Search Results (484)

Porous materials are characterized by the pore surface area (S) and volume (V) accessible to a confined fluid. For mesoporous materials NMR measurements of diffusion are used to assess the S/V ratio, because at short times, only the diffusivity of molecules in the adsorbed layer is affected by confinement and the fractional population of these molecules is proportional to the S/V ratio. For materials with sub-nanometer pores, this might not be true, as the adsorbed layer can encompass the entire pore volume. Here, using molecular simulations, we explore the role played by S and S/V in determining the dynamical behavior of two carbon-bearing fluids—CO₂ and ethane—confined in sub-nanometer pores of silica. S and V in a silicalite model representing a sub-nanometer porous material are varied by selectively blocking a part of the pore network by immobile methane molecules. Three classes of adsorbents were thus obtained with either all of the straight (labeled ‘S-major’) or zigzag channels (‘Z-major’) remaining open or a mix of a fraction of both types of channel blocked, resulting in half of the total pore volume being blocked (‘Half’). While the adsorption layers from opposite surfaces overlap, encompassing the entire pore volume for all pores except the intersections, the diffusion coefficient is still found to be reduced at high S/V, especially for CO₂, albeit not so strongly as would be expected in the case of wider pores. This is because of the presence of channel intersections that provide a wider pore space with non-overlapping adsorption layers. Full article

In this work, the symmetries of a H₂⁺ molecule confined within potential energy wells of various shapes are highlighted. This system has been long regarded as a model for small molecules trapped in crystalline cavities and molecular cages; in this context, the role of symmetry assumes significant importance. Symmetries are determined by the well shape, molecular position, and orientation. They allow the classification of H₂⁺ states, the identification of fixed nodal surfaces for the identification of excited states in Monte Carlo simulations, and the estimation of potential energy surfaces. Full article

Carotid atherosclerotic stenosis (CAS) is a leading cause of ischemic stroke. Current understanding of plaque vulnerability remains largely confined to histopathological characterization. Consequently, identifying molecular determinants of plaque stability represents a major challenge to advance prevention strategies. Untargeted metabolomic analysis was performed using mass spectrometry coupled to liquid chromatography on carotid plaques removed from patients with CAS undergoing endarterectomy. To identify factors influencing plaque stability, we compared 42 asymptomatic with 30 symptomatic CAS patients. Associations between each annotated metabolite in plaques and asymptomatic CAS status were assessed using logistic regression models. Asymptomatic patients exhibited lower plasmatic levels of C-reactive protein (CRP) and higher HDL-cholesterol. Within the plaques, caffeine and its catabolites, paraxanthine and methylxanthine, were associated with plaque stability and were correlated with HDL-cholesterol. Additional plant-based diet biomarkers including N5-acetylornithine, gentisic acid, proline betaine, and homostachydrine were also associated with plaque stability. In contrast, N-methylpyridone carboxamides, reflecting niacin excess, involved in vascular inflammatory processes, were both associated with plaque vulnerability and also correlated with higher CRP. Our findings provide molecular evidence that plant-based diets, including coffee, promote carotid plaque stability, while excessive niacin intake, linked to processed foods, may be detrimental. Metabolomics offers new insights into food exposome-related vascular risk. Full article

This scoping review explores the expression patterns and molecular features of sarcoglycans (SGs) in non-muscle organs, challenging the long-standing assumption that their function is confined to skeletal and cardiac muscle. By analyzing evidence from both animal models and human studies, the review highlights the widespread presence of SG subunits in organs, including the nervous system, glands, adipose tissue, oral mucosa, retina, and other structures, with distinct regional and cell-type-specific patterns. Studies on the central nervous system demonstrate a widespread “spot-like” distribution of SG subunits in neurons and glial cells, implicating their involvement in synaptic organization and neurotransmission. Similarly, SGs maintain cellular integrity and homeostasis in glands and adipose tissue. At the same time, the altered expression of SGs is associated with pathological conditions in the gingival epithelium of the oral mucosa. These findings underscore the multifaceted roles of SGs beyond muscle, suggesting that they may contribute to cellular signaling, membrane stability, and neurovascular coupling. However, significant gaps remain regarding SG post-translational modifications and functional implications in non-muscle organs. Future research integrating molecular, cellular, and functional approaches in animal models and human tissues is essential to fully elucidate these roles and explore their potential as therapeutic targets in various diseases. Full article

Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symbolic regression framework is trained on simulation data from molecular dynamics and correlates the values of the self-diffusion coefficients with macroscopic properties, such as density, temperature, and the width of confinement. New expressions are derived for nine different molecular fluids, while an all-fluid universal equation is extracted to capture molecular behavior as well. In such a way, a highly computationally demanding property is predicted by easy-to-define macroscopic parameters, bypassing traditional numerical methods based on mean squared displacement and autocorrelation functions at the atomistic level. To achieve generalizability and interpretability, simple symbolic expressions are selected from a pool of genetic programming-derived equations. The obtained expressions present physical consistency, and they are discussed in terms of explainability. The accurate prediction of the self-diffusion coefficient both in bulk and confined systems is important for advancing the fundamental understanding of fluid behavior and leading the design of nanoscale confinement devices containing real molecular fluids. Full article

This study investigates the behavior of spark discharges under various environmental conditions to simulate aspects of early-stage lightning dynamics, with a focus on their spectral characteristics, propagation, and ionization behavior. In a laboratory setting, spark discharges generated by a Tesla coil operating with high-frequency alternating current (AC) were analyzed under varying air humidity and water surface conductivity. Spectral analysis revealed that the discharges are dominated by the second positive system of molecular nitrogen N₂ (2P) and also exhibit the first negative system of molecular nitrogen ions N₂⁺ (1N). Notably, the N₂ (2P) emissions show strong peaks in the 350–450 nm range, closely matching spectral features typically associated with corona and streamer discharges in natural lightning. Environmental factors significantly influenced discharge morphology: in dry air, sparks exhibited longer and more branched paths, while in moist air, the discharges were shorter and more confined. Over water surfaces, the sparks spread radially, forming star-shaped patterns. Deionized (DI) water, with low conductivity, supported wider lateral propagation, whereas higher conductivity in tap water and saltwater suppressed discharge spread. The gap between the electrode tip and the surface also affected discharge extent and brightness. These findings demonstrate that Tesla coil discharges reproduce key features of early lightning processes and offer insights into how environmental factors influence discharge development. Full article

Background/Objectives: Changnienia amoena is a rare and endangered terrestrial orchid endemic to China, valued for its ornamental and medicinal properties. However, limited genomic resources hinder its effective conservation strategies and sustainable utilization. This study aimed to generate comprehensive plastome resources and develop molecular markers to support the phylogenetics, identification, and conservation management of C. amoena. Methods: Genome skimming was employed to assemble and annotate the complete plastomes of seven geographically distinct C. amoena accessions. Comparative analyses were conducted to assess structural features and sequence divergence within C. amoena and across related species in the Calypsoinae subtribe. Phylogenetic relationships were inferred from protein-coding genes. Simple sequence repeats (SSRs), dispersed repeats, and hypervariable regions were identified from the plastomes, while nuclear SSRs were developed from assembled nuclear sequences. Results: All seven plastomes exhibited a conserved quadripartite structure with identical gene content and order, showing only minor variations in genome size. Sequence divergence was mainly confined to non-coding regions. Across Calypsoinae species, mycoheterotrophic taxa exhibited reduced plastomes. Phylogenetic analyses resolved four well-supported intergeneric clades within Calypsoinae and revealed a notable divergence between the HuNGZ accession and other C. amoena accessions, which otherwise showed low plastome-level differentiation. We also identified 69–74 plastome-derived SSRs, 22–25 dispersed repeats, and three hypervariable regions that may serve as informative molecular markers for C. amoena. Additionally, 16 polymorphic nuclear SSRs were developed from assembled nuclear sequences. Conclusions: These findings significantly expand the genomic resources available for C. amoena and provide essential insights for its phylogeny, molecular identification, conservation management, and future breeding efforts. Full article

Despite their outstanding thermal insulation and ultralight structure, silica aerogels suffer from inherent mechanical fragility, making the investigation of their mechanical behavior crucial for expanding their practical utility in advanced applications. To enhance their mechanical performance, this study introduces a dual-phase reinforcement strategy by anisotropically incorporating carbon nanotubes (CNTs) and graphene oxide (GO) sheets into the aerogel matrix. Using molecular dynamic simulations, we systematically investigate the tensile behavior and pore structure evolution of these hetero-structured composites. The results reveal a non-monotonic dependence of tensile strength on loading ratio, distinguishing three strain-dependent reinforcement regimes. High loading content (11.1%) significantly improves strength under low strain (0–26%), whereas low loading levels (1.8%) are more effective at preserving structural integrity under large strain (44–50%). Moderate loading (5.1%) yields balanced performance in intermediate regimes. While increasing carbon content reduces initial pore size by partially filling the framework, tensile deformation leads to interfacial debonding and the formation of larger pores due to CNT–GO hybrid structure interactions. This work elucidates a dual reinforcement mechanism—physical pore confinement and interfacial coupling—highlighting the critical role of nanostructure geometry in tuning strain-specific mechanical responses. The findings provide mechanistic insights into anisotropic nanocomposite behavior and offer guidance for designing robust porous materials for structural and functional applications. Full article

We conduct extensive Monte Carlo simulations to investigate the factors that control the isotropic-to-nematic transition of hard colloidal polymers in bulk and under various conditions of confinement. Utilizing a highly idealized model, polymers are represented as linear chains of tangent hard spheres of uniform length, whose stiffness is controlled by a bending potential leading to rod-like configurations. Confinement is realized through the presence of flat, parallel, and impenetrable walls in one, two, or three dimensions while periodic boundary conditions are applied on the unconstrained dimensions. All simulations are performed through the Simu-D software, composed of conventional and advanced, chain-connectivity-altering Monte Carlo algorithms. We explore in detail how distinct factors, including chain length, stiffness, confinement, and packing density affect the isotropic-to-nematic transition exhibited by the polymer chains and identify with high precision the concentration range where this phase change takes place as a function of the applied conditions. Full article

Peripheral myelination is driven by the intricate interplay between Schwann cells and axons, coordinated through molecular signaling and the structural organization of their shared environment. While the biochemical regulation of this process has been extensively studied, the influence of spatial architecture and mechanical cues remains poorly understood. Here, we use in vitro co-culture models—featuring microfluidic devices and hydrogel-based scaffolds—to explore how extracellular organization, cellular density, and spatial constraints shape Schwann cell behavior. Our results show that (i) pro-myelinating effects triggered by ascorbic acid administration is distally propagated along axons in Schwann cell-DRG co-cultures, (ii) ascorbic acid modulates Neuregulin-1 expression, (iii) a critical threshold of cellular density is required to support proper Schwann cell differentiation and myelin formation, and (iv) spatial confinement promotes myelination in the absence of ascorbic acid. Together, these findings highlight how spatial and structural parameters regulate the cellular and molecular events underlying peripheral myelination, offering new physiologically relevant models of myelination and opening new avenues for peripheral nerve repair strategies. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Membrane tubes, a class of soft biological confinement for ubiquitous transport intermediates, are essential for cell trafficking and intercellular communication. However, the confinement interaction and directional migration of diffusive nanoparticles (NPs) are widely dismissed as improbable due to the surrounding environment compressive force. Here, combined with the mechanics analysis of nanoparticles (such as extracellular vesicles, EVs) to study their interaction in confinement, we perform dissipative particle dynamics (DPD) simulations to construct a model that is as large as possible to clarify the submissive behavior of NPs. Both molecular simulations and mechanical analysis revealed that the interactions between NPs are controlled by confinement deformation and the centroid distance of the NPs. When the centroid distance exceeds a threshold value, the degree of crowding variation becomes invalid for NPs motion. The above conclusions are further supported by the observed dynamics of multiple NPs under confinement. These findings provide new insights into the physical mechanism, revealing that the confinement squeeze generated by asymmetric deformation serves as the key factor governing the directional movement of the NPs. Therefore, the constraints acting on NPs differ between rigid confinement and soft confinement environments, with NPs maintaining relative stillness in rigid confinement. Full article

Ductal carcinoma in situ (DCIS) is the most common form of non-invasive breast cancer and a recognized precursor to invasive ductal carcinoma (IDC). Although DCIS itself is confined to the milk duct and not immediately life-threatening, its potential for progression to invasive disease necessitates careful clinical management. The increased detection of DCIS due to advancements in imaging and widespread screening programs has raised critical questions regarding its classification, prognosis, and optimal treatment strategies. While most cases exhibit indolent behavior, others harbor molecular characteristics that drive malignant transformation. A key challenge lies in distinguishing low-risk DCIS, which may never progress, from aggressive cases requiring intervention. Tumor microenvironment dynamics, immune cell infiltration, and molecular alterations, including hormone receptor (HR) status, human epidermal growth factor 2 (HER2) expression, and genetic mutations, play crucial roles in determining disease trajectory. This review explores the biological and molecular mechanisms underlying DCIS progression, with an emphasis on myoepithelial cells, tumor-infiltrating lymphocytes, and microenvironmental factors. By integrating recent findings, this article aims to refine risk stratification approaches and guide future strategies for personalized DCIS management. Improved prognostic biomarkers and targeted therapeutic interventions could help optimize treatment decisions, balancing the need for effective cancer prevention while minimizing overtreatment in low-risk patients. Full article

Monomer-to-excimer transition has become a valuable technique in fluorescence imaging because of its ability to enhance imaging contrast. However, from a practical perspective, the accuracy of excimer formation at target sites warrants further exploration. Enzyme-triggered peptide self-assembly provides a promising solution to this limitation. As a proof-of-concept, in this study, we developed a furin-triggered peptide self-assembling fluorescent probe RF-Cou by coupling a coumarin dye 7-(diethylamino)-2-oxo-2H-chromene-3-carboxylic acid (Cou) with a furin-responsive peptide scaffold for precision live-cell imaging. Upon entering furin-overexpressing 4T1 tumor cells, RF-Cou underwent enzymatic cleavage, releasing an amphiphilic peptide motif and self-assembling into nanoparticles largely concentrated in the Golgi apparatus to confine the diffusion of Cou. During this process, the Cou excimers were formed and induced a red shift in the fluorescence emission, validating the feasibility of RF-Cou in efficient excimer imaging of furin-overexpressing tumor cells. We expect that our findings will highlight the potential of stimuli-responsive small molecular peptide probes to advance excimer-based imaging platforms, particularly for enzyme-specific cell imaging and therapeutic monitoring. Full article

Metabolic enzymes and cancer-driving transcriptions factors are often overexpressed in neoplastic cells, and their exposed cysteine residues are amenable to chemical modification. This review explores cysteine alkylation as a cancer treatment strategy, focusing on Michael acceptors like curcumin and helenalin, which interact with transcription factors NF-κB, STAT3 and HIF-1α. Molecular docking studies using AutoDockFR revealed distinct binding affinities: curcumin showed strong interactions with STAT3 and NF-κB, while helenalin exhibited high affinity for STAT3 and HIF-1α. Synthetic compounds like STAT3-IN-1 and CDDO-Me demonstrated superior binding in most targets, except for CDDO-Me with HIF-1α, suggesting unique structural incompatibilities. Natural products such as zerumbone and umbelliferone displayed moderate activity, while palbociclib highlighted synthetic-drug advantages. These results underscore the importance of ligand−receptor structural complementarity, particularly for HIF-1α’s confined binding site, where helenalin’s terminal Michael acceptor system proved optimal. The findings advocate for integrating computational and experimental approaches to develop cysteine-targeted therapies, balancing synthetic precision with natural product versatility for context-dependent cancer treatment strategies. Full article