BioMedical Engineering OnLine最新文献

Currently, bone tissue engineering is a research hotspot in the treatment of orthopedic diseases, and many problems in orthopedics can be solved through bone tissue engineering, which can be used to treat fractures, bone defects, arthritis, etc. More importantly, it can provide an alternative to traditional bone grafting and solve the problems of insufficient autologous bone grafting, poor histocompatibility of grafts, and insufficient induced bone regeneration. Growth factors are key factors in bone tissue engineering by promoting osteoblast proliferation and differentiation, which in turn increases the efficiency of osteogenesis and bone regeneration. 3D printing technology can provide carriers with better pore structure for growth factors to improve the stability of growth factors and precisely control their release. Studies have shown that 3D-printed scaffolds containing growth factors provide a better choice for personalized treatment, bone defect repair, and bone regeneration in orthopedics, which are important for the treatment of orthopedic diseases and have potential research value in orthopedic applications. This paper aims to summarize the research progress of 3D printed scaffolds containing growth factors in orthopedics in recent years and summarize the use of different growth factors in 3D scaffolds, including bone morphogenetic proteins, platelet-derived growth factors, transforming growth factors, vascular endothelial growth factors, etc. Optimization of material selection and the way of combining growth factors with scaffolds are also discussed.

Articular cartilage injury is a serious bone disease that can result in disabilities. With the rapid increase in the aging population, this disorder has become an increasingly important public health issue. Recently, stem cell-based cartilage tissue engineering has emerged as a promising therapeutic option for treating articular cartilage damage. Cellular scaffolds, which are among three key elements of tissue engineering, play significant roles in the repair of damaged articular cartilage by regulating cellular responses and promoting cartilage tissue regeneration. Biological macromolecules are commonly used as scaffold materials owing to their unique properties. For example, natural and synthetic polymer hydrogel scaffolds can effectively mimic the microenvironment of the natural extracellular matrix; exhibit high cytocompatibility, biocompatibility, and biodegradability; and have attracted increasing attention in bone and cartilage tissue engineering and regeneration medicine. Several types of hydrogel scaffolds have been fabricated to treat articular cartilage abnormalities. This article outlines the recent progress in the field of hydrogel scaffolds manufactured from various biomaterials for repairing damaged articular cartilage, discusses their advantages and disadvantages, and proposes directions for their future development.

The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability of patients, which can be stratified into single-(e.g., disc herniation, prolapse, or bulge) and comorbidity-type degeneration (e.g., simultaneous presence of two or more conditions), respectively. A sample of lumbar magnetic resonance imaging (MRI) images from multiple clinical hospitals in China was collected and used in the proposal assessment. We devised a weighted transfer learning framework WDRIV-Net by ensembling four pre-trained models including Densenet169, ResNet101, InceptionV3, and VGG19. The proposed approach was applied to the clinical data and achieved 96.25% accuracy, surpassing the benchmark ResNet101 (87.5%), DenseNet169 (82.5%), VGG19 (88.75%), InceptionV3 (93.75%), and other state-of-the-art (SOTA) ensemble deep learning models. Furthermore, improved performance was observed as well for the metric of the area under the curve (AUC), producing a ≥ 7% increase versus other SOTA ensemble learning, a ≥ 6% increase versus most-studied models, and a ≥ 2% increase versus the baselines. WDRIV-Net can serve as a guide in the initial and efficient type screening of complex degeneration of lumbar intervertebral discs (LID) and assist in the early-stage selection of clinically differentiated treatment options.

Objectives: Respiratory syncytial virus (RSV) is a leading cause of hospitalization for lower respiratory tract infections amongst infants under 1 year, posing a significant global health challenge. The incidence of RSV exhibits marked seasonality and is influenced by various meteorological factors, which vary across regions and climates. This study aimed to analyze seasonal trends in RSV-related hospitalization in Tianjin, a region with a semi-arid and semi-humid monsoon climate, and to explore the relationship between these trends and meteorological factors. This research intends to inform RSV prevention strategies, optimize public health policies and medical resource allocation while also promoting vaccine and therapeutic drug development.

Methods: This study analyzed data from a cohort of 6222 children hospitalized with RSV-related infections. Meteorological data were collected from the Tianjin Binhai International Airport meteorological station, encompassing temperature (℃), air pressure (mmHg), wind speed (m/s), humidity (%), and precipitation (mm). We employed seasonal ARIMA and GAM models to investigate the association between meteorological factors and RSV-related hospitalizations.

Results: The SARIMA (1,0,0) (0,1,2)12 model effectively predicted RSV-related hospital admissions. Spearman correlation and GAM analysis revealed a significant negative association between the monthly average temperature and RSV hospitalizations.

Conclusions: Our findings indicated that meteorological factors influence RSV infection-related hospital admissions, with higher monthly average temperatures associated with fewer hospitalizations. The predictive capabilities of the SARIMA model bolster the formulation of targeted RSV prevention strategies, enhancing public health policy and medical resource allocation. Furthermore, continued research into vaccines and therapeutic drugs remains indispensable for augmenting public health outcomes.

Background: Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics.

Methods: This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains. Additionally, a multi-attribute classification network is proposed, leveraging attribute correlations to optimize parameters and enhance classification performance.

Results: Comprehensive evaluation on both public and clinical datasets showcases the superiority of the proposed framework, achieving an impressive average accuracy of 90.02%. The developed Self-supervised Pre-training and Multi-Attribute (SPMA) network achieves a best match ratio (MR) score of 71.38% and a low Hamming loss (HL) of 0.0425%, demonstrating its efficacy in orthodontic diagnosis from lateral cephalograms.

Conclusions: This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments.

Background: Coronavirus disease 19 (COVID-19) patients might develop sequelae after apparent resolution of the infection. Autonomic dysfunction and baroreflex failure have been frequently reported. However, the long-term effect of COVID-19 on cardiorespiratory and cardiovascular neural controls has not been investigated with directional approaches able to open the closed-loop relationship between physiological variables.

Methods: A model-based causal spectral approach, namely causal squared coherence (CK²), was applied to the beat-to-beat variability series of heart period (HP) and systolic arterial pressure (SAP), and to the respiratory signal (RESP) acquired at rest in supine position and during active standing (STAND) in COVID-19 survivors 9 months after their hospital discharge. Patients were categorized according to their need of ventilatory support during hospitalization as individuals that had no need of continuous positive airway pressure (noCPAP, n = 27), need of continuous positive airway pressure in sub-intensive care unit (CPAP, n = 14) and need of invasive mechanical ventilation in intensive care unit (IMV, n = 8).

Results: The expected decrease of the strength of the HP-RESP dynamic interactions as well as the expected increase of the dependence of HP on SAP along baroreflex during STAND was not observed and this result held regardless of the severity of the disease, namely in noCPAP, CPAP and IMV cohorts. Regardless of the experimental condition, spectral causality markers did not vary across groups either.

Conclusions: CK² markers, in association with an orthostatic challenge, were able to characterize the impairment of cardiorespiratory control and baroreflex in COVID-19 patients long after acute infection resolution and could be exploited to monitor the evolution of the COVID-19 patients after hospital discharge.

Background: Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.

Methods: All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. We segmented regions of interest according to the diagnostic results, which were evaluated by radiologists. These regions were then classified regions into four categories. We employed an efficient deep learning model and various image augmentation techniques (DenseNet-ECA). The classification performance of the different deep learning models was assessed, and receiver operating characteristic (ROC) curves and accuracy (ACC) were used to determine the optimal algorithm for classifying CWP clinical imaging features obtained from HRCT images.

Results: Four primary clinical imaging features in HRCT images, with a total of more than 1700 regions of interest (ROIs), were annotated, augmented, and used as a training set for tenfold cross-validation to generate the model. We selected DenseNet-Attention Net as the optimal model through assessing the performance of different classification algorithms, which yielded an average area under the ROC curve (AUC) of 0.98, and all clinical imaging features were classified with an AUC greater than 0.92. For the individual classifications, the AUCs were as follows: small miliary opacities, 0.99; nodular opacities, 1.0; interstitial changes, 0.92; and emphysema, 1.0.

Conclusion: We successfully applied a data augmentation strategy to develop a deep learning model by combining DenseNet with ECA-Net. We used our novel model to automatically classify CWP clinical imaging features from 2D HRCT images. This successful application of a deep learning-data augmentation algorithm can help clinical radiologists by providing reliable diagnostic information for classification.

Trial registration: Chinese Clinical Trial Registry, ChiCTR2100050379. Registered on 27 August 2021, https://www.chictr.org.cn/bin/project/edit?pid=132619 .

Recently, the incidence rate and mortality of various acute or chronic vascular occlusive diseases have increased yearly. As one of the most effective measures to treat them, vascular stents have been widely studied by researchers, and presently, the most commonly used is a drug-eluting stent, which reduces the process of rapid endothelialization because the drug is not selective. Fortunately, with the discovery and exploration of micro-nanostructures that can regulate cells selectively, reducing the incidence of "intravascular restenosis" and achieving rapid endothelialization simultaneously are possible through a special structure that cannot only improve endothelial cells (ECs), but also inhibit smooth muscle cells (SMCs). Therefore, this paper mainly introduces the preparation methods of micro-nanostructures used in the past, as well as the detection methods of EC and SMC. Then, the various functions of different dimensional structures for different cells are summarized and analyzed. Finally, the application of micro-nanostructure in future stent materials is summarized and proposed.

With precise control of smart materials deformation in time dimension, doctors can customize orthopedic implants. This review focuses on the advances of 4D printing technology in orthopedics, including its applications in bone repair and reconstruction, personalized treatment, and drug delivery. 4D printing enables the creation of bionic scaffolds and fixation devices for bone repair, customized implants matching patients' conditions for personalized treatment, and specific carriers for accurate drug release and delivery, which together contribute to accelerating bone healing, providing exclusive treatments, enhancing therapeutic effects and reducing side effects, thus helping improve orthopedic medicine. It offers comprehensive reference materials for relevant medical personnel.

Objective: This study aims to investigate the monthly variation patterns of bioelectrical impedance (BEI) along 24 meridian pathways in healthy individuals.

Methods: A cohort of 684 healthy middle-aged participants from North China was enrolled between July 1, 2017, and September 5, 2020. BEI measurements were consistently recorded along the 24 meridian pathways over the study period. The collected BEI data were subjected to statistical analysis, and line charts were constructed to depict the temporal variation patterns.

Results: Analysis revealed that BEI values along the 24 meridian pathways followed a normal distribution over a 12-month period. In the first group of meridians, which includes the lung, large intestine, heart, small intestine, pericardium, and triple-energizer meridians, significant monthly variations were observed. The second group, comprising the spleen, stomach, bladder, kidney, gallbladder, and liver meridians, exhibited marked differences primarily between March and April (P < 0.05), with a peak in April and relatively stable values thereafter. Synchronous BEI fluctuations were evident on the left and right sides of the body, and both groups of meridian pathways displayed similar variation patterns. These patterns largely corresponded to fluctuations observed in the spleen meridian.

Conclusion: The consistent monthly variation patterns in BEI along the 24 meridian pathways among healthy middle-aged individuals align with Traditional Chinese Medicine (TCM) concepts of meridians and collaterals. The spleen meridian, in particular, appears to play a crucial role in influencing these bioelectrical fluctuations, as posited in TCM theory. From a bioelectrical standpoint, this study provides empirical support for the potential existence and functionality of meridians and collaterals, offering a scientific perspective that complements ancient TCM principles.