Genistein resists castration-resistant prostate cancer by inhibiting the androgen receptor pathway.

Prostate cancer is a prevalent malignancy, often diagnosed at advanced stages and associated with poor prognosis. Genistein, a soybean isoflavone, has demonstrated antitumor effects against castration-resistant prostate cancer . Our results indicate that genistein effectively downregulates the expression of key enzymes involved in the synthesis pathway, namely AKR1C3, SRD5A2, CYP11A1, and 3βHSD, thus blocking androgen synthesis in CRPC cells.

Head pose-assisted localization of facial landmarks for enhanced fast registration in skull base surgery.

In skull base surgery, the method of using a probe to draw or 3D scanners to acquire intraoperative facial point clouds for spatial registration presents several issues. Manual manipulation results in inefficiency and poor consistency. Traditional registration algorithms based on point clouds are highly dependent on the initial pose.

A Novel Nano-Spherical Tip for Improving Precision in Elastic Modulus Measurements of Polymer Materials via Atomic Force Microscopy.

Micro-nano-scale mechanical properties are vital for engineering and biological materials. The elastic modulus is generally measured by processing the force-indentation curves obtained by atomic force microscopy (AFM). However, the measurement precision is largely affected by tip shape, tip wear, sample morphology, and the contact model.

Real-time simulation for multi-component biomechanical analysis using localized tissue constraint progressive transfer learning.

In virtual surgical training, it is crucial to achieve real-time, high-fidelity simulation of the tissue deformation. The anisotropic and nonlinear characteristics of the organ with multi-component make accurate real-time deformation simulation difficult. A localized tissue constraint progressive transfer learning method is proposed in this paper, where the base-compensated dual-output transfer learning strategy and the localized tissue constraint progressive learning architecture are developed.

STQD-Det: Spatio-Temporal Quantum Diffusion Model for Real-Time Coronary Stenosis Detection in X-Ray Angiography.

Detecting coronary stenosis accurately in X-ray angiography (XRA) is important for diagnosing and treating coronary artery disease (CAD). However, challenges arise from factors like breathing and heart motion, poor imaging quality, and the complex vascular structures, making it difficult to identify stenosis fast and precisely. In this study, we proposed a Quantum Diffusion Model with Spatio-Temporal Feature Sharing to Real-time detect Stenosis (STQD-Det).

Three-Dimensional Label-Free Observing of the Self-Assembled Nanoparticles inside a Single Cell at Nanoscale Resolution.

Understanding the intracellular behavior of nanoparticles (NPs) plays a key role in optimizing the self-assembly performance of nanomedicine. However, conducting the 3D, label-free, quantitative observation of self-assembled NPs within intact single cells remains a substantial challenge in complicated intracellular environments. Here, we propose a deep learning combined synchrotron radiation hard X-ray nanotomography approach to visualize the self-assembled ultrasmall iron oxide (USIO) NPs in a single cell.

Bi-Fusion of Structure and Deformation at Multi-Scale for Joint Segmentation and Registration.

Medical image segmentation and registration are two fundamental and highly related tasks. However, current works focus on the mutual promotion between the two at the loss function level, ignoring the feature information generated by the encoder-decoder network during the task-specific feature mapping process and the potential inter-task feature relationship. This paper proposes a unified multi-task joint learning framework based on bi-fusion of structure and deformation at multi-scale, called BFM-Net, which simultaneously achieves the segmentation results and deformation field in a single-step estimation.

DSC-Recon: Dual-Stage Complementary 4-D Organ Reconstruction From X-Ray Image Sequence for Intraoperative Fusion.

Accurately reconstructing 4D critical organs contributes to the visual guidance in X-ray image-guided interventional operation. Current methods estimate intraoperative dynamic meshes by refining a static initial organ mesh from the semantic information in the single-frame X-ray images. However, these methods fall short of reconstructing an accurate and smooth organ sequence due to the distinct respiratory patterns between the initial mesh and X-ray image.

Simulation Study and Optimization Strategies for Vacuum Infusion of GFRP Hoses Based on Resin Time-Viscosity Variables.

During the infusion process of a glass-fiber-reinforced thermosetting composite hose, the viscosity of its resin matrix undergoes temporal variations. Consequently, if the impact of resin viscosity changes over time on the internal resin fluidity is not considered during the infusion process, this may result in the incomplete impregnation of the hose, characterized by the presence of numerous voids. This phenomenon adversely affects the quality of the pipe's curing and forming process.

Facial augmented reality based on hierarchical optimization of similarity aspect graph.

Background: The existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points.

Methods: This work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face.

Embedding-Alignment Fusion-Based Graph Convolution Network With Mixed Learning Strategy for 4D Medical Image Reconstruction.

In recent years, 4D medical image involving structural and motion information of tissue has attracted increasing attention. The key to the 4D image reconstruction is to stack the 2D slices based on matching the aligned motion states. In this study, the distribution of the 2D slices with the different motion states is modeled as a manifold graph, and the reconstruction is turned to be the graph alignment.

Pose estimation via structure-depth information from monocular endoscopy images sequence.

Image-based endoscopy pose estimation has been shown to significantly improve the visualization and accuracy of minimally invasive surgery (MIS). This paper proposes a method for pose estimation based on structure-depth information from a monocular endoscopy image sequence. Firstly, the initial frame location is constrained using the image structure difference (ISD) network.

Dual-correlate optimized coarse-fine strategy for monocular laparoscopic videos feature matching via multilevel sequential coupling feature descriptor.

Feature matching of monocular laparoscopic videos is crucial for visualization enhancement in computer-assisted surgery, and the keys to conducting high-quality matches are accurate homography estimation, relative pose estimation, as well as sufficient matches and fast calculation. However, limited by various monocular laparoscopic imaging characteristics such as highlight noises, motion blur, texture interference and illumination variation, most exiting feature matching methods face the challenges of producing high-quality matches efficiently and sufficiently. To overcome these limitations, this paper presents a novel sequential coupling feature descriptor to extract and express multilevel feature maps efficiently, and a dual-correlate optimized coarse-fine strategy to establish dense matches in coarse level and adjust pixel-wise matches in fine level.

Automatic marker-based alignment method for a nano-resolution full-field transmission X-ray microscope.

Driven by the development of X-ray optics, the spatial resolution of the full-field transmission X-ray microscope (TXM) has reached tens of nanometers and plays an important role in promoting the development of biomedicine and materials science. However, due to the thermal drift and the radial/axial motion error of the rotation stage, TXM computed tomography (CT) data are often associated with random image jitter errors along the horizontal and vertical directions during CT measurement. A nano-resolution 3D structure information reconstruction is almost impossible without a prior appropriate alignment process.

Polyguluronate alleviates ulcerative colitis by targeting the gut commensal Lactobacillus murinus and its anti-inflammatory metabolites.

Polyguluronate (PG) is a fermentable polysaccharide from edible algae. The present study was designed to investigate the therapeutic effect of PG on ulcerative colitis (UC) and its underlying mechanisms. Our results suggest that oral intake of PG attenuates UC and improves gut microbiota dysbiosis by promoting the growth of Lactobacillus spp.

Automatic literature screening using the PAJO deep-learning model for clinical practice guidelines.

Background: Clinical practice guidelines (CPGs) are designed to assist doctors in clinical decision making. High-quality research articles are important for the development of good CPGs. Commonly used manual screening processes are time-consuming and labor-intensive.

Biomimetic synthesis of polydopamine-graphene oxide/hydroxyapatite for efficient and fast uranium(VI) capture from aqueous solution.

A novel and efficient mesoporous nano-absorbent for U(VI) removal was developed through an environment-friendly route by inducing the biomimetic mineralization of hydroxyapatite (HAP) on the bioinspired surface of polydopamine-graphene oxide (PDA-GO). PDA-GO/HAP exhibited the greatly rapid and efficient U(VI) removal within 2 min, and much higher U(VI) adsorption capacity of 433.07 mg·g than that of GO and PDA-GO.

Local Contractive Registration with Biomechanical Model: Assessing Microwave Ablation after Compensation for Tissue Shrinkage.

Microwave ablation (MWA) is a minimally invasive procedure for the treatment of liver tumor. Accumulating clinical evidence has considered the minimal ablative margin (MAM) as a significant predictor of local tumor progression (LTP). In clinical practice, MAM assessment is typically carried out through image registration of pre- and post-MWA images.

CT2X-IRA: CT to x-ray image registration agent using domain-cross multi-scale-stride deep reinforcement learning.

In computer-assisted minimally invasive surgery, the intraoperative x-ray image is enhanced by overlapping it with a preoperative CT volume to improve visualization of vital anatomical structures. Therefore, accurate and robust 3D/2D registration of CT volume and x-ray image is highly desired in clinical practices. However, previous registration methods were prone to initial misalignments and struggled with local minima, leading to issues of low accuracy and vulnerability.

Research on the Heating Process of CFRP Circular Tubes Based on Electromagnetic Induction Heating Method.

Based on the electromagnetic induction heating method, heating and curing of Carbon Fiber Reinforced Polymer (CFRP) have the advantages of high energy utilization and no pollution. However, in the heating process, both the material weaving structure and mold material can affect the temperature field. Therefore, in this study, an electromagnetic heating finite element analysis model for CFRP circular tubes was established based on the equivalent electromagnetic thermal characteristics of CFRP.

Robot-assisted augmented reality surgical navigation based on optical tracking for mandibular reconstruction surgery.

Purpose: This work proposes a robot-assisted augmented reality (AR) surgical navigation system for mandibular reconstruction. The system accurately superimposes the preoperative osteotomy plan of the mandible and fibula into a real scene. It assists the doctor in osteotomy quickly and safely under the guidance of the robotic arm.

An alternately optimized generative adversarial network with texture and content constraints for deformable registration of 3D ultrasound images.

3D ultrasound non-rigid registration is significant for intraoperative motion compensation. Nevertheless, distorted textures in the registered image due to the poor image quality and low signal-to-noise ratio of ultrasound images reduce the accuracy and efficiency of the existing methods.A novel 3D ultrasound non-rigid registration objective function with texture and content constraints in both image space and multiscale feature space based on an unsupervised generative adversarial network based registration framework is proposed to eliminate distorted textures.

Isolation of Alginate-Degrading Bacteria from the Human Gut Microbiota and Discovery of AY11-1 as a Novel Anti-Colitis Probiotic Bacterium.

Alginate has been documented to prevent the development and progression of ulcerative colitis by modulating the gut microbiota. However, the bacterium that may mediate the anti-colitis effect of alginate has not been fully characterized. We hypothesized that alginate-degrading bacteria might play a role here since these bacteria could utilize alginate as a carbon source.

Deep-learning-based ring artifact correction for tomographic reconstruction.

X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography.