new peptides with high antimicrobial potential were proposed. Full article

This story began half a century ago with the discovery of an unusually high presence of tryptophan (Trp, W) in transthyretin (TTR), one of the three carrier proteins of thyroid hormones. With the Trp-rich retinol-binding protein (RBP), TTR forms a plasma complex implicated in the delivery of retinoid compounds to body tissues. W has the lowest concentration among all AAs involved in the sequencing of human body proteins. The present review proposes molecular maps focusing on the ratio of W/AA residues found in the sequence of proteins involved in immune events, allowing us to ascribe the guidance of inflammatory processes as fully under the influence of W. Under the control of cytokine stimulation, plasma biomarkers of protein nutritional status work in concert with major acute-phase reactants (APRs) and with carrier proteins to release, in a free and active form, their W and hormonal ligands, interacting to generate hot spots affecting the course of acute stress disorders. The prognostic inflammatory and nutritional index (PINI) scoring formula contributes to identifying the respective roles played by each of the components prevailing during the progression of the disease. Glucagon demonstrates ambivalent properties, remaining passive under steady-state conditions while displaying stronger effects after cytokine activation. In developing countries, inappropriate weaning periods lead to toddlers eating W-deficient cereals as a staple, causing a dramatic reduction in the levels of W-rich biomarkers in plasma, constituting a novel nutritional deficiency at the global scale. Appropriate counseling should be set up using W implementations to cover the weaning period and extended until school age. In adult and elderly subjects, the helpful immune protections provided by W may be hindered by the surge in harmful catabolites with the occurrence of chronic complications, which can have a significant public health impact but lack the uncontrolled surges in PINI observed in young infants and teenagers. Biomarkers of neurodegenerative and neoplastic disorders measured in elderly patients indicate the slow-moving elevation of APRs due to rampant degradation processes. Full article