Rhinoplasty can achieve both aesthetic and functional improvements by altering the internal and external structures of the nose. Given the complex anatomy and significant individual variability of the nasal structure, rhinoplasty requires high precision. 3D printing technology, with its advantages of personalization and rapid prototyping, is increasingly being applied in the field of nasal reconstruction. This technology allows for the creation of three-dimensional models from patient imaging data, enhancing preoperative planning and simulation, enabling custom prosthetic design, and providing intraoperative guidance. Additionally, it aids in postoperative evaluation and care, while providing preoperative training models for surgeons. Clinical applications demonstrate that 3D printing can shorten surgical time, reduce complications, and improve patient satisfaction. However, 3D printing still faces several challenges, including insufficient imaging accuracy, high costs, and technical limitations in bioprinting. Future advancements are needed in imaging and modeling technologies, the development of advanced biomaterials, and cost reduction to further enhance and expand the application of 3D printing in rhinoplasty.

We present overhang_surrogates , a lightweight Python package that streamlines surrogate-model workflows for building-energy studies. It provides space-filling Monte Carlo sampling utilities including a Python reimplementation of the MIPT sampler with incremental extension, helpers to build batched building energy model samples and run EnergyPlus simulations, a simple interface for k -fold cross-validated XGBoost ensembles and grid predictions, and a vedo-based 3D plotting helper. By automating sampling, batched simulation, ensemble training, prediction and visualization, the package shortens time-to-prototype and lowers the barrier to reproduce and extend simulation driven surrogate experiments. The software is open-source and designed for easy reuse and extension. • Lightweight Python package for surrogate workflows in building energy. • Implements MIPT space-filling sampling with incremental extension. • Batch EnergyPlus sampling and simulation helper for rapid prototyping. • Cross-validated XGBoost ensembles and grid prediction interface. • Vedo-based helper to make publication-quality 3D diagrams easily.

Identifying aberrations in live surveillance data representing before year-end data reconciliation can improve public health response and data quality. We developed an automated, customized data report to communicate potential aberrations to jurisdictions submitting syphilis data to the Centers for Disease Control and Prevention. Our multiphase approach encompassed: requirements gathering, exploratory data analyses, rapid prototyping, evaluating relative performance of different aberration detection methodologies, and soliciting end-user feedback on the prototype. The final product encompasses statistical code that generates user-friendly quarterly data reports on live syphilis surveillance data, customized to each US jurisdiction. Data reports include an executive summary highlighting critical issues followed by detailed charts and tables showing anomalies in priority variables. The flexible code includes options for alternative methods, figures, and descriptions. End-users preferred a mix of tables and figures, with simple data representations. Evolving epidemics, decentralized data collection, jurisdictions' use of various data systems, and public health workforce shortages are critical challenges for national disease surveillance. Additional technical challenges include setting thresholds for aberrations and jurisdictions' different information needs. End users expressed satisfaction with the prototype, identifying multiple use cases for how jurisdictions with variable morbidity and surveillance capacities could leverage the information for action. Our multiphase, user-centered design approach identified challenges in determining and communicating data quality aberrations in a complex data ecosystem. An important consideration for developing such products is balancing complex methodologies with visualizations that are easy to interpret by nonstatistical audiences.

Bottom-up micro/nanofabrication complements photolithography in multilayer, 3D, and cost-effective manufacturing, but lacks resist patterning technology with photoresist-level reproducibility, processability, and universality. Here, we explore a surface-selective nucleation (SSN) effect to derive polymeric resist patterns with nanoscale resolution, inherent 3D compatibility, low defect density, clean lift-off, and broad applicability across fabrication platforms. The SSN phenomenon is achieved through solution-phase polymerization of dual-ended acrylic monomers on prepatterned substrates and surface-mediated creation of area-dependent nucleation barriers using adsorption inhibitors and chain terminators, which synergistically modulate surface and bulk free energy of nucleation, respectively. The resulting resist forms a coherent resin film physically adhered to the substrate, while featuring tunable thickness (13-150nm) and minimal surface roughness (0.91nm). This method delivers 15nm linewidth patterning with 99.97% coverage across wafer-scale arrays within 10s and demonstrates high compatibility with mainstream thin-film deposition techniques (e.g., e-beam evaporation, sputtering, and atomic layer deposition).

Bacterial cellulose (BC) hydrogels produced by Gluconacetobacter species hold considerable promise for a wide range of applications owing to their exceptional mechanical properties, biocompatibility, and biodegradability. Achieving precise control over their structural and mechanical characteristics is crucial for the engineering of BC-based materials. In this study, we investigated the formation dynamics and structural features of BC hydrogels, emphasizing the complex interplay between cellulose nanofibril secretion and bacterial motility. Comprehensive tracking of bacterial movement during hydrogel formation has validated mechanisms underlying the development of branching and merging junctions, which are key elements that define the network's physical properties. Additionally, we observed the emergence of vortex-lattice and chiral-nematic structures during hydrogel development, depending on bacterial and cellulose densities. These insights contribute to a fundamental understanding of bottom-up 3D fabrication of BC hydrogels that harness the collective behavior of cellulose-producing bacteria.

PurposeThis study illustrates the use of design thinking (DT) as a structured, participatory approach to explore contemporary complex challenges in health professions admissions, including the rise of generative AI, remote interviews, and the elimination of standardized admissions tests. This work focuses on stakeholder-driven problem framing and idea generation rather than evaluating outcomes.MethodsA two-hour workshop engaged 15 purposively sampled stakeholders, including faculty, staff, application readers, and student ambassadors, in collaborative problem framing, ideation, and rapid prototyping of admissions concepts. Generative artifacts (brainstorming outputs, reflection worksheets, facilitator notes) and post-session surveys were analyzed using thematic synthesis and descriptive statistics to characterize emergent ideas and participant perspectives.Major findingsParticipants generated ideas that clustered into three major themes: interview restructuring, GenAI integration and compliance, and broadening of admissions criteria, illustrating how stakeholders reframed challenges and proposed diverse solution pathways. Survey responses reflected descriptive indicators of participant experience, suggesting the workshop supported creative problem solving (Mean 4.7 ± 0.5), idea generation (4.8 ± 0.4), and collaboration (4.4 ± 0.5).ConclusionsFindings suggest that DT offers a structured, iterative framework for stakeholder-driven idea generation and problem reframing in an evolving admissions context. The workshop demonstrates the potential of collaborative, reflective processes to surface assumptions and generate diverse perspectives to inform future exploration of admissions practices.

Neurodegenerative diseases (NDs) such as Alzheimer's, Parkinson's, and ALS remain some of the most challenging disorders to diagnose at an early stage. Conventional approaches rely on costly neuroimaging or invasive cerebrospinal fluid sampling, which limit accessibility and early intervention. Recent advances in 3D printing have enabled rapid prototyping of lab-on-chip (LOC) platforms that integrate microfluidics, biosensors, and biological models to detect disease-specific biomarkers with high sensitivity and throughput. Herein, we explore the synergistic role of 3D printing technologies and biomaterials in fabricating LOC systems for NDs. We highlight key biomarkers, and neuron- and organoid-on-chip platforms, and discuss the challenges and opportunities in clinical translation. By combining technical innovation in additive manufacturing with biological relevance, 3D-printed LOC devices represent a transformative approach toward precision diagnostics in neuro-medicine.

This study introduces a novel geometric marker, derived from CT image data analysis via grey theory and prototyping verification, to enhance the planning of sacroiliac joint screw placement, aiming at optimizing safety. A dataset comprising 107 adult cases with hip thin-slice CT scans in DICOM format was collected for statistical analysis. The sacral bone model was segmented from the scanning data sets to facilitate planning for sacroiliac screw surgery. The saddle point (SP), located at the intersection of the contralateral sacral promontory and the sacral wing, was identified as the target direction for needle insertion. The entry point on the anterior convexity and posterior concavity of the ear-shaped surface was connected to the posterior superior aspect to determine the retrograde positioning of the screw channel. Utilizing a home-made software, E3D, the safe channel was visualized using three cross-sectional views with the screw channel as the axis. Adjustment of the central screw channel's position in the transverse and coronal planes facilitated its establishment. The distances between the highest (up, u) and lowest (down, d) entry points on the ear-shaped surface (ud, mm), as well as the distances between the most anterior (ahead, a) and most posterior (back, b) points (ba, mm), were measured to approximate the area (surface, s, mm2) within the four points, aiding in assessing the size of the safe range. Out of the 107 cases, two were unable to undergo sacroiliac screw placement due to sacral morphological variations (1 male and 1 female). In the remaining 105 cases (61 male and 44 female), 98.13% successfully underwent bilateral safe screw placement (53 cases on the left side and 52 on the right side). Statistical tools and grey relation analysis are applied to extract templates for data reuse and validation. The utilization of the central screw channel as the axis in the three-section view facilitated a direct three-dimensional assessment of the screw channel's safety, ensuring its containment within the bone tissue. Consequently, planning the sacroiliac screw channel with the contralateral saddle point as the target direction and the posterior superior aspect of the ear-shaped surface as the entry point is deemed safe and feasible, notwithstanding individual variations. To validate the findings, a printed sacroiliac joint prototype and a real human sacroiliac joint specimen were employed to simulate puncture insertion surgery. The results demonstrate the practicality of the newly identified marker as a crucial reference point for surgical planning. Subsequent steps will involve the statistical analysis of additional medical specimens and the execution of clinical surgeries to demonstrate its application.

Constructing multicellular mechanistic models traditionally requires extensive time and computational expertise. We introduce intelligent tool orchestration via Model Context Protocol (MCP) servers, enabling Large Language Model (LLM) agents to act as AI laboratory assistants for rapid model prototyping. We demonstrate this approach by constructing a multiscale model of cancer cell fate in response to TNF using an AI agent connected to MCP servers interfacing with three complementary tools: NeKo for gene regulatory networks construction, MaBoSS for Boolean models simulation, and PhysiCell for setting up multicellular agent-based models. This workflow was executed entirely through natural language interactions, without manual coding, direct parameter editing, or manual modification of generated model files. Through this use case, we identified key principles for biological AI-tool integration, specifically regarding tool granularity, session management, and flexible orchestration. Testing across multiple LLMs demonstrated our framework's portability, though model-dependent variations emphasize the need for rigorous validation. Ultimately, this work establishes a foundation for AI-assisted rapid prototyping, enabling researchers to explore computational hypotheses more rapidly through natural language interaction.

Cell-free expression systems offer a method for the rapid prototyping of DNA circuits and functional protein synthesis. While crude extracts remain a black box with many components carrying out unknown reactions, PURE contains only the required transcription and translation components for protein production. All proteins and small molecules are at known concentrations, enabling detailed modeling of reliable computational predictions. However, there are few experimental data supporting the expression of target proteins for PURE-based models. In this work, we generalized the PURE detailed translation model for proteins with arbitrary amino acid compositions and lengths. We then built a chemical reaction network (CRN) for transcription in PURE, validating the transcription models using DNA expression for the malachite-green aptamer (MGapt) to measure RNA production. Lastly, we coupled the transcription and the generalized translation models to create a PURE protein synthesis model built purely of mass-action reactions. We used the combined model to capture the kinetics of MGapt and deGFP expressed from plasmids at various concentrations.

ABSTRACT This study offers an in-depth qualitative examination of how music teachers conceptualise and integrate artificial intelligence (AI) within their pedagogical and creative practices. Through interviews conducted across Australia and Italy using thematic analysis, two overarching themes emerged: (1) music teaching, and (2) ethics, ownership, and challenges. Findings reveal that teachers are developing sophisticated, domain-specific uses of AI extending beyond administrative convenience. Participants identified AI as a time-saving and organisational tool supporting report writing, rubric generation, lesson planning, and content summarisation. Teachers also described AI as a catalyst for creative scaffolding, enabling rapid musical prototyping, idea generation, notation workflows, and the structuring of DAW-based tasks, while positioning these applications as supplementary rather than substitutive. Hybrid practices, in which AI functions as a 'third ear' or co-creative reference, were evident alongside in examples of human musicianship guided by interpretive and embodied decision-making. Teachers expressed concerns regarding authorship, reliability, aesthetic homogenisation, and the erosion of student agency and critical thinking. Inconsistent institutional responses prompted calls for redesigned assessment strategies emphasising oral, practical, and iterative work. Overall, the study highlights a cautious optimism in which teachers strategically incorporate AI while reaffirming the centrality of human judgement, creativity, and relational pedagogy in music education.

Energy harvesting is gaining importance in the 21st century, with thermoelectric (TE) technology offering a promising method for converting thermal energy into electrical energy. However, the application of TE devices remains relatively low because of the limitations of conventional manufacturing methods. Fabricating complex-shaped TE devices using traditional manufacturing processes is challenging and leads to a low efficiency. Unlike conventional subtractive methods, additive manufacturing (AM) builds three-dimensional (3D) objects layer by layer, enabling the creation of intricate and complex structures with precision. This study explores recent trends in AM of thermoelectric systems, with a focus on materials, synthesis methods, and device fabrication. It also discusses the challenges associated with these AM techniques and explores potential areas for improvement. Recent studies have shown that AM can produce thermoelectric units with hollow and layered structures, enhancing temperature gradients and power density compared with conventional designs. The ability to customize geometries through AM offers promising opportunities to enhance the performance of the TE materials and devices. AM technologies can produce highly efficient, functionally graded TE devices. By enabling rapid prototyping and high-performance structures, AM can improve the efficiency and application of the TE materials. Future work focuses on further advancing these AM techniques by integration with machine learning (ML) and development of multimaterial TE devices.

KSSOLV (Kohn-Sham Solver) is a MATLAB package for plane-wave Kohn-Sham density functional theory (DFT) calculations. Here, we present the KSSOLV Toolbox, a MATLAB-based graphical and workflow-oriented environment built on KSSOLV to simplify the setup, execution, and analysis of plane-wave DFT calculations. The toolbox integrates structure import, symmetry analysis, self-consistent and non-self-consistent calculations, postprocessing, and visualization within a unified interface, while retaining access to the underlying KSSOLV computational backend. To improve reproducibility and project management, calculation settings, workflows, and results are organized in a single project file. We further assess the numerical consistency and practical computational behavior of the toolbox through benchmark calculations against established plane-wave codes. Comparisons with Quantum ESPRESSO show close agreement in total energies and atomic forces, and single-thread CPU timing tests against M-SPARC, DFTK.jl, PyPWDFT, and Quantum ESPRESSO clarify the computational performance of KSSOLV for PBE and HSE06 calculations on Si systems of different sizes. The KSSOLV Toolbox is intended as a transparent and accessible platform for plane-wave DFT workflow organization, method development, validation, and rapid prototyping.

Differentiation of vitreoretinal interface disorders on optical coherence tomography (OCT) relies on expert interpretation and can be challenging in borderline cases. Automated machine learning (AutoML) platforms may enable clinician-driven artificial intelligence development without coding expertise. This study evaluated the performance of a code-free AutoML approach for OCT-based classification. In this cross-sectional image classification study, 434 OCT B-scans from publicly available datasets were manually labeled into four categories: epiretinal membrane (ERM), lamellar macular hole (LMH), full-thickness macular hole (MH), and normal retina. Images were uploaded to a cloud-based AutoML platform (Google Cloud Vertex AI), which automatically performed data splitting (80% training, 10% validation, 10% test), model training, and optimization. Performance was assessed using precision, recall, average precision, and confusion matrix analysis. The model achieved an overall average precision of 0.988, with precision and recall of 97.6%. MH and normal retina were classified with perfect precision and recall (100%). ERM showed high precision (100%) with slightly reduced recall (92.9%), while LMH demonstrated complete recall (100%) with lower precision (83.3%). Misclassifications were limited to anatomically related entities. Code-free AutoML enables accurate OCT-based classification of vitreoretinal interface disorders using a clinician-driven workflow. This approach may facilitate broader adoption of artificial intelligence in ophthalmology and support rapid clinical research prototyping.

AimsSegmental bone defects of the femur and tibia present a major challenge for reconstructive surgery, especially in joint-sparing oncological and post-traumatic cases. Patient-specific implants (PSIs), enabled by 3D imaging and additive manufacturing, offer tailored solutions. This review aimed to systematically evaluate clinical applications of PSIs for femoral and tibial shaft reconstruction in terms of mechanical survival rate, and introduce a classification framework to guide implant design and fixation, as well as communication.MethodsA systematic search of PubMed and Embase identified studies reporting joint-sparing reconstructions using PSIs for segmental femoral or tibial defects. Data were extracted on implant design, fixation method, material, biological augmentation, and clinical outcomes. A novel classification system was developed to categorize implants by defect location (unicortical, diaphyseal, meta-extended) and fixation type (screws, plates, nails, stems, hybrid). Kaplan-Meier survival analysis was used to assess five-year implant survival across subgroups.ResultsA total of 53 studies involving 299 patients were included. Titanium was the predominant material, often featuring lattice structures to support bone ingrowth or grafting. Fixation methods varied, with hybrid fixation showing the highest five-year implant survival rate (97%), followed by stems (95%) and plate- or nail-only fixation (each 90%). Most implants remained failure-free at final follow-up; structural failure was the most common complication. The classification system enabled structured comparison across implant types. A notable concentration of studies (27/53) originated from China, suggesting regional leadership in PSI innovation, potentially linked to regulatory flexibility compared to the European Union.ConclusionThis review provides a structured overview of PSI design and fixation, and outcomes for joint-sparing femoral and tibial reconstruction. The proposed classification framework facilitates standardized reporting and interdisciplinary communication. Hybrid fixation methods demonstrated superior mechanical survival, supporting their use in future PSI designs.Cite this article: Bone Joint Res 2026;15(6):647–661.

To create modular solutions for interactive real-time MRI using reconstruction algorithms implemented in BART. A new protocol for streaming of multidimensional arrays is presented and integrated into BART. The new functionality is demonstrated using examples for cardiac interactive real-time MRI based on radial FLASH, where iterative reconstruction is combined with advanced features such as dynamic coil compression and gradient-delay correction. We analyze the latency of the reconstruction and measure end-to-end latency of the full imaging process. Reconstruction pipelines with iterative reconstruction and advanced functionality were built in a modular way using scripting. Latency measurements demonstrate latency sufficient for interactive real-time MRI, on the order of 30 ms for BART processing and network transfer time, or 200 ms for end-to-end latency including acquisition, vendor processing, and display. With the new streaming capabilities, real-time reconstruction pipelines can be assembled using BART in a flexible way, enabling rapid prototyping of advanced applications such as interactive real-time MRI.

In response to the challenges of insufficient precision and limited safety associated with traditional techniques in the diagnosis and treatment of complex spinal diseases, intelligent technologies-represented by 3D visualization, additive manufacturing (3D printing), surgical navigation and robotics, artificial intelligence, and telemedicine-are driving spine surgery into a new era of precision and personalized treatment. However, there is currently a lack of standardized protocols or guidelines for the clinical application of these intelligent technologies in spine surgery. To standardize and promote their clinical use, this guideline was developed through an initiative led by the Spinal Trauma Group of the Chinese Association of Orthopaedic Surgeons (CAOS) and the Intelligent Orthopaedics Group of CAOS, in collaboration with Shaanxi Medical Doctor Association Professional Committee on Orthopaedic Minimally Invasive Surgery. Multidisciplinary experts participated, formulating recommendations based on evidence-based medicine and multi-center Delphi expert consensus. This guideline systematically summarizes the application scope of intelligent technologies in spine surgery, covering six aspects: 3D visualization, 3D printing, surgical navigation and robotics, artificial intelligence, telemedicine, and the implementation of intelligent technologies. It finally forms 15 recommendations, clarifies the applicable scenarios, evidence levels, and recommendation strength for each technology, and aims to promote their scientific, standardized, and efficient application, provide practical suggestions for medical institutions at different levels, and ultimately enhance the overall clinical diagnosis and treatment level and clinical decision-making support capacity in spine surgery.

Creativity has been a key driver of innovation; thus, cultivating creative problem solvers is a central goal of STEM education. Making and tinkering practices supported by 3D printing offer a promising avenue for fostering creativity, particularly because the relatively low cost and accessibility of 3D printing make such opportunities feasible in rural educational settings. However, empirical evidence linking these practices to measurable gains in rural students' creativity remains limited. To address this gap, this study investigated whether integrating making and tinkering experiences through 3D printing affected rural school students' creativity. A convergent mixed method design was employed in which quantitative and qualitative data were collected in parallel, combining quantitative data from a single-group pretest-posttest design with qualitative insights from semi-structured interviews. After receiving institutional review board approval, parental consent and student assent, this study involved eleven students (ages 16-18) from a rural public high school in the southeastern United States, seven of whom participated in semi-structured interviews. The results indicated a significant increase in students' creativity scores after participating in the 3D printing session, with a moderate effect size (Cohen's d = 0.58). Qualitative findings revealed that the hands-on, iterative nature of the 3D printing process fostered students' creative thinking, in particular, originality and usefulness. The makerspace program provided a tangible platform that allowed students to translate abstract ideas into physical products via rapid prototyping. These findings added preliminary evidence for the potential of integrating 3D printing into educational settings as a means of cultivating creativity, particularly among rural students.

The limitations of traditional gel-based Ag/AgCl electrodes, such as skin irritation, unsuitability for long-term monitoring, etc., have spurred the development of next-generation "dry" electrodes for bioelectricity monitoring applications. This study reports the fabrication of high-performance and low-cost thermoplastic elastomer multi-material 3D printed (3DP) dry electrodes, utilizing Fused Filament Fabrication (FFF). The electrodes consist of neat and conductive thermoplastic polyurethane (cTPU) simultaneously printed to incorporate the conductive material properties into the electrodes' bulk structure, as well as "tune" the compliance, stretchability, and flexibility with respect to the human skin stiffness. Different electrode designs are proposed to optimize skin conformity, employing nature-inspired triply periodic minimal surface (TPMS) geometries, namely, gyroid (G) and square honeycomb (SH), with a 3mm unit cell size. The final electrodes are characterized through thermogravimetric analyses, tensile testing, cyclic bending, and electrode-skin impedance, while different ECG signal acquisition scenarios are demonstrated, i.e., stationary, 24 h monitoring, standing, and walking. The multi-material gyroid electrode exhibited the lowest electrode-skin impedance (298 kΩ at 20Hz) and the highest ECG signal quality. The proposed multi-material structure and TPMS design strategy enable a scalable route to comfortable, reusable, and high-performance electrodes by spatially combining conductivity and skin-compliant mechanics, supporting continuous bioelectricity monitoring in wearable applications.

Trends in Micro- and Nano-Lithography required for future development of large area applications ranging from high-packing-density electronics to solar cells are surveyed and outlined. Strategies to use direct laser writing to define etch masks over large areas by: (i) fixed beam moving stage and (ii) moving beam moving stage approaches are presented. The extension of planar 2D and stacked 2D (or 2.5D) fabrication methods into 3D micro- and nano-fabrication is discussed. One of the essential future characteristics of 3D nanolithography is real-time feedback capability. This can be realised via inherent 3D-capable holography, which bridges lithographic exposure control, wavefront sensing, and adaptive feedback, providing a pathway to stitch-free, large-area 3D patterning. The future of micro-fabrication is expected to evolve via highly specialised 3D architecture design and reduction in post-processing steps.