Search Results (362)

Background/Objectives: Young adulthood is a critical developmental period during which lifestyle behaviors may shape intrinsic brain network dynamics that support cognition. This pilot longitudinal intervention study examined whether variability in physical activity and sedentary behavior during an 8-week exercise and/or cognitive intervention protocol was associated with changes in intrinsic brain dynamics and cognitive and mood outcomes in undergraduate young adults. Methods: Participants (n = 32) completed resting-state functional magnetic resonance imaging (rs-fMRI) at baseline (T1) and post-intervention (T2). Dynamic functional network connectivity (dFNC) was estimated from 53 intrinsic connectivity networks derived using spatially constrained independent component analysis (ICA). Ten recurring dynamic connectivity states were identified and individualized using constrained dynamic double functional independent primitives (c-ddFIPs). State occupancy and dynamic convergence and divergence metrics were computed to characterize network flexibility. Results: Greater moderate-to-vigorous physical activity was modestly but consistently associated with increased occupancy of integrative higher-order states, particularly States 6 and 7, and reduced occupancy of more segregated configurations. More physically active individuals also demonstrated greater divergence between integrative and low-engagement states, whereas greater sedentary time corresponded to increased similarity among segregated configurations. Working memory performance showed parallel associations with more integrative and better-differentiated dynamic patterns. Conclusions: These findings suggest that dynamic functional network reconfiguration may represent a neurobiological mechanism linking lifestyle behaviors and cognitive health in young adulthood. Furthermore, they highlight the translational promise of engagement-driven, low-burden programs for college-aged young adults, showing that even modest variability in habitual physical activity corresponds to greater engagement and differentiation of integrative connectivity states linked to executive and broader cognitive functions. Full article

Background/Objectives: Inclusion complexes among drugs and cyclodextrin-modified polymers are a topic of recent interest in pharmaceutical research and industry as they might expand the solubility, bioavailability, and stability of the guest molecules. Polyurethanes derived from cyclodextrins show some biomedical applications. In this study, two cross-linked polyurethane networks based on hydroxypropyl-β-cyclodextrin (HPβCD) and polyethylene glycols (PEG 2000 or PEG 6000) were synthesized with NCO/OH molar ratio 4.3 and 6.3 by the typical two-step polymerization method. Methods: Inclusion complexes of clotrimazole (CLOT) with two HPβCD-modified polyurethane networks and their corresponding physical mixtures were prepared using kneading methods and physical mixing in a 1:6 weight ratio of CLOT:HPβCD. Results: Obtained prepolymers, previously end-capped with isocyanate groups forming urethane links with HPβCD, which were confirmed by FTIR analysis. TGA results indicate a slight increase in thermal stability of the prepared complexes. The characteristic endothermic peak of the CLOT at around 145.90 °C did not appear in the DSC curve of the drug-loaded inclusion complexes. The XRD patterns of physical mixtures showed specific peaks corresponding to pure clotrimazole. SEM micrographs confirmed an elliptical/spherical- and plate-shaped particles without phase segregation, indirectly confirming that CLOT is not separately present due to inclusion into HPβCD and entrapment into polyurethane networks. Novel complexes PUR2/HPβCD-CLOT-IC and PUR3/HPβCD-CLOT-IC were applied as drug carriers, and diffusion-controlled kinetics of CLOT release were best described using Higuchi model. Conclusions: The obtained in vitro results showed surprisingly slow/prolonged clotrimazole release from modified polyurethane networks due to the significant influence of NCO/OH molar ratio and the chosen polyol soft segments chain length with potential in vivo applications. Full article

Lightweight aggregate concrete (LWAC) is a practical alternative to conventional concrete in civil engineering, offering advantages such as reduced density, enhanced insulation properties, and improved seismic performance. However, segregation during compaction remains a limitation, as it can lead to non-uniform material distribution and reduced compressive strength. This study addresses this issue by combining non-destructive techniques with deep learning methods to predict the compressive strength of LWAC. We propose an explainable approach based on a convolutional recurrent neural network architecture, enhanced by unsupervised clustering and SHapley Additive exPlanations (SHAP), to improve interpretability. To optimize predictive performance, several aggregation strategies are evaluated at the recurrent layer before the dense layers, including full-sequence flattening, max pooling, average pooling, and an attention mechanism over the full sequence. Experimental results show that the proposed model outperforms conventional machine learning methods such as multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR), as well as ensemble methods such as gradient boosting (GBR), XGBoost, and weighted average ensemble (WAE). Furthermore, when combined with unsupervised clustering, the model identifies latent behavioral patterns that are not observable through traditional evaluation techniques. This demonstrates the potential of integrating non-destructive testing with interpretable deep learning as a reliable approach for the structural assessment of LWAC. Full article

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS. Full article

Background: Conventional omics analysis often treats outliers as noise, yet they may harbor critical biological insights. Objetive: This study proposes a paradigm shift: actively investigating outliers to discover biologically relevant subtypes within metabolic–inflammatory syndromes. Methods: We applied a comprehensive analytical framework for outlier detection based on a multi-algorithm consensus (IQR, MAD, Isolation Forest) to a clinical cohort of diabetic neuropathy (n = 93) and an in vitro 3T3-L1 adipocyte model (n = 39). The identified outliers were characterized using robust PCA, co-expression networks, unsupervised clustering, and Random Forest predictive modeling. Results: In the clinical cohort, an outlier subgroup (47.3%) exhibited an extreme immune–metabolic phenotype characterized by hyperactivation of Th1/Th17 pathways (elevated T-bet and IL-17; p < 0.001), hypertriglyceridemia, and network reconfiguration (TGFβ and STAT4 hubs). In the cellular model, outlier samples (12.8%) showed autonomous pro-inflammatory behavior characterized by IL-6 overproduction (p = 0.002) and IL-10 suppression. Conclusions: Multivariate analysis confirmed spatial segregation of these profiles. Systematic outlier investigation revealed discrete pathophysiological subtypes invisible to mean-focused analyses, demonstrating that extreme values encapsulate potent biological signals. This framework offers a generalizable approach for uncovering clinical heterogeneity and identifying therapeutic targets in complex diseases. Full article

Phytophthora infestans, a devastating oomycete pathogen responsible for late blight in solanaceous crops, relies on cellulose synthase (CesA) complexes for cell wall biosynthesis and virulence. Unlike plant CesAs that form homomeric trimers, oomycete CesA complexes are hypothesized to assemble as heteromeric units, yet their structural organization remains poorly defined. Here, we employed AlphaFold-Multimer and molecular docking to resolve the structural assembly of the PiCesA1–PiCesA2–PiCesA4 heterotrimer in P. infestans and identify potential ligand-binding sites for targeted inhibition. Structural modeling revealed a conserved transmembrane architecture combined with a distinctive cytosolic organization, in which N-terminal pleckstrin homology domains play a central role in heteromeric assembly. AlphaFold-Multimer consistently predicted a stable heterotrimer stabilized by cyclic interactions between pleckstrin homology domains and glycosyltransferase-A domains, forming an extensive interface network that is spatially segregated from the conserved UDP-glucose–binding catalytic core. Structure-guided docking identified potential ligands targeting pleckstrin homology–glycosyltransferase interface regions. Notably, these sites are absent or structurally divergent in plant cellulose synthases, underscoring their potential for pathogen-selective targeting. This work advances mechanistic understanding of cellulose biosynthesis in filamentous pathogens and proposes new avenues for selective disease control in agriculture. Full article

Background: Metabolic enzymes catalyze biochemical pathways that sustain cellular metabolism. Their activity, stability, and molecular interactions are extensively regulated by post-translational modifications (PTMs). However, an integrated systems-level understanding of how diverse PTMs are organized across the human metabolic network remains poorly defined. Methods: We integrated experimentally reported PTM annotations from PhosphoSitePlus, dbPTM, and the quantitative PTM database (qPTM), and identified 29 distinct PTM types present across the 771 human metabolic enzymes. PTM features were quantitatively characterized at multiple levels, including sequence- and composition-based metrics (modification density and PTM potentiality rate), recurrence- and co-occurrence-based features (predominant sites, hotspot regions and PTM crosstalk), and functional-context annotations (protein-region localization and mutation overlap). These integrated features were subsequently used for unsupervised clustering to evaluate higher-order organizational patterns. Results: The analysis revealed that PTMs are unevenly distributed across metabolic enzymes, with phosphorylation, acetylation, ubiquitination, and methylation representing the most prevalent and recurrent regulatory modifications. Clustering segregated enzymes into two regulatory groups: (i) a PTM-enriched regulatory group characterized by high PTM density, frequent hotspot and crosstalk regions, and enrichment of rate-limiting enzymes, and (ii) a broad metabolic group with comparatively sparse PTM regulation. This non-uniform organization reflects the preferential accumulation of multiple regulatory PTMs on enzymes occupying key control points in central metabolic pathways, thereby forming a discrete regulatory subnetwork within metabolism. Conclusions: This study presents a systems-level, multi-PTM atlas of human metabolic enzymes and provides a quantitative framework for prioritizing PTM-regulated enzymes and pathways relevant to signaling–metabolism integration and disease-associated metabolic regulation. Full article

Psychedelic-assisted therapies (PATs) can produce rapid and sustained antidepressant effects, yet variability in response remains substantial. Identifying predictors and moderators is essential for optimising patient selection, preparation, and delivery. To map and synthesise the evidence on the predictors of antidepressant response to classic/serotonergic psychedelics administered with psychotherapeutic support in adults with depressive disorders, including treatment-resistant depression. Following PRISMA-ScR principles, we conducted a scoping review of major biomedical and psychology databases (PubMed (MEDLINE), Embase, PsycINFO, and Web of Science) and trial registries (searches September–October 2025), supplemented by reference-list screening. We included randomised trials, open-label studies, and naturalistic cohorts reporting associations between candidate predictors (baseline traits/clinical features, set/setting variables, acute in-session phenomenology, and biological measures) and validated depression outcomes. We charted study characteristics, analytic approaches (including moderation/mediation where available), and indicators of robustness (e.g., adjustment for overall intensity, preregistration, external validation). A total of 48 studies were included in the review. Across study designs, process-level features during the dosing session were the most consistent correlates of antidepressant improvement. Greater emotional breakthrough, mystical/unitive experiences, and ego dissolution-linked reappraisal/insight generally predicted larger and more durable symptom reductions, whereas anxiety-dominant or dysphoric states tended to attenuate benefit, often independent of overall subjective intensity. Set and setting—particularly a stronger therapeutic alliance and music experienced as resonant—predicted both the emergence of therapeutically salient acute experiences and downstream clinical gains. Baseline moderators showed smaller and mixed effects: PTSD comorbidity sometimes weakened trajectories; extensive prior psychedelic exposure was associated with smaller incremental gains; demographics were typically uninformative. Converging biological findings associated better outcomes with markers consistent with increased neural flexibility and plasticity (e.g., less segregated network dynamics; EEG indices), alongside peripheral changes implicating neurotrophic, inflammatory, and HPA axis pathways. Current evidence suggests that antidepressant response in PATs is driven less by static patient characteristics and more by what occurs during dosing and how the context shapes that experience. Optimising preparation, alliance, and music; facilitating emotional breakthrough and meaning making; and mitigating anxious dysregulation are actionable levers. Future trials should harmonise measures, pre-specify and validate moderators/mediators, intensively sample in-session experience and physiology, and report benefits and harms more consistently. Full article

Both the electrical conductivity and tailored mechanical characteristics—showing flexibility and structural integrity—are key properties of polymer composites. In this work, a novel, simple, and water-based strategy for synthesizing rGO-MWCNT/polymer composites was developed. Namely, carbon nanofillers in a mixture of reduced graphene oxide (rGO) and multi-walled carbon nanotubes (MWCNTs) were incorporated in a waterborne methacrylic polymer matrix at loadings of 0.25, 0.5, and 1.0 wt.% nanofiller, and with rGO-to-MWCNT ratios of 10:1, 1:1, and 1:10 (w/w) at room temperature. Electrically conductive composites were obtained with all tested filler rates showing the highest conductivity (up to 8.2 × 10⁻³ Sm⁻¹) for the MWCNT-rich filler due to the formation of a segregated network of the filler in the matrix. The mechanical properties of the composites—characterized by their Young’s modulus and elongation at break—strongly depended on both the filler incorporation rate and the rGO:MWCNT ratio. For instance, soft and flexible composites were obtained by incorporating 0.25 wt.% of the MWCNT-rich filler, which increased the elongation at break from 154.2% (neat polymer) to 252.4%. Overall, this study emphasizes the sensitive interplay between carbon filler introduction incorporating conductivity and the fillers’ impact on the mechanical properties of a polymer composite, both necessitating careful optimization for applications, e.g., in flexible electronics. Full article

High-density lipoprotein-binding protein (HDLBP), also called Vigilin, is a multifunctional RNA-binding protein with established roles in RNA transport and regulation, chromosome segregation, lipid homeostasis, and translational regulation. Frequently detected to be perturbed in phosphoproteome analysis, phosphorylation is indicated as a major mechanism in the regulation of HDLBP functions; however, its phosphorylation landscape remains unexplored. We performed a meta-phosphoproteome analysis of HDLBP to map site-specific functional and regulatory roles of its two most frequently detected phosphosites, S31 and S944. Co-occurrence analysis across multiple datasets indicated that they can be phosphorylated together, suggesting potential co-ordinated regulation. Site-specific co-regulation analysis revealed distinct phospho-regulatory networks, with upstream kinases identified exclusively for S944. Functional enrichment of co-regulated protein phosphosites (CPPs) highlighted its role in RNA metabolism, chromosome organization, and nucleoplasmic transport, while functional annotation of site-specific phosphorylation of CPPs indicates its involvement in cell cycle regulation, apoptosis, and carcinogenesis. Additionally, the potential role of CPPs in the lipid homeostasis network was explored. Furthermore, the differential expression of HDLBP phosphosites across multiple cancers was observed using UALCAN, suggesting a potential role for phospho-regulation of HDLBP in tumor-associated pathways. Together, these findings provide the first integrated view of HDLBP phosphorylation and could serve as a valuable framework for future targeted studies to elucidate the mechanistic roles of site-specific HDLBP phosphorylation in cellular and pathophysiological processes. Full article

Reproductive dysfunction in captive-bred Senegalese sole (Solea senegalensis) limits aquaculture production consolidation, particularly due to reduced fertility and poor sperm quality in F1 males. To elucidate the molecular mechanisms underlying this problem, a quantitative proteomic analysis was conducted using LC–MS/MS on gonadal tissues from wild and F1 males and females. A total of 2221 proteins were identified, of which 1797 were retained after quality filtering. Comparative analyses revealed clear segregation by origin (F1 [cultivated] and wild) and sex (male and female), and 86 proteins were differentially expressed between F1 and wild males. Functional enrichment showed significant downregulation of key reproductive processes in F1 males, including sperm–egg recognition, binding of sperm to zona pellucida, and acrosome reaction, suggesting impaired gamete interaction and fertilization ability. Conversely, F1 males displayed metabolic and proteolytic pathway enrichment, which is indicative of compensatory energy demands. Protein–protein interaction network analysis identified a reproductive subnetwork dominated by zona pellucida sperm-binding proteins, which exhibited reduced connectivity in F1 males. These results demonstrate a coordinated suppression of molecular components essential for sperm–egg communication and acrosomal exocytosis, providing proteomic evidence for the systemic deregulation of the reproductive machinery in F1 fish. This study identifies potential protein biomarkers linked to reproductive performance, offering molecular targets to improve broodstock management and fertilization success in S. senegalensis aquaculture. Full article

The rapid development of medical technologies requires architects to implement a future-proofing approach while designing medical facilities, despite the inherent uncertainty of long-term change. This challenge is particularly visible within hospital emergency departments (HEDs), which play a critical role as first-contact units and life-saving infrastructures. Due to their specific function, HEDs are a challenging environment for implementing new solutions, as they rely on proven frameworks designed to ensure continuity of care and operational efficiency. This raises the key question: how can modern technologies and architectural strategies streamline workflows in HEDs without overwhelming medical staff? Considering current challenges, an equally important factor in the development of emergency departments is their preparedness for crisis situations, such as pandemics, war threats and natural disasters. How can architectural design enable the implementation of given design strategies, aiming to ensure opportunities for development while simultaneously preparing for all-hazard scenarios? The authors gathered existing trends and solutions aimed at preparing hospital emergency departments for future challenges: positive/neutral, such as technological development, but also negative, such as currently ongoing war threats or risk of the next pandemic. Despite the apparent thematic extremity, certain systematic architectural solutions using a transdisciplinary approach may be the answer to these occurrences. The mentioned architectural solutions and factors were synthesized and subjected to design-oriented review based on existing case studies of a few Polish hospitals, which are simultaneously studied as case studies for broader doctoral research in the field of effectiveness assessment. The selected Polish hospital emergency departments are used as an illustrative, analytical reference to support the interpretation and synthesis of the reviewed literature. The contextual analysis enables the identification of transferable, design-oriented strategies relevant to broader emergence medicine architecture and applicable within European units. Examples from Polish units in particular are used as reference and background for discussion, rather than as empirical case studies. The study provides an overview of contemporary and future-oriented solutions in hospital architecture, focusing on the impact and feasibility within the hospital emergency departments. The synthesis highlights the importance of designing flexible spaces prepared for future technological advances, such as oversized service shafts, increased floor heights, and modular layouts. Additionally, the study focuses on the spatial connotations of emerging technologies like medical robotics, their maintenance areas and possible challenges. All of this is interrelated to social, demographic, and economic trends. These include the development of hospital networks, the evolving patient profile, inter-hospital information flow, and the growing role of highly specialized medical units. In terms of rapid challenges like wars or armed threats, factors revealed within the review indicate levels of HED readiness to face the conflict, mainly in terms of surge capacity but also structural durability and reserve resources. The post-pandemic context, in turn, assumes rapid expansion of the hospital into temporary and flexible structures and reversible zoning allowing for patient segregation and separation. Together, these insights outline pathways for creating resilient, adaptable, and efficient emergency care environments resilient to unforeseen challenges. Considering future scenarios of emergency departments, two main scenarios were identified: “the hospital of the future”, continuing overall development and adapting to rapid technological innovations, and “the crisis-resilient hospital”, resistant to various crisis scenarios, such as pandemics or war threats. The optimal development of the unit assumes both openness to technological changes and preparation of key zones for all-hazard scenarios. This review aims to synthesize architectural implications of technological and socio-demographic changes, not to provide a full empirical study. Adopting an exploratory framework, the review refers to technological innovations and crisis preparedness as external drivers shaping the spatial organization of hospital emergency departments and their adaptability to future challenges. Because of various inhibitors (economic, political, hierarchical), not all hospitals can introduce the described improvements, but the synthesis may serve as a knowledge source for future investments. The review was also conducted to support design decisions under conditions of uncertainty. The choice to address all the external factors collectively was induced to provide transferability of solutions and coherence of possible scenarios, which may happen simultaneously. Full article

Mutants with a bright green appearance due to wax synthesis or deposition defects have been reported in various plants such as Arabidopsis thaliana, corn, and rice, but they are relatively rare in broccoli (a brassicaceae crop). Here, we describe SY03, a natural mutant of broccoli with a glossy green phenotype owing to epidermal wax deficiency. Genetic analysis indicated that the leaf luster trait of SY03 was controlled by a single recessive gene. By using the F₂ generation and combining bulked segregant analysis and molecular marker techniques, the candidate gene BoFAR3a, homologous to the Arabidopsis FAR gene, was identified within a 96.678 kb interval of chromosome C01. The A→G point mutation in exon 1 of the BoFAR3a coding sequence substitutes the canonical ATG start codon with GTG, which is predicted to abrogate or severely reduce translation initiation. RT-qPCR indicated that the expression levels of BoFAR3a were significantly decreased in the leaves of the glossy green phenotype mutant. Heterologous expression of BoFAR3a in A. thaliana restored the phenotype of A. thaliana mutant FAR3. The discovery of BoFAR3a is of great significance for breeding lustrous and commercially appealing broccoli varieties. This study systematically analyzed the molecular basis of the lustrous green phenotype in broccoli, providing new insights into the epidermal waxy regulatory network of cruciferous crops. In the future, the wax synthesis pathway can be precisely improved through gene editing technology, achieving a coordinated enhancement of the appearance quality and stress resistance of broccoli. Full article

The spatial distribution of immigrants and associated patterns of residential segregation and integration can manifest not only at the metropolitan scale but also at finer micro-spatial resolutions, reflecting the interaction between path dependence and structural reconfiguration. This article examines the micro-spatial residential patterns of Chinese immigrants in Seoul under institutional and market constraints. Using a Spatial Durbin Model and Multiscale Geographically Weighted Regression, it shows that from 2011 to 2025, immigrant settlements shifted from a monocentric pattern to a polycentric, functionally differentiated, and networked structure. While overall spatial embeddedness is high and segregation remains low, traditional cores such as Guro–Daerim persist. Selective clustering is shaped by path-dependent migrant networks, urban redevelopment policies, and intra-group differentiation, while infrastructure homogenization renders transportation accessibility a background condition. The findings support segmented assimilation theory in high-density East Asian cities and underscore the importance of incorporating immigrant needs into urban policy to promote inclusive integration. Full article

A CoCrNi-AlTi medium-entropy alloy was fabricated via laser powder bed fusion (L-PBF), and its microstructural evolution and mechanical response during aging at 500–900 °C for 1 h were systematically investigated. The as-built alloy exhibits a hierarchical microstructure consisting of elongated columnar grains and dislocation-rich cellular substructures, which is associated with an excellent strength–ductility combination (YS: 848 MPa, UTS: 1136 MPa, EF: 32.6%). Upon aging, a pronounced precipitation-hardening response is observed, with a peak hardness of 501 ± 7 HV and an ultimate tensile strength of 1429 MPa achieved at 800 °C. TEM and STEM-EDS analyses indicate that Ti preferentially segregates along dislocation networks and grain boundaries at early aging stages, promoting the heterogeneous nucleation of nanoscale Ni–Al–Ti–rich precipitates that effectively impede dislocation motion. At elevated aging temperatures, additional Cr-enriched regions with diffuse compositional partitioning are observed within the FCC matrix, occurring concurrently with the peak mechanical performance. Further aging at 900 °C leads to strength degradation, which is attributed to precipitate coarsening and recovery-induced dislocation annihilation. These results highlight the critical role of L-PBF-induced defect structures in governing precipitation behavior and the resulting strength–ductility trade-off during post-build heat treatment of CoCrNi-AlTi medium-entropy alloys. Full article