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. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face.

RESULTS

The proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods.

CONCLUSIONS

Our experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality.

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. An embedding-alignment fusion-based graph convolution network (GCN) with a mixed-learning strategy is proposed to align the graphs. Herein, the embedding and alignment processes of graphs interact with each other to realize a precise alignment with retaining the manifold distribution. The mixed strategy of self- and semi-supervised learning makes the alignment sparse to avoid the mismatching caused by outliers in the graph. In the experiment, the proposed 4D reconstruction approach is validated on the different modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). We evaluate the reconstruction accuracy and compare it with those of state-of-the-art methods. The experiment results demonstrate that our approach can reconstruct a more accurate 4D image.

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. Firstly, a novel sequential coupling swin transformer layer is designed in feature descriptor to learn and extract multilevel feature representations richly without increasing complexity. Then, a dual-correlate optimized coarse-fine strategy is proposed to match coarse feature sequences under low resolution, and the correlated fine feature sequences is optimized to refine pixel-wise matches based on coarse matching priors. Finally, the sequential coupling feature descriptor and dual-correlate optimization are merged into the Sequential Coupling Dual-Correlate Network (SeCo DC-Net) to produce high-quality matches. The evaluation is conducted on two public laparoscopic datasets: Scared and EndoSLAM, and the experimental results show the proposed network outperforms state-of-the-art methods in homography estimation, relative pose estimation, reprojection error, matching pairs number and inference runtime. The source code is publicly available at https://github.com/Iheckzza/FeatureMatching.

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. in dextran sulfate sodium-fed mice. Five different species of Lactobacillus were isolated from the feces of PG-treated mice and L. murinus was identified to have the best anti-colitis effect, suggesting a critical role for L. murinus in mediating the therapeutic effect of PG. Furthermore, PG was degraded potentially by the beta-glucuronidase from L. murinus and adding PG to the culture medium of L. murinus remarkably increased its production of anti-inflammatory metabolites, including itaconic acid, cis-11,14-eicosadienoic acid, and 3-amino-3-(2-chlorophenyl)-propionic acid. Additionally, L. salivarius, a human intestine-derived PG-utilizing species that is closely related to L. murinus, was also demonstrated to have potent anti-colitis effects, suggesting that it is a candidate target of PG in the human gut. Altogether, our study illustrates an unprecedented application of PG in the treatment of UC and establishes the basis for understanding its therapeutic effect from the perspective of L. murinus and its metabolites.

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.

METHODS

The proposed system mainly consists of two modules: the AR guidance module of the mandible and fibula and the robot navigation module. In the AR guidance module, we propose an AR calibration method based on the spatial registration of the image tracking marker to superimpose the virtual models of the mandible and fibula into the real scene. In the robot navigation module, the posture of the robotic arm is first calibrated under the tracking of the optical tracking system. The robotic arm can then be positioned at the planned osteotomy after the registration of the computed tomography image and the patient position. The combined guidance of AR and robotic arm can enhance the safety and precision of the surgery.

RESULTS

The effectiveness of the proposed system was quantitatively assessed on cadavers. In the AR guidance module, osteotomies of the mandible and fibula achieved mean errors of 1.61 ± 0.62 and 1.08 ± 0.28 mm, respectively. The mean reconstruction error of the mandible was 1.36 ± 0.22 mm. In the AR-robot guidance module, the mean osteotomy errors of the mandible and fibula were 1.47 ± 0.46 and 0.98 ± 0.24 mm, respectively. The mean reconstruction error of the mandible was 1.20 ± 0.36 mm.

CONCLUSIONS

The cadaveric experiments of 12 fibulas and six mandibles demonstrate the proposed system's effectiveness and potential clinical value in reconstructing the mandibular defect with a free fibular flap.

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. Secondly, endoscopy image depth information is used to estimate the pose of sequence frames. Finally, adaptive boundary constraints are used to optimize continuous frame endoscopy pose estimation, resulting in more accurate intraoperative endoscopy pose estimation. Evaluations were conducted on publicly available datasets, with the pose estimation error in bronchoscopy and colonoscopy datasets reaching 1.43 mm and 3.64 mm, respectively. These results meet the real-time requirements of various scenarios, demonstrating the capability of this method to generate reliable pose estimation results for endoscopy images and its meaningful applications in clinical practice. This method enables accurate localization of endoscopy images during surgery, assisting physicians in performing safer and more effective procedures.

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. To solve this problem, a fully automatic gold particle marker-based alignment approach without human intervention was proposed in this study. It can automatically detect, isolate, and register gold particles for projection image alignment with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. Simulated and experimental results confirmed the reliability and robustness of this method.

Research on Robot Screwing Skill Method Based on Demonstration Learning

A robot screwing skill learning framework based on teaching-learning is proposed to improve the generalization ability of robots for different scenarios and objects, combined with the experience of a human operation. This framework includes task-based teaching, learning, and summarization. We teach a robot to twist and gather the operation's trajectories, define the obstacles with potential functions, and counter the twisting of the robot using a skill-learning-based dynamic movement primitive (DMP) and Gaussian mixture model-Gaussian mixture regression (GMM-GMR). The hole-finding and screwing stages of the process are modeled. In order to verify the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios, obstacle avoidance trends and tightening experiments were conducted. Obstacle avoidance and tightening experiments were conducted on the robot tightening platform for bolts, plastic bottle caps, and faucets. The robot successfully avoided obstacles and completed the twisting task, verifying the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios.

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. Artificial intelligence (AI)-based techniques have been widely used to analyze unstructured data, including texts and images. Currently, there are no effective/efficient AI-based systems for screening literature. Therefore, developing an effective method for automatic literature screening can provide significant advantages.

METHODS

Using advanced AI techniques, we propose the Paper title, Abstract, and Journal (PAJO) model, which treats article screening as a classification problem. For training, articles appearing in the current CPGs are treated as positive samples. The others are treated as negative samples. Then, the features of the texts (e.g., titles and abstracts) and journal characteristics are fully utilized by the PAJO model using the pretrained bidirectional-encoder-representations-from-transformers (BERT) model. The resulting text and journal encoders, along with the attention mechanism, are integrated in the PAJO model to complete the task.

RESULTS

We collected 89,940 articles from PubMed to construct a dataset related to neck pain. Extensive experiments show that the PAJO model surpasses the state-of-the-art baseline by 1.91% (F1 score) and 2.25% (area under the receiver operating characteristic curve). Its prediction performance was also evaluated with respect to subject-matter experts, proving that PAJO can successfully screen high-quality articles.

CONCLUSIONS

The PAJO model provides an effective solution for automatic literature screening. It can screen high-quality articles on neck pain and significantly improve the efficiency of CPG development. The methodology of PAJO can also be easily extended to other diseases for literature screening.

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-1 than that of GO and PDA-GO. The enhanced adsorption capacity was mainly attributed to the synergistic effect of O-H, -C=N-, and PO43- functional groups and the incorporation of uranyl ions by the formation of a new phase (chernikovite, H2(UO2)2(PO4)2·8H2O). The adsorption process of U(VI) fitted well with pseudo-second-order kinetic and Langmuir isotherm model. Moreover, PDA-GO/HAP showed a high U(VI) adsorption capacity in a broad range of pH values and owned good thermal stability. PDA-GO/HAP with various excellent properties made it a greatly promising adsorbent for extracting uranium. Our work developed a good strategy for constructing fast and efficient uranium-adsorptive biomimetic materials.

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. However, this process faces two main challenges: non-homologous match between tumor and coagulation with inconsistent image appearance, and tissue shrinkage caused by thermal dehydration. These challenges result in low precision when using traditional registration methods for MAM assessment. In this paper, we present a local contractive nonrigid registration method using a biomechanical model (LC-BM) to address these challenges and precisely assess the MAM. The LC-BM contains two consecutive parts: (1) local contractive decomposition (LC-part), which reduces the incorrect match between the tumor and coagulation and quantifies the shrinkage in the external coagulation region, and (2) biomechanical model constraint (BM-part), which compensates for the shrinkage in the internal coagulation region. After quantifying and compensating for tissue shrinkage, the warped tumor is overlaid on the coagulation, and then the MAM is assessed. We evaluated the method using prospectively collected data from 36 patients with 47 liver tumors, comparing LC-BM with 11 state-of-the-art methods. LTP was diagnosed through contrast-enhanced MR follow-up images, serving as the ground truth for tumor recurrence. LC-BM achieved the highest accuracy (97.9%) in predicting LTP, outperforming other methods. Therefore, our proposed method holds significant potential to improve MAM assessment in MWA surgeries.

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

Objective.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.Approach.To improve registration performance, we propose a novel CT/x-ray image registration agent (CT2X-IRA) within a task-driven deep reinforcement learning framework, which contains three key strategies: (1) a multi-scale-stride learning mechanism provides multi-scale feature representation and flexible action step size, establishing fast and globally optimal convergence of the registration task. (2) A domain adaptation module reduces the domain gap between the x-ray image and digitally reconstructed radiograph projected from the CT volume, decreasing the sensitivity and uncertainty of the similarity measurement. (3) A weighted reward function facilitates CT2X-IRA in searching for the optimal transformation parameters, improving the estimation accuracy of out-of-plane transformation parameters under large initial misalignments.Main results.We evaluate the proposed CT2X-IRA on both the public and private clinical datasets, achieving target registration errors of 2.13 mm and 2.33 mm with the computation time of 1.5 s and 1.1 s, respectively, showing an accurate and fast workflow for CT/x-ray image rigid registration.Significance.The proposed CT2X-IRA obtains the accurate and robust 3D/2D registration of CT and x-ray images, suggesting its potential significance in clinical applications.

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. The study investigated the temperature rise mechanism of the material weaving structure under the magnetic field, and explored in-depth the influence of molds made of 45# steel and glass fiber-reinforced plastic (FRP) on the heating process of CFRP. The CFRP circular tubes with weaving structures of 89-degree winding angle, 45-degree winding angle, and plain weave were studied. The study found that when the metal mold was heated, the CFRP structure had almost no effect on the temperature distribution. However, when the glass fiber-reinforced plastic mold was heated, the temperature field changed with the CFRP structure, and the more fiber cross points, the more uniform the temperature field. The accuracy of the finite element model was verified through experiments. The aim of this research is to provide theoretical guidance for actual industrial production.

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

OBJECTIVE

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.

APPROACH

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 (GAN) based registration framework is proposed to eliminate distorted textures. A similarity metric in the image space is formulated based on combining self-structural constraint with intensity to strengthen the robustness to abnormal intensity change compared with common intensity-based metrics. The proposed framework takes two discriminators as feature extractors to formulate the texture and content similarity between the registered image and the fixed image in the multiscale feature space respectively. A distinctive alternating training strategy is established to jointly optimize the combination of various similarity loss functions to overcome the difficulty and instability of training convergence and balance the training of generator and discriminators.

MAIN RESULTS

Compared with five registration methods, the proposed method is evaluated both with small and large deformations, and achieves the best registration accuracy with average target registration error (TRE) of 1.089 mm and 2.139 mm in cases of small and large deformations, respectively. The performance on peak signal to noise ratio (PSNR) and structural similarity (SSIM) also proves the effective constraints on distorted textures of the proposed method (PSNR is 31.693 dB and SSIM is 0.9 in the case of small deformation; PSNR is 28.177 dB and SSIM is 0.853 in the case of large deformation).

SIGNIFICANCE

The proposed 3D ultrasound non-rigid registration method based on texture and content constraints with the distinctive alternating training strategy can eliminate the distorted textures with improving the registration accuracy.

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. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.

Robotic versus laparoscopic left colectomy with complete mesocolic excision for left-sided colon cancer: a multicentre study with propensity score matching analysis

BACKGROUND

Robotic surgery for right-sided colon and rectal cancer has rapidly increased; however, there is limited evidence in the literature of advantages of robotic left colectomy (RLC) for left-sided colon cancer. The purpose of this study was to compare the outcomes of RLC versus laparoscopic left colectomy (LLC) with complete mesocolic excision (CME) for left-sided colon cancer.

METHODS

Patients who had RLC or LLC with CME for left-sided colon cancer at 5 hospitals in China between January 2014 and April 2022 were included. A one-to-one propensity score matched analysis was performed to decrease confounding. The primary outcome was postoperative complications occurring within 30 days of surgery. Secondary outcomes were disease-free survival, overall survival and the number of harvested lymph nodes.

RESULTS

A total of 292 patients (187 males; median age 61.0 [20.0-85.0] years) were eligible for this study, and propensity score matching yielded 102 patients in each group. The clinical-pathological characteristics were well-matched between groups. The two groups did not differ in estimated blood loss, conversion to open rate, time to first flatus, reoperation rate, or postoperative length of hospital stay (p > 0.05). RLC was associated with a longer operation time (192.9 ± 53.2 vs. 168.9 ± 52.8 minutes, p=0.001). The incidence of postoperative complications did not differ between the RLC and LLC groups (18.6% vs. 17.6%, p = 0.856). The total number of lymph nodes harvested in the RLC group was higher than that in the LLC group (15.7 ± 8.3 vs. 12.1 ± 5.9, p< 0.001). There were no significant differences in 3-year and 5-year overall survival or 3-year and 5-year disease-free survival.

CONCLUSIONS

Compared to laparoscopic surgery, RLC with CME for left-sided colon cancer was found to be associated with higher numbers of lymph nodes harvested and similar postoperative complications and long-term survival outcomes.

Multimodal registration of ultrasound and MR images using weighted self-similarity structure vector

PROPOSE

Multimodal registration of 2D Ultrasound (US) and 3D Magnetic Resonance (MR) for fusion navigation can improve the intraoperative detection accuracy of lesion. However, multimodal registration remains a challenge because of the poor US image quality. In the study, a weighted self-similarity structure vector (WSSV) is proposed to registrate multimodal images.

METHOD

The self-similarity structure vector utilizes the normalized distance of symmetrically located patches in the neighborhood to describe the local structure information. The texture weights are extracted using the local standard deviation to reduce the speckle interference in the US images. The multimodal similarity metric is constructed by combining a self-similarity structure vector with a texture weight map.

RESULTS

Experiments were performed on US and MR images of the liver from 88 groups of data including 8 patients and 80 simulated samples. The average target registration error was reduced from 14.91 ± 3.86 mm to 4.95 ± 2.23 mm using the WSSV-based method.

CONCLUSIONS

The experimental results show that the WSSV-based registration method could robustly align the US and MR images of the liver. With further acceleration, the registration framework can be potentially applied in time-sensitive clinical settings, such as US-MR image registration in image-guided surgery.

Adaptive tetrahedral interpolation for reconstruction of uneven freehand 3D ultrasound

Objective.Freehand 3D ultrasound volume reconstruction has received considerable attention in medical research because it can freely perform spatial imaging at a low cost. However, the uneven distribution of the original ultrasound images in space reduces the reconstruction effect of the traditional method.Approach.An adaptive tetrahedral interpolation algorithm is proposed to reconstruct 3D ultrasound volume data. The algorithm adaptively divides the unevenly distributed images into numerous tetrahedrons and interpolates the voxel value in each tetrahedron to reconstruct 3D ultrasound volume data.Main results.Extensive experiments on simulated and clinical data confirm that the proposed method can achieve more accurate reconstruction than six benchmark methods. Specifically, the averaged interpolation error at the gray level can be reduced by 0.22-0.82, and the peak signal-to-noise ratio and the mean structure similarity can be improved by 0.32-1.83 dB and 0.01-0.05, respectively.Significance.With the parallel implementation of the algorithm, one 3D ultrasound volume data with size 279 × 279 × 276 can be reconstructed from 100 slices 2D ultrasound images with size 200 × 200 at 1.04 s. Such a quick and accurate approach has practical value in medical research.

Automatic spatial calibration of freehand ultrasound probe with a multilayer N-wire phantom

The classic N-wire phantom has been widely used in the calibration of freehand ultrasound probes. One of the main challenges of the phantom is accurately identifying N-fiducials in ultrasound images, especially with multiple N-wire structures. In this study, a method using a multilayer N-wire phantom for the automatic spatial calibration of ultrasound images is proposed. All dots in the ultrasound image are segmented, scored, and classified according to the unique geometric features of the multilayer N-wire phantom. A recognition method for identifying N-fiducials from the dots is proposed for calibrating the spatial transformation of the ultrasound probe. At depths of 9, 11, 13, and 15 cm, the reconstruction error of 50 points is 1.24 ± 0.16, 1.09 ± 0.06, 0.95 ± 0.08, 1.02 ± 0.05 mm, respectively. The reconstruction mockup test shows that the distance accuracy is 1.11 ± 0.82 mm at a depth of 15 cm.

Inhibition of castration-resistant prostate cancer growth by genistein through suppression of AKR1C3

Background

Prostate cancer is the second leading cause of cancer-related death among males in America. The patients' survival time is significantly reduced after prostate cancer develops into castration-resistant prostate cancer (CRPC). It has been reported that AKR1C3 is involved in this progression, and that its abnormal expression is directly correlated with the degree of CRPC malignancy. Genistein is one of the active components of soy isoflavones, and many studies have suggested that it has a better inhibitory effect on CRPC.

Objective

This study aimed to investigate the antitumor effect of genistein on CRPC and the potential mechanism of action.

Design

A xenograft tumor mouse model established with 22RV1 cells was divided into the experimental group and the control group, and the former was given 100 mg/kg.bw/day of genistein, with 22RV1, VCaP, and RWPE-1 cells cultured in a hormone-free serum environment and treated with different concentrations of genistein (0, 12.5, 25, 50, and 100 μmol/L) for 48 h. Molecular docking was used to elucidate the molecular interactions between genistein and AKR1C3.

Results

Genistein inhibits CRPC cell proliferation and in vivo tumorigenesis. The western blot analysis confirmed that the genistein significantly inhibited prostate-specific antigen production in a dose-dependent manner. In further results, AKR1C3 expression was decreased in both the xenograft tumor tissues and the CRPC cell lines following genistein gavage feeding compared to the control group, with the reduction becoming more obvious as the concentration of genistein was increased. When the genistein was combined with AKR1C3 small interfering ribonucleic acid and an AKR1C3 inhibitor (ASP-9521), the inhibitory effect on the AKR1C3 was more pronounced. In addition, the molecular docking results suggested that the genistein had a strong affinity with the AKR1C3, and that it could be a promising AKR1C3 inhibitor.

Conclusion

Genistein inhibits the progression of CRPC via the suppression of AKR1C3.

ELANE Promotes M2 Macrophage Polarization by Down-Regulating PTEN and Participates in the Lung Cancer Progression

BACKGROUND

Macrophages are one of the most important immunoinflammatory cell populations in the tumor microenvironment (TME). In this study, we preliminarily investigated the upstream pathway of M2 macrophage polarization affecting lung cancer progression.

METHODS

Bioinformatics analysis was used to evaluate genes closely associated with lung adenocarcinoma and their relationship with immune cells. THP-1 monocytes were induced into M2 macrophages. The expression of markers in M2 macrophages was detected by quantitative reverse transcription-PCR (qRT-PCR), enzyme linked immunosorbent assay (ELISA), and flow cytometry. The effects of neutrophil elastase (ELANE)-mediated M2 macrophages on lung cancer cell proliferation, migration and invasion and tumor growth were investigated by in vitro and in vivo experiments after co-culture of macrophage conditioned medium (CM) and lung cancer cell lines A549 and H1299. The PTEN protein expression was detected by Western blotting.

RESULTS

ELANE was significantly positively correlated with M2 macrophages. ELANE up-regulated the expression of the M2 macrophage markers CD206, CCL22, IL-10 and CCL18 and increased the proportion of CD206+ macrophages. Compared with M0-CM, M2-CM promoted cell proliferation, migration, and invasion, and (M2+ELANE)-CM further enhanced this effect. In vivo, ELANE promoted M2 macrophage-induced tumor growth in lung cancer mice model. In vitro experiments showed that ELANE can down-regulate the expression of PTEN and promote the polarization of M2 macrophages.

CONCLUSION

ELANE promotes the polarization of M2 macrophages by down-regulating PTEN, thus promoting cell proliferation, migration, and invasion in vitro and growth of lung cancer cells in vivo.

Polysaccharide from Edible Alga Enteromorpha clathrata Improves Ulcerative Colitis in Association with Increased Abundance of Parabacteroides spp. in the Gut Microbiota of Dextran Sulfate Sodium-Fed Mice

Polysaccharide from the edible alga Enteromorpha clathrata has been demonstrated to exert beneficial effects on human health. However, what effect it has on inflammatory bowel diseases has not been investigated. Here, using a mouse model of dextran sulfate sodium (DSS)-induced ulcerative colitis, we illustrate that Enteromorpha clathrata polysaccharide (ECP) could alleviate body weight loss, reduce incidences of colonic bleeding, improve stool consistency and ameliorate mucosal damage in diseased mice. 16S rRNA high-throughput sequencing and bioinformatic analysis indicated that ECP significantly changed the structure of the gut microbiota and increased the abundance of Parabacteroides spp. in DSS-fed mice. In vitro fermentation studies further confirmed that ECP could promote the growth of Parabacteroides distasonis F1-28, a next-generation probiotic bacterium isolated from the human gut, and increase its production of short-chain fatty acids. Additionally, Parabacteroides distasonis F1-28 was also found to have anti-ulcerative colitis effects in DSS-fed mice. Altogether, our study demonstrates for the first time a beneficial effect of ECP on ulcerative colitis and provides a possible basis for understanding its therapeutic mechanisms from the perspective of symbiotic gut bacteria Parabacteroides distasonis.

Multi3: multi-templates siamese network with multi-peaks detection and multi-features refinement for target tracking in ultrasound image sequences

Objective. Radiation therapy requires a precise target location. However, respiratory motion increases the uncertainties of the target location. Accurate and robust tracking is significant for improving operation accuracy. Approach. In this work, we propose a tracking framework Multi3, including a multi-templates Siamese network, multi-peaks detection and multi-features refinement, for target tracking in ultrasound sequences. Specifically, we use two templates to provide the location and deformation of the target for robust tracking. Multi-peaks detection is applied to extend the set of potential target locations, and multi-features refinement is designed to select an appropriate location as the tracking result by quality assessment. Main results. The proposed Multi3 is evaluated on a public dataset, i.e. MICCAI 2015 challenge on liver ultrasound tracking (CLUST), and our clinical dataset provided by the Chinese People’s Liberation Army General Hospital. Experimental results show that Multi3 achieves accurate and robust tracking in ultrasound sequences (0.75 ± 0.62 mm and 0.51 ± 0.32 mm tracking errors in two datasets). Significance. The proposed Multi3 is the most robust method on the CLUST 2D benchmark set, exhibiting potential in clinical practice.

Augmented reality calibration using feature triangulation iteration-based registration for surgical navigation

BACKGROUND

Marker-based augmented reality (AR) calibration methods for surgical navigation often require a second computed tomography scan of the patient, and their clinical application is limited due to high manufacturing costs and low accuracy.

METHODS

This work introduces a novel type of AR calibration framework that combines a Microsoft HoloLens device with a single camera registration module for surgical navigation. A camera is used to gather multi-view images of a patient for reconstruction in this framework. A shape feature matching-based search method is proposed to adjust the size of the reconstructed model. The double clustering-based 3D point cloud segmentation method and 3D line segment detection method are also proposed to extract the corner points of the image marker. The corner points are the registration data of the image marker. A feature triangulation iteration-based registration method is proposed to quickly and accurately calibrate the pose relationship between the image marker and the patient in the virtual and real space. The patient model after registration is wirelessly transmitted to the HoloLens device to display the AR scene.

RESULTS

The proposed approach was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with six advanced AR calibration methods. The proposed method obtained average fusion errors of 0.70 ± 0.16 and 0.91 ± 0.13 mm in phantom and volunteer experiments, respectively. The fusion accuracy of the proposed method is the highest among all comparison methods. A volunteer liver puncture clinical simulation experiment was also conducted to show the clinical feasibility.

CONCLUSIONS

Our experiments proved the effectiveness of the proposed AR calibration method, and revealed a considerable potential for improving surgical performance.

Feature detection network-based correction method for accurate nano-tomography reconstruction

Driven by the development of advanced x-ray optics such as Fresnel zone plates, nano-resolution full-field transmission x-ray microscopy (Nano-CT) has become a powerful technique for the non-destructive volumetric inspection of objects and has long been developed at different synchrotron radiation facilities. However, Nano-CT data are often associated with random sample jitter because of the drift or radial/axial error motion of the rotation stage during measurement. Without a proper sample jitter correction process prior to reconstruction, the use of Nano-CT in providing accurate 3D structure information for samples is almost impossible. In this paper, to realize accurate 3D reconstruction for Nano-CT, a correction method based on a feature detection neural network, which can automatically extract target features from a projective image and precisely correct sample jitter errors, is proposed, thereby resulting in high-quality nanoscale 3D reconstruction. Compared with other feature detection methods, even if the target feature is overlapped by other high-density materials or impurities, the proposed Nano-CT correction method still acquires sub-pixel accuracy in geometrical correction and is more suitable for Nano-CT reconstruction because of its universal and faster correction speed. The simulated and experimental datasets demonstrated the reliability and validity of the proposed Nano-CT correction method.

InterNet: Detection of Active Abdominal Arterial Bleeding Using Emergency Digital Subtraction Angiography Imaging With Two-Stage Deep Learning

Objective

Active abdominal arterial bleeding is an emergency medical condition. Herein, we present our use of this two-stage InterNet model for detection of active abdominal arterial bleeding using emergency DSA imaging.

Methods

Firstly, 450 patients who underwent abdominal DSA procedures were randomly selected for development of the region localization stage (RLS). Secondly, 160 consecutive patients with active abdominal arterial bleeding were included for development of the bleeding site detection stage (BSDS) and InterNet (cascade network of RLS and BSDS). Another 50 patients that ruled out active abdominal arterial bleeding were used as negative samples to evaluate InterNet performance. We evaluated the mode's efficacy using the precision-recall (PR) curve. The classification performance of a doctor with and without InterNet was evaluated using a receiver operating characteristic (ROC) curve analysis.

Results

The AP, precision, and recall of the RLS were 0.99, 0.95, and 0.99 in the validation dataset, respectively. Our InterNet reached a recall of 0.7, the precision for detection of bleeding sites was 53% in the evaluation set. The AUCs of doctors with and without InterNet were 0.803 and 0.759, respectively. In addition, the doctor with InterNet assistant could significantly reduce the elapsed time for the interpretation of each DSA sequence from 84.88 to 43.78 s.

Conclusion

Our InterNet system could assist interventional radiologists in identifying bleeding foci quickly and may improve the workflow of the DSA operation to a more real-time procedure.

Endoscopy image enhancement method by generalized imaging defect models based adversarial training

Objective. Smoke, uneven lighting, and color deviation are common issues in endoscopic surgery, which have increased the risk of surgery and even lead to failure. Approach. In this study, we present a new physics model driven semi-supervised learning framework for high-quality pixel-wise endoscopic image enhancement, which is generalizable for smoke removal, light adjustment, and color correction. To improve the authenticity of the generated images, and thereby improve the network performance, we integrated specific physical imaging defect models with the CycleGAN framework. No ground-truth data in pairs are required. In addition, we propose a transfer learning framework to address the data scarcity in several endoscope enhancement tasks and improve the network performance. Main results. Qualitative and quantitative studies reveal that the proposed network outperforms the state-of-the-art image enhancement methods. In particular, the proposed method performs much better than the original CycleGAN, for example, the structural similarity improved from 0.7925 to 0.8648, feature similarity for color images from 0.8917 to 0.9283, and quaternion structural similarity from 0.8097 to 0.8800 in the smoke removal task. Experimental results of the proposed transfer learning method also reveal its superior performance when trained with small datasets of target tasks. Significance. Experimental results on endoscopic images prove the effectiveness of the proposed network in smoke removal, light adjustment, and color correction, showing excellent clinical usefulness.

Marinifilum caeruleilacunae sp. nov., isolated from Yongle Blue Hole in the South China Sea

A Gram-stain-negative, non-motile, facultatively anaerobic and non-flagellated marine bacterium, designated JC070T was isolated from the Yongle Blue Hole in the South China Sea. The temperature, pH and NaCl ranges for growth of strain JC070T were 4–37 °C (optimum, 16 °C), pH 6.0–9.0 (optimum, pH 7.0) and 1.0 –6.0% (w/v; optimum, 3.0%). The predominant isoprenoid quinone of strain JC070T was identified as menaquinone-7. The dominant fatty acids (>10%) were iso-C15:0 (59.6%) and iso-C17:0 3-OH (17.2%). The major polar lipids were aminophospholipid, aminolipid, two unknown phospholipids and two unidentified lipids. The genomic DNA G+C content was determined to be 37.0 mol%. Based on the results of polyphasic analysis, a new species, named Marinifilum caeruleilacunae sp. nov., within the genus Marinifilum was proposed. The type strain is JC070T (= JCM 39045T=MCCC 1K03774T).

Metal-Organic Framework-Based Materials for Adsorption and Detection of Uranium(VI) from Aqueous Solution

The steady supply of uranium resources and the reduction or elimination of the ecological and human health hazards of wastewater containing uranium make the recovery and detection of uranium in water greatly important. Thus, the development of effective adsorbents and sensors has received growing attention. Metal-organic frameworks (MOFs) possessing fascinating characteristics such as high surface area, high porosity, adjustable pore size, and luminescence have been widely used for either uranium adsorption or sensing. Now pertinent research has transited slowly into simultaneous uranium adsorption and detection. In this review, the progress on the research of MOF-based materials used for both adsorption and detection of uranium in water is first summarized. The adsorption mechanisms between uranium species in aqueous solution and MOF-based materials are elaborated by macroscopic batch experiments combined with microscopic spectral technology. Moreover, the application of MOF-based materials as uranium sensors is focused on their typical structures, sensing mechanisms, and the representative examples. Furthermore, the bifunctional MOF-based materials used for simultaneous detection and adsorption of U(VI) from aqueous solution are introduced. Finally, we also discuss the challenges and perspectives of MOF-based materials for uranium adsorption and detection to provide a useful inspiration and significant reference for further developing better adsorbents and sensors for uranium containment and detection.

Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network

Building an in vivo three-dimensional (3D) surface model from a monocular endoscopy is an effective technology to improve the intuitiveness and precision of clinical laparoscopic surgery. This paper proposes a multi-loss rebalancing-based method for joint estimation of depth and motion from a monocular endoscopy image sequence. The feature descriptors are used to provide monitoring signals for the depth estimation network and motion estimation network. The epipolar constraints of the sequence frame is considered in the neighborhood spatial information by depth estimation network to enhance the accuracy of depth estimation. The reprojection information of depth estimation is used to reconstruct the camera motion by motion estimation network with a multi-view relative pose fusion mechanism. The relative response loss, feature consistency loss, and epipolar consistency loss function are defined to improve the robustness and accuracy of the proposed unsupervised learning-based method. Evaluations are implemented on public datasets. The error of motion estimation in three scenes decreased by 42.1%,53.6%, and 50.2%, respectively. And the average error of 3D reconstruction is 6.456 ± 1.798mm. This demonstrates its capability to generate reliable depth estimation and trajectory reconstruction results for endoscopy images and meaningful applications in clinical.

Feature matching for texture-less endoscopy images via superpixel vector field consistency

Feature matching is an important technology to obtain the surface morphology of soft tissues in intraoperative endoscopy images. The extraction of features from clinical endoscopy images is a difficult problem, especially for texture-less images. The reduction of surface details makes the problem more challenging. We proposed an adaptive gradient-preserving method to improve the visual feature of texture-less images. For feature matching, we first constructed a spatial motion field by using the superpixel blocks and estimated its information entropy matching with the motion consistency algorithm to obtain the initial outlier feature screening. Second, we extended the superpixel spatial motion field to the vector field and constrained it with the vector feature to optimize the confidence of the initial matching set. Evaluations were implemented on public and undisclosed datasets. Our method increased by an order of magnitude in the three feature point extraction methods than the original image. In the public dataset, the accuracy and F1-score increased to 92.6% and 91.5%. The matching score was improved by 1.92%. In the undisclosed dataset, the reconstructed surface integrity of the proposed method was improved from 30% to 85%. Furthermore, we also presented the surface reconstruction result of differently sized images to validate the robustness of our method, which showed high-quality feature matching results. Overall, the experiment results proved the effectiveness of the proposed matching method. This demonstrates its capability to extract sufficient visual feature points and generate reliable feature matches for 3D reconstruction and meaningful applications in clinical.

Novel insights into heat tolerance using metabolomic and high-throughput sequencing analysis in dairy cows rumen fluid

Heat stress influences rumen fermentative processes with effects on the physiology and production of dairy cows. However, the underlying relationship between rumen microbiota and its associated metabolism with heat tolerance in cows have not been extensively described yet. Therefore, the main objective of this study was to investigate differential heat resistance in Holstein cows using rumen bacterial and metabolome analyses. We performed both principal component analysis and membership function analysis to select seven heat-tolerant (HT) and seven heat-sensitive (HS) cows. Under heat stress conditions, the HT cows had a significantly (P < 0.05) higher propionic acid content than the HS cows; while measures of the respiratory rate, acetic, and butyric acid in the HT cows were significantly (P < 0.05) lower compared with the HS cows. Also, the HT cows showed lower (P < 0.01) rectal temperature and acetic acid to propionic acid ratio than the HS group of cows. Omics sequencing revealed that the relative abundances of Muribaculaceae, Rikenellaceae, Acidaminococcaceae, Christensenellaceae, Rikenellaceae_RC9_gut_group, Succiniclasticum, Ruminococcaceae_NK4A214_group and Christensenellaceae_R-7_group were significantly (P < 0.01) higher in the HT cows; whereas Prevotellaceae, Prevotella_1, Ruminococcaceae_UCG-014, and Shuttleworthia were significantly (P < 0.01) lower in HT cows compared to HS cows. Substances mainly involved in carbohydrate metabolism, including glycerol, mannitol, and maltose, showed significantly higher content in the HT cows (P < 0.05) compared to that in the HS cows. Simultaneously, distinct metabolites were significantly correlated with differential bacteria, suggesting that glycerol, mannitol, and maltose could serve as potential biomarkers for determining heat resistance that require further study. Overall, distinct changes in the rumen microbiota and metabolomics in the HT cows may be associated with a better adaptability to heat stress. These findings suggest their use as diagnostic tools of heat tolerance in dairy cattle breeding schemes.

Fusion Siamese network with drift correction for target tracking in ultrasound sequences

Motion tracking techniques can revise the bias arising from respiration-caused motion in radiation therapy. Tracking key structures accurately and at a real-time speed is necessary for effective motion tracking. In this work, we propose a fusion Siamese network with drift correction for target tracking in ultrasound sequences. Specifically, the network fuses four response maps generated by the cross-correlation between convolution layers at different resolutions to reduce up-sampling error. A correction strategy combining local structural similarity and target trajectory is proposed to revise the target drift predicted by the network. Moreover, a coarse-to-fine strategy is proposed to train the network with a limited number of annotated images, in which an augmented dataset is generated by corner points to learn network features with high generalizability. The proposed method is evaluated on the basis of the public dataset of the MICCAI 2015 Challenge on Liver UltraSound Tracking (CLUST) and our ultrasound image dataset, which is provided by the Chinese People’s Liberation Army General Hospital (CPLAGH). A tracking error of 0.80 ± 1.16 mm is observed for 85 targets across 39 ultrasound sequences in the CLUST dataset. A tracking error of 0.61 ± 0.36 mm is observed for 20 targets across 10 ultrasound sequences in the CPLAGH dataset. The effectiveness of the proposed fusion and correction strategies is verified via two ablation experiments. Overall, the experimental results demonstrate the effectiveness of the proposed fusion Siamese network with drift correction and reveal its potential in clinical practice.

Polysaccharide from edible alga Gloiopeltis furcata attenuates intestinal mucosal damage by therapeutically remodeling the interactions between gut microbiota and mucin O-glycans

Gloiopeltis furcata is an edible alga that has long been consumed in China. However, the bioactive polysaccharides from G. furcata have been largely unexplored. Here, we show for the first time that a sulfated polysaccharide from G. furcata (SAO) could improve the integrity of the colonic epithelial layer and protect against dextran sulfate sodium-induced intestinal mucosal damage. Mechanistically, SAO attenuated colonic mucosal damage by therapeutically remodeling the interactions between gut microbiota and mucin O-glycans. Specifically, SAO increased the proportions of complex long-chain mucin O-glycans in the epithelial layer with two terminal N-acetylneuraminic acid residues and promoted the growth of probiotic bacteria including Roseburia spp. and Muribaculaceae. Altogether, our study demonstrates a novel application of SAO for the treatment of inflammatory bowel disease-associated mucosal damage and forms the basis to understand the therapeutic effects of natural polysaccharides from the perspective of symbiotic interactions between host mucin O-glycome and gut microbiome.

Deep-learning-based image registration for nano-resolution tomographic reconstruction

Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.

Hole-filling based on content loss indexed 3D partial convolution network for freehand ultrasound reconstruction

BACKGROUND AND OBJECTIVE

During the 3D reconstruction of ultrasound volume from 2D B-scan ultrasound images, holes are usually found in the reconstructed 3D volumes due to the fast scans. This condition will affect the positioning and judgment of the doctor to the lesion. Hence, in this study, we propose to fill the holes by using a novel content loss indexed 3D partial convolution network for 3D freehand ultrasound volume reconstruction. The network can synthesize novel ultrasound volume structures and reconstruct ultrasound volume with missing regions with variable sizes and at arbitrary locations.

METHODS

First, the 3D partial convolution is introduced into the convolutional layer, which is masked and renormalized to be conditioned on only valid voxels. Then, the mask in the next layer is automatically updated as a part of the forward pass. To better preserve texture and structure details of the reconstruction results, we couple the adversarial loss of the least squares generative adversarial network (LSGAN) with the innovative content loss, which consists of the context loss, the feature-matching loss and the total variation loss. Thereafter, we introduce a novel spectral-normalized LSGAN by adding spectral normalization (SN) to the generator and discriminator of the LSGAN. The proposed method is simple in formulation, and is stable in training.

RESULTS

Experiments on public and in-vivo ultrasound datasets and comparisons with popular algorithms demonstrate that the proposed approach can generate high-quality hole-filling results with preserved perceptual image details.

CONCLUSIONS

Considering the high quality of the hole-filling results, the proposed method can effectively fill the missing regions in the reconstructed 3D ultrasound volume from 2D ultrasound image sequences.

An optimal ablation time prediction model based on minimizing the relapse risk

OBJECTIVE

Percutaneous microwave ablation is an essential and safe method for the treatment of liver cancer. As one therapeutic dose, ablation time is crucial to the treatment effect determined by the physicians. However, due to the different experiences of physicians and the significant individual differences of patients, the final treatment effect is also different, which makes it difficult for the ablation time recorded in the electronic health records (EHRs) to follow the same pattern. To solve this problem, we propose a data mining method based on historical treatment data recorded in EHR, which uses a robust relapse risk as strong supervision to correct the ablation time. The prediction results of this method are closer to the situation of patients without relapse, which can provide physicians with reference.

METHODS

In the proposed method, we introduce the optimization method to iteratively minimize the postoperative relapse risk and utilize gradient propagation between the risk and ablation time during iteration to correct the latter. We also apply a self-attention mechanism to find the global dependencies between each feature in EHR to improve the final prediction performance of the model.

RESULTS

Comparative experimental results show that compared with other baseline model, the proposed model achieves better performance on R-square, MAE, and MSE metric. The results of ablation experiments show that the integration of label correction and self-attention mechanism can improve the model performance.

CONCLUSIONS

We using relapse risk as strong supervision related to the ablation time can effectively correct the deviation of the ablation time as weak supervision. The self-attention mechanism in the proposed model can significantly improve the prediction performance.

The factors involved in the induction of neutrophil gelatinase-associated lipocalin overexpression in renal tubular epithelial cells under endoplasmic reticulum stress

Our previous work found that neutrophil gelatinase-associated lipocalin (NGAL) expression increases when endoplasmic reticulum stress (ERS) occurs in human kidney-2 (HK-2) tubular epithelial cells. However, the reason for this is not yet known. This study investigated the factors involved when inducing NGAL overexpression in HK-2 cells during ERS. The cells were divided into six groups: the control group (normal HK-2 cells), the ERS group (HK-2 cells cultured in complete medium with thapsigargin (TG)), the transfection group (HK-2 cells transfected with activating transcription factor 4 small interfering ribonucleic acid (ATF4 siRNA), the ERS after transfection group (HK-2 cells transfected with ATF4 siRNA, then cultured in complete medium with TG), the negative control group (HK-2 cells transfected with siRNA-negative contrast), and the dimethyl sulfoxide (DMSO) group (HK-2 cells cultured in complete medium with DMSO). Western blot and a real-time polymerase chain reaction were used to measure the expression of protein and messenger ribonucleic acid (mRNA). As a result NGAL, ATF4, C/EBP homologous protein, glucose-regulated protein 78 kDa, ATF4 mRNA, and NGAL mRNA were clearly overexpressed in the ERS group compared with the control group (p < 0.05). The expression of NGAL and ATF4 were similar in the control group, the negative control group, and the DMSO group (p > 0.05). Meanwhile, ATF4, NGAL, ATF4 mRNA, and NGAL mRNA in the ERS after transfection group were significantly lower compared with the ERS group (p < 0.05), which showed that NGAL was affected by ATF4. There was a close correlation between NGAL and ATF4; when the expression of ATF4 was inhibited, NGAL was significantly lower. Therefore, ATF4 may be one of the upstream regulators of NGAL.

Dietary Polysaccharide from Enteromorpha clathrata Attenuates Obesity and Increases the Intestinal Abundance of Butyrate-Producing Bacterium, Eubacterium xylanophilum, in Mice Fed a High-Fat Diet

Previous studies have suggested that polysaccharide from Enteromorpha clathrata (ECP) could be used as a potential prebiotic to treat dysbiosis-associated diseases. However, whether it has any therapeutic effects on obesity has not been investigated. In the present study, we explored the anti-obesity effect of ECP and illustrated that it can significantly reduce the body weight and decrease the serum levels of triacylglycerol and cholesterol in high-fat diet (HFD)-fed mice. As revealed by 16S rRNA high-throughput sequencing and bioinformatic analysis, HFD remarkably changed the composition of the gut microbiota and promoted the growth of opportunistic pathogens such as Mucispirillum, Desulfobacterota and Alphaproteobacteria in obese mice. Interestingly, ECP improved intestinal dysbiosis caused by HFD and reshaped the structure of the gut microbiota in diseased mice by increasing the abundance of butyrate-producing bacterium, Eubacterium xylanophilum, in the gut. Altogether, we demonstrate for the first time an anti-obesity effect of ECP and shed new light into its therapeutic mechanisms from the perspective of gut microbiota. Our study will pave the way for the development of ECP as new prebiotic for the treatment of obesity and its associated disorders.

Fast Li Plating Behavior Probed by X-ray Computed Tomography

Uneven lithium plating/stripping is an essential issue that inhibits stable cycling of a lithium metal anode and thus hinders its practical applications. The investigation of this process is challenging because it is difficult to observe lithium in an operating device. Here, we demonstrate that the microscopic lithium plating behavior can be observed in situ in a close-to-practical cell setup using X-ray computed tomography. The results reveal the formation of porous structure and its progressive evolution in space over the charging process with a large current. The elaborated analysis indicates that the microstructure of deposited lithium makes a significant impact on the subsequent lithium plating, and the impact of structural inhomogeneity, further exaggerated by the large-current charging, can lead to severely uneven lithium plating and eventually cell failure. Therefore, a codesign strategy involving delicate controls of microstructure and electrochemical conditions could be a necessity for the next-generation battery with lithium metal anode.

In vitro fermentation of hyaluronan by human gut microbiota: Changes in microbiota community and potential degradation mechanism

Hyaluronan (HA) has been widely used as a dietary supplement which can be degraded by gut microbiota. However, the interactions between HA and gut microbiota have not been fully characterized. Here, using an in vitro system, we found that HA is readily fermented by human gut microbiota but with differing fermentative activities among individuals. HA-fermentation boosted Bacteroides spp., Bifidobacterium spp., Dialister spp., Faecalibacterium spp. and produced a significant amount of acetate, propionate and butyrate. Fermentation products profiling indicated that HA could be degraded into unsaturated even-numbered and saturated odd-numbered oligosaccharides. Further, polysaccharide lyases (PLs) and glycoside hydrolases (GHs) including GH88, PL8, PL29, PL35 and PL33 were identified from B. ovatus E3, which can help to explain the structure of the fermentation products. Collectively, our study sheds new light into the metabolism of HA and forms the basis for understanding the bioavailability of HA from a gut microbiota perspective.

POS0761 INVESTIGATION ON THE EFFECT AND MECHANISM OF ABNORMALLY ACTIVATED CD8+ T CELLS FROM BONE MARROW ON HEMATOPOIETIC STEM CELLS IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

Background:

SLE is an autoimmune disease characterized by the abnormal function of lymphocytes. The impairment of hematopoietic function of bone marrow participates in its pathogenesis, in which T cells play an important role. However, study on bone marrow T cells in SLE patients is very limited.

Objectives:

This study aims to characterize the phenotype and molecular characteristics of abnormally activated CD8+T cells in bone marrow of SLE patients and explore the mechanism of hematopoietic stem cells (HSCs) reduction caused by the abnormally activated CD8+T cells in bone marrow of patients with SLE.

Methods:

A total of 8 SLE patients and 5 age- and sex-matched controls were recruited in our study. Among them, 3 SLE patients and 4 donors were collected bone marrow and peripheral blood samples for Single-cell RNA sequencing (scRNA-seq) and functional studies. BM and peripheral T cell subsets were measured by flow cytometry. Plasma cytokines and secreted immunoglobulins were detected by Luminex. Disease activity of SLE patients was measured using the SLE Disease Activity Index (SLEDAI). All analyses were performed using R language and Flowjo 9.

Results:

In the present study, SLE patients had increased CD8+T%αβT cells and decreased CD4+T%αβT cells in bone marrow of SLE, compared to healthy controls. A large number of CD38+HLADR+CD8+T cells existed in the bone marrow and peripheral blood of SLE patients. Those patients also showed reduced number of HSCs, and with a downward trend of the numbers of peripheral red blood cells, white blood cells, neutrophils, hemoglobin, and platelets. By scRNA-seq, the CD38+HLADR+CD8+T cells contained high levels of GZMK, GZMA, PRF1, IFNG, and TNF in the bone marrow of SLE patients. the CD38+HLADR+CD8+T cells exhibited significant relationship with HSCs, white blood cells, neutrophils, and platelets.

Conclusion:

These findings demonstrated that the abnormally activated CD8+T cells in bone marrow can reduce the number of HSCs by the expression of killer molecules, which contributes to the impairment of hematopoietic function and the development of SLE. This project focuses on the specific bone marrow T cell subset in SLE. The completement of this project provides information for exploring the mechanism of hematopoiesis involvement.

References:

[1]Anderson E, Shah B, Davidson A, Furie R. Lessons learned from bone marrow failure in systemic lupus erythematosus: Case reports and review of the literature. Semin Arthritis Rheum. 2018;48(1):90-104.

[2]Sun LY, Zhou KX, Feng XB, Zhang HY, Ding XQ, Jin O, Lu LW, Lau CS, Hou YY, Fan LM. Abnormal surface markers expression on bone marrow CD34+cells and correlation with disease activity in patients with systemic lupus erythematosus. Clin Rheumatol. 2007;26(12):2073-2079.

Acknowledgements:

We want to thank Lu Meng, Teng Li, Wei Zhou, and Jiaxin Guo for their assistance with this study.

Disclosure of Interests:

None declared

Fermentation of alginate and its derivatives by different enterotypes of human gut microbiota: Towards personalized nutrition using enterotype-specific dietary fibers

Alginate and its derivatives are widely used as food additives and dietary fibers. Previous studies indicated that alginate, polyguluronate (PG) and polymannuronate acid (PM) could be fermented by human gut microbiota. However, how different compositions of the microbiota may affect the fermentation outcomes of these polysaccharides remains unknown. Here we show that Bacteroides-dominated microbiota (Bacteroides enterotype) is more proficient at degrading and utilizing PG and PM as compared to Prevotella-dominated (Prevotella enterotype) and Escherichia-dominated microbiota (Escherichia enterotype). Enterotype dictates the fermentation outcomes of the three fibers and the amount of short-chain fatty acids (SCFAs) that are produced. Fermentation of alginate and PM by Bacteroides-dominated microbiota produced the highest amount of total SCFAs and butyrate. Our study demonstrates an enterotype-specific effect of microbiota on the fermentation of alginate and its derivatives and highlights that personalized nutrition using dietary fibers should be tailored according to individual's composition of the gut microbiome.

Effects of the circadian rhythm on milk composition in dairy cows: Does day milk differ from night milk?

Metabolism in most organisms can show variations between the day and night. These variations may also affect the composition of products derived from livestock. The aim of the present study was to investigate the difference in composition between the day milk and night milk of dairy cows. Ten multiparous Holstein cows (milk yield = 25.2 ± 5.00 kg/d) were randomly selected during mid lactation. Milk samples were collected at 0500 h ("night milk") and 1500 h ("day milk") and analyzed to determine their composition. Mid-infrared spectroscopy was used to analyze macronutrient content of milk. Metabolomics and lipidomics were used to detect and analyze small molecules and fatty acids, respectively. An automatic biochemical analyzer and ELISA kits were used to determine biochemical indicators, as well as antioxidant and immune parameters in the milk. Though milk fat, protein, lactose, and total milk solids were not different between day milk and night milk, small molecules, metabolites and lipids, and hormones and cytokines differed between day milk and night milk. Regarding biochemical and immune-related indicators, the concentrations of malondialdehyde, HSP70, and HSP90 in night milk were lower than that in day milk. However, interferon-γ levels were higher in night milk. Additionally, night milk was naturally rich in melatonin. Lipidomics analyses showed that the levels of some lipids in night milk were higher than those in day milk. Metabolomics analyses identified 36 different metabolites between day milk and night milk. Higher concentrations of N-acetyl-d-glucosamine, cis-aconitate, and d-sorbitol were observed in day milk. However, the other 33 metabolites analyzed, including carbohydrates, lipids, AA, and aromatic compounds, showed lower concentrations in day milk than in night milk. The present findings show that the composition of night milk differs considerably from that of day milk. Notable changes in the circadian rhythm also altered milk composition. These results provide evidence to support the strategic use and classification of day milk and night milk.

Multiple feature-based portal vein classification for liver segment extraction

PURPOSE

The liver segments divided by Couinaud classification method are used to understand the functional anatomy of liver, which is significant in hepatic resection surgery. In Couinaud classification method, each third-order branch of the portal vein (PV) defines the supplied territory of a corresponding liver segment. However, the accuracies of the reconstruction and classification of PV are affected by the complicated structure of the vein. The purpose of this paper is to develop a separation and classification method that can accurately extract the liver segments.

METHODS

In this paper, a multiple feature-based method is proposed to obtain liver segments. Because the portal and hepatic veins usually connect in the vessel segmentation result, the PV is first completely separated based on the different strategies for minimal node cut using fused features of topology and appearance. Meanwhile, all bifurcation nodes of PV are detected. The bifurcation nodes are initial ordered through their linkages to classify the branches. Then, the feature of the vascular topology is used to refine the orders of bifurcation nodes. The bifurcation nodes with the refined orders classify the branches between them, and the third-order branches of PV are obtained. The liver segments are eventually obtained through the third-order branches.

RESULTS

The separation and classification in the proposed method are evaluated on the CT and MR datasets. The average values of Dice, Jaccard, Recall, and Precision obtained by the proposed method are 93.00%, 87.90%, 93.47%, and 93.19%, respectively. Compared with the state-of-the-art methods, the separation results obtained by the proposed method are more accurate. The branches of PV are classified based on the separation result. According to the third-order branches, eight liver segments correspond to the different functional areas are precisely extracted.

CONCLUSIONS

The proposed method achieves a high accuracy for the liver segment extraction. And the extracted liver segments are significant for the preplanning of resection surgery.

Divergence-Free Fitting-Based Incompressible Deformation Quantification of Liver

Liver is an incompressible organ that maintains its volume during the respiration-induced deformation. Quantifying this deformation with the incompressible constraint is significant for liver tracking. The constraint can be accomplished with retaining the divergence-free field obtained by the deformation decomposition. However, the decomposition process is time-consuming, and the removal of non-divergence-free field weakens the deformation. In this study, a divergence-free fitting-based registration method is proposed to quantify the incompressible deformation rapidly and accurately. First, the deformation to be estimated is mapped to the velocity in a diffeomorphic space. Then, this velocity is decomposed by a fast Fourier-based Hodge-Helmholtz decomposition to obtain the divergence-free, curl-free, and harmonic fields. The curl-free field is replaced and fitted by the obtained harmonic field with a translation field to generate a new divergence-free velocity. By optimizing this velocity, the final incompressible deformation is obtained. Moreover, a deep learning framework (DLF) is constructed to accelerate the incompressible deformation quantification. An incompressible respiratory motion model is built for the DLF by using the proposed registration method and is then used to augment the training data. An encoder-decoder network is introduced to learn appearance-velocity correlation at patch scale. In the experiment, we compare the proposed registration with three state-of-the-art methods. The results show that the proposed method can accurately achieve the incompressible registration of liver with a mean liver overlap ratio of 95.33%. Moreover, the time consumed by DLF is nearly 15 times shorter than that by other methods.