Bioengineering最新文献

Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction-volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20-24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30-100%), and FBP. In two blinded surveys, radiologists ranked eight reconstructions and assessed four using a 5-point Likert scale in arterial and portal venous phases. DLIR consistently outperformed other methods in SNR, CNR, image quality, image interpretation, structural differentiability and diagnostic certainty. Pixelshine performed comparably only to ASIR-V 50%. No significant differences were observed between junior and senior radiologists. In conclusion, DLIR-based techniques have the potential to establish a new benchmark in clinical CT imaging, offering superior image quality for tumor staging, enhanced diagnostic capabilities, and seamless integration into existing workflows without requiring an extensive learning curve.

Coronary artery disease (CAD) remains a major global health concern, significantly contributing to morbidity and mortality. This study aimed to investigate the co-occurrence patterns of diagnoses and comorbidities in CAD patients using a network-based approach. A retrospective analysis was conducted on 195 hospitalized CAD patients from a single hospital in Guangxi, China, with data collected on age, sex, and comorbidities. Network analysis, supported by sensitivity analysis, revealed key diagnostic clusters and comorbidity hubs, with hypertension emerging as the central node in the co-occurrence network. Unstable angina and myocardial infarction were identified as central diagnoses, frequently co-occurring with metabolic conditions such as diabetes. The results also highlighted significant age- and sex-specific differences in CAD diagnoses and comorbidities. Sensitivity analysis confirmed the robustness of the network structure and identified clusters, despite the limitations of sample size and data source. Modularity analysis uncovered distinct clusters, illustrating the complex interplay between cardiovascular and metabolic disorders. These findings provide valuable insights into the relationships between CAD and its comorbidities, emphasizing the importance of integrated, personalized management strategies. Future studies with larger, multi-center datasets and longitudinal designs are needed to validate these results and explore the temporal dynamics of CAD progression.

Biogenic hydroxyapatite is known for its osteoinductive potential due to its similarity to human bone and biocompatibility, but insufficient vascularization compared to autogenous bone during early implantation limits bone integration and osteogenesis. Fluorine has been shown to improve hydroxyapatite's mechanical properties and the coupling of osteogenic and angiogenic cells. In this study, fluorine-modified biogenic hydroxyapatite (FPHA) with varying fluorine concentrations was prepared and tested for its ability to promote vascularized osteogenesis. FPHA prepared in this study retained the natural porous structure of biological cancellous bone and released F^- ions when immersed in cell culture medium. The extraction solutions of FPHA0.25 and FPHA0.50 promoted the formation of capillary-like tubes by human umbilical vein endothelial cells (HUVECs), with FPHA0.25 significantly upregulating vegf mRNA and VEGF protein levels in co-cultured human bone marrow mesenchymal stem cells (HBMSCs). Additionally, FPHA0.25 and FPHA0.50 upregulated pdgf-bb mRNA and PDGF-BB protein levels in HUVECs. In vivo experiments using a rabbit cranial defect model demonstrated that FPHA0.25 promoted early bone formation and angiogenesis in the defect area, enhanced VEGF secretion, and increased PDGFR-β expression in endothelial and mesenchymal cells. These findings suggest that fluorine-modified biogenic hydroxyapatite with an optimal fluorine concentration (FPHA0.25) may offer a promising strategy to enhance the body's innate bone-healing potential by accelerating vascularization.

The use of artificial intelligence in orthodontics is emerging as a tool for localizing cephalometric points in two-dimensional X-rays. AI systems are being evaluated for their accuracy and efficiency compared to conventional methods performed by professionals. The main objective of this study is to identify the artificial intelligence algorithms that yield the best results for cephalometric landmark localization, along with their learning system. A literature search was conducted across PubMed-MEDLINE, Cochrane, Scopus, IEEE Xplore, and Web of Science. Observational and experimental studies from 2013 to 2023 assessing the detection of at least 13 cephalometric landmarks in two-dimensional radiographs were included. Studies requiring advanced computer engineering knowledge or involving patients with anomalies, syndromes, or orthodontic appliances, were excluded. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Newcastle-Ottawa Scale (NOS) tools. Of 385 references, 13 studies met the inclusion criteria (1 diagnostic accuracy study and 12 retrospective cohorts). Six were high-risk, and seven were low-risk. Convolutional neural networks (CNN)-based AI algorithms showed point localization accuracy ranging from 64.3 to 97.3%, with a mean error of 1.04 mm ± 0.89 to 3.40 mm ± 1.57, within the clinical range of 2 mm. YOLOv3 demonstrated improvements over its earlier version. CNN have proven to be the most effective AI system for detecting cephalometric points in radiographic images. Although CNN-based algorithms generate results very quickly and reproducibly, they still do not achieve the accuracy of orthodontists.

The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information about these datasets is more readily available, yet current methods struggle to generate tabular data solely from statistical inputs. This study addresses the gaps by introducing a novel approach that converts statistical data into tabular datasets using a modified Generative Adversarial Network (GAN) architecture. A custom loss function was incorporated into the training process to enhance the quality of the generated data. The proposed method is evaluated using fidelity and utility metrics, achieving "Good" similarity and "Excellent" utility scores. While the generated data may not fully replace real databases, it demonstrates satisfactory performance for training machine-learning algorithms. This work provides a promising solution for synthetic data generation when real datasets are inaccessible, with potential applications in medical data privacy and beyond.

Cell functionality, driven by remarkable plasticity, is strongly influenced by mechanical forces that regulate mesenchymal stem cell (MSC) fate. This study explores the biomechanical properties of jaw periosteal cells (JPCs) and induced mesenchymal stem cells (iMSCs) under different culture conditions. We cultured both JPCs and iMSCs (n = 3) under normoxic and hypoxic environments, with and without osteogenic differentiation, and on laminin- or gelatin-coated substrates. Using atomic force microscopy, we measured cellular elasticity and Young's modulus of calcium phosphate precipitates (CaPPs) formed under osteogenic conditions. Correlation analyses between cellular stiffness, quantity of CaPP deposition, and stiffness of formed CaPPs were evaluated. The results showed that iMSCs, despite their softer cellular consistency, tended to form CaPPs of higher elastic moduli than osteogenically differentiated JPCs. Particularly under normoxic conditions, JPCs formed stronger CaPPs with lower cellular stiffness profiles. Conversely, iMSCs cultivated under hypoxic conditions on laminin-coated surfaces produced stronger CaPPs while maintaining lower cellular stiffness. We conclude that JPCs and iMSCs display distinct biomechanical responses to culture conditions. While JPCs increase cellular stiffness during osteogenic differentiation, in particular under hypoxic conditions, iMSCs exhibit a decrease in stiffness, indicating a higher resistance to lower oxygen levels. In both cell types, a lower cellular stiffness profile correlates with enhanced mineralization, indicating that this biomechanical fingerprint serves as a critical marker for osteogenic differentiation.

Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification.

Electromyographic (EMG) sensors are essential tools for analyzing muscle activity, but traditional designs often face challenges such as motion artifacts, signal variability, and limited wearability. This study introduces a novel EMG sensor fabricated using Aerosol Jet Printing (AJP) technology that addresses these limitations with a focus on precision, flexibility, and stability. The innovative sensor design minimizes air interposition at the skin-electrode interface, thereby reducing variability and improving signal quality. AJP enables the precise deposition of conductive materials onto flexible substrates, achieving a thinner and more conformable sensor that enhances user comfort and wearability. Performance testing compared the novel sensor to commercially available alternatives, highlighting its superior impedance stability across frequencies, even under mechanical stress. Physiological validation on a human participant confirmed the sensor's ability to accurately capture muscle activity during rest and voluntary contractions, with clear differentiation between low and high activity states. The findings highlight the sensor's potential for diverse applications, such as clinical diagnostics, rehabilitation, and sports performance monitoring. This work establishes AJP technology as a novel approach for designing wearable EMG sensors, providing a pathway for further advancements in miniaturization, strain-insensitive designs, and real-world deployment. Future research will explore optimization for broader applications and larger populations.

Antibiotic therapy has been a common method for treating bacterial infections over the past century, but with the rise in bacterial resistance caused by antibiotic abuse, better control and more rational use of antibiotics have been increasingly demanded. At the same time, a journey to explore alternatives to antibiotic therapies has also been undertaken. Chitosan and its derivatives, materials with good biocompatibility, biodegradability, and excellent antibacterial properties, have garnered significant attention, and more and more studies on chitosan and its derivatives have been conducted in recent years. In this work, we aim to elucidate the biological properties of chitosan and its derivatives and to track their clinical applications, as well as to propose issues that need to be addressed and possible solutions to further their future development and application.

Introduction: This study reports of the use of a rigid-plate fixation (RPF) system designed for sternal closure after minimally invasive cardiac surgery (MICS).

Methods: This retrospective analysis included all patients undergoing MICS with RPF (Zimmer Biomet, Jacksonville, FL, USA) at our institution. We analyzed in-hospital complications, as well as sternal complications and sternal pain at discharge and at follow-up 7 to 14 months after surgery.

Results: Between June and December 2023, 12 patients underwent RPF during MICS, of which 9 patients were included in the study. The median (IQR) age was 64 years (63 to 71) and two patients (22%) were female. All patients underwent aortic valve replacement, with two patients (22%) undergoing concomitant aortic surgery. RPF was successfully performed in all patients. ICU and in-hospital stay were 1 day (1 to 1) and 9 days (7 to 13), respectively. Patients were first mobilized in the standing position on postoperative day 2 (2 to 2). Four patients (44%) required opiates on the general ward. In-hospital mortality was 0%. At discharge, rates of sternal pain, sternal instability or infection were 0%. After a follow-up time of 343.6 days (217 to 433), median pain intensity using the Visual Analog Scale was 0 (0 to 2). Forty-four percent (n = 4) of patients reported pain at rest. No sternal complications (sternal dehiscence, sternal mal-union, sternal instability, superficial wound infections and deep sternal wound infections) were reported.

Conclusions: In the evolving landscape of cardiac therapies with incentives to reduce surgical burden, RPF showed safety and feasibility. It might become an important tool for sternal closure in minimally invasive cardiac surgery.