Biocybernetics and Biomedical Engineering最新文献

The transition from fetal to newborn condition involves complex physiological adaptations for extrauterine life. A crucial event in this process is the clamping of the umbilical cord, which can be categorized as immediate or delayed. The type of clamping significantly influences the hemodynamics of the newborn. In this study, we developed a simulator based on existing cardiovascular models to better understand this practice. The simulator covers the period from late gestation to 24 h after birth and faithfully reproduces flow patterns observed in real-life situations (as evaluated by clinical specialists), considering factors such as the timing of cord clamping and the altitude of the birth location. It also reproduces blood pressure values reported in clinical data. Under similar conditions, the simulation results indicate that delayed cord clamping leads to increased oxygen concentration and improved blood volume compared to immediate cord clamping. Delayed cord clamping also had a positive impact on sustained placental respiration. Furthermore, this study provides further evidence that umbilical cord clamping should be based on physiological criteria rather than predefined time intervals.

Breast cancer is a prevalent malignant tumour with high global incidence. Its diagnosis relies primarily on the analysis of pathological breast images. Owing to the complex organisation of the tumour microenvironment, neural network models are essential as efficient classification tools in the field of pathological image analysis. This study introduced spatially-aware attention swift parallel convolution network (SPA-SPCNet), a lightweight and low-latency model for classifying breast pathologies. A novel module for multi-scale feature extraction was constructed using a depthwise separable convolution method. It focuses on the multi-scale features of pathological images to alleviate recognition problems caused by similar local features in breast cancer tissues. The module concatenates the convolutions of different kernels from three branches. Second, a lightweight dynamic spatially-aware attention module was introduced to integrate the visual graph convolutional architecture in a branch. This allowed the model to capture the spatial structure and relationships in image, enabling better handling of the unique spatial distribution relationship between breast cancer tissue structures. The other branch utilises a self-attention mechanism in the transformer. The module can dynamically adjust the attention of the model to different regions in the image, allowing it to focus on the key features of the complex spatial distribution of breast cancer tissue. This feature fusion method enabled the model to capture both global semantics and local details. Compared with existing lightweight models, the proposed model has advantages in terms of tissue structure classification accuracy, parameter quantity, floating-point operations, and real-time inference speed, providing a powerful tool for computer-aided breast pathological image classification.

Breast cancer is a prevalent global disease where early detection is crucial for effective treatment and reducing mortality rates. To address this challenge, a novel Computer-Aided Diagnosis (CAD) framework leveraging Artificial Intelligence (AI) techniques has been developed. This framework integrates capabilities for the simultaneous detection and classification of breast lesions. The AI-based CAD framework is meticulously structured into two pipelines (Stage 1 and Stage 2). The first pipeline (Stage 1) focuses on detectable cases where lesions are identified during the detection task. The second pipeline (Stage 2) is dedicated to cases where lesions are not initially detected. Various experimental scenarios, including binary (benign vs. malignant) and multi-class classifications based on BI-RADS scores, were conducted for training and evaluation. Additionally, a verification and validation (V&V) scenario was implemented to assess the reliability of the framework using unseen multimodal datasets for both binary and multi-class tasks. For the detection tasks, the recent AI detectors like YOLO (You Only Look Once) variants were fine-tuned and optimized to localize breast lesions. For classification tasks, hybrid AI models incorporating ensemble convolutional neural networks (CNNs) and the attention mechanism of Vision Transformers were proposed to enhance prediction performance. The proposed AI-based CAD framework was trained and evaluated using various multimodal ultrasound datasets (BUSI and US2) and mammogram datasets (MIAS, INbreast, real private mammograms, KAU-BCMD, and CBIS-DDSM), either individually or in merged forms. Visual t-SNE techniques were applied to visually harmonize data distributions across ultrasound and mammogram datasets for effective various datasets merging. To generate visually explainable heatmaps in both pipelines (stages 1 and 2), Grad-CAM was utilized. These heatmaps assisted in finalizing detected boxes, especially in stage 2 when the AI detector failed to automatically detect breast lesions. The highest evaluation metrics achieved for merged dataset (BUSI, INbreast, and MIAS) were 97.73% accuracy and 97.27% mAP50 in the first pipeline. In the second pipeline, the proposed CAD achieved 91.66% accuracy with 95.65% mAP50 on MIAS and 95.65% accuracy with 96.10% mAP50 on the merged dataset (INbreast and MIAS). Meanwhile, exceptional performance was demonstrated using BI-RADS scores, achieving 87.29% accuracy, 91.68% AUC, 86.72% mAP50, and 64.75% mAP50-95 on a combined dataset of INbreast and CBIS-DDSM. These results underscore the practical significance of the proposed CAD framework in automatically annotating suspected lesions for radiologists.

Radial flow packed-bed bioreactors (rPBBs) overcome the transport limitations of static and axial-flow perfusion bioreactors and enable development of clinical-scale bioengineered tissues. We developed criteria to design rPBBs with uniform medium radial flux distribution along bioreactor length ensuring uniform construct perfusion. We report a model-based analysis of the effect of non-uniform axial distribution of medium radial flux on pericellular concentration of oxygen and glucose. Albeit pseudo-homogeneous, the model predicts how medium flux, solutes transport and cellular consumption interact and determine the pericellular oxygen and glucose concentrations in the presence of pore transport resistance to design optimal axisymmetric rPBBs and enable control of pericellular environment. Thus, oxygen and glucose supply may match cell requirements as tissue matures. Flow and solute transport in bioreactor empty spaces and construct was described with Navier-Stokes and Darcy-Brinkman equations, and with convection–diffusion and convection–diffusion-reaction equations, respectively. Solute transport in construct accounted for Michaelian cellular consumption and bulk medium-to-cell surface oxygen transport resistance in terms of a transport-equivalent bed of Raschig rings. The effect of relevant dimensionless groups on pericellular and bulk solute concentrations was predicted under typical tissue engineering operation and evaluated against literature data for bone tissue engineering. Axial distribution of medium radial flux influenced the distribution of pericellular solutes concentration, more so at high cell metabolic activity. Increasing medium feed flow rates relieved non-uniform solute concentration distribution and decayed at cell surface for metabolic consumption, also starting from axially non-uniform radial flux distribution. Model predictions were obtained in runtimes compatible with on-line control strategies.

Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging differs notably between healthy individuals and those with sleep apnea (SA). SA disrupts the regularity of sleep stages, affecting the performance of sleep stage detection and influencing the accuracy of sleep staging, thereby impacting sleep quality assessment results. The study compares the accuracy of sleep staging between healthy individuals and SA patients using the same algorithm, revealing variations in performance based on different severities of sleep apnea. This suggests limitations in the generalization ability of current sleep staging methods. Accordingly, researchers are working to develop sleep staging methods that can diminish the impact of sleep apnea and exhibit better generalization capabilities. Furthermore, the study emphasizes the advantages of automated methods over manual scoring due to being less subjective and resource-intensive, making them more suitable for practical applications. The emphasis is on recent research findings on automatic sleep stage classification based on electroencephalography (EEG). The study outlines potential applications and distinctions of various algorithm models rooted in machine learning and deep learning within the context of sleep staging. These methods are applied to the well-known public EEG dataset Sleep-EDF. The study applies four widely studied algorithms to the single-channel EEG of 20 subjects, comparing the results of the models’ automatic sleep staging with the manual sleep staging annotations by clinical experts.

Artificial pancreas systems should be designed considering different patient profiles, which is challenging from a control theory perspective. In this paper, a flexible-hybrid dual-hormone control algorithm for an artificial pancreas is proposed. The algorithm handles announced/unannounced meals by means of a non-interacting feedforward scheme that safely incorporates prandial boluses. Also, a coordination strategy is employed to distribute the counter-regulatory actions, which can be delivered as a continuous glucagon infusion via an automated pump, as an oral rescue carbohydrate recommendation, or as a rescue glucagon dose recommendation to be administrated through a glucagon pen. The different configurations of the proposed controller were evaluated in silico using a 14-day virtual scenario with random meal intakes and exercise sessions, achieving above 80% time-in-range and low time spent in hypoglycemia.

Background

Intraventricular hemorrhage (IVH) is a common and significant complication in premature infants. While cranial ultrasound is the golden standard for IVH detection, it may not identify lesions until hours or days after occurring, which limits early intervention. Predicting IVH in premature infants would be highly advantageous. Recent studies have shown that EEG data’s amplitude and frequency modulation features could offer predictive insights for neurological diseases in adults.

Methods

To investigate the association between IVH and EEG monitoring, a retrospective case-control study was conducted in preterm infants. All infants underwent amplitude integrated EEG monitoring for at least 3 days after birth. The study included 20 cases who had an IVH diagnosed on cranial ultrasound and had a negative ultrasound 24 h earlier, and 20 matched controls without IVH. Amplitude and frequency modulation features were extracted from single-channel EEG data, and various machine learning algorithms were evaluated to create a predictive model.

Results

Cases had an average gestational age and birth weight of 26.4 weeks and 965 g, respectively. The best-performing algorithm was adaptive boosting. EEG data from 24 h before IVH detection proved predictive with an area under the receiver operating characteristic curve of 93 %, an accuracy of 91 %, and a Kappa value of 0.85. The most informative features were the slow varying instantaneous frequency and amplitude in the Delta frequency band.

Conclusion

Amplitude and frequency modulation features obtained from single-channel EEG signals in extremely preterm infants show promise for predicting IVH occurrence within 24 h before detection on cranial ultrasound.

Chronic kidney disease (CKD) affects the morphological structure and causes significant degradation in kidney function, leading to renal replacement treatment in affected individuals. Vascular rarefaction is thought to be an important factor in accelerating kidney damage in CKD patients, therefore, the assessment of renal morphology and vasculature is crucial in nephrology. The objective of this study was to evaluate the morphological and vascular changes caused by CKD in mice kidneys. In this study, dual photoacoustic microscopy (PAM) and optical coherence microscopy (OCM) oriented wide-field high-resolution imaging modalities were employed for diseased renal imaging. The unilateral ureteral obstruction (UUO) model was used to prepare renal samples with CKD, and the developed wide-field dual imaging system was used to image both control and CKD-affected kidneys for assessing vascular and morphological changes during CKD progression. The obtained results reveal a gradual alteration in vascular intensity and pelvis space with the progress of UUO disease. Furthermore, a quantitative micro-vessel analysis was performed based on the node, junction, and mesh of the vessel, which provides details on the increasing microvascular-related characteristics in the peripheral area as the disease progresses. Thus, by concurrently employing the advantages of each optical imaging technique, the proposed method of assessing the OCM-based morphological and PAM-based vascular properties of the renal sample using a wide-field multimodal imaging system can be an efficient technique for whole volume analysis without any exogenous contrast agents in kidney histopathology.

Congestive heart failure (CHF) is a serious threat to human health. Electrocardiogram (ECG) signals have been proven to be useful in the detection of CHF. However, the low amplitude and short duration of the ECG signals, as well as the superimposed noise during the real-time acquisition of the signal, seriously affect the CHF detection. To improve the detection rate of CHF, this paper proposes a congestive heart failure detection method based on Gramian angular field (GAF) and two-dimensional symbolic phase permutation entropy (SPPE2D). The significant advantage of this method is that it reduces the sensitivity to noise, and good performance can be obtained without denoising using raw ECG signals. We segment the original ECG signals into 2 s non-overlapping segments and convert them into images using the GAF method. Then, the SPPE2D algorithm is proposed to measure the complexity between normal sinus rhythm (NSR) and CHF, and analyze the anti-noise performance of the algorithm. Finally, the SPPE2D features of GAF images are computed and input into a support vector machine (SVM) for CHF detection. Classification accuracy on the Massachusetts Institute of Technology − Beth Israel Hospital Normal Sinus Rhythm Database and Beth Israel Deaconess Medical Center Congestive Heart Failure Database is 99.59%, sensitivity is 99.42%, specificity is 99.80%, and F1-score is 99.62%. The accuracy of detecting CHF reach more than 97.75% in the other five CHF databases. The experimental results show that the method based on GAF and SPPE2D can effectively detect CHF by images of ECG signals and has good robustness. CHF can be detected using the 2 s sample lengths of ECG signals recording with high sensitivity, giving clinicians ample time to treat patients with CHF.

This study aimed to establish a blood vessel model within a hollow fiber bioreactor to evaluate the impact of high and fluctuating glucose levels on human umbilical vein endothelial cells (HUVECs) under laminar shear stress (LSS). HUVECs were cultured for 48 h in normal (5 mM), high (20 mM), and variable (20 mM / 5 mM alternating every 24 h) glucose concentrations under LSS of 0.66 Pa. An automated medium replacement system was developed. The control cultures remained static. The analysis included cell viability via cytometric analysis, glucose consumption, lactate production via electroenzymatic methods, and the expression of 21 genes via qPCR. The percentage of apoptotic cells did not significantly differ across glucose concentrations under LSS. HUVECs favor glycolysis for energy regardless of LSS. Under LSS, the IL1B, CCL2, and SELE genes were upregulated under high-glucose conditions and downregulated under variable-glucose conditions. A few other genes related to inflammation, oxidative stress, cell adhesion and apoptosis were upregulated under high-glucose conditions. In conclusion, using the blood vessel model we effectively examined the impact of glucose profiles on HUVECs under LSS in a device replicating the cylindrical geometry of blood vessels. LSS and tubular cell arrangement might mitigate the adverse effects of variable glucose on endothelial cells.