BioMedical Engineering OnLine最新文献

Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Both radiological and clinical factors have been investigated as predictors of curve progression. With the evolution of machine learning technologies, the integration of multidimensional information now enables precise predictions of curve progression. This review focuses on the application of machine learning methods to predict AIS curve progression, analyzing 15 selected studies that utilize various machine learning models and the risk factors employed for predictions. Key findings indicate that machine learning models can provide higher precision in predictions compared to traditional methods, and their implementation could lead to more personalized patient management. However, due to the model interpretability and data complexity, more comprehensive and multi-center studies are needed to transition from research to clinical practice.

Objectives: The abnormal anatomical alterations of blood vessels during DSA angiography in patients with hematological disorders were retrospectively examined, and the influencing factors of short-term (≤ 6 months) recurrent hemoptysis were statistically analyzed, and the consistency between admission diagnosis and intraoperative diagnosis was evaluated.

Methods: The intraoperative angiography data of patients who underwent selective bronchial artery embolization for hemoptysis in our hospital from January 2022 to December 2022 were reviewed. They were divided into the observation group and the control group based on whether there was recurrent hemoptysis. The Logistic regression model and forest map were employed to analyze the factors influencing the recurrence rate.

Results: A total of 104 patients were encompassed in this study (12 cases of tuberculosis, 35 cases of infection, 4 cases of lung cancer, 8 cases of bronchiectasis, 22 cases of arteriovenous fistula, 16 cases of aneurysm, and 7 cases of pulmonary hypertension). The coincidence rate of preoperative and intraoperative diagnoses was 73.1%. Pulmonary arteriovenous fistula and aneurysm were the predominant types of diseases that were misdiagnosed. The short-term recurrence rate was 16.3%, mainly attributed to the reopening of responsible vessels related to embolization, angiography leakage, and leaky embolization of specific types of vessels. The recurrence rate of only patients with arteriovenous fistula and aneurysm accounted for 47% of the total recurrence rate. The right bronchial artery, right internal thoracic artery, right thyroid neck trunk, and age were the independent factors influencing the recurrence of hemoptysis (p < 0.05).

Conclusions: The main reason for angiographic leakage and embolization leakage in cases of hemoptysis is the lack of understanding of the anatomic variations of the vessels responsible. Careful examination of the specific types and locations of the vessels is the principal approach to reducing secondary operations.

The analysis of cardiac wall mechanics is of importance for understanding coronary heart diseases (CHD). The inhalation of ultrafine particles could deteriorate CHD. The aim of the study is to investigate the effects of cardiac wall mechanics on rats of myocardial infarction (MI) after long-term inhalation of ultrafine Zn particles. Cardiac wall stresses and strains were computed, based on echocardiographic and hemodynamic measurements. It was found that MI resulted in the significantly elevated stresses and the reduced strains. The short-term inhalation of ultrafine Zn particles decreased stresses and increased strains in MI rats, but the long-term inhalation had the opposite effects. Hence, the short-term inhalation of ultrafine Zn particles could alleviate the MI-induced LV dysfunction while the long-term inhalation impaired it.

Background: Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes.

Methods: From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction.

Results: We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value.

Conclusions: The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

Background: Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.

Methods: This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.

Results: The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.

Conclusion: The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.

The effects of the novel synthetic peptide, A7-1, on wound healing and skin grafts were evaluated in a C57BL/6 mouse model. Two 15-mm wide circular skin excisions were made on the backs of mice and to each excision, 100 µM A7-1 or normal saline was applied daily. The treatments were applied and sutured for skin graft analysis. Digital photos were acquired on days 4, 7, 11, and 14 and fluorescein angiography was conducted. Wound sizes were verified using stereoscopic microscopy. Histological analysis was performed via hematoxylin and eosin staining and Masson's trichrome staining. Western blotting was performed using vascular endothelial growth factor. Using a stereoscopic microscope, significantly faster wound healing (17.3%) and skin graft healing (16.5%) were observed in the A7-1 treatment group in comparison to that of the control. The angiogenesis was significantly faster in fluorescein angiography examination in wound healing (11%) and skin grafts (15%). However, the average completion of epithelization (overall time for wound healing), did not show any significant differences. In comparison to the control, the new protein, A7-1, led to significantly faster wound healing in the initial angiogenesis.

Background: Minimally invasive glaucoma surgery (MIGS) has experienced a surge in popularity in recent years. Glaucoma micro-stents serve as the foundation for these minimally invasive procedures. Nevertheless, the utilization of these stents still presents certain short-term and long-term complications. This study aims to elucidate the creation of a novel drainage stent implant featuring a diverging channel, produced through microfluidic template processing technology. Additionally, an analysis of the mechanical properties, biocompatibility, and feasibility of implantation is conducted.

Results: The stress concentration value of the proposed stent is significantly lower, approximately two to three times smaller, compared to the currently available commercial XEN gel stent. This indicates a stronger resistance to bending in theory. Theoretical calculations further reveal that the initial drainage efficiency of the gradient diverging drainage stent is approximately 5.76 times higher than that of XEN stents. Notably, in vivo experiments conducted at the third month demonstrate a favorable biocompatibility profile without any observed cytotoxicity. Additionally, the drainage stent exhibits excellent material stability in an in vitro simulation environment.

Conclusions: In summary, the diverging drainage stent presents a novel approach to the cost-effective and efficient preparation process of minimally invasive glaucoma surgery (MIGS) devices, offering additional filtering treatment options for glaucoma.

Nanotechnology has contributed important innovations to medicine and dentistry, and has also offered various applications to the field of orthodontics. Intraoral appliances must function in a complex environment that includes digestive enzymes, a diverse microbiome, mechanical stress, and fluctuations of pH and temperature. Nanotechnology can improve the performance of orthodontic brackets and archwires by reducing friction, inhibiting bacterial growth and biofilm formation, optimizing tooth remineralization, improving corrosion resistance and biocompatibility of metal substrates, and accelerating or decelerating orthodontic tooth movement through the application of novel nanocoatings, nanoelectromechanical systems, and nanorobots. This comprehensive review systematically explores the orthodontic applications of nanotechnology, particularly its impacts on tooth movement, antibacterial activity, friction reduction, and corrosion resistance. A search across PubMed, the Web of Science Core Collection, and Google Scholar yielded 261 papers, of which 28 met our inclusion criteria. These selected studies highlight the significant benefits of nanotechnology in orthodontic devices. Recent clinical trials demonstrate that advancements brought by nanotechnology may facilitate the future delivery of more effective and comfortable orthodontic care.

Background: Personalized modeling is a promising tool to improve abdominal aortic aneurysm (AAA) rupture risk assessment. Computed tomography (CT) and quantitative flow (Q-flow) magnetic resonance imaging (MRI) are widely regarded as the gold standard for acquiring patient-specific geometry and velocity profiles, respectively. However, their frequent utilization is hindered by various drawbacks. Ultrasound is used extensively in current clinical practice and offers a safe, rapid and cost-effective method to acquire patient-specific geometries and velocity profiles. This study aims to extract and validate patient-specific velocity profiles from Doppler ultrasound and to examine the impact of the velocity profiles on computed hemodynamics.

Methods: Pulsed-wave Doppler (PWD) and color Doppler (CD) data were successfully obtained for six volunteers and seven patients and employed to extract the flow pulse and velocity profile over the cross-section, respectively. The US flow pulses and velocity profiles as well as generic Womersley profiles were compared to the MRI velocities and flows. Additionally, CFD simulations were performed to examine the combined impact of the velocity profile and flow pulse.

Results: Large discrepancies were found between the US and MRI velocity profiles over the cross-sections, with differences for US in the same range as for the Womersley profile. Differences in flow pulses revealed that US generally performs best in terms of maximum flow, forward flow and ratios between forward and backward flow, whereas it often overestimates the backward flow. Both spatial patterns and magnitude of the computed hemodynamics were considerably affected by the prescribed velocity boundary conditions. Larger errors and smaller differences between the US and generic CFD cases were observed for patients compared to volunteers.

Conclusion: These results show that it is feasible to acquire the patient-specific flow pulse from PWD data, provided that the PWD acquisition could be performed proximal to the aneurysm region, and resulted in a triphasic flow pattern. However, obtaining the patient-specific velocity profile over the cross-section using CD data is not reliable. For the volunteers, utilizing the US flow profile instead of the generic flow profile generally resulted in improved performance, whereas this was the case in more than half of the cases for the patients.