Bioengineering最新文献

The management of segmental bone defects presents a complex reconstruction challenge for orthopedic surgeons. Current treatment options are limited by efficacy across the spectrum of injury, morbidity, and cost. Regional gene therapy is a promising tissue engineering strategy for bone repair, as it allows for local implantation of nucleic acids or genetically modified cells to direct specific protein expression. In cell-based gene therapy approaches, a variety of different cell types have been described including mesenchymal stem cells (MSCs) derived from multiple sources-bone marrow, adipose, skeletal muscle, and umbilical cord tissue, among others. MSCs, in particular, have been well studied, as they serve as a source of osteoprogenitor cells in addition to providing a vehicle for transgene delivery. Furthermore, MSCs possess immunomodulatory properties, which may support the development of an allogeneic "off-the-shelf" gene therapy product. Identifying an optimal cell type is paramount to the successful clinical translation of cell-based gene therapy approaches. Here, we review current strategies for the management of segmental bone loss in orthopedic surgery, including bone grafting, bone graft substitutes, and operative techniques. We also highlight regional gene therapy as a tissue engineering strategy for bone repair, with a focus on cell types and cell sources suitable for this application.

A novel, portable chemiluminescence (CL) sensing platform powered by deep learning and smartphone integration has been developed for cost-effective and selective glucose detection. This platform features low-cost, wax-printed micro-pads (WPµ-pads) on paper-based substrates used to construct a miniaturized CL sensor. A 3D-printed black box serves as a compact WPµ-pad sensing chamber, replacing traditional bulky equipment, such as charge coupled device (CCD) cameras and optical sensors. Smartphone integration enables a seamless and user-friendly diagnostic experience, making this platform highly suitable for point-of-care (PoC) applications. Deep learning models significantly enhance the platform's performance, offering superior accuracy and efficiency in CL image analysis. A dataset of 600 experimental CL images was utilized, out of which 80% were used for model training, with 20% of the images reserved for testing. Comparative analysis was conducted using multiple deep learning models, including Random Forest, the Support Vector Machine (SVM), InceptionV3, VGG16, and ResNet-50, to identify the optimal architecture for accurate glucose detection. The CL sensor demonstrates a linear detection range of 10-1000 µM, with a low detection limit of 8.68 µM. Extensive evaluations confirmed its stability, repeatability, and reliability under real-world conditions. This deep learning-powered platform not only improves the accuracy of analyte detection, but also democratizes access to advanced diagnostics through cost-effective and portable technology. This work paves the way for next-generation biosensing, offering transformative potential in healthcare and other domains requiring rapid and reliable analyte detection.

A total of 75% of the oxygen humans inhale is exhaled without being utilized. To help organisms better utilize oxygen in exercise training, we designed the singlet oxygen energy generator (SOEG), a device that converts ambient air into energy-rich oxygen. The SOEG comprises an LED light source, a photosensitizer kit, and an air pump. Based on the principle of photosynthesis, the photosensitizer activates oxygen to produce excited-state singlet oxygen under the irradiation of light, which releases about 94 kJ/mol of singlet oxygen energy (SOE) after the relaxation process. After comparing data from 14 volunteers, we found that inhaling SOE during exercise significantly reduces energy consumption during running, decreases oxygen uptake, and improves running efficiency. At the same time, SOE effectively lowers blood lactate levels and improves oxygen utilization, indicating that SOE may enhance endurance and efficiency during exercise.

Fatigue management is critical for high-risk professions such as pilots, firefighters, and healthcare workers, where physical and mental exhaustion can lead to catastrophic accidents and loss of life. Traditional fatigue assessment methods, including surveys and physiological measurements, are limited in real-time monitoring and user convenience. To address these issues, this study introduces a novel contactless fatigue level diagnosis system leveraging multimodal sensor data, including video, thermal imaging, and audio. The system integrates non-contact biometric data collection with an AI-driven classification model capable of diagnosing fatigue levels on a 1 to 5 scale with an average accuracy of 89%. Key features include real-time feedback, adaptive retraining for personalized accuracy improvement, and compatibility with high-stress environments. Experimental results demonstrate that retraining with user feedback enhances classification accuracy by 11 percentage points. The system's hardware is validated for robustness under diverse operational conditions, including temperature and electromagnetic compliance. This innovation provides a practical solution for improving operational safety and performance in critical sectors by enabling precise, non-invasive, and efficient fatigue monitoring.

Anaerobic digestion (AD) is a promising and yet a complex waste-to-energy technology. To optimize such a process, precise modeling is essential. Developing complex, mechanistically inspired AD models can result in an overwhelming number of parameters that require calibration. This study presents a novel approach that considers the role of trace metals (Ca, K, Mg, Na, Co, Cr, Cu, Fe, Ni, Pb, and Zn) in the modeling, numerical simulation, and optimization of the AD process in a batch bioreactor. In this context, BioModel is enhanced by incorporating the influence of metal activities on chemical, biochemical, and physicochemical processes. Trace metal-related parameters are also included in the calibration of all model parameters. The model's reliability is rigorously validated by comparing simulation results with experimental data. The study reveals that perturbations of 5% in model parameter values significantly increase the discrepancy between simulated and experimental results up to threefold. Additionally, the study highlights how precise optimization of metal additives can enhance both the quantity and quality of biogas production. The optimal concentrations of trace metals increased biogas and CH₄ production by 5.4% and 13.5%, respectively, while H₂, H₂S, and NH₃ decreased by 28.2%, 43.6%, and 42.5%, respectively.

CD44, a pivotal cell surface molecule, plays a crucial role in many cellular functions, including cell-cell interactions, adhesion, and migration. It serves as a receptor for hyaluronic acid and is involved in lymphocyte activation, recirculation, homing, and hematopoiesis. Moreover, CD44 is a commonly used cancer stem cell marker associated with tumor progression and metastasis. The development of CD44 aptamers that specifically target CD44 can be utilized to target CD44-positive cells, including cancer stem cells, and for drug delivery. Building on the primary sequences of our previously selected thioaptamers (TAs) and observed variations, we developed a bead-based X-aptamer (XA) library by conjugating drug-like ligands (X) to the 5-positions of certain uridines on a complete monothioate backbone. From this, we selected an XA with high affinity to the CD44 hyaluronic acid binding domain (HABD) from a large combinatorial X-aptamer library modified with N-acetyl-2,3-dehydro-2-deoxyneuraminic acid (ADDA). This XA demonstrated an enhanced binding affinity for the CD44 protein up to 23-fold. The selected CD44 X-aptamers (both amine form and ADDA form) also showed enhanced binding affinity to CD44-overexpressing human ovarian cancer IGROV cells. Secondary structure predictions of CD44 using MFold identified several binding motifs and smaller constructs of various stem-loop regions. Among our identified binding motifs, X-aptamer motif 3 and motif 5 showed enhanced binding affinity to CD44-overexpressing human ovarian cancer IGROV cells with ADDA form, compared to the binding affinities with amine form and scrambled sequence. The effect of ADDA as a binding affinity enhancer was not uniform within the aptamer, highlighting the importance of optimal ligand positioning. The incorporation of ADDA not only broadened the XA's chemical diversity but also increased the binding surface area, offering enhanced specificity. Therefore, the strategic use of site-directed modifications allows for fine-tuning aptamer properties and offers a flexible, generalizable framework for developing high-performance aptamers that target a wide range of molecules.

Background: Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs' storage and query performance based on distributed graph databases. This study evaluates the feasibility and performance of distributed graph databases in managing large-scale drug knowledge graphs.

Methods: First, a drug knowledge graph storage and query system is designed based on the Nebula Graph database. Second, the system's writing and query performance is evaluated. Finally, two drug repurposing benchmarks are used to provide a more extensive and reliable assessment.

Results: The performance of distributed graph databases surpasses that of single-machine databases, including data writing, regular queries, constrained queries, and concurrent queries. Additionally, the advantages of distributed graph databases in writing performance become more pronounced as the data volume increases. The query performance benefits of distributed graph databases also improve with the complexity of query tasks. The drug repurposing evaluation results show that 78.54% of the pathways are consistent with currently approved drug treatments according to repoDB. Additionally, 12 potential pathways for new drug indications are found to have literature support according to DrugRepoBank.

Conclusions: The proposed system is able to construct, store, and query a large graph of multisource drug knowledge and provides reliable and explainable drug-disease paths for drug repurposing.

In image-guided surgery (IGS) practice, combining intraoperative 2D X-ray images with preoperative 3D X-ray images from computed tomography (CT) enables the rapid and accurate localization of lesions, which allows for a more minimally invasive and efficient surgery, and also reduces the risk of secondary injuries to nerves and vessels. Conventional optimization-based methods for 2D X-ray and 3D CT matching are limited in speed and precision due to non-convex optimization spaces and a constrained searching range. Recently, deep learning (DL) approaches have demonstrated remarkable proficiency in solving complex nonlinear 2D-3D registration. In this paper, a fast and robust DL-based registration method is proposed that takes an intraoperative 2D X-ray image as input, compares it with the preoperative 3D CT, and outputs their relative pose in x, y, z and pitch, yaw, roll. The method employs a dual-channel Swin transformer feature extractor equipped with attention mechanisms and feature pyramid to facilitate the correlation between features of the 2D X-ray and anatomical pose of CT. Tests on three different regions of interest acquired from open-source datasets show that our method can achieve high pose estimation accuracy (mean rotation and translation error of 0.142° and 0.362 mm, respectively) in a short time (0.02 s). Robustness tests indicate that our proposed method can maintain zero registration failures across varying levels of noise. This generalizable learning-based 2D (X-ray) and 3D (CT) registration algorithm owns promising applications in surgical navigation, targeted radiotherapy, and other clinical operations, with substantial potential for enhancing the accuracy and efficiency of image-guided surgery.

This study introduced a novel force-driven automated staging design method for clear aligners, aimed at enhancing treatment planning efficiency and outcomes. The method simplified the alignment process into a force-driven mechanics model that calculates forces and moments exerted on teeth while adhering to Newton's third law, determining their displacement and rotation at each position. An optimal path was generated by iteratively moving teeth from their initial to target positions and subsequently divided into stages based on a predefined step size. The algorithm was implemented in C++ and incorporated into the WebGL-based SmarteeCheck3.0 software for visualization. In a maxillary extraction case, the automated staging method (0.25 mm step size) generated 51 stages in merely 5 s, while manual staging (>0.25 mm step size) necessitated 30 min to achieve 55 stages. In a molar distalization case, the automated method demonstrated similar efficiency advantages, generating 30 stages for the maxilla and 34 for the mandible, compared to 41 stages each in manual staging. The automated staging approach yielded shorter and more precise tooth movement paths that adhered to aligner biomechanics and physical principles, surpassing the limitations of manual staging. For cases requiring entire arch displacement, the method incorporated sequential movements with anchorage control to maintain force equilibrium. This innovative method substantially improved design efficiency and accuracy, ultimately elevating the efficacy of clear aligner therapy, although further biomechanical analyses and experimental validations are needed to refine the model parameters.

Knee joint stability comprises passive (ligaments), active (muscles), and static (articular congruency) contributors. The stability of total knee replacement (TKR) implants can be assessed pre-clinically using joint motion simulators. However, contemporary testing methods with these platforms do not accurately reproduce the biomechanical contributions of passive stabilizers, active stabilizers, or both. A key component of joint stability is therefore missing from laxity tests. A recently developed muscle actuator system (MAS) pairs the quadriceps-driven motion capabilities of an Oxford knee simulator with the prescribed displacements and laxity testing methods of a VIVO robotic knee testing system, which also includes virtual ligament capabilities. Using a TKR-embedded non-cadaveric joint analogue, TKR with two different virtual ligament models were compared to TKR with no active ligaments. Laxity limits were then obtained for both developed models using the conventional style of laxity testing (the VIVO's force/displacement control) and compared with results obtained under similar conditions with the MAS (gravity-dependent muscle control). Differences in joint control methods identified the need for muscle forces providing active joint stability, while differences in the effects of the virtual ligament models identified the importance of physiological representations of collateral ligaments during testing.