BioMedical Engineering OnLine最新文献

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.

Background: Epidermal growth factor receptor (EGFR) gene mutations can lead to distant metastasis in non-small cell lung cancer (NSCLC). When the primary NSCLC lesions are removed or cannot be sampled, the EGFR status of the metastatic lesions are the potential alternative method to reflect EGFR mutations in the primary NSCLC lesions. This review aimed to evaluate the potential of magnetic resonance imaging (MRI) radiomics based on extrapulmonary metastases in predicting EGFR mutations through a systematic reviews and meta-analysis.

Materials and methods: A systematic review of the studies on MRI radiomics based on extrapulmonary metastases in predicting EGFR mutations. The area under the curve (AUC), sensitivity (SNEC), and specificity (SPEC) of each study were separately extracted for comprehensive evaluation of MRI radiomics in predicting EGFR mutations in primary or metastatic NSCLC.

Results: Thirteen studies were ultimately included, with 2369 cases of metastatic NSCLC, including five studies predicting EGFR mutations in primary NSCLC, eight studies predicting EGFR mutations in metastatic NSCL. In terms of EGFR mutations in the primary lesion of NSCLC, the pooled AUC was 0.90, with SENC and SPEC of 0.80 and 0.85, respectively, which seems superior to the radiomics meta-analysis based on NSCLC primary lesions. In terms of EGFR mutations in NSCLC metastases, the pooled AUC was 0.86, with SENC and SEPC of 0.79 and 0.79, respectively, indicating moderate evaluation performance.

Conclusions: MRI radiomics helps to predict the EGFR mutation status in the primary or metastatic lesions of NSCLC, serve as a high-precision supplement to current molecular detection methods.

Objective: This study presents a novel digital interproximal enamel reduction (IER) clinical procedure, aiming to improve the effectiveness of IER processes in orthodontic treatment.

Methods: A malocclusion case of skeletal-class I and angle-class I was selected for the experimental investigation. A three-dimensional (3D) model of the dentition was constructed using scanning data from a plaster model. The IER volume was measured by the overlay area of two neighboring crowns on the arranged virtual teeth. For the upper dentition, a guide plate was innovatively designed based on the original surface of the dentition and the calculated IER volume. The guide plate was fabricated using stereolithography 3D printing (SLA), and was successfully employed during the IER operation. For the lower dentition, the IER procedure was performed using the free-hand method, guided by the predesigned IER volume. Preoperative and postoperative 3D models of the dentition were compared to assess the accuracy of both IER methods.

Results: The standard deviation of upper dentition IER with guide plate was calculated as 0.13 mm, while that of lower dentition IER by freehand was 0.24 mm.

Conclusion: Through the integration of laser scanning, 3D reconstruction, virtual arrangement, guide plate design, and 3D printing, this study not only explores a novel digital IER method, but also demonstrates its clinical applicability. The findings provide compelling evidence of the method's superior accuracy in clinical practice, offering a new approach for high-precision IER operations in orthodontic treatment.

Corrective osteotomy for upper limb deformities caused by fractures, trauma, or degeneration necessitates detailed preoperative planning to ensure accurate anatomical alignment, restore limb length, and correct angular deformities. This review evaluates the effectiveness of a three-dimensional (3D) preoperative planning program and an image fusion system designed for intraoperative guidance during corrective osteotomy procedures. The application processes and clinical outcomes observed with these technologies in various surgical scenarios involving the upper extremities were summarized. The systems proved beneficial in allowing surgeons to visualize surgical steps and optimize implant placement. However, despite these technological advancements, we found no significant impact on clinical outcomes compared to conventional methods. This indicates a need for further enhancements in system efficiency and user-friendliness to significantly improve patient results. Future developments should focus on addressing these limitations to enhance the practical utility of such advanced systems.

Background: A biodegradable nonwoven fabric that can be used to extract adipose-derived stem cells (ADSCs) from adipose tissue slices was developed, which were cultured rapidly without enzymatic treatment. The extracted and cultured ADSCs remain on the nonwoven fabric and form a thick cell sheet. The aim was to use the thick cell sheet as a treatment by transplanting it into the living body. In addition, the expectation was that it will be possible to observe the cell sheet in the living body using X-ray computed tomography (CT) because the nonwoven fabric used to produce the cell sheet contains 50% (by weight) hydroxyapatite.

Results: Thick cell sheets of ADSCs supported by two layers of nonwoven fabric were cut to size and transplanted into the cheeks of rats. No health damage was observed in the rats in which the cell sheets were implanted, except for one in which the surgery appeared to have failed. X-ray CT imaging showed that the fabric of the implanted cell sheet biodegraded over 12 weeks. Changes in the position, shape, and size of the cell sheet within the rat's body were tracked by X-ray CT. The thick cell sheets, which can be easily produced by simply seeding tissue slices, can be cut into appropriate shapes and transplanted safely, and it was confirmed that they slowly biodegraded when transplanted into the rats' bodies.

Conclusions: We demonstrated not only that the thick ADSC sheets can be transplanted successfully into animals, but also that the transplanted sheets can be observed in vivo by X-ray CT, which also allows changes in the ADSC sheets to be tracked. The results suggest that the biodegradable nonwoven fabric will be a useful transplantation device to ensure cell engraftment throughout the affected area, and facilitate monitoring of the transplant's subsequent status. We expect that this transplantation device will promote the development of regenerative therapy.

The aim of this study was to evaluate the feasibility of using a biofeedback-enhanced robotics-assisted tilt table (RATT) to investigate time- and intensity-dependent changes in heart rate variability (HRV) at rest and during heart rate-controlled exercise in patients recovering from a stroke. Twelve patients (age 55.3 years ± 15.6 years, 7 women) completed two separate measurement sessions. The first involved familiarization and system identification to determine parameters of a feedback system for automatic control of heart rate (HR). The second comprised 14 min of rest and 21 min of active exercise during which HR was held constant using feedback control to eliminate cardiovascular drift. HR data were collected using a chest-belt HR sensor, and raw RR intervals were employed for HRV analysis during periods of rest (0-7 min and 7-14 min) and exercise (5-13 min and 13-21 min). A biofeedback-enhanced, robotics-assisted tilt table can be successfully employed to perform heart rate-controlled exercises in patients after a stroke. All HRV metrics were substantially lower during exercise compared to rest. In the rest period, HRV values during 0-7 min were lower than during 7-14 min, in line with a slight HR decrease over the entire rest period. During exercise, HRV values during 5-13 min were higher than during 13-21 min, suggesting a time-dependent HRV decrease. All HRV metrics exhibited intensity- and time-dependent changes: higher HRV at rest and decreasing HRV over time. Understanding these HRV characteristics will support the development of heart rate-controlled exercise regimens and protocols for examining HRV changes during exercise in patients.

Purpose: This study aims to accurately predict the effects of hormonal therapy on prostate cancer (PC) lesions by integrating multi-modality magnetic resonance imaging (MRI) and the clinical marker prostate-specific antigen (PSA). It addresses the limitations of Convolutional Neural Networks (CNNs) in capturing long-range spatial relations and the Vision Transformer (ViT)'s deficiency in localization information due to consecutive downsampling. The research question focuses on improving PC response prediction accuracy by combining both approaches.

Methods: We propose a 3D multi-branch CNN Transformer (CNNFormer) model, integrating 3D CNN and 3D ViT. Each branch of the model utilizes a 3D CNN to encode volumetric images into high-level feature representations, preserving detailed localization, while the 3D ViT extracts global salient features. The framework was evaluated on a 39-individual patient cohort, stratified by PSA biomarker status.

Results: Our framework achieved remarkable performance in differentiating responders and non-responders to hormonal therapy, with an accuracy of 97.50%, sensitivity of 100%, and specificity of 95.83%. These results demonstrate the effectiveness of the CNNFormer model, despite the cohort's small size.

Conclusion: The findings emphasize the framework's potential in enhancing personalized PC treatment planning and monitoring. By combining the strengths of CNN and ViT, the proposed approach offers robust, accurate prediction of PC response to hormonal therapy, with implications for improving clinical decision-making.