Bioengineering最新文献

Background: Lingual orthodontic systems have recently advanced with the introduction of fully customized CAD/CAM-based designs featuring self-ligating (SL) mechanisms. This study aimed to evaluate the three-dimensional accuracy of a customized SL lingual system in reproducing digitally planned tooth positions. Methods: A total of 280 teeth were analyzed following treatment with a fully customized self-ligating lingual system (Harmony^®, Aso International Inc., Tokyo, Japan). Digital models obtained before treatment (T0), from the setup (TS), and after treatment (T1) were superimposed using a best fit algorithm in GOM Inspect. Tooth movements were quantified across seven biomechanically relevant parameters including tip, torque, rotation, buccolingual, mesiodistal, vertical, and overall displacement. Predicted and achieved movements were compared using paired t tests and Bland-Altman analysis. Results: The fully customized SL lingual appliance achieved an overall dentition accuracy of 92.1%. Mean accuracy for linear tooth movements was 94.5% ± 2.1% in the maxilla and 93.8% ± 2.5% in the mandible. For angular movements, mean accuracy was 90.8% ± 3.4% in the maxilla and 89.3% ± 3.9% in the mandible. The highest precision was observed in anterior teeth for mesiodistal (96.2%) and buccolingual (95.8%) movements, whereas the lowest accuracy occurred in rotational movements of the posterior segments (87.1%). No statistically significant differences were found between predicted and achieved movements for most parameters (p > 0.05). Conclusions: The fully customized SL lingual orthodontic system demonstrated high accuracy in reproducing digitally planned tooth movements, particularly in the anterior segments. Although accuracy was slightly lower in the posterior regions, the overall outcomes remained mechanically and clinically acceptable across all evaluated dimensions.

Mitral valve orifice area is a key functional metric that depends on complex geometric features, motivating a systematic assessment of the relative influence of these parameters. In this study, the mitral valve geometry is parameterized using twelve geometric variables, and a global sensitivity analysis based on Sobol indices is performed to quantify their relative importance. Because global sensitivity analysis requires many simulations, a Gaussian Process regressor is developed to efficiently predict the orifice area from the geometric inputs. Structural simulations of the mitral valve are carried out in Abaqus, focusing exclusively on the valve mechanics. The predicted distribution of orifice areas obtained from the Gaussian Process shows strong agreement with the ground-truth simulation results, and similar agreement is observed when only the most influential geometric parameters are varied. The analysis identifies a subset of geometric parameters that dominantly govern the mitral valve orifice area and can be reliably extracted from medical imaging modalities such as echocardiography. These findings establish a direct link between echocardiographic measurements and physics-based simulations and provide a framework for patient-specific assessment of mitral valve mechanics, with potential applications in guiding interventional strategies such as MitraClip placement.

Radiographically visible carotid artery calcifications are typically seen at the level of the C3-C4 cervical vertebrae and can be detected on panoramic dental radiographs. Their early identification is clinically relevant, as they represent a potential marker for increased risk of stroke. In this context, the present study proposes a deep learning method for automatic identification of carotid atheromas using MobileNetV2. From a publicly available dataset, 378 region-of-interest (ROI) images (640 × 320) were prepared and split into train/val/test = 264/57/57 with class counts train 157/107, val 34/23, test 34/23 (negatives/positives). Images underwent standardized preprocessing and on-the-fly augmentation; training used a two-stage scheme (backbone frozen "head" training followed by partial fine-tuning of the top layers), class-weighting, dropout = 0.3, batch normalization (BN) head, early stopping, and partial unfreezing (~70% of the backbone). The decision threshold was selected on validation by Youden's J. On the independent test set, the model achieved an accuracy (ACC) of 94.7%, sensitivity (SEN) of 95,7%, specificity (SPE) of 0.941, area under the receiver operating characteristic curve (AUC) 0.963, and area under the precision-recall curve (AUPRC) of 0.968. Using a sensitivity-targeted threshold (SEN ≈ 0.80), the model yielded ACC = 91.2%, SEN = 82.6%, and SPE = 97.1%. These results support panoramic radiographs as an opportunistic screening modality for systemic vascular risk and highlight the potential of artificial intelligence (AI)-assisted methods to enable earlier identification within preventive healthcare.

Knee osteoarthritis prognostic tools often target structural progression or surgery and require imaging or biomarker inputs that are not routinely available. Using Osteoarthritis Initiative data, we developed a fully clinical nomogram to estimate both the probability of long-term pain non-resilience (clinically important worsening) and, by complement, maintenance of acceptable pain in radiographic knee osteoarthritis. We included participants with radiographic knee osteoarthritis and complete worst-knee WOMAC pain scores at baseline, 24 and 48 months; non-resilience was defined as a ≥9-point increase on the 0-100 WOMAC pain scale over 4 years. A six-predictor Firth logistic regression model (age, body mass index, Kellgren-Lawrence grade, baseline pain, 0-24-month pain change and Center for Epidemiologic Studies Depression Scale score) was fitted and translated into a point-based nomogram. Among 2365 eligible participants, 527 (22.3%) were non-resilient. The model showed good performance, with optimism-corrected AUC 0.74 and Brier score 0.15, and decision-curve analysis indicated positive net benefit versus treat-none across 1-15% thresholds and small gains versus treat-all. Early pain worsening and higher depressive symptoms were the strongest predictors of non-resilience. This six-variable, clinic-ready nomogram provides a simple, well-calibrated tool for prognostic counseling and risk stratification in radiographic knee osteoarthritis and requires external validation before wider clinical use.

Wound dressing coverages (WDC) play a key role in protecting skin lesions and preventing infection. Polymeric membranes have been widely explored as WDC due to their ability to incorporate bioactive agents, including antimicrobial nanoparticles and non-steroidal anti-inflammatory drugs (NSAIDs). In this study, polycaprolactone (PCL)-based membranes functionalized with titanium dioxide nanoparticles (TiO₂ NPs) and ibuprofen (IBP) were fabricated using a film manufacturing approach, and their structural and biocompatibility profiles were evaluated. The membranes were characterized by SEM, FTIR and XPS. Bands at 1725 cm^-1, 2950 cm^-1, 2955 cm^-1, 2865 cm^-1 and 510 cm^-1 proved molecular stability of reagents during manufacture. In SEM, the control shows the flattest surface, while the PCL-IBP and PCL-IBP-TiO₂ NPs groups had increased rugosity. In vitro biocompatibility was evaluated using human fetal osteoblasts (hFOB). On day 3, the cell adhesion response of hFOB seeded in PCL-IBP and PCL-IBP-TiO₂ NPs groups showed the biggest absorbances (p = 0.0014 and p = 0.0491, respectively). On day 7 PCL-IBP group had lower lectin binding than the control (p = 0.007) and the PCL-IBP-TiO₂ NPs (p = 0.015) membranes, but no evidence of cytotoxicity was observed in any group. Furthermore, the Live/Dead test adds more biocompatibility evidence to conveniently discriminate between live and dead cells. The PCL polymeric membrane elaborated in this study may confer antiseptic, analgesic and anti-inflammatory properties, making these membranes ideal for skin lesions.

Background: General Movement Assessment is a strong early predictor of adverse neurodevelopmental outcomes but remains qualitative and examiner-dependent. Quantitative, video-based kinematic analysis may complement General Movement Assessment by providing objective, scalable metrics. Methods: In this pilot study, a computer-vision-based pipeline was used to extract trunk center-of-mass kinematics from video recordings of spontaneous General Movements in infants under three months corrected age during the Writhing and Fidgety stage. Two measures were derived: trunk quantity of motion and movement duration. Group differences were examined using t-tests and effect sizes, and associations with corrected age and sex were explored with correlation analyses. Results: Writhing Movements were substantially longer than Fidgety Movements, with a large effect size, whereas trunk quantity of motion did not differ meaningfully between movement types. Correlations between corrected age and both the quantity of motion and duration were small and imprecise. Sex did not moderate duration changes, but trunk motion showed a significant age-sex interaction effect. Conclusions: Video-based extraction of trunk kinematics is feasible in early infancy and reveals robust differences in GMs type duration between Writhing and Fidgety Movements. Larger longitudinal studies are needed to clarify the value of these measures as early quantitative markers of postural control and neuromotor development.

Background/Objectives: Effective removal of organic tissue extruded beyond the apex is crucial in regenerative endodontics, particularly in teeth with immature apices; therefore, this study aims to compare the efficacy of standard needle irrigation (SNI), ultrasonic irrigation (UI), photon-induced photoacoustic streaming (PIPS), and shock wave-enhanced emission photoacoustic streaming (SWEEPS) techniques in dissolving periapical tissue in a simulated model. Methods: Sixty single-rooted human premolars and sixty bovine palatal mucosa specimens were used. A custom model was created by placing mucosal tissue in contact with the apical area. Specimens were divided into four groups (n = 15) according to the irrigation method: SNI, UI, PIPS, and SWEEPS. Each canal received 15 mL of 2% NaOCl. Tissue samples were weighed before and after treatment. One-way ANOVA and Tukey's post hoc test were used for statistical analysis (p < 0.05). Results: UI showed significantly less tissue dissolution than the other methods (p < 0.05). SNI, PIPS, and SWEEPS showed no significant differences (p > 0.05). Conclusions: All methods led to tissue loss, but UI was significantly less effective. SNI, PIPS, and SWEEPS performed similarly.

This study aimed to evaluate the effects of laser photobiomodulation (PBM) therapy in SaOs-2 osteosarcoma cells treated with zoledronic acid (ZA), a bisphosphonate, in vitro, mimicking a bisphosphonate-related osteonecrosis of the jaw (BRONJ) situation. Cells were treated with 100 μM ZA for 24 h and subjected to PBM using wavelengths of 660 nm and 808 nm at energy delivered of 1, 5, 10, and 20 J. After 24 h, metabolic activity, apoptosis, and BAX and BCL-2 gene expression were analyzed. Data were compared using one-way ANOVA followed by Tukey's post hoc test (p < 0.05). ZA significantly reduced metabolic activity (p < 0.05), an effect attenuated by PBM at 808 nm with 1 J, while BCL-2 expression increased with 1 J at 660 nm and with 1 J and 20 J at 808 nm. However, PBM did not reverse ZA-induced apoptosis. In conclusion, PBM modulated the response of SaOs-2 osteoblastic cells treated with ZA in a wavelength- and dose-dependent manner. PBM at 808 nm and 1 J stimulated cell metabolic activity and upregulated BCL-2 expression, suggesting a potential protective effect against ZA-induced cytotoxicity.

Bio-electric fields-manifested as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG), and Electrooculogram (EOG)-are fundamental to modern medical diagnostics but often suffer from severe data imbalance, scarcity, and environmental noise. Generative Adversarial Networks (GANs) offer a powerful, nonlinear solution to these modeling hurdles. This review presents a comprehensive survey of GAN methodologies specifically tailored for bio-electric signal processing. We first establish a theoretical foundation by detailing GAN principles, training mechanisms, and critical structural variants, including advancements in loss functions and conditional architectures. Subsequently, the paper extensively analyzes applications ranging from high-fidelity signal synthesis and noise reduction to multi-class classification. Special attention is given to clinical anomaly detection, specifically covering epilepsy, arrhythmia, depression, and sleep apnea. Furthermore, we explore emerging applications such as modal transformation, Brain-Computer Interfaces (BCI), de-identification for privacy, and signal reconstruction. Finally, we critically evaluate the computational trade-offs and stability issues inherent in current models. The study concludes by delineating prospective research avenues, emphasizing the necessity of interdisciplinary synergy to advance personalized medicine and intelligent diagnostic systems.