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.