Long-term outcomes of intraoperative chemotherapy with 5-FU for colorectal cancer patients receiving curative resection (IOCCRC): a randomized, multicenter, prospective, phase III trial.

BACKGROUND: We aimed to compare combined intraoperative chemotherapy and surgical resection with curative surgical resection alone in colorectal cancer patients. METHODS: We performed a multicenter, open-label, randomized, phase III trial. All eligible patients were randomized and assigned to intraoperative chemotherapy and curative surgical resection or curative surgical resection alone (1:1). Survival actualization after long-term follow-up was performed in patients analyzed on an intention-to-treat basis. RESULTS: From January 2011 to January 2016, 696 colorectal cancer patients were enrolled and randomly assigned to intraoperative chemotherapy and radical surgical resection (n=341) or curative surgical resection alone (n=344). Intraoperative chemotherapy with surgical resection showed no significant survival benefit over surgical resection alone in colorectal cancer patients (3-year DFS: 91.1% vs. 90.0%, P=0.328; 3-year OS: 94.4% vs. 95.9%, P=0.756). However, colon cancer patients benefitted from intraoperative chemotherapy, with a relative 4% reduction in liver and peritoneal metastasis (HR=0.336, 95% CI: 0.148-0.759, P=0.015) and a 6.5% improvement in 3-year DFS (HR=0.579, 95% CI: 0.353-0.949, P=0.032). Meanwhile, patients with colon cancer and abnormal pretreatment CEA levels achieved significant survival benefits from intraoperative chemotherapy (DFS: HR=0.464, 95% CI: 0.233-0.921, P=0.029 and OS: (HR=0.476, 95% CI: 0.223-1.017, P=0.049). CONCLUSIONS: Intraoperative chemotherapy showed no significant extra prognostic benefit in total colorectal cancer patients who underwent radical surgical resection; however, in colon cancer patients with abnormal pretreatment serum CEA levels (> 5 ng/ml), intraoperative chemotherapy could improve long-term survival.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

CSCA U-Net: A channel and space compound attention CNN for medical image segmentation.

Image segmentation is one of the vital steps in medical image analysis. A large number of methods based on convolutional neural networks have emerged, which can extract abstract features from multiple-modality medical images, learn valuable information that is difficult to recognize by humans, and obtain more reliable results than traditional image segmentation approaches. U-Net, due to its simple structure and excellent performance, is widely used in medical image segmentation. In this paper, to further improve the performance of U-Net, we propose a channel and space compound attention (CSCA) convolutional neural network, CSCA U-Net in abbreviation, which increases the network depth and employs a double squeeze-and-excitation (DSE) block in the bottleneck layer to enhance feature extraction and obtain more high-level semantic features. Moreover, the characteristics of the proposed method are three-fold: (1) channel and space compound attention (CSCA) block, (2) cross-layer feature fusion (CLFF), and (3) deep supervision (DS). Extensive experiments on several available medical image datasets, including Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS, CVC-T, 2018 Data Science Bowl (2018 DSB), ISIC 2018, and JSUAH-Cerebellum, show that CSCA U-Net achieves competitive results and significantly improves generalization performance. The codes and trained models are available at https://github.com/xiaolanshu/CSCA-U-Net.

Synthesis of Carboranylated Dihydropyrrolo[1,2-a]quinoxalines and Dihydroindolo[1,2-a]quinoxalines by BF3·OEt2-Catalyzed Heterocyclization of C-Formyl-o-carboranes and Investigation of Their Oxidation Stability.

A BF3·OEt2-catalyzed synthesis of carboranylated dihydropyrrolo[1,2-a]quinoxalines and dihydroindolo[1,2-a]quinoxalines in 30-99% yields is presented through the heterocyclization of various C-modified C-formyl-o-carboranes with 1-(2-aminophenyl)-pyrroles/indoles. A systematic comparative investigation of their oxidation stability in air confirmed that 4-carboranyl-4,5-dihydropyrrolo[1,2-a]quinoxaline had better stability than the 4-phenyl analogue. A cage-deboronation reaction for N-acetyl-substituted carboranylated dihydropyrrolo[1,2-a]quinoxaline produced the corresponding 7,8-nido-carborane cesium salt. A kinetic resolution was also realized to obtain an optically pure carboranylated N-heterocycle scaffold bearing a carborane cage carbon-bonded chiral stereocenter.

Total Flavonoids of Astragalus Inhibit Activated CD4[Formula: see text] T Cells and Regulate Differentiation of Th17/Th1/Treg Cells in Experimental Autoimmune Encephalomyelitis Mice by JAK/STAT and NF[Formula: see text]B Signaling Pathways.

Multiple sclerosis (MS) is a neuroinflammatory disease characterized by CD4[Formula: see text] T cell-mediated immune cell infiltration and demyelination in the central nervous system (CNS). The subtypes of CD4[Formula: see text] T cells are T helper cells 1 (Th1), Th2, Th17, and regulatory T cells (Treg), while three other types of cells besides Th2 play a key role in MS and its classic animal model, experimental autoimmune encephalomyelitis (EAE). Tregs are responsible for immunosuppression, while pathogenic Th1 and Th17 cells cause autoimmune-associated demyelination. Therefore, suppressing Th1 and Th17 cell differentiation and increasing the percentage of Treg cells may contribute to the treatment of EAE/MS. Astragali Radix (AR) is a representative medicine with immunoregulatory, anti-inflammatory, antitumor, and neuroprotective effects.The active ingredients in AR include astragalus flavones, polysaccharides, and saponins. In this study, it was found that the total flavonoids of Astragus (TFA) could effectively treat EAE in mice by ameliorating EAE motor disorders, reducing inflammatory damage and demyelination, inhibiting the proportion of Th17 and Th1 cells, and promoting Tregs differentiation by regulating the JAK/STAT and NF[Formula: see text]B signaling pathways. This novel finding may increase the possibility of using AR or TFA as a drug with immunomodulatory effects for the treatment of autoimmune diseases.

TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network.

The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range dependencies are directly neglected in existing CNN fusion approaches, impeding balancing the entire image-level perception for complex scenario fusion. In this paper, therefore, we propose an infrared and visible image fusion algorithm based on the transformer module and adversarial learning. Inspired by the global interaction power, we use the transformer technique to learn the effective global fusion relations. In particular, shallow features extracted by CNN are interacted in the proposed transformer fusion module to refine the fusion relationship within the spatial scope and across channels simultaneously. Besides, adversarial learning is designed in the training process to improve the output discrimination via imposing competitive consistency from the inputs, reflecting the specific characteristics in infrared and visible images. The experimental performance demonstrates the effectiveness of the proposed modules, with superior improvement against the state-of-the-art, generalising a novel paradigm via transformer and adversarial learning in the fusion task.

Towards Robust Visual Object Tracking with Independent Target-Agnostic Detection and Effective Siamese Cross-Task Interaction.

Advanced Siamese visual object tracking architectures are jointly trained using pair-wise input images to perform target classification and bounding box regression. They have achieved promising results in recent benchmarks and competitions. However, the existing methods suffer from two limitations: First, though the Siamese structure can estimate the target state in an instance frame, provided the target appearance does not deviate too much from the template, the detection of the target in an image cannot be guaranteed in the presence of severe appearance variations. Second, despite the classification and regression tasks sharing the same output from the backbone network, their specific modules and loss functions are invariably designed independently, without promoting any interaction. Yet, in a general tracking task, the centre classification and bounding box regression tasks are collaboratively working to estimate the final target location. To address the above issues, it is essential to perform target-agnostic detection so as to promote cross-task interactions in a Siamese-based tracking framework. In this work, we endow a novel network with a target-agnostic object detection module to complement the direct target inference, and to avoid or minimise the misalignment of the key cues of potential template-instance matches. To unify the multi-task learning formulation, we develop a cross-task interaction module to ensure consistent supervision of the classification and regression branches, improving the synergy of different branches. To eliminate potential inconsistencies that may arise within a multi-task architecture, we assign adaptive labels, rather than fixed hard labels, to supervise the network training more effectively. The experimental results obtained on several benchmarks, i.e., OTB100, UAV123, VOT2018, VOT2019, and LaSOT, demonstrate the effectiveness of the advanced target detection module, as well as the cross-task interaction, exhibiting superior tracking performance as compared with the state-of-the-art tracking methods.

Fast Self-Guided Multi-View Subspace Clustering.

Multi-view subspace clustering is an important topic in cluster analysis. Its aim is to utilize the complementary information conveyed by multiple views of objects to be clustered. Recently, view-shared anchor learning based multi-view clustering methods have been developed to speed up the learning of common data representation. Although widely applied to large-scale scenarios, most of the existing approaches are still faced with two limitations. First, they do not pay sufficient consideration on the negative impact caused by certain noisy views with unclear clustering structures. Second, many of them only focus on the multi-view consistency, yet are incapable of capturing the cross-view diversity. As a result, the learned complementary features may be inaccurate and adversely affect clustering performance. To solve these two challenging issues, we propose a Fast Self-guided Multi-view Subspace Clustering (FSMSC) algorithm which skillfully integrates the view-shared anchor learning and global-guided-local self-guidance learning into a unified model. Such an integration is inspired by the observation that the view with clean clustering structures will play a more crucial role in grouping the clusters when the features of all views are concatenated. Specifically, we first learn a locally-consistent data representation shared by all views in the local learning module, then we learn a globally-discriminative data representation from multi-view concatenated features in the global learning module. Afterwards, a feature selection matrix constrained by the l2,1 -norm is designed to construct a guidance from global learning to local learning. In this way, the multi-view consistent and diverse information can be simultaneously utilized and the negative impact caused by noisy views can be overcame to some extent. Extensive experiments on different datasets demonstrate the effectiveness of our proposed fast self-guided learning model, and its promising performance compared to both, the state-of-the-art non-deep and deep multi-view clustering algorithms. The code of this paper is available at https://github.com/chenzhe207/FSMSC.

Discriminative Dictionary Pair Learning With Scale-Constrained Structured Representation for Image Classification.

The dictionary pair learning (DPL) model aims to design a synthesis dictionary and an analysis dictionary to accomplish the goal of rapid sample encoding. In this article, we propose a novel structured representation learning algorithm based on the DPL for image classification. It is referred to as discriminative DPL with scale-constrained structured representation (DPL-SCSR). The proposed DPL-SCSR utilizes the binary label matrix of dictionary atoms to project the representation into the corresponding label space of the training samples. By imposing a non-negative constraint, the learned representation adaptively approximates a block-diagonal structure. This innovative transformation is also capable of controlling the scale of the block-diagonal representation by enforcing the sum of within-class coefficients of each sample to 1, which means that the dictionary atoms of each class compete to represent the samples from the same class. This implies that the requirement of similarity preservation is considered from the perspective of the constraint on the sum of coefficients. More importantly, the DPL-SCSR does not need to design a classifier in the representation space as the label matrix of the dictionary can also be used as an efficient linear classifier. Finally, the DPL-SCSR imposes the l2,p -norm on the analysis dictionary to make the process of feature extraction more interpretable. The DPL-SCSR seamlessly incorporates the scale-constrained structured representation learning, within-class similarity preservation of representation, and the linear classifier into one regularization term, which dramatically reduces the complexity of training and parameter tuning. The experimental results on several popular image classification datasets show that our DPL-SCSR can deliver superior performance compared with the state-of-the-art (SOTA) dictionary learning methods. The MATLAB code of this article is available at https://github.com/chenzhe207/DPL-SCSR.

LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images.

Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role in the fusion process. However, in general, it is hard to specify a good fusion architecture, and consequently, the design of fusion networks is still a black art, rather than science. To address this problem, we formulate the fusion task mathematically, and establish a connection between its optimal solution and the network architecture that can implement it. This approach leads to a novel method proposed in the paper of constructing a lightweight fusion network. It avoids the time-consuming empirical network design by a trial-and-test strategy. In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model. The low-rank representation (LRR) objective is the foundation of our learnable model. The matrix multiplications, which are at the heart of the solution are transformed into convolutional operations, and the iterative process of optimisation is replaced by a special feed-forward network. Based on this novel network architecture, an end-to-end lightweight fusion network is constructed to fuse infrared and visible light images. Its successful training is facilitated by a detail-to-semantic information loss function proposed to preserve the image details and to enhance the salient features of the source images. Our experiments show that the proposed fusion network exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. Interestingly, our network requires a fewer training parameters than other existing methods.

Catalytic asymmetric synthesis of carboranylated diols bearing two adjacent stereocenters located at the α,ß-position of o-carborane cage carbon.

Despite the great interest in carborane-containing molecules, there is a lack of literature on the generation of central chiralities, via catalytic asymmetric transformations using prochiral carboranyl substrates. Herein, we have synthesized novel optically active icosahedral carborane-containing diols via Sharpless catalytic asymmetric dihydroxylation of carborane-derived alkenes, under mild conditions. The reaction showed a good substrate scope with 74-94% yields and 92->99% ee. This synthetic approach facilitated the creation of two adjacent stereocenters respectively located at the α,ß-position of o-carborane cage carbon, with a single syn-diastereoisomer. In addition, the obtained chiral carborane-containing diol product can be transformed to cyclic sulfate and can subsequently undergo a nucleophilic substitution and reduction to obtain the unexpected nido-carboranyl derivatives of chiral amino alcohols in the form of zwitterions.

U-SPDNet: An SPD manifold learning-based neural network for visual classification.

With the development of neural networking techniques, several architectures for symmetric positive definite (SPD) matrix learning have recently been put forward in the computer vision and pattern recognition (CV&PR) community for mining fine-grained geometric features. However, the degradation of structural information during multi-stage feature transformation limits their capacity. To cope with this issue, this paper develops a U-shaped neural network on the SPD manifolds (U-SPDNet) for visual classification. The designed U-SPDNet contains two subsystems, one of which is a shrinking path (encoder) making up of a prevailing SPD manifold neural network (SPDNet (Huang and Van Gool, 2017)) for capturing compact representations from the input data. Another is a constructed symmetric expanding path (decoder) to upsample the encoded features, trained by a reconstruction error term. With this design, the degradation problem will be gradually alleviated during training. To enhance the representational capacity of U-SPDNet, we also append skip connections from encoder to decoder, realized by manifold-valued geometric operations, namely Riemannian barycenter and Riemannian optimization. On the MDSD, Virus, FPHA, and UAV-Human datasets, the accuracy achieved by our method is respectively 6.92%, 8.67%, 1.57%, and 1.08% higher than SPDNet, certifying its effectiveness.

Safety profile of 0.0015% tafluprost eye drops in China: a post-marketing observational study.

AIM: To investigate the treatment pattern and safety of tafluprost for glaucoma and ocular hypertension (OH) in clinical practice in China. METHODS: This post-marketing observational study included patients who received tafluprost to lower intraocular pressure (IOP) within 30d between September 2017 and March 2020 in 20 hospitals in China. Adverse drug reactions (ADRs) during tafluprost treatment and within 30d after the treatment were collected. RESULTS: A total of 2544 patients were included in this study, of them 58.5% (1488/2544) had primary open angle glaucoma (POAG), 21.9% (556/2544) had OH and 19.7% (500/2544) used tafluprost for other reasons. Of 359 ADRs occurred in 10.1% (258/2544) patients, and no serious adverse event occurred. The most common ADR was conjunctival hyperemia (128 ADRs in 124 patients, 4.9%). Totally 1670 participants (65.6%) combined tafluprost with carbonic anhydrase inhibitors (CAIs; 37.1%, 620/1670), sympathomimetics (33.5%, 559/1670), ß-blockers (33.2%, 555/1670), other prostaglandin analogs (PGAs; 15.6%, 260/1670) and other eye drops (15.1%, 253/1670). The highest incidence of conjunctival hyperemia was noted in patients who received tafluprost in combination with other PGAs (23 ADRs in 23 patients, 8.8%, 23/260) and the lowest was in combination with CAIs (16 ADRs in 16 patients, 2.6%, 16/620). Tafluprost was applied in primary angle-closure glaucoma (41.6%, 208/500), after glaucoma surgery (17.8%, 89/500) and after non-glaucoma surgery (15.8%, 79/500). CONCLUSION: Tafluprost is safe for POAG and OH, and tolerable when combined with other eye drops and under various clinical circumstances.

CPInformer for Efficient and Robust Compound-Protein Interaction Prediction.

Recently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches.

Astragaloside IV ameliorates metabolic disorder in db/db obese mice as a PPARγ antagonist.

Metabolic disorder is highly related to obesity, insulin resistance, hypertension, and hyperlipidemia. The present study found that astragaloside IV (ASI) attenuated metabolic disorder related symptoms and modulated hepatic lipid metabolism associated gene mRNA expression in db/db mice. ASI inhibited rosiglitazone-induced adipocyte differentiation of 3T3-L1 cells, and lipid accumulation in palmitic acid (PA)-induced HepG2 cells with down-regulated mRNA expression of lipogenesis-related genes. In addition, it was predicted to bind to the ligand binding domain (LBD) of PPARγ and inhibit its transactivity. Collectively, our study suggested that ASI improves lipid metabolism in obese mice probably through suppressing PPARγ activity.

Neoadjuvant Chemotherapy With CAPOX Versus Chemoradiation for Locally Advanced Rectal Cancer With Uninvolved Mesorectal Fascia (CONVERT): Initial Results of a Phase III Trial.

OBJECTIVE: To compare neoadjuvant chemotherapy (nCT) with CAPOX alone versus neoadjuvant chemoradiotherapy (nCRT) with capecitabine in locally advanced rectal cancer (LARC) with uninvolved mesorectal fascia (MRF). BACKGROUND DATA: nCRT is associated with higher surgical complications, worse long-term functional outcomes, and questionable survival benefits. Comparatively, nCT alone seems a promising alternative treatment in lower-risk LARC patients with uninvolved MRF. METHODS: Patients between June 2014 and October 2020 with LARC within 12 cm from the anal verge and uninvolved MRF were randomly assigned to nCT group with 4 cycles of CAPOX (Oxaliplatin 130 mg/m2 IV day 1 and Capecitabine 1000 mg/m2 twice daily for 14 d. Repeat every 3 wk) or nCRT group with Capecitabine 825 mg/m² twice daily administered orally and concurrently with radiation therapy (50 Gy/25 fractions) for 5 days per week. The primary end point is local-regional recurrence-free survival. Here we reported the results of secondary end points: histopathologic response, surgical events, and toxicity. RESULTS: Of the 663 initially enrolled patients, 589 received the allocated treatment (nCT, n=300; nCRT, n=289). Pathologic complete response rate was 11.0% (95% CI, 7.8-15.3%) in the nCT arm and 13.8% (95% CI, 10.1-18.5%) in the nCRT arm ( P =0.33). The downstaging (ypStage 0 to 1) rate was 40.8% (95% CI, 35.1-46.7%) in the nCT arm and 45.6% (95% CI, 39.7-51.7%) in the nCRT arm ( P =0.27). nCT was associated with lower perioperative distant metastases rate (0.7% vs. 3.1%, P =0.03) and preventive ileostomy rate (52.2% vs. 63.6%, P =0.008) compared with nCRT. Four patients in the nCT arm received salvage nCRT because of local disease progression after nCT. Two patients in the nCT arm and 5 in the nCRT arm achieved complete clinical response and were treated with a nonsurgical approach. Similar results were observed in subgroup analysis. CONCLUSIONS: nCT achieved similar pCR and downstaging rates with lower incidence of perioperative distant metastasis and preventive ileostomy compared with nCRT. CAPOX could be an effective alternative to neoadjuvant therapy in LARC with uninvolved MRF. Long-term follow-up is needed to confirm these results.

Differential affinity chromatography reveals a link between Porphyromonas gingivalis-induced changes in vascular smooth muscle cell differentiation and the type 9 secretion system.

Porphyromonas gingivalis is implicated in adverse pregnancy outcome. We previously demonstrated that intrauterine infection with various strains of P. gingivalis impairs the physiologic remodeling of the uterine spiral arteries (IRSA) during pregnancy, which underlies the major obstetrical syndromes. Women diagnosed with IRSA also have a greater risk for premature cardiovascular disease in later life. The dysregulated plasticity of vascular smooth muscle cells (VSMCs) is present in both IRSA and premature cardiovascular events. We hypothesized that VSMCs could serve as a bait to identify P. gingivalis proteins associated with dysregulated VSMC plasticity as seen in IRSA. We first confirmed that dams with P. gingivalis A7UF-induced IRSA also show perturbed aortic smooth muscle cell (AoSMC) plasticity along with the P. gingivalis colonization of the tissue. The in vitro infection of AoSMCs with IRSA-inducing strain A7UF also perturbed AoSMC plasticity that did not occur with infection by non-IRSA-inducing strain W83. Far-Western blotting with strain W83 and strain A7UF showed a differential binding pattern to the rat aorta and primary rat AoSMCs. The affinity chromatography/pull-down assay combined with mass spectrometry was used to identify P. gingivalis/AoSMC protein interactions specific to IRSA. Membrane proteins with a high binding affinity to AoSMCs were identified in the A7UF pull-down but not in the W83 pull-down, most of which were the outer membrane components of the Type 9 secretion system (T9SS) and T9SS cargo proteins. Additional T9SS cargo proteins were detected in greater abundance in the A7UF pull-down eluate compared to W83. None of the proteins enriched in the W83 eluate were T9SS components nor T9SS cargo proteins despite their presence in the prey preparations used in the pull-down assay. In summary, differential affinity chromatography established that the components of IRSA-inducing P. gingivalis T9SS as well as its cargo directly interact with AoSMCs, which may play a role in the infection-induced dysregulation of VSMC plasticity. The possibility that the T9SS is involved in the microbial manipulation of host cell events important for cell differentiation and tissue remodeling would constitute a new virulence function for this system.

Robust Graph Regularized NMF with Dissimilarity and Similarity Constraints for ScRNA-seq Data Clustering.

The notable progress in single-cell RNA sequencing (ScRNA-seq) technology is beneficial to accurately discover the heterogeneity and diversity of cells. Clustering is an extremely important step during the ScRNA-seq data analysis. However, it cannot achieve satisfactory performances by directly clustering ScRNA-seq data due to its high dimensionality and noise. To address these issues, we propose a novel ScRNA-seq data representation model, termed Robust Graph regularized Non-Negative Matrix Factorization with Dissimilarity and Similarity constraints (RGNMF-DS), for ScRNA-seq data clustering. To accurately characterize the structure information of the labeled samples and the unlabeled samples, respectively, the proposed RGNMF-DS model adopts a couple of complementary regularizers (i.e., similarity and dissimilar regularizers) to guide matrix decomposition. In addition, we construct a graph regularizer to discover the local geometric structure hidden in ScRNA-seq data. Moreover, we adopt the l2,1-norm to measure the reconstruction error and thereby effectively improve the robustness of the proposed RGNMF-DS model to the noises. Experimental results on several ScRNA-seq datasets have demonstrated that our proposed RGNMF-DS model outperforms other state-of-the-art competitors in clustering.

Preliminary verification of the anti-hypoxia mechanism of Gentiana straminea maxim based on UPLC-triple TOF MS/MS and network pharmacology.

BACKGROUND: Anoxia is characterized by changes in the morphology, metabolism, and function of tissues and organs due to insufficient oxygen supply or oxygen dysfunction. Gentiana straminea Maxim (G.s Maxim) is a traditional Tibetan medicine. Our previous work found that G.s Maxim mediates resistance to hypoxia, and we found that the ethyl acetate extract had the best effect. Nevertheless, the primary anti-hypoxia components and mechanisms of action remain unclear. METHODS: Compounds from the ethyl acetate extraction of G.s Maxim were identified using UPLC-Triple TOF MS/MS. Then Traditional Chinese Medicine Systematic Pharmacology Database was used to filtrate them. Network pharmacology was used to forecast the mechanisms of these compounds. Male specific pathogen-free Sprague Dawley rats were randomly divided into six groups: (1) Control; (2) Model; (3) 228 mg/kg body weight Rhodiola capsules; (4) 6.66 g/kg body weight the G.s Maxim's ethyl acetate extraction; (5) 3.33 g/kg body weight the G.s Maxim's ethyl acetate extraction; (6) 1.67 g/kg body weight the G.s Maxim's ethyl acetate extraction. After administering intragastric ally for 15 consecutive days, an anoxia model was established using a hypobaric oxygen chamber (7000 m, 24 h). Then Histology, enzyme-linked immunosorbent assays, and western blots were performed to determine these compounds' anti-hypoxic effects and mechanisms. Finally, we performed a molecular docking test to test these compounds using Auto Dock. RESULTS: Eight drug-like compounds in G.s Maxim were confirmed using UPLC-Triple TOF MS/MS and Lipinski's rule. The tumor necrosis factor (TNF) signaling pathway, the hypoxia-inducible factor 1 (HIF-1) signaling pathway, and the nuclear factor kappa-B (NF-κB) signaling pathway was signaling pathways that G.s Maxim mediated anti-anoxia effects. The critical targets were TNF, Jun proto-oncogene (JUN), tumor protein p53 (TP53), and threonine kinase 1 (AKT1). Animal experiments showed that the ethyl acetate extraction of G.s Maxim ameliorated the hypoxia-induced damage of hippocampal nerve cells in the CA1 region and reversed elevated serum expression of TNF-α, IL-6, and NF-κ B in hypoxic rats. The compound also reduced the expression of HIF-1α and p65 and increased the Bcl-2/Bax ratio in brain tissue. These findings suggest that G.s Maxim significantly protects against brain tissue damage in hypoxic rats by suppressing hypoxia-induced apoptosis and inflammation. Ccorosolic acid, oleanolic acid, and ursolic acid had a strong affinity with core targets. CONCLUSIONS: The ethyl acetate extraction of G.s Maxim mediates anti-hypoxic effects, possibly related to inhibiting apoptosis and inflammatory responses through the HIF-1/NF-κB pathway. The primary active components might be corosolic, oleanolic, and ursolic acids.

Impact of posttransplant cyclophosphamide on the outcome of patients undergoing unrelated single-unit umbilical cord blood transplantation for pediatric acute leukemia.

BACKGROUND: Umbilical cord blood transplantation (UCBT) from unrelated donors is one of the successful treatments for acute leukemia in childhood. The most frequent side effect of UCBT is peri-engraftment syndrome (PES), which is directly associated with the greater prevalence of acute and chronic graft-versus-host-disease (aGvHD and cGvHD). In haploidentical stem cell transplantation, posttransplant cyclophosphamide (PTCY) has been demonstrated to be an effective method against GvHD. However, the effects of PTCY as a GvHD prophylactic in UCBT had not been investigated. This study aimed to evaluate the effects of PTCY on the outcomes of UCBT for pediatric acute leukemia. METHODS: This retrospective study included 52 children with acute leukemia who underwent unrelated single-unit UCBT after myeloablative conditioning regimens. The results from the PTCY and non-PTCY groups were compared. RESULTS: The incidence of transplantation-related mortality in non-PTCY and PTCY were 5% and 10% (p = 0.525), respectively. The incidence of relapse in non-PTCY and PTCY were 5% and 23% (p = 0.095), respectively. Second complete remission status (CR2) was an independent risk factor for relapse-free survival (hazard ratio = 9.782, p = 0.001). The odds ratio for sepsis or bacteremia incidence was significantly greater in the PTCY group (9.524, p = 0.017). PTCY group had increased rates of cytomegalovirus activity and fungal infection. The incidence of PES, aGvHD, cGvHD, and hemorrhagic cystitis in the PTCY group was lower than that in the non-PTCY group, although it was not significantly different. Additionally, higher doses of PTCY (29 mg/kg and 40 mg/kg) were associated with lower incidences of aGvHD and severe GvHD (65% and 29%, respectively) than lower doses (93% and 57%, respectively). Engraftment time and graft failure incidence were similar across groups. CONCLUSION: The results support the safety and efficiency of PTCY as part of PES controlling and GvHD prophylaxis in single-unit UCBT for children with acute leukemia. A PTCY dosage of 29 mg/kg to 40 mg/kg appears to be more effective in GvHD prophylaxis for UCBT patients.