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SIRT1 mediated autophagy enhancement by Lactobacillus fermentum derived oligosaccharides accelerates wound healing in biofilm associated infection
Abstract The present study explores the structural elucidation, antimicrobial properties, and wound-healing potential of a novel oligosaccharide, OligoF, derived from Lactobacillus fermentum . Structural analysis using paper chromatography, and 1D & 2D NMR spectroscopy indicated that OligoF is a linear tetrasaccharide comprising β-D-glucose, α-L-rhamnose, and D-glucuronic acid in a defined sequence. This structural elucidation was further confirmed by key chemical shifts, coupling constants, and glycosidic linkage assignments. OligoF exhibited potent antibacterial activity against multidrug-resistant Pseudomonas aeruginosa isolates, demonstrating inhibition zones of 60–63 mm, a minimum inhibitory concentration (MIC) of 16 µg/mL, and a minimum bactericidal concentration (MBC) of 32 µg/mL. Biofilm eradication assays revealed a significant reduction in biofilm viability by ~ four-fold at sub-MIC concentrations, as evidenced by scanning electron microscopy. OligoF-treated biofilms displayed notable disruption of extracellular matrix and severe bacterial cell distortion compared to untreated controls. In vivo wound-healing assays in rats showed that OligoF significantly reduced wound area and enhanced skin repair compared to untreated controls. Treatment with OligoF notably increased the concentration and gene expression of SIRT1 as well as upregulated the gene expression of beclin1 and ATG5 which are critical regulators of autophagy and cellular repair processes. Histopathological analysis corroborated these findings, revealing enhanced re-epithelialization, granulation tissue formation, and vascularized connective tissue deposition in OligoF-treated wounds. These findings underscore the potential of OligoF as a multifunctional agent with antibacterial and wound-healing properties, paving the way for its application in managing multidrug-resistant infections and promoting tissue repair.
Lymphatic–stem cell crosstalk promotes tendon regeneration via Notch1–Srebp2–mediated cholesterol metabolism
Sociodemographic variation and childhood predictors of showing love and care for others in 22 countries
Abstract Showing love and care for other people is a vital aspect of human relationships. However, little is known about how levels of love/care expression differ across cultures and across sociodemographic groups within those different cultures, or about the potential childhood antecedents that are associated with love/care expression in adulthood. Based on nationally-representative data from 22 countries in six continents in the Global Flourishing Study (N = 202,898), we present ordered means of love/care expression across countries, observe its distributions across key sociodemographic characteristics, and evaluate the strength of its childhood predictors (with E -values as a robustness check), revealing the extent to which the distributions and associations are uniform throughout the world or differ by country. The mean levels of love/care expression (scaled 0–10) ranged from 9.05 in the Philippines to 5.96 in Japan and tended to be higher in countries in the Global South. Based on a random effects meta-analysis of means and a multivariate regression analysis of childhood predictors, we find evidence of both universal and country-specific influences. Age cohort, gender, self-rated health, and religious service attendance were the strongest childhood predictors. These findings enhance our understanding of the country-specific variations in love/care expression and early-life predictors of adult outcomes, providing a foundation for future investigations into sociocultural influences that might shape love/care expression.
Oxygen-modulated engineering of Cu0–Cu+ interfaces for CO2-to-C2H4 photoreduction
A decision making algorithm for economic growth in the digital economy using CRITIC WASPAS based circular picture fuzzy information
Abstract In this study, we explore the use of fuzzy set (FS) theory to address uncertainty in evaluating strategies that drive economic growth within the digital economy. Specifically, we employ a circular picture fuzzy (CPF) set (CPFS). As economies become increasingly digital, decision-makers face complex challenges in selecting the most effective strategies for growth, ranging from digital infrastructure investment to innovation support and policy reform. To assess these strategic options, we consider five alternatives, each evaluated against five key attributes. To ensure objectivity in weighing these attributes, we first apply the CRITIC (Criteria Importance Through Intercriteria Correlation) method, which calculates weights based on the contrast intensity and interdependencies among the attributes. Next, we use the WASPAS (Weighted Aggregated Sum Product Assessment) method to rank the alternatives. Furthermore, we develop a decision-making algorithm tailored for multi-attribute group decision-making (MAGDM), integrating the CRITIC-WASPAS approach within the CPFS framework. This model is applied to a hypothetical case study focused on digital economy growth strategies, demonstrating its practical utility in uncertain and complex decision environments. After the evaluation, the second alternative, E-Governance and Digital Policy Reform, emerged as the most optimal strategy for promoting economic growth in the digital economy. To validate the robustness of the proposed approach, we conduct a sensitivity analysis and a comparative evaluation with existing methods. The results confirm that our model provides reliable and effective support for strategic decision-making aimed at fostering economic growth in the digital age.
Near-surface liquid water on Mars inferred from seasonal marsquakes
Micro RNA miR-726-3p targets CYB5A in Hen ovaries to modulate granulosa cell proliferation and differentiation
Backpropagation through space, time and the brain
Green approach in saponin extraction from Acanthophyllum caryophyllaceae root based on ultrasonication coupled with solvent extraction method and its lyophilization
Photochromism of pyridine-substituted merocyanine through reversible C-N bond formation
A linear programming framework for optimizing molecular combinations of asphalt four components
Ultraweak interactions drive cap-mediated positioning and elongation of the bacterial flagellar filament
Modelling of heat addition to near-critical and supercritical fluids
Abstract This work presents a coupled flow model to describe heat addition to near-critical and supercritical fluids. In a first approximation only forced convection is considered, while buoyancy forces, viscous and volume expansion losses are neglected. The steady, one-dimensional balance equations are solved simultaneously through an iterative procedure, taking into account real gas effects. The analysis shows that the intrinsic high compressibility of near-critical fluids strongly reduces the region of validity for the incompressible flow assumption. Indeed, depending on the initial conditions, compressible flow effects may occur at Mach numbers below 0.1. For a given mass flux, the occurrence of heat transfer deterioration (HTD) correlates directly with the maximum amount of heat ( $$Q_{max}$$ ) that a compressible flow can absorb at a specific local Mach number. For near-critical fluids, $$Q_{max}$$ depends upon two parameters, namely the heat flux to mass flux ratio and the thermal dilatation. The latter induces not only the early inception of compressibility effects, but it also poses an additional volumetric constraint to $$Q_{max}$$ . Indeed, in confined flows the enhancement of the thermal dilatation parameter strongly limits the capability of the flow to increase the local mass flux by expanding the flow, which eventually decreases the $$Q_{max}$$ value. These findings provide a generalization of the well-known dependence of $$Q_{max}$$ upon the local Mach number for an ideal gas. The local enhancement of the thermal dilatation also explains the counterintuitive cooling of the fluid upon heat addition. Overall, our analysis advocates for a more comprehensive flow analysis in the design of regenerative cooling systems.
A systematic review and meta-analysis of disease clusters in multimorbidity
ATF3 overexpression is associated with cardiac hypertrophy and electrical dysfunction accompanied by enhanced cardiac cell proliferation in zebrafish
Abstract Activating transcription factor 3 (ATF3) is a crucial regulator of gene expression in response to physiological stress across various tissues. Abnormal ATF3 expression is associated with cardiac dysfunction; however, the mechanisms by which it affects cardiac pathology remain unclear. In this study, we developed a cardiac-specific ATF3-expressing zebrafish line, Tg( myl7 : ATF3 ), using a well-established vertebrate model for cardiovascular research to investigate the role of human ATF3 in the zebrafish heart. We conducted morphological assessments, immunofluorescence staining, electrocardiography, and transcriptome analysis on transgenic and wild-type zebrafish. Compared to wild-type siblings, Tg( myl7 : ATF3 ) zebrafish exhibited significant pathophysiological changes, including cardiac hypertrophy and features resembling hypertrophic cardiomyopathy. Histological analysis revealed increased fibrotic scarring and disorganized sarcomeres. Electrocardiogram measurements indicated that ATF3 overexpression induced symptoms resembling long QT syndrome, suggesting electrical dysfunction. Transcriptome analysis demonstrated downregulation of apoptosis-related genes and upregulation of proliferation-related genes in the hearts of transgenic zebrafish. These findings suggest that ATF3 expression is associated with cardiac hypertrophy accompanied by increased proliferation of cardiac cells, including cardiomyocytes. The findings of this study provide novel insights into the role of ATF3 in cardiovascular disease progression.
Size-dependent translocation of polystyrene nanoplastics across biological barriers in mammals
Automated modeling to high level-of-detail composite object using spatial BIM objects and properties
Abstract Building Information Modeling (BIM) procedures for geometric and information modeling are laborious and time-intensive. Traditional methods focus on individual objects, with designers manually modeling each item. This paper presents an approach to high Level of Development (LoD) indoor spatial modeling using space-centered BIM composite objects. By automatically positioning these composite objects within a space, designers can achieve more efficient indoor design modeling early on. The study defines BIM composite objects and demonstrates a visual-language-based parametric modeling process integrated into BIM authoring tools. The process involves inputting data, selecting and placing interior design objects, and deriving the indoor layout. The feasibility of the space-centered design approach is demonstrated in interior architecture, showcasing its potential to reduce inefficiencies and enhance the modeling process, especially in the initial design stage. This approach not only streamlines the early design phase but also reduces inefficiencies across a wide range of applications. The demonstration is implemented and presented in the paper.