Vasoconstriction of the resistance artery is mainly determined by an integrated action of multiple local stimuli acting on the vascular smooth muscle cells, which include neuronal delivery of α-adrenoceptor agonists and intraluminal pressure. The contractile activity of the arterial wall has been extensively studied ex vivo using isolated arterial preparations and myography techniques. However, agonist-mediated vasoconstriction response is often confounded by local effects of other stimuli (e.g., pressure) and, it remained unclear whether the pressure-induced myogenic response has any implication on the efficacy of agonist-mediated vasoconstriction during blood flow regulation in tissues. A quantitative understanding of the influence of each stimulus is necessary to understand the interaction between multiple regulatory mechanisms, which is required to ensure timely oxygen delivery to meet tissue needs.

Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.

Hypertension is a major preventable risk factor for cardiovascular disease, affecting over 1.5 billion adults worldwide. Antihypertensive peptides (AHTPs) have gained attention as a natural therapeutic option with minimal side effects. This study proposes a Deep Forest-based machine learning framework for AHTP prediction, leveraging a multi-granularity cascade structure to enhance classification accuracy. We integrated data from BIOPEP-UWM and three previously used datasets, totaling 2000 peptide sequences, and introduced novel feature extraction methods to build a comprehensive dataset for model training. This study represents the first application of Deep Forest for AHTP identification, demonstrating substantial classification performance advantages over traditional methods (e.g., SVM, CNN, and XGBoost) as well as recent mainstream prediction models (Ensemble-AHTPpred, CNN-SVM Ensemble, and mAHTPred). Requiring no complex manual feature engineering, the model adapts flexibly to various data needs, offering a novel perspective for efficient AHTP prediction and promising utility in hypertension management. On the benchmark dataset, the model achieved high accuracy, sensitivity, and AUC, providing a robust tool for identifying safe and effective AHTPs. However, future efforts should incorporate larger and more diverse independent validation datasets to further improve the model and enhance its generalizability. Additionally, the model's predictive accuracy relies on known AHTP targets and sequence features, potentially limiting its ability to detect AHTPs with uncharacterized or atypical properties.

Oxygen transport in the heart is crucial, and its impairment can lead to pathological conditions such as hypoxia, ischemia, and heart failure. However, investigating oxygen transport in the heart using in vivo measurements is difficult due to the small size of the coronary capillaries and their deep embedding within the heart wall.

Whole slide image (WSI) classification is of great clinical significance in computer-aided pathological diagnosis. Due to the high cost of manual annotation, weakly supervised WSI classification methods have gained more attention. As the most representative, multiple instance learning (MIL) generally aggregates the predictions or features of the patches within a WSI to achieve the slide-level classification under the weak supervision of WSI labels. However, most existing MIL methods ignore spatial position relationships of the patches, which is likely to strengthen the discriminative ability of WSI-level features.

Predicting post-operative flow is essential for assessing the risk of adverse events in cerebrovascular stenosis patients following endovascular treatment (EVT). This study aimed to evaluate the accuracy of the CFD simulation model in predicting post-operative velocity, flow and pressure distal to a stenosis, based on cerebrovascular microcirculatory resistance.

The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer.

globalization and population mobility have increased the spread of infectious diseases and challenged public health security. This paper proposes a complex network epidemic model with nonlinear incidence rate and quadratic transmission. The Turing pattern, sensitivity analysis and parameter identification of the epidemic model under different network structures are studied; METHODS:: this paper discusses the Turing pattern of the model under different network structures, and identifies the key parameters of the model through sensitivity analysis. The influence of network dimension on the spread of infectious diseases on random networks is also explored, and the problems of minimum path and minimum cover set of random networks are further discussed. We also carry out parameter identification experiments, adopt gradient descent algorithm to realize heterogeneous spatial fitting pattern of red blood cell plasma and simulate the transmission path of COVID-19 through Markov chain Monte Carlo fitting experiment, verifying the effectiveness of the model; RESULTS:: the necessary conditions for Turing instability on homogeneous and heterogeneous networks are found. On the heterogeneous lattice network, we observe the special patterns of equal density population. Sensitivity analysis shows that the higher the infection rate, the more infected people. On random networks, the higher the dimension, the better the effect of suppressing the spread of infectious diseases. Through comparison experiment, it is found that gradient descent algorithm has the best performance in parameter identification experiments. Red blood cell plasma fitting experiment reveals the spatial density distribution of infection rate; CONCLUSIONS:: this study provides theoretical support for the prevention and control of infectious diseases, and the complex network model can simulate the transmission process of infectious diseases more accurately. Sensitivity analysis and parameter identification experiments reveal the key influencing factors of propagation and the role of network structure. The effectiveness of the model is supported by actual data, which is helpful for the government health departments to formulate scientific prevention and control strategies.

Target motion monitoring plays a significant role in several computer-assisted interventions. However, ensuring the reliability of tracking algorithms can be challenging without adequate tools. We introduce SlicerCineTrack, a free open-source research toolkit, designed to provide users with a user-friendly interface for visualizing their target tracking results.

Accurate localization of the seizure onset zone (SOZ) is crucial for surgical treatment in patients with drug-resistant epilepsy (DRE). However, clinical identification of SOZ often relies on physician experience and has a certain subjectivity. Therefore, it is emergent to develop quantitative computational tools to assist clinicians in identifying SOZ.

Transcranial sonography-based grading of Parkinson's Disease has gained increasing attention in recent years, and it is currently used for assistive differential diagnosis in some specialized centers. To this end, accurate midbrain segmentation is considered an important initial step. However, current practice is manual, time-consuming, and bias-prone due to the subjective nature. Relevant studies in the literature are scarce and lacks comprehensive model evaluations from application perspectives. Herein, we aimed to benchmark the best-performing U-Net model for objective, stable and robust midbrain auto-segmentation using transcranial sonography images.

Automatic generation of medical reports reduces both the burden on radiologists and the possibility of errors due to the inexperience of radiologists. The model that utilizes attention mechanism and contrastive learning can generate medical reports by capturing both general and specific semantics. However, existing contrastive learning methods ignore the specificity of medical data, that is, a patient may suffer from multiple diseases at the same time. This means that the lack of fine-grained relationships for contrastive learning will lead to the problem of insufficient specificity.

Tumor Electric Field Therapy (TEFT) is a new treatment for glioblastoma cells with significant effect and few side effects. However, it is difficult to directly measure the intracranial electric field generated by TEFT, and the inability to control the electric field intensity distribution in the tumor target area also limits the clinical therapeutic effect of TEFT. It is a safe and effective way to construct an efficient and accurate prediction model of intracranial electric field intensity of TEFT by numerical simulation.

Magnetocardiography undoubtedly has exceptionally high sensitivity to electrophysiological changes in the myocardium. This is an absolutely non-invasivemethod with no contraindications. However, several barriers exist to the widespread adoption of this technique into clinical routine. One of the most important is the lack of a clear and consistent medical algorithm for interpreting magnetocardiographic data, leading to a clinically significant decision.

Rehabilitation after stroke is essential to minimize permanent disability. Gamification, the integration of game elements into non-game environments, has emerged as a promising strategy for increasing motivation and rehabilitation effectiveness. This article systematically reviews the gamified devices used in stroke rehabilitation and evaluates their impact on emotional, social, and personal effects on patients, providing a comprehensive view of gamified rehabilitation.

Breast cancer is the most common cancer type among women worldwide and a leading cause of female death. Accurately interpreting these complex tumors, involving small size and morphology, requires a significant amount of expertise and time. Developing a breast tumor segmentation model to assist clinicians in treatment, therefore, holds great practical significance.

Ischemic stroke is the most common type of stroke and the second leading cause of global mortality. Prompt and accurate diagnosis is crucial for effective treatment. Non-contrast CT (NCCT) scans are commonly employed as the first-line imaging modality to identify the infarct lesion and affected brain areas, as well as to make prognostic predictions to guide the subsequent treatment planning. However, visual evaluation of infarct lesions in NCCT scans can be subjective and inconsistent due to reliance on expert experience.

Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study, we propose a hybrid pipeline that, starting from plasma glucose data acquired during the Oral Glucose Tolerance Test (OGTT), allows us to classify the two types of patients through computational modeling and artificial intelligence.

This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to generate autocorrelation functions (ACFs) to train the DL network. We assessed the accuracy of the proposed DL using simulated and milk phantom data. Compared to convolutional neural networks (CNN), our lightweight DL architecture achieves 66.7% and 18.5% improvement in MSE for BFi and the coherence factor β, using synthetic data evaluation. The accuracy of rBFi over different algorithms was also investigated. We further simplified the DL computing primitives using subtraction for feature extraction, considering further hardware implementation. We extensively explored computing parallelism and fixed-point quantization within the DL architecture. With the DL model's compact size, we employed unrolling and pipelining optimizations for computation-intensive for-loops in the DL model while storing all learned parameters in on-chip BRAMs. We also achieved pixel-wise parallelism, enabling simultaneous, real-time processing of 10 and 15 autocorrelation functions on Zynq-7000 and Zynq-UltraScale+ field programmable gate array (FPGA), respectively. Unlike existing FPGA accelerators that produce BFi and the β from autocorrelation functions on standalone hardware, our approach is an encapsulated, end-to-end on-chip conversion process from intensity photon data to the temporal intensity ACF and subsequently reconstructing BFi and β. This hardware platform achieves an on-chip solution to replace post-processing and miniaturize modern DCS systems that use single-photon cameras. We also comprehensively compared the computational efficiency of our FPGA accelerator to CPU and GPU solutions.

Background subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to structures with different intensities and independent motion patterns, making XCA background subtraction challenging.

If left untreated, cracked teeth can lead to tooth loss, of which the incidence is 70%. Dental preparation is an effective treatment, but it is difficult to meet the clinical requirements when traditionally prepared by dentists. Grinding-based tooth preparation robot (TPR) shows promise for clinical applications to assist dentists. However, current TPR has problems with chipping and crack extension when preparing real teeth.