Bioengineering最新文献

In the original publication [...].

(1) Background: Variations in intracellular and extracellular sodium levels have been hypothesized to serve as biomarkers for tumour characterization and therapeutic response. While previous research has explored the feasibility of 23Na-MRI, a comprehensive review of its clinical utility in breast cancer is lacking. This scoping review aims to synthesize existing literature on the potential role of 23Na-MRI in breast cancer diagnosis and treatment monitoring. (2) Methods: This review included English-language studies reporting on quantitative applications of 23Na-MRI in breast cancer. Systematic searches were conducted across PubMed, Emcare, Embase, Scopus, Google Scholar, Cochrane Library, and Medline. (3) Results: Seven primary studies met the inclusion criteria, highlighting the ability of 23Na-MRI to differentiate between malignant and benign breast lesions based on elevated total sodium concentration (TSC) in tumour tissues. 23Na-MRI also showed potential in early prediction of treatment response, with significant reductions in TSC observed in responders. However, the studies varied widely in their protocols, use of phantoms, field strengths, and contrast agent application, limiting inter-study comparability. (4) Conclusion: 23Na-MRI holds promise as a complementary imaging modality for breast cancer diagnosis and treatment monitoring. However, standardization of imaging protocols and technical optimization are essential before it can be translated into clinical practice.

Background: Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults. Early detection is crucial to reducing DR-related vision loss risk but is fraught with challenges. Manual detection is labor-intensive and often misses tiny DR lesions, necessitating automated detection.

Objective: We aimed to develop and validate an annotation-free deep learning strategy for the automatic detection of exudates and bleeding spots on color fundus photography (CFP) images and ultrawide field (UWF) retinal images.

Materials and methods: Three cohorts were created: two CFP cohorts (Kaggle-CFP and E-Ophtha) and one UWF cohort. Kaggle-CFP was used for algorithm development, while E-Ophtha, with manually annotated DR-related lesions, served as the independent test set. For additional independent testing, 50 DR-positive cases from both the Kaggle-CFP and UWF cohorts were manually outlined for bleeding and exudate spots. The remaining cases were used for algorithm training. A multiscale contrast-based shape descriptor transformed DR-verified retinal images into contrast fields. High-contrast regions were identified, and local image patches from abnormal and normal areas were extracted to train a U-Net model. Model performance was evaluated using sensitivity and false positive rates based on manual annotations in the independent test sets.

Results: Our trained model on the independent CFP cohort achieved high sensitivities for detecting and segmenting DR lesions: microaneurysms (91.5%, 9.04 false positives per image), hemorrhages (92.6%, 2.26 false positives per image), hard exudates (92.3%, 7.72 false positives per image), and soft exudates (90.7%, 0.18 false positives per image). For UWF images, the model's performance varied by lesion size. Bleeding detection sensitivity increased with lesion size, from 41.9% (6.48 false positives per image) for the smallest spots to 93.4% (5.80 false positives per image) for the largest. Exudate detection showed high sensitivity across all sizes, ranging from 86.9% (24.94 false positives per image) to 96.2% (6.40 false positives per image), though false positive rates were higher for smaller lesions.

Conclusions: Our experiments demonstrate the feasibility of training a deep learning neural network for detecting and segmenting DR-related lesions without relying on their manual annotations.

Myocardial infarction (MI) is a severe hypoxic event, resulting in the loss of up to one billion cardiomyocytes (CMs). Due to the limited intrinsic regenerative capacity of the heart, cell-based regenerative therapies, which feature the implantation of stem cell-derived cardiomyocytes (SC-CMs) into the infarcted myocardium, are being developed with the goal of restoring lost muscle mass, re-engineering cardiac contractility, and preventing the progression of MI into heart failure (HF). However, such cell-based therapies are challenged by their susceptibility to oxidative stress in the ischemic environment of the infarcted heart. To maximize the therapeutic benefits of cell-based approaches, a better understanding of the heart environment at the cellular, tissue, and organ level throughout MI is imperative. This review provides a comprehensive summary of the cardiac pathophysiology occurring during and after MI, as well as how these changes define the cardiac environment to which cell-based cardiac regenerative therapies are delivered. This understanding is then leveraged to frame how cell culture treatments may be employed to enhance SC-CMs' hypoxia resistance. In this way, we synthesize both the complex experience of SC-CMs upon implantation and the engineering techniques that can be utilized to develop robust SC-CMs for the clinical translation of cell-based cardiac therapies.

Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI's potential in advancing the field of ophthalmology and improving patient care.

Voice onset is the sequence of events between the first detectable movement of the vocal folds (VFs) and the stable vibration of the vocal folds. It is considered a critical phase of phonation, and the different modalities of voice onset and their distinctive characteristics are analysed. Oscillation of the VFs can start from either a closed glottis with no airflow or an open glottis with airflow. The objective of this article is to provide a comprehensive survey of this transient phenomenon, from a biomechanical point of view, in normal modal (i.e., nonpathological) conditions of vocal emission. This synthetic overview mainly relies upon a number of recent experimental studies, all based on in vivo physiological measurements, and using a common, original and consistent methodology which combines high-speed imaging, sound analysis, electro-, photo-, flow- and ultrasound glottography. In this way, the two basic parameters-the instantaneous glottal area and the airflow-can be measured, and the instantaneous intraglottal pressure can be automatically calculated from the combined records, which gives a detailed insight, both qualitative and quantitative, into the onset phenomenon. The similarity of the methodology enables a link to be made with the biomechanics of sustained phonation. Essential is the temporal relationship between the glottal area and intraglottal pressure. The three key findings are (1) From the initial onset cycles onwards, the intraglottal pressure signal leads that of the opening signal, as in sustained voicing, which is the basic condition for an energy transfer from the lung pressure to the VF tissue. (2) This phase lead is primarily due to the skewing of the airflow curve to the right with respect to the glottal area curve, a consequence of the compressibility of air and the inertance of the vocal tract. (3) In case of a soft, physiological onset, the glottis shows a spindle-shaped configuration just before the oscillation begins. Using the same parameters (airflow, glottal area, intraglottal pressure), the mechanism of triggering the oscillation can be explained by the intraglottal aerodynamic condition. From the first cycles on, the VFs oscillate on either side of a paramedian axis. The amplitude of these free oscillations increases progressively before the first contact on the midline. Whether the first movement is lateral or medial cannot be defined. Moreover, this comprehensive synthesis of onset biomechanics and the links it creates sheds new light on comparable phenomena at the level of sound attack in wind instruments, as well as phenomena such as the production of intervals in the sung voice.

Deep learning technology has been widely used in brain tumor segmentation with multi-modality magnetic resonance imaging, helping doctors achieve faster and more accurate diagnoses. Previous studies have demonstrated that the weighted fusion segmentation method effectively extracts modality importance, laying a solid foundation for multi-modality magnetic resonance imaging segmentation. However, the challenge of fusing multi-modality features with single-modality features remains unresolved, which motivated us to explore an effective fusion solution. We propose a multi-modality and single-modality feature recalibration network for magnetic resonance imaging brain tumor segmentation. Specifically, we designed a dual recalibration module that achieves accurate feature calibration by integrating the complementary features of multi-modality with the specific features of a single modality. Experimental results on the BraTS 2018 dataset showed that the proposed method outperformed existing multi-modal network methods across multiple evaluation metrics, with spatial recalibration significantly improving the results, including Dice score increases of 1.7%, 0.5%, and 1.6% for the enhanced tumor core, whole tumor, and tumor core regions, respectively.

The aim of this randomized clinical trial was to evaluate the efficacy of two different remineralising toothpastes in preventing dental caries and promoting oral health. Patients aged 6-18 years old with healthy and fully erupted first permanent molars (C1 and C2 DIAGNOdent scores) were enrolled and randomized into two groups according to the home-hydroxyapatite-based remineralising treatment used: the Trial group used zinc carbonate hydroxyapatite-based treatment (Biorepair Total Protective Repair), while the Control group used magnesium strontium carbonate hydroxyapatite conjugated with chitosan toothpaste (Curasept Biosmalto Caries Abrasion & Erosion). Dental and periodontal parameters were measured over a six-month period, including the DIAGNOdent Pen Index (primary outcome), BEWE Index, Plaque Index, Bleeding Score, Schiff Air Index, and ICDAS assessed with DIAGNOcam. A total of 40 patients were equally allocated in the two groups and finally analyzed. A significant reduction in the DIAGNOdent Pen score was reported in the Trial group after 1 month of treatment, while in the Control group, no significant change was found. The Trial group also showed a significant reduction in plaque levels after 3 months of treatment, while in the Control group, it occurred after 1 month. However, the Bleeding Score and Schiff Air Index showed no significant differences between the groups, suggesting that additional measures may be required to address gingival inflammation and hypersensitivity. The ICDAS index also showed no statistically significant changes, due to the limited duration of this study. Overall, zinc-hydroxyapatite-based toothpaste was more effective than magnesium strontium carbonate hydroxyapatite toothpaste in enhancing enamel remineralisation in the short-term period. The assigned treatments did not result in significant improvements in the oral indexes assessed in this study.

The objective of this study was to develop a musculoskeletal model incorporated with a subject-specific knee joint to predict the tibiofemoral contact force (TFCF) during daily motions. For this purpose, 18 healthy participants were recruited to perform the motion data acquisition using synchronized motion capture and force platform systems, and motion simulation based on an improved musculoskeletal model for five daily activities, including normal walking, stair ascent, stair descent, sit-to-stand, and stand-to-sit. The proposed musculoskeletal model included subject-specific models of bones, cartilages, and meniscus, detailed knee ligaments and muscles, deformable elastic contacts, and multiple degrees of freedom (DOFs) of the knee joint. The prediction accuracy was demonstrated by the good agreements of TFCF curves between the model predictions and in vivo measurements for the five activities (RMSE: 0.216~0.311 BW, R²: 0.928~0.992, and C_E: 0.048~0.141). Based on the validated model, the TFCF on total, medial, and lateral compartments (TFCF_Total, TFCF_Medial, and TFCF_Lateral) during the five daily activities were predicted. For TFCF_Total, the peak force for stair descent or sit-to-stand was the largest, followed by stair ascent or stand-to-sit, and finally normal walking. For TFCF_Medial, stair descent had the largest peak, followed by stair ascent. There were no significant differences between the peak TFCF_Medial values of normal walking, sit-to-stand, and stand-to-sit. For TFCF_Lateral, the peak of sit-to-stand was the largest, followed by stand-to-sit or stair descent, and finally normal walking or stair ascent. This study is valuable for further understanding the biomechanics of a healthy knee joint and providing theoretical guidance for the treatment of knee osteoarthritis (KOA).

Biomechanical asymmetries between children's left and right feet can affect stability and coordination, especially during dynamic movements. This study aimed to examine plantar pressure distribution, foot balance, and center of pressure (COP) trajectories in children during walking, running, and turning activities to understand how different movements influence these asymmetries. Fifteen children participated in the study, using a FootScan plantar pressure plate to capture detailed pressure and balance data. The parameters, including time-varying forces, COP, and Foot Balance Index (FBI), were analyzed through a one-dimensional Statistical Parametric Mapping (SPM1d) package. Results showed that asymmetries in COP and FBI became more pronounced, particularly during the tasks of running and directional turns. Regional plantar pressure analysis also revealed a more significant load on specific foot areas during these dynamic movements, indicating an increased reliance on one foot for stability and control. These findings suggest that early identification of asymmetrical loading patterns may be vital in promoting a balanced gait and preventing potential foot health issues in children. This study contributes to understanding pediatric foot biomechanics and provides insights for developing targeted interventions to support healthy physical development in children.