Biomedical Engineering Research Articles

The Origins of Engineered Biomaterials: NSF-Funded, University of Washington Engineered Biomaterials (UWEB).

The University of Washington Engineered Biomaterials (UWEB) Engineering Research Center (ERC) was funded from 1996 to 2007 by the U.S. National Science Foundation. The mission of UWEB was to advance biomaterials by integrating modern biology with materials science. UWEB specifically focused on the healing and integration of medical implants. UWEB teamed biologists, physicians, engineers, and industry and demonstrated three paths that might advance biomaterials so they could seamlessly integrate and heal in the body. The three primary lines of investigation were precision porous scaffolds, super-non-fouling surfaces, and the control of matricellular proteins. The UWEB program set the groundwork for the modern field of immunoengineering. Also, UWEB invested significantly in training scientists/engineers who could freely integrate advances in biological sciences, state-of-the-art materials science, and medical technology. This historical summary of the UWEB program demonstrates that federal investment in interfacing forefront fields can yield dividends with benefits for society and the economy.

Bacterial Cellulose-Silk Hydrogel Biosynthesized by Using Coconut Skim Milk as Culture Medium for Biomedical Applications.

In this study, hydrogel films of biocomposite comprising bacterial cellulose (BC) and silk (S) were successfully fabricated through a simple, facile, and cost-effective method via biosynthesis by Acetobacter xylinum in a culture medium of coconut skim milk/mature coconut water supplemented with the powders of thin-shell silk cocoon (SC). Coconut skim milk/mature coconut water and SC are the main byproducts of coconut oil and silk textile industries, respectively. The S/BC films contain protein, carbohydrate, fat, and minerals and possess a number of properties beneficial to wound healing and tissue engineering, including nontoxicity, biocompatibility, appropriate mechanical properties, flexibility, and high water absorption capacity. It was demonstrated that silk could fill into a porous structure and cover fibers of the BC matrix with very good integration. In addition, components (fat, protein, etc.) in coconut skim milk could be well incorporated into the hydrogel, resulting in a more elastic structure and higher tensile strength of films. The tensile strength and the elongation at break of BC film from coconut skim milk (BCM) were 212.4 MPa and 2.54%, respectively, which were significantly higher than BC film from mature coconut water (BCW). A more elastic structure and relatively higher tensile strength of S/BCM compared with S/BCW were observed. The films of S/BCM and S/BCW showed very high water uptake ability in the range of 400-500%. The presence of silk in the films also significantly enhanced the adhesion, proliferation, and cell-to-cell interaction of Vero and HaCat cells. According to multiple improved properties, S/BC hydrogel films are high-potential candidates for application as biomaterials for wound dressing and tissue engineering.

Advanced Biomaterials for Lacrimal Tissue Engineering: A Review.

The lacrimal gland (LG) is vital for ocular health, producing tears that lubricate and protect the eye. Dysfunction of the LG leads to aqueous-deficient dry eye disease (DED), significantly impacting quality of life. Current treatments mainly address symptoms rather than the underlying LG dysfunction, highlighting the need for regenerative therapies. Tissue engineering offers a promising solution, with biomaterials playing crucial roles in scaffolding and supporting cell growth for LG regeneration. This review focuses on recent advances in biomaterials used for tissue engineering of the lacrimal gland. We discuss both natural and synthetic biomaterials that mimic the extracellular matrix and provide structural support for cell proliferation and differentiation. Natural biomaterials, such as Matrigel, decellularized extracellular matrices, chitosan, silk fibroin hydrogels, and human amniotic membrane are evaluated for their biocompatibility and ability to support lacrimal gland cells. Synthetic biomaterials, like polyethersulfone, polyesters, and biodegradable polymers (PLLA and PLGA), are assessed for their mechanical properties and potential to create scaffolds that replicate the complex architecture of the LG. We also explore the integration of growth factors and stem cells with these biomaterials to enhance tissue regeneration. Challenges such as achieving proper vascularization, innervation, and long-term functionality of engineered tissues are discussed. Advances in 3D bioprinting and scaffold fabrication techniques are highlighted as promising avenues to overcome current limitations.

Transformative Biomaterials in Burn Care: Utilizing Scaffolds, Hydrogels, and Chitosan for Optimized Healing and Scar Reduction

With over a million patients per year in the USA alone, burns are among the most common injuries in the world. In addition to preventing difficulties during burn injuries, the growing impact of scaffolds, hydrogels, cell scaffolds, and cell sheets with healing boosting elements expedite triggers and strengthens re-epithelialization and wound healing, which limits the creation of scars. While superficial partial-thickness and superficial burns typically heal without surgery, severe burns require special care, including topical antimicrobial surgery or bandages. Usually composed of polymeric biomaterials, scaffolds offer the structural support needed for cell adhesion and the subsequent formation of tissue. The term "tissue engineering triad" often refers to cells, scaffolds, and growth-stimulating signals. This may be accomplished in several ways by combining different polymers. Simple (planar) 3D structures may be produced using conventional and rapid prototyping techniques, but complicated structures have been successfully produced by carefully controlling the Molds and processing parameters. For biomedical and tissue engineering applications, chitosan has been widely employed (skin, bone, cartilage, and vascular grafts to substrates for mammalian cell culture). Additionally, it is renewable, biocompatible, biodegradable, nonantigenic, bioactive, and nontoxic.

Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting.

Melt electrowriting (MEW) is an emerging high-resolution 3D printing technology used in biomedical engineering, regenerative medicine, and soft robotics. Its transition from academia to industry faces challenges such as slow experimentation, low printing throughput, poor reproducibility, and user-dependent operation, largely due to the nonlinear and multiparametric nature of the MEW process. To address these challenges, we applied computer vision and machine learning to monitor and analyze the process in real-time through imaging of the MEW jet between the nozzle-collector gap. To collect data for training we developed an automated data collection methodology that eases the experimental time from days to hours. A feedforward neural network, working in concert with optimization methods and a feedback loop, is used to develop closed-loop control ensuring reproducibility of the printed parts. We demonstrate that machine learning allows streamlining the MEW operation via closed-loop control of the highly nonlinear 3D printing technology.

3D-Printed Polymeric Biomaterials for Health Applications.

3D printing, also known as additive manufacturing, holds immense potential for rapid prototyping and customized production of functional health-related devices. With advancements in polymer chemistry and biomedical engineering, polymeric biomaterials have become integral to 3D-printed biomedical applications. However, there still exists a bottleneck in the compatibility of polymeric biomaterials with different 3D printing methods, as well as intrinsic challenges such as limited printing resolution and rates. Therefore, this review aims to introduce the current state-of-the-art in 3D-printed functional polymeric health-related devices. It begins with an overview of the landscape of 3D printing techniques, followed by an examination of commonly used polymeric biomaterials. Subsequently, examples of 3D-printed biomedical devices are provided and classified into categories such as biosensors, bioactuators, soft robotics, energy storage systems, self-powered devices, and data science in bioplotting. The emphasis is on exploring the current capabilities of 3D printing in manufacturing polymeric biomaterials into desired geometries that facilitate device functionality and studying the reasons for material choice. Finally, an outlook with challenges and possible improvements in the near future is presented, projecting the contribution of general 3D printing and polymeric biomaterials in the field of healthcare.

Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies

Article Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies Heng Zhao 1, Like Qian 1, Yaqi Zhu 1 and Dingcheng Tian 1,2,∗ 1 Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo 315211, China 2 College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110016, China ∗ Correspondence: 2310520@stu.neu.edu.cn Received: 8 August 2024; Revised: 10 October 2024; Accepted: 14 October 2024; Published: 5 November 2024 Abstract: Low-dose computed tomography (LDCT) denoising is an important topic in CT image research. Compared with normal-dose CT images, LDCT can reduce the radiation dose of X-rays, decreasing the radiation burden on the human body, which is beneficial to human health. However, quantum noise caused by low-dose rays will reduce the quality of CT images, thereby decreasing the accuracy of clinical diagnosis. In recent years, deep learning-based denoising methods have shown promising advantages in this field. Researchers have proposed some optimized models for low-dose CT image denoising. These methods have enhanced the application of low-dose CT image denoising from different aspects. From the perspective of experimental research, this paper investigates and evaluates some top deep learning models proposed in the field of low-dose image denoising in recent years, with the aim of determining the best models and training strategies for this task. We conducted experiments on seven deep learning models (REDCNN, EDCNN, QAE, OCTNet, UNet, WGAN, CTformer) on the AAPM dataset and the Piglet dataset. Our research shows that UNet has the best denoising effect among the models, obtaining PSNR = 33.06 (AAPM dataset) and PSNR = 31.21 (Piglet dataset), and good generalization capacity is also observed. However, UNet has a large number of parameters, and the time it takes to process an image is about 8 ms, while EDCNN takes about 4.8 ms to process an image, and its average PSNR is ranked second after UNet. EDCNN strikes a balance between denoising performance and processing efficiency, making it ideal for low-dose CT image denoising tasks.

Modeling via peridynamics for damage and failure of hyperelastic composites

Modeling damage and failure behaviors of hyperelastic composites under large deformations is pivotal for advancing the design of cutting-edge elastomers used in biomedical engineering and soft robotics. However, existing methods struggle with capturing the non-linearities and singularities in the displacement field under such conditions. To address these difficulties, we propose a novel bond-based peridynamics (PD) framework with multiple advancements. First, we develop a PD bond strain model grounded in the nonlinear Piola-Kirchhoff stress-stretch relationship, precisely capturing hyperelasticity and ensuring full compliance with thermodynamic laws and kinematics in large deformation scenarios. Second, our framework employs a particle discretization technique that not only sidesteps the mesh distortion issues commonly encountered in grid-based methods subjected to large deformation but also significantly lowers the computational complexity due to the ease of numerical implementation of random inclusion distributions. Third, we propose, for the first time, a refined 3D hyperelastic model within the PD framework that enables a more comprehensive and accurate prediction of material responses to external loads, surpassing the limitations of conventional 2D simulations. Validation against experimental data demonstrates that our model accurately captures key physical phenomena in hyperelastic composites, such as spontaneous crack initiation and propagation, interface debonding, crack coalescence, and the formation of non-smooth crack surfaces. Crucially, this framework is versatile and adaptable to a wide range of engineered composite systems with different inclusions and matrices, making it a powerful tool for predicting and analyzing large deformation behaviors in various advanced applications.

Optimizing extraction of collagen from hazardous leather shaving dusts by crude protease enzyme: Chrome recovery and reuse for leather rechroming

This study explores an eco-friendly method to achieve 99.98 % dechroming (reducing Cr from 7700 to 4.71 ppm) of Chrome-tanned leather shaving dust (CSD), through enzymatic hydrolysis using crude protease. The enzyme exhibited strong activity (>40 U mL−1) across a temperature range of 50 °C to 65 °C and a pH range of 5 to 11. The pH of the mixture containing CSD, water, MgO, and enzyme was maintained at 8.9 during hydrolysis. The temperature, hydrolysis time, and enzyme concentration were optimized using response surface methodology (RSM). Under optimal conditions of 17.57 % enzyme concentration, 62.01 °C, and a hydrolysis time of 7 h, the predicted collagen yield was 31.66 %. Laboratory experiments conducted with these predicted parameters resulted in a collagen recovery of 30.9 %, achieving an accuracy of 97.6 % compared to the predictions. In contrast, hydrolysis with MgO alone achieved only 87.01 % dechroming with about 2 % collagen yield, highlighting the enzyme's superior effectiveness. The enzymatic cleavage of peptide bonds in the leather matrix enhanced collagen solubilization. Characterization techniques, including FT-IR, NMR, XRD, DSC, and TGA, confirmed successful extraction and purity, with the collagen being rich in glycine and proline. Cytotoxicity results demonstrated its non-toxic nature, indicating potential applications in biomedical fields, tissue engineering, food, pharmaceuticals, and cosmetics. The recovered chromium sulfate was reused for rechroming goat wet blue leather, with chrome content in crust leather treated with recovered basic chromium sulfate (rBCS) and fresh basic chromium sulfate (BCS) being 3.23 % and 3.35 %, respectively, both meeting industry standards for goat crust leather. SynopsisAn eco-friendly method for dechroming chrome-tanned leather shaving dust (CSD) and extracting collagen was established. The process achieved 99.98 % dechroming and 30.9 % collagen yield using crude protease enzyme. Characterization confirmed the collagen's purity, and recovered chromium sulfate was reused for rechroming goat leather, meeting industry standards.

Integrating machine learning for predicting biomechanical properties of layered manufactured bone implants

AbstractThe layered manufacturing (LM) based locking bone plates (LBPs) are highly favored for biomedical applications due to its implant customization flexibility, control over porosity, and functional parameters. Design of experiments mainly relies on predefined experimental run orders; however, machine learning (ML) can handle complex and nonlinear relationships for predicting output responses. This research investigates the application of various ML models for predicting the impact strength, torque values, and punch shear strength of poly lactic acid (PLA) based LBPs. A dataset of 100 data points for each response variable was developed, analyzing the influence of printing parameters, like infill density (ID), layer height (LH), wall thickness (WT), and print speed (PS). The ID, LH, WT, and PS were varied within the ranges of 20%–100%, 0.1–0.5 mm, 0.4–1.2 mm, and 20–100 mm/s, respectively. Among the ML models evaluated, XGBoost and Adaboost exhibited strong alignment of the predicted values with actual values. The decision tree regression, showed the lowest predictive accuracy due to its tendency to overfit and lack of iterative refinement. The findings suggest that ML models can effectively predict the mechanical properties of PLA‐based LBPs, offering valuable insights for optimizing printing parameters to enhance the performance of orthopedic implants. The findings can guide biomedical engineers in making data‐driven decisions to enhance the strength of LBPs, thereby improving patient outcomes in orthopedic treatments.Highlights Predicted impact strength, torque, and shear strength of biomedical specimens. Evaluated 100 data points per variable to assess printing parameters' influence. XGBoost and Adaboost showed superior accuracy, with R2 consistently above 0.9. Decision Tree regression exhibited the lowest accuracy due to overfitting issues. Aims to guide biomedical engineers in data‐driven decisions for better patient outcomes.

Integrating an Annotation Project in a Senior-Level Biomedical Engineering Course to Develop Primary Literature Analysis and Science Communication Skills

Abstract Purpose This paper describes a senior-level biomedical engineering course designed to educate students on primary literature analysis and effective scientific communication. The course integrates elements from both disciplines through in-class discussions of primary research articles and an innovative annotation project utilizing the Science in the Classroom (SitC) program. Methods The course incorporates weekly discussions of seminal research articles led by the instructor in the field of biomedical microdevices, bi-weekly quizzes to assess comprehension, and a novel final project where student groups collaborate to annotate a high-impact scientific article through the SitC program. The annotations aim to make the article accessible to a broader audience while training the students in primary literature analysis and effective scientific communication. Results The course effectively enhances student understanding of primary literature, as evidenced by the significant improvement in quiz scores following in-class discussions. Additionally, student participation in the annotation project contributes valuable educational resources to the SitC program, promoting the dissemination of scientific knowledge to a wider audience. Conclusions The novel aspects of the course, specifically the annotation project, offer a unique learning experience by equipping students with the necessary skills to navigate and critically analyze primary research articles. The course also fosters effective scientific communication, transforming students into active contributors to the scientific community. Recommendations are included for instructors interested in adopting this novel approach to teaching primary literature analysis and scientific communication.

Building Biomedical Engineering Education, Research, and Innovation at HBCUs (NIBIB)

Building Biomedical Engineering Education, Research, and Innovation at HBCUs (NIBIB)

Enhancing biocompatibility and degradation control of biodegradable polymers through amino acid grafting: A study on 4arm-PLGA-Amino acid copolymers

Non-degradable polymers, when retained in the body for long periods, can cause chronic inflammation and immune responses. This issue increases the costs related to secondary surgeries and overall healthcare. Conversely, while biodegradable polymers such as PLGA offer an alternative, their degradation rate is challenging to precisely control. Additionally, their mechanical properties deteriorate during the degradation process, and the degradation by-products may lead to localized acidification or other adverse reactions. In this study, we introduce a new biodegradable copolymer called 4arm-PLGA-Amino acid (4aPLGA-Amino acid). This copolymer is synthesized by grafting amino acids onto 4arm-PLGA (4aPLGA) using triphosgene, which is both environmentally friendly and cost-effective. The incorporation of amino acids aims to enhance the biocompatibility and functionality of the copolymer. By varying the types of amino acids grafted, the degradation rate of 4aPLGA can be modulated, thereby improving its potential for applications in drug delivery systems. This modification also promotes cell attachment and proliferation, making it suitable for tissue engineering and regenerative medicine. The study demonstrated that 4aPLGA-Amino acid significantly improved biocompatibility, degradation control, and mechanical properties, broadening its applicability in various biomedical engineering fields.

Monitoring of lung stiffness for long-COVID patients using magnetic resonance elastography (MRE)

PurposeTransaxial CT imaging is the main clinical imaging modality for the assessment of COVID-induced lung damage. However, this type of data does not quantify the functional properties of the lung. The objective is to provide non-invasive personalized cartographies of lung stiffness for long-COVID patients using MR elastography (MRE) and follow-up the evolution of this quantitative mapping over time. MethodsSeven healthy and seven long-COVID participants underwent CT and MRE imaging at total lung capacity. After CT test, a senior radiologist visually analyzed the lung structure. Less than one month later, a first MRI (1.5 T, GRE sequence) lung density test followed by a first MRE (SE-EPI sequence) test were performed. Gadolinium-doped water phantom and a pneumatic driver (vibration frequency: 50 Hz), placed on the sternum, were used for MRI and MRE tests, respectively. Personalized cartographies of the stiffness were obtained, by two medical imaging engineers, using a specific post processing (MMDI algorithm). The monitoring (lung density, stiffness) was carried out no later than 11 months for each COVID patient. Wilcoxon's tests and an intra-class correlation coefficient (ICC) were used for statistical analysis. ResultsThe density for long-COVID patients was significantly (P = 0.047) greater (170 kg.m−3) compared to healthy (125 kg.m−3) subjects. After the first MRE test, the stiffness measured for the healthy subjects was in the same range (median value (interquartile range, IQR): 0.93 (0.09) kPa), while the long-COVID patients showed a larger stiffness range (from 1.39 kPa to 2.05 kPa). After a minimum delay of 5 months, the second MRE test showed a decrease of stiffness (from 22 % to 40 %) for every long-COVID patient. The inter-operator agreement was excellent (intra-class correlation coefficient: 0.93 [0.78–0.97]). ConclusionThe MRE test is sensitive enough to monitor disease-induced change in lung stiffness (increase with COVID symptoms and decrease with recovery). This non-invasive modality could yield complementary information as a new imaging biomarker to follow up long-COVID patients.

Monitoring Metacognitive Strategies Use During Interaction Collaborative Groups

This study of the use of metacognitive strategies during learning interaction in collaborative groups is considered referential for understanding the development of such strategies. The use of metacognitive strategies is related to better learning outcomes. We monitored the interaction in five collaborative groups at three points in time (initial, intermediate and final). These groups consisted of undergraduate health science (n = 9) and biomedical engineering students (n = 10). The aims were the following: (1) To check whether there were significant differences in the use of metacognitive strategies between the groups of students monitored depending on the type of degree or the point in time measurement. (2) To test whether there were significant differences in students’ learning outcomes depending on whether or not they had participated in the interaction monitoring during collaborative interaction. (3) To test clustering without a pre-assignment variable against learning outcomes in collaborative groups. Significant differences were detected in the use of some of the metacognitive strategies of orientation, planning and elaboration in favor of the collaborative groups of the biomedical engineering students. Also, students who participated in monitoring collaborative groups in both clusters obtained better learning outcomes. Lastly, the assignment groups were exactly related to the collaboration groups with respect to the use of metacognitive strategies in the final measurement.

Preface

Between June 6 and 7, 2024, the Technical University of Cluj-Napoca hosted the second edition of the scientific conference ICEMS-BIOMED - International Conference on Electromagnetic Fields, Signals and BioMedical Engineering - dedicated to the research in electrical and biomedical engineering. Organized under the aegis of EUT+ and based on the collaborative relations between prestigious academic institutions from Romania (Technical University of Cluj-Napoca, „Nicolae Bălcescu” Land Forces Academy of Sibiu and „Ştefan cel Mare” University of Suceava), the 2024 edition enjoyed the presence of researchers from 8 European countries: Spain, Greece, Poland, Latvia, Austria, Switzerland, Czech Republic and Romania. As in the case of the previous edition, the conference was held in a hybrid format; following the evaluation process, out of a total of 40 papers initially uploaded on the site, only 27 were presented, the 13 rejected papers representing a percentage of 32.5%. The Conference was attended by 4 invited speakers, who held lectures of forty minutes each, the rest of the papers being presented exclusively orally. In this way, a total of 27 papers were presented: 4 by invited speakers and 23 regular papers. The main objective of this scientific event was to create emulation in the academic world in topics of great relevance and interest and to offer an opportunity to young researchers to present their newest results in approaching the following topics: • Electromagnetic Fields • Electromagnetic Compatibility • Interactions between Electromagnetic Waves and Biological Systems • Biomedical Engineering • Modern Solutions for Diagnostics and Treatment The event took place in the new conference center of the Technical University of Cluj-Napoca (UTC-N Hub) and in the presentation room there were about 50 people: authors (senior researchers or doctoral students), master’s students, sponsors and guests. The regular presentations took place over a period of 15 minutes - 10 minutes for the presentation itself and 5 minutes for discussions and questions and the online presentations were made through the Teams platform. This scientific event was made possible thanks to the efforts of generous people, who are grouped into the following committees: List of Steering committee, Conference Co-Chairs, Scientific and Reviewers Committee and Organizing Committee are available in this Pdf.

Solidification of Polyurethane Model Foams via Catalyst Drainage from a Secondary Foam.

Due to their unique mechanical and thermal properties, polyurethane foams are widely used in multiple fields of applications, including cushioning, thermal insulation or biomedical engineering. However, the way polyurethane foams are usually manufactured - via chemical foaming - produces samples where blowing and gelling occur at the same time, resulting in a morphology control achieved by trial and error processes. Here, a novel strategy is introduced to build model homogeneous polyurethane foams of controlled density with millimetric bubbles from liquid templates. By producing a polyurethane foam via physical bubbling without a catalyst and gently depositing a secondary foam containing catalyst on the top of this first foam, it is possible to take advantage of drainage mechanisms to trigger the solidification of the bottom foam. The characterization of the samples performed by X-ray microtomography allows to study quantitatively the structure of the final solid foam, at the global and at the local scale. Using the tomographic 3D images of the foam architectures, the superimposed foam technique introduced in this article is shown to be promising to produce foams with a good homogeneity along the vertical direction, with a density controlled by varying the concentration of catalyst in the secondaryfoam.

State-of-the-Art CFD Simulation: A Review of Techniques, Validation Methods, and Application Scenarios

This review examines recent advancements in Computational Fluid Dynamics (CFD) simulation, focusing on state-of-the-art techniques, validation methods, and their application across various fields. CFD techniques, including Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), Reynolds-Averaged Navier-Stokes (RANS), and hybrid methods, are compared in terms of computational demands, accuracy, and suitability for different fluid flow scenarios. Effective validation methods such as wind tunnel testing, analytical solutions, and field measurements are highlighted to ensure simulation reliability and align models with experimental data. Additionally, this review explores application scenarios in aerospace, automotive, environmental, and biomedical engineering, discussing how each field adapts CFD techniques to optimize performance and ensure realistic simulation outcomes. The comparative analysis offers insights into the trade-offs between accuracy and computational cost. It underscores the need for careful technique selection and robust validation to enhance CFD's applicability in both research and industry. The review aims to guide practitioners in selecting the most appropriate CFD approaches for their specific engineering challenges.

Comparing scissors and scalpels to a novel surgical instrument: a biomechanical sectioning study

BackgroundThis study introduces a novel surgical instrument to reduce iatrogenic nerve injuries during procedures such as carpal tunnel and ulnar nerve decompression surgery. These injuries often result from direct damage to surrounding tissues by surgical instruments, whose designs have remained largely unchanged over the past decades. The novel device is a modified surgical forceps that has a deployable surgical scalpel that runs along a groove on the forceps. This design protects important anatomical structures while allowing fast dissection and cutting of fascial layers.MethodsThe process used to develop a novel instrument included computer-aided design (CAD) modeling, 3D printing for prototyping, and the fabrication of an aluminum prototype. Biomechanical testing was performed with the novel device, iris scissors, bandage scissors, and a scalpel on an MTS Static Materials Test System. The peak force to slide-cut, number of cut attempts, and percentage cut on first attempt were compared between the prototype and traditional surgical tools. The materials cut in testing were Ace™ bandage, stockinette, and gauze. Statistical analyses were performed using Welch’s t-tests and Fisher’s exact tests.ResultsCompared to conventional bandage and iris scissors, the novel surgical instrument required significantly less force to cut through an Ace™ bandage, stockinette, and gauze (p < 0.01). The number of cuts required to transect those same materials with the novel device was comparable to that of the scalpel and bandage scissors. Additionally, while there were no differences between the novel device and the other devices for an Ace™ bandage and stockinette, the novel device tended to cut a greater percentage of gauze in one pass than did the iris scissors.ConclusionThe novel surgical instrument designed in this study required less force compared to conventional scissors, demonstrated cutting efficiency similar to that of a scalpel blade, and had more safety features than either instrument. This study highlights the value of collaboration between biomedical engineering and orthopedic surgery departments on innovation in medical technology, through which new technologies with improved design and functionality demonstrate the potential to reduce iatrogenic injuries.

Tailoring carrier-free nanoparticles based on natural small molecule assembly for synergistic anti-tumor efficacy

Interfacial modular assemblies of versatile polyphenols have attracted widespread interest in surface and materials engineering. In this study, natural polyphenol (tannic acid, TA) and nobiletin (NOB) can directly form binary carrier-free spherical nanoparticles (NT NPs) through synergistically driven by a variety of interactions (such as hydrogen bonding, oxidative reactions, etc.). The synthesis involves polyphenolic deposition on hydrophobic NOB nanoaggregates, followed by in situ oxidative self-polymerization. Interestingly, the assembled NT NPs exhibit controllable and dynamic changes in particle size during the initial stage. Ultimately, uniform and spherical NT NPs appear stable, with high loading capability, enabling incorporated NOB to preserve their function. Furthermore, in vitro evaluations demonstrate that the rational combination of polyphenol module and NOB can induce apoptosis and inhibit tumor metastasis for both lung cancer H1299 and human fibrosarcoma HT1080 cell lines. Notably, the optimized NT48 NPs were then verified in vivo experiments to achieve a promising synergistic anti-tumor efficacy. These findings not only provide new opportunities for the streamlined and sensible engineering of future polyphenol-based biomaterials, but also open up new prospects for the design of small-molecule nature phytochemicals.