Bioengineering最新文献

Accurate segmentation of the intercondylar notch of the femur is of great significance for the diagnosis of knee joint diseases, surgical planning, and anterior cruciate ligament (ACL) reconstruction. Among them, the obvious anatomical heterogeneity, the interference of structurally similar tissues, and the blurred boundaries in MRI images make the segmentation of the intercondylar notch challenging. The segmentation of the intercondylar notch is often regarded as a standard semantic segmentation problem, but doing so leaves the inherent high-order internal variation and low-contrast features of its anatomical structure unresolved. We proposed a new Wavelet Multi-scale Contextual Attention Network (WMCA-Net). We have coordinated the Shallow High-frequency Feature Dense Extraction Block (SHFDEB) and Wavelet Split and Fusion Block (WSFB) modules with each other. The SHFDEB intensively extracts high-frequency detailed features at the shallowest layer of the network, while the WSFB effectively splits and fuses features at various resolutions, suppressing noise while better preserving the high-frequency detailed structural information we need. The Multi-scale Depth-wise Convolution Block (MDCB) captures cross-scale features from the narrow intercondylar notch (5-8 mm wide) to the surrounding femoral structure (approximately 50 mm diameter), dynamically adapting to different morphologies, including pathological changes caused by osteophyte formation. The Contextual-Weighted Attention Module (CWAM) establishes long-term semantic associations between fuzzy regions and clear anatomical landmarks by precisely locating uncertain regions through foreground and background decomposition. The Dice Similarity Coefficient of WMCA-Net on the intercondylar notch dataset is 93.16%, and the 95% Hausdorff Distance is 1.42 mm, demonstrating its advanced segmentation performance and good anatomical adaptability.

This study presents a finite element (FE) investigation of intervertebral disc (IVD) degeneration in the human lumbar spine (L1-L3 segment). The model, based on CT-derived geometry and isotropic hyperelastic representation of disc tissues, incorporates controlled simplifications, detailed in the limitations section. Degenerative changes were parametrically simulated across healthy, mild, moderate, and severe stages by reducing disc height (up to 60%), nucleus pulposus volume (up to 70%), and adjusting tissue stiffness to reflect dehydration and fibrosis. Displacement-controlled compressive loading was applied to assess von Mises stress distributions, reaction forces, and load transfer mechanisms. Results indicate significant load redistribution: annulus fibrosus stresses increased by up to 175% in severe degeneration, while nucleus pulposus stresses decreased by ~70%, indicating a diminished compressive load-bearing contribution of the nucleus. Model predictions were validated against cadaveric and in vivo data, confirming trends in intradiscal pressure (IDP) reductions (40-70%) and stress elevations. The parametric framework elucidates interactions between geometric and material changes, providing clinicians with insights into degeneration progression and guiding biomedical engineers in implant design and interventions.

This study presents a fully automatic hybrid framework for circle detection and geometric feature extraction from anteroposterior (AP) X-ray images. Detecting circular structures in X-ray imagery is challenging due to low contrast, noise, and metal-induced artifacts, which often limit the robustness of purely learning-based or purely geometric approaches. To address these challenges, a hybrid deep learning and computer vision pipeline is proposed that combines data-driven region localization with robust geometric fitting. A YOLOv5-based detector is first employed to identify a compact region of interest (ROI) containing circular components. Within this ROI, edge-based processing using Canny detection is applied, followed by an Edge-Snap refinement stage and robust RANSAC-based circle fitting with a Hough-transform fallback to ensure anatomically plausible circle estimation. The resulting circle centers and radii provide stable geometric parameters that can be consistently extracted across images with varying contrast, noise levels, and prosthesis appearances. The applicability of the proposed framework is demonstrated through a case study on hip prosthesis wear analysis, where the automatically detected circle parameters are used to compute medial, superior, and resultant displacement components using established two-dimensional radiographic formulations. Experimental evaluation on AP hip radiographs shows that the YOLOv5 detector achieves high ROI localization performance (mAP@0.5 = 0.971) and that the hybrid pipeline produces consistent circle parameters across longitudinal image sequences. Overall, the proposed method provides an end-to-end automatic solution for robust circle detection in X-ray imagery, with hip prosthesis wear presented solely as a case study without clinical or diagnostic claims.

Background: Periapical pathosis in periapical radiographs must be properly diagnosed for the success of endodontic treatment but is often muddled by 2D imaging limitations and subjective interpretation. Artificial intelligence (AI) offers a solution, but whether the diagnostic granularity of AI versus human clinicians in everyday clinical practice has been adequately explored remains to be addressed. The purpose of this study was to evaluate the diagnostic accuracy of ChatGPT-5 in detecting periapical radiographic abnormalities compared with the three-expert consensus reference standard. Methods: In this diagnostic accuracy retrospective study, 270 periapical radiographs were independently read by a large language model (ChatGPT-5) and a three-board-certified oral radiologist consensus. The AI was given a standardized prompt to label radiographic features, like the presence of periapical radiolucency, border, shape, and integrity of lamina dura. Diagnostic accuracy, agreement (Cohen's κ), and predictors of correct AI classification were compared with the expert consensus reference standard. Results: ChatGPT-5 demonstrated high sensitivity (87.5%) but low specificity (12.5%), resulting in an overall diagnostic accuracy of 50.0%. This performance profile reflects a tendency toward over-identification of pathology, with the model classifying 87.5% of radiographs as abnormal compared with 50.0% by expert consensus. Agreement was almost perfect for anatomical localization (arch, κ = 0.857) but poor for binary abnormality detection (κ = 0.000). For morphological descriptors, statistically significant disagreement was observed for lesion border characterization (κ = 0.127; p < 0.001), whereas lesion shape demonstrated only descriptive divergence without reaching statistical significance (κ = 0.359). Root resorption assessment also differed significantly between evaluators (p = 0.046). Regression analysis showed that well-defined corticated borders (OR = 60.25, p < 0.001) and first molar-associated lesions (OR = 32.55, p < 0.001) were significant predictors of correct AI classification. Conclusions: This study demonstrates that while ChatGPT-5 Vision can visually interpret periapical radiographs with high sensitivity, limited specificity and inconsistent morphological feature characterization restrict its reliability for independent clinical diagnosis. The AI system tends to over-diagnose systematically and categorizes lesions more structurally and defined compared to dental experts. AI has the potential for being optimized as a sensitive first-screening test, but its findings must be validated by dental professionals to avoid false positives and ensure proper characterization.

Temperature fluctuations strongly affect microbial viability, often inducing adaptive responses. In this study, we employed the psychrophilic bacterium Bacillus mycoides 41-22 and its associated phage VMY22, originally isolated from the Mingyong Glacier, to investigate phage adaptability under varied temperature conditions. Through selective enrichment at 4 °C, 15 °C, 28 °C, and 32 °C, we observed clear differences in phage infectivity, as assessed by plaque assays, along with genomic mutations and protein structural changes. Notably, mutations predominantly occurred in functional genes (ATPase, endolysin), while the examined structural loci remained conserved. Homology modeling revealed distinct adaptations in protein tertiary structures corresponding to environmental temperatures, suggesting that phage evolution mainly affects post-adsorption processes. Our findings elucidate a novel mechanism of temperature-driven functional protein evolution among cold-adapted bacteriophages (phage) and providing insights into their potential applications in microbial ecology and biotechnology.

Intracortical microstimulation (ICMS) is a promising approach for visual prostheses. We recently proposed using retinal neuromorphic spike trains derived from visual images as ICMS pulse sequences, and preliminarily recorded cortical voltage-sensitive dye (VSD) responses to such stimulation. To examine whether these cortical responses contain image information, we explore the feasibility of machine-learning-based decoding. However, constructing such a decoder requires large-scale datasets linking visual images, spike trains, and cortical responses, which are not yet experimentally available. Therefore, we generated surrogate data with a Wiener-system model that simulates VSD responses of the visual cortex to ICMS pulse trains. A convolutional neural network trained on these synthetic datasets successfully reconstructed images from the simulated cortical responses. This simulation work serves as a proof-of-concept study, demonstrating the computational feasibility of estimating visual information contained in neuromorphic ICMS-evoked cortical activity and providing a foundation for future physiological validation.

This study investigated antibacterial dentures fabricated by peening titanium apatite onto a polymethyl methacrylate (PMMA) denture base resin using a peening device. The effects of different peening mass flow rates and total peening masses on the deposition and antibacterial properties of titanium apatite were investigated. Titanium apatite was peened onto PMMA specimens at mass flow rates of 1, 2, and 5 g/s, with total peening masses of 5, 10, and 15 g. The surface morphology, elemental distribution, and mass changes were analyzed before and after peening and after immersion and water rinsing. The antibacterial activity against Staphylococcus aureus was evaluated using a crystal violet assay. The results showed that reducing the peening mass flow rate increased the amount of titanium apatite transferred and enhanced the antibacterial properties, with the highest deposition achieved at 1 g/s. Varying the total peening mass did not significantly affect the deposition pattern or antibacterial activity. The arithmetic mean roughness of the denture base remained unchanged after peening, indicating its clinical applicability. In conclusion, peening titanium apatite onto PMMA at a lower mass flow rate enabled stronger bonding and incorporation of antibacterial properties, potentially contributing to the development of novel antibacterial denture base materials.

Orthopedic surgery is undergoing a transformation driven by artificial intelligence (AI), which is reshaping clinico-surgical decision-making. While the operative strategy and professional responsibility traditionally relied on the surgeon's intuition and manual skills, advanced algorithms now provide predictive, analytical, and procedural decision supports. This paradigm shift is redefining the concept of human error as well as the relationship between technological tools and human decision-makers. As a result, the foundational elements of the healthcare liability framework are being affected. This paper offers a narrative discussion on selected applications of artificial intelligence in orthopedic surgical practice, including patient risk stratification, surgical indication and prosthesis positioning, with a particular focus on the liability implications for healthcare professionals who rely on these systems in terms of therapeutic decision-making. The aim is then to provide a comprehensive medico-legal perspective within the highly regulated and high-risk field of biomedicine, acknowledging and critically assessing the roles and responsibilities of all stakeholders involved-patients, healthcare professionals, innovative technologies, healthcare organizations, and facility management-while balancing innovation, evidence-based practice, and accountability in healthcare delivery.

Traditional statistical approaches identify group-level associations between biomarkers and rupture status in intracranial aneurysms (IAs) but often miss nonlinear interactions at the patient level. Methods: The authors retrospectively analyzed 35 saccular IAs in 35 patients (57.1% ruptured) from a single center (2021-2023). Demographics, detailed morphology (e.g., neck width, aspect ratio, VERTI, irregular shape), and multi-site inflammatory/immune markers (CRP; complement C3/C4; IgA/IgG/IgM) were included. After preprocessing (min-max scaling; one-hot encoding), five algorithms (DT, AdaBoost, GBM, XGBoost, RF) were evaluated with stratified five-fold CV and class balancing via random oversampling. The primary model (Random Forest) was tuned with Optuna and explained using global feature importance and LIME. The results showed that baseline RF achieved CV ROC-AUC 0.81 and test ROC-AUC 0.92 (test accuracy 0.857). The tuned RF (with oversampling and Optuna) yielded a mean CV accuracy of 0.85 ± 0.09 and CV ROC-AUC of 0.98 ± 0.07 while maintaining test ROC-AUC of 0.92. The average precision on the test PR curve was 0.97. The most influential predictors combined inflammatory markers (CRP, C3, C4) with morphology (neck width, irregular shape). LIME revealed consistent local patterns: low A.CRP/C.CRP and lower C3/C4 favored Not-Broken, whereas higher CRP/complement with smaller neck and irregular shape pushed toward Broken classifications. It can be concluded that an interpretable machine learning (ML) pipeline captured clinically plausible, nonlinear interactions between inflammation and aneurysm geometry. Integrating explainable ML with conventional statistics may enhance rupture risk stratification, enable patient-level rationale, and inform personalized management. These results could significantly contribute to the quality of treatment for patients with intracranial aneurysms.

Background/objectives: Cervical degenerative disc disease (DDD) is associated with decreased disc height, spinal arthrosis, decreased spinal stability, neck pain (NP), and increased years living with disability and global disease burden.

Methods: A total of 64 patients (19 males, 45 females) between 23 and 77 years (mean age of 49.05 ± 3.34 years) presented to a private practice with NP and disability. Pre-treatment radiographs revealed decreased cervical curvature (ARA C2-C7) measuring -6.18 ± 3.06° (ideal is -42.0°), anterior head translation (Tz C2-C7) measuring 22.03 ± 2.39 mm (ideal is 0 mm), anterior cervical disc height (ADH C2-C7) measuring 3.68 ± 0.20 mm, and posterior cervical disc height (PDH C2-C7) measuring 3.21 ± 0.15 mm. Pre-treatment NP numeric rating scale (NRS) scored 6.66 ± 0.27, and neck disability index (NDI) scored 40.28 ± 1.42%, indicating moderate disability due to NP. Patients were treated using Chiropractic BioPhysics^® (CBP^®) Mirror Image^® spinal rehabilitation for mean values of 37.80 ± 2.44 treatment visits over 19.48 ± 3.89 weeks at a frequency of 2.89 ± 0.45 treatment visits per week.

Results: Post-treatment radiographs revealed improvements in ARA C2-C7 to -19.95 ± 3.05°, Tz C2-C7 to 12.11 ± 2.34 mm, ADH C2-C7 to 5.19 ± 0.21 mm, and PDH C2-C7 to 4.36 ± 0.16 mm. Post-treatment patient-reported outcomes showed improvements in NP NRS to 1.52 ± 0.26 and NDI to 12.66 ± 0.96, indicating minimal NP and disability.

Conclusions: CBP^® helps improve sagittal cervical spinal alignment and posture, which may help improve cervical disc height and NP and disability in adult patients with cervical DDD.