A crystalline starch model and anisotropic pressure field for MD simulation of starch-polyphenol interactions: Using HPH regulating starch-C3G interactions as a case study.

High-Frequency wall vibration correlates with growth of a vertebrobasilar dolichoectatic Aneurysm: A case study.

Cellular biomechanics have emerged as powerful, label-free indicators for stratifying heterogeneous cell populations across disease contexts. However, existing deformability-based cytometry techniques often suffer from limited specificity, low interpretability, and poor compatibility with real-time sorting, limiting their translational utility in functional screening. Here, we demonstrate an extensional-flow cytometry platform for tumor cell profiling and sorting, enabled by a mechanistic reinterpretation of cellular deformation dynamics. Fluid-structure interaction simulations facilitated a mechanistic reinterpretation of strain-induced morphological transitions, allowing deformation phenotypes to be reliably captured and interpreted through image-derived features. Then we developed a novel lightweight detection algorithm incorporating auto-localization filters and a normalized block attention module to enhance spatial precision and morphological sensitivity, and achieved a mean average precision upon 96.8%. The final sorting is accomplished through a fully integrated pipeline comprising droplet encapsulation, electrostatic charging, and voltage-controlled deflection, yielding a sorting purity of 90.2% ± 4.4% while maintaining cell viability above 95%, which approaches the state-of-the-art technology for image-driven, label-free deformability-based systems. Our work establishes a robust and scalable cytometry platform that bridges mechanistic insight with real-time, label-free sorting, offering an interpretable solution for mechanophenotyping and functional cytometric decision-making.

A CFPD-FSI analysis of the impact of nasal hairs on airflow patterns, nasal resistance, and particle filtration in a realistic human nasal airway.

Hemodynamic changes during simulation of Valsalva-derived cardiovascular conditions in small basilar tip aneurysms: A computational fluid dynamics and one-way fluid-structure interaction analysis.

We present the implementation of a fragment-based, quantum-centric supercomputing workflow for computing molecular electronic structure using quantum hardware. The workflow is applied to predict the relative energies of two conformers of the 303-atom Trp-cage miniprotein. The methodology employs wave function-based embedding (EWF) as the underlying fragmentation framework, in which all atoms in the system are explicitly included in the configuration interaction (CI) simulations. We employ sample-based quantum diagonalization (SQD) solver for challenging fragments and full configuration interaction (FCI) solver for trivial fragments. The EWF-(FCI,SQD) results are compared against EWF-MP2 and EWF-CCSD benchmarks. The impact of fragmentation on the predicted relative energies of the Trp-cage conformers is further evaluated by comparison with unfragmented RI-MP2 and DLPNO-CCSD calculations. The results demonstrate that large-scale electronic configuration interaction (CI) simulations of protein systems containing hundreds or even thousands of atoms can be realized through the combined use of quantum and classical computing resources.

Scaling contact force parameters across body size, limb count, and number of contact spheres.

This umbrella review will synthesize evidence on the effectiveness of immersive virtual reality (iVR) simulation using head-mounted-display (HMD) on educational outcomes for entry-to-practice health professional learners. HMD-based iVR is increasingly used in health professions education because it can offer engaging, interactive simulation experiences. However, educators, regulators, and decision-makers remain uncertain about when iVR provides unique educational benefits that justify widespread implementation. A high-level synthesis that isolates HMD-based iVR outcomes is needed to support evidence-informed policy, curricular, and budgetary decisions. We will include systematic reviews and meta-analyses that evaluate entry-to-practice learners' quantifiable outcomes aligned with the Kirkpatrick Four-Level Training Evaluation Model when using HMD-delivered iVR interventions. Reviews will be excluded if they focus on practicing clinicians, continuing professional development, patient-facing therapeutic virtual reality, veterinary education, or if they combine immersive and non-immersive modalities without reporting HMD-specific results separately. This umbrella review will follow JBI methodology. We will systematically search PubMed, APA PsycINFO (EBSCOhost), CINAHL Plus with Full Text (EBSCOhost), Education Full Text (EBSCOhost), Embase, ERIC (EBSCOhost), and Scopus. Two independent reviewers will conduct screening and data extraction. Methodological quality will be appraised using the JBI critical appraisal instrument and data will be extracted using a modified JBI umbrella review extraction tool. Outcomes will be analyzed using the Kirkpatrick Four-Level Training Evaluation Model and the National League for Nursing/Jeffries Simulation Theory. Certainty of evidence will be assessed using the GRADE approach. PROSPERO CRD420251075197.

ABSTRACT Reflective practice serves as an important mechanism for developing interpersonal skills in communication training. This paper reports on an online chat-based exercise conducted at a university in Sweden since 2021 as part of social work communication training. The exercise responds to a growing demand for digital communication training for future social workers and aims to stimulate students’ reflection on the skills required for online, text-based interactions with anonymous users. The exercise is structured as a three-part session including (1) an introduction to online text-based counseling, (2) a simulation of anonymous chat interactions in which students take on the roles of support seeker and support provider, and (3) a structured reflection phase in which students reflect on their experiences. The paper outlines the pedagogical foundations and design of the exercise and presents a thematic analysis of students’ written reflections. Three main themes are identified: the novelty of the communication format and resulting shifts in perspective; enhanced understanding of communication skills required for text-based interaction; and increased awareness of the affordances and limitations of anonymous online communication. The paper concludes by discussing possible directions for further development of the exercise.

Carbon fixation and metabolic efficiency in extremophiles offer promising avenues for sustainable bioproduction. Priestia megaterium SR7 is a supercritical CO2-tolerant extremophile, whose central carbon metabolism may be particularly well adapted toward efficient heterotrophic CO2 fixation. However, the regulatory organization of carbon flux in this organism remains unclear, especially around the pyruvate node, a central metabolic hub that directs carbon toward energy generation, metabolic replenishment, and biosynthesis. In this work, we proposed and substantiated the formation of multiple potential metabolons, associated enzyme complexes that channel metabolites between sequential reactions, surrounding the pyruvate node in P. megaterium SR7, which serves as an important regulatory mechanism in central carbon metabolism by enabling the channeling of metabolites toward different pathways. Cross-linking experiments, together with subsequent proteomics, provided in vitro evidence of metabolon formation among phosphoenolpyruvate carboxylase (PPC), NAD+-dependent malic enzyme (NDME), lactate dehydrogenase (LDH), and pyruvate kinase (PYK) protein-protein interactions. In silico simulations of protein-protein interactions revealed possible interacting residues between different proteins. Furthermore, observed dilution of metabolite pools from 13C isotopic labeling experiments provides in vivo support for metabolon formation. These insights suggested a possible novel regulatory mechanism and provided additional avenues for future metabolic engineering efforts that leverage enzyme organization to enhance CO2-linked bioproduction.

AI-assisted development of interactive web simulations for analytical chemistry: a personal account.

This study investigates how uncertainty in the reaction rate parameters of an atomistic kinetic Monte Carlo (KMC) model propagates to model outputs, such as the defect growth rate in carbon materials used for thermal protection systems. A KMC model consisting of key processes between carbon and nitrogen is used to model defect growth caused by nitridation in graphene. Uncertainties in rate parameters of nitrogen adsorption, surface diffusion, and CN formation reactions were identified from density functional theory or other model calculations. These uncertainties result from variations in the model parameters of these methods and, thus, constitute epistemic uncertainties in the KMC model. The KMC model, along with the identified uncertainties, was used to perform a sensitivity analysis to quantify and study the influence of these uncertainties on the resulting defect growth rate uncertainty. The analysis revealed that the activation energy of CN formation contributed significantly higher uncertainties to the defect growth rate than other reaction rate parameters. This result is attributed to the strong limiting effect of CN formation on defect growth, as well as the greater propagation of uncertainty through activation energies rather than prefactor terms. In addition, uncertainty propagation was studied across temperature from 1700 to 2100K. Results further demonstrated that a reaction's uncertainty contribution strongly depends on its limiting effect, which varies with temperature. Together, these results highlight the factors influencing uncertainty propagation from key processes in carbon nitridation.

Modern education emphasizes the development of critical thinking and problem-solving skills among students. This article explores the potential for effectively implementing the STEAM approach in mathematics lessons through the use of artificial intelligence technologies. It analyzes methods for teaching mathematical concepts in a practical context using AI-powered tools such as adaptive learning platforms and interactive simulations. This approach deepens students' understanding of interdisciplinary connections and fosters their creativity and innovative abilities Consequently, students acquire essential skills for future professions and develop a greater interest in mathematics

The aim is to develop and test a framework that integrates real-time catheter shape reconstruction, interactive simulations, and mixed reality visualization to enable accurate monitoring of catheter vessel interactions during endovascular navigation. A finite element model (FEM) of the venous pathway from the right femoral vein to the inferior vena cava was generated from computed tomography data and implemented into an interactive simulation. Catheter motion was imposed as boundary condition, and catheter vessel contact was modeled with a Lagrange multiplier formulation to compute vessel deformation. The framework was integrated and tested in vitro using a sensorized catheter with fiber Bragg grating and electromagnetic sensors to provide real-time 3D shape and location as it was advanced by a catheter driver through a silicone replica of the vascular anatomy. Upon registration, real-time sensor read-outs fed the simulation, and the updated catheter and vessel geometries were streamed to Hololens (HL2). The performance of the simulation and the accuracy of FEM-computed vessel wall displacement were validated vs. experimental ground truth obtained via stereo frames triangulation. Complexity and extent of catheter vessel interaction affected FEM performance by increasing the computational cost. The simulated time exceeded the real temporal extent of the physical phenomenon by 12% during the initial navigation phase and by 45% when the catheter reached the most tortuous portion of the vessel. The HL2 rendering remained stable between 35 and 40 frames per second. Across these two phases, the median relative displacement error between FEM-computed vessel wall displacements and the ground truth remained below 1mm and 2.33 mm, respectively. The study demonstrates the feasibility of integrating interactive biomechanical simulation with real-time sensor data to enable continuous monitoring of catheter vessel interactions, with mixed reality visualization serving as a user interface to support a more engaged and better-informed operator throughout the navigation.

Correctly setting and reading a manual air displacement micropipette is an essential yet challenging skill for life-science students. The incorrect delivery of volumes affects both experimental outcomes and data interpretation, potentially reducing student confidence in the laboratory. To address this issue, we developed and evaluated an interactive simulation using Articulate Storyline. Our novel approach isolated and addressed the critical cognitive task of reading a micropipette dial from the physical skill of using the tool. This intervention offers a uniquely scalable and cost-effective solution compared to the use of physical micropipettes. Existing training often focuses on the physical action, leaving a gap for a scalable digital tool that allows students to first master the cognitive challenge of reading the dial. Using a prepost mixed methods design, we measured student perceptions of the approach and changes in student performance across confidence, unit conversion, and micropipette dial reading accuracy. Students at two independent universities showed significant improvements in self-rated confidence and accuracy in reading micropipette scales after using the simulation. Student feedback was highly positive regarding the benefits of the simulation in learning these skills and enhancing their self-efficacy. This simulation is an ideal preparatory tool within a blended learning framework, significantly improving student confidence and accuracy. By mastering the cognitive prerequisite in a safe, virtual environment, students are better prepared for effective hands-on practice in a face-to-face setting.

PurposeAccurate assessment of the pressure gradient (ΔP) across aortic coarctation (CoA) is critical for determining disease severity and the need for intervention. Current non-invasive methods are unreliable, while invasive catheterization remains the clinical gold standard. This study evaluates a novel MRI acquisition strategy, 4D-FlowP, that simultaneously encodes blood velocity and acceleration to enable reliable non-invasive pressure gradient mapping in CoA.MethodsPatient-specific compliant aortic phantoms were created from clinical MRI data of two patients with CoA. Additional geometries were synthetically generated by increasing stenosis severity. Phantoms were studied in an MRI-compatible flow loop under physiologically realistic flow and pressure conditions. Pressure gradients were estimated using conventional 4D-Flow MRI, 4D-FlowP, and fluid–structure interaction (FSI) simulations. Results were compared against ground-truth catheter-based measurements across multiple flow rates and stenosis severities.ResultsConventional 4D-Flow consistently underestimatedΔP(slope = 0.63,R2=0.75) relative to catheter measurements. In contrast, 4D-FlowP demonstrated substantially improved agreement (slope = 0.95,R2=0.75). FSI simulations showed the highest overall agreement with catheter-derivedΔP(slope = 1.14,R2=0.82). Scan times for 4D-FlowP were comparable to 4D-Flow (26 vs. 24 minutes).Conclusion4D-FlowP enables a more accurate MRI-based pressure gradient mapping in CoA than conventional 4D-Flow, when compared to ground truth catheter measurements. These findings support furtherin vivoevaluation of 4D-FlowP as a non-invasive alternative for functional assessment of CoA severity.

Immune checkpoint modulation has emerged as a promising strategy in cancer therapy, including the treatment of aggressive tumors such as glioblastoma. Among these targets, programmed death-ligand 1 (PD-L1) plays a key role in tumor immune evasion and represents an attractive target for small-molecule inhibitor development. In this study, a virtual screening approach was applied to identify potential PD-L1 modulators within a library of nucleoside-related compounds and structurally similar molecules. A dataset of 400 compounds was evaluated using molecular docking to predict their binding affinity (free energy values and binding pose) toward PD-L1. The resulting complexes were analyzed to identify nonbond interactions within the hydrophobic pocket formed at the PD-L1 dimer interface. In addition to docking results, physicochemical descriptors associated with drug-likeness and blood-brain barrier penetration were calculated, including lipophilicity, molecular weight, hydrogen bond donors and acceptors, as well as topological polar surface area. To integrate these parameters, a multiparameter optimization (MPO) score was implemented. Finally, molecular dynamics simulations of protein-ligand interactions were performed to explore the structural stability for 100 ns using the most promising ligands. The analysis revealed that several top-ranked compounds exhibited favorable docking scores and physicochemical properties compatible with drug-like behavior. Interestingly, BMS-1, a known PD-L1 inhibitor, was identified among the highest-scoring compounds, supporting the reliability of the MPO protocol. Furthermore, multiple candidates displaying nucleoside-like scaffolds combined with reduced polarity and moderate lipophilicity emerged as promising molecules according to the MPO ranking. Overall, the results suggest that nucleoside-derived scaffolds may represent a viable starting point for the development of small-molecule PD-L1 modulators with potential applicability in glioblastoma therapy.

Actual microstructure-based FEM simulation of crack interaction in thick thermal barrier coating using cohesive zone models

Energy harvesting properties of flexible piezoelectrets under wind-induced vibration

This pilot randomized controlled trial evaluated whether a Gagné's model-based interactive desktop simulation program could reduce ageism and improve nursing students' attitudes toward older adult care. Hundred and twenty nursing students were randomized to an intervention group (n = 60) receiving eight sessions combining Gagné's instructional events, desktop simulations, case discussions, and structured reflection, or a control group (n = 60) receiving no intervention. Groups were comparable at baseline across all outcome measures (WEPS, FSA, PCOP-SV; all p > 0.05). Post-intervention, the intervention group demonstrated significantly greater improvement in willingness to work with older adults (WEPS), reduced ageist attitudes (FSA), and more positive perspectives on caring for older patients (PCOP-SV), with statistically significant group-by-time interactions across all three outcomes sustained at follow-up. A simulation-enhanced instructional program based on Gagné's model shows promise for improving attitudinal outcomes in gerontological nursing education. Findings are preliminary and should be interpreted cautiously given the pilot design and complete-case analysis. Trial Registration: ClinicalTrials.gov identifier: NCT0613947.