Publications by authors named "Tianzhu Zhang"

79 Publications

SATB2 overexpression promotes oral squamous cell carcinoma progression by up-regulating NOX4.

Cell Signal 2021 Jun 3;82:109968. Epub 2021 Mar 3.

Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing 210029, China; Depatment of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing 210029, China. Electronic address:

While atypical expression of special AT-rich sequence-binding protein 2 (SATB2) has been approved associated with tumor progression, metastasis and unfavourable prognosis in various carcinomas. However, in oral squamous cell carcinoma (OSCC), both the expressive state and associated functions of SATB2's are still undefined. Here we show that, in clinical samples from a retrospective cohort of 58 OSCC patients, high expression of SATB2 is associated with poor prognosis of OSCC patients. In this study, we investigated SATB2 is highly expressed in OSCC tissues and cell lines, which can promote OSCC cells' proliferation, migration, invasion and tumor growth. According to sequencing results based on previous literature, we identified NOX4 is a bona fide downstream target of SATB2, when it was knockdown, OSCC's proliferation can be partially suppressed. Furthermore, NOX4 knockdown inhibits tumorigenicity, which can be rescued partially by ectopic expression of SATB2 in HNSCC cell line, and vice versa. Collectively, our findings not only indicate overexpression of SATB2 triggers the proliferative, migratory and invasive mechanisms which are important in the malignant phenotype of OSCC, but also identify NOX4 as the downstream gene for SATB2. These findings indicate that SATB2 may play a key role in OSCC tumorigenicity and may be a future target for the development of new therapeutic regimens.
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http://dx.doi.org/10.1016/j.cellsig.2021.109968DOI Listing
June 2021

Local Correspondence Network for Weakly Supervised Temporal Sentence Grounding.

IEEE Trans Image Process 2021 2;30:3252-3262. Epub 2021 Mar 2.

Weakly supervised temporal sentence grounding has better scalability and practicability than fully supervised methods in real-world application scenarios. However, most of existing methods cannot model the fine-grained video-text local correspondences well and do not have effective supervision information for correspondence learning, thus yielding unsatisfying performance. To address the above issues, we propose an end-to-end Local Correspondence Network (LCNet) for weakly supervised temporal sentence grounding. The proposed LCNet enjoys several merits. First, we represent video and text features in a hierarchical manner to model the fine-grained video-text correspondences. Second, we design a self-supervised cycle-consistent loss as a learning guidance for video and text matching. To the best of our knowledge, this is the first work to fully explore the fine-grained correspondences between video and text for temporal sentence grounding by using self-supervised learning. Extensive experimental results on two benchmark datasets demonstrate that the proposed LCNet significantly outperforms existing weakly supervised methods.
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http://dx.doi.org/10.1109/TIP.2021.3058614DOI Listing
March 2021

Expansion of Ovarian Cancer Stem-like Cells in Poly(ethylene glycol)-Cross-Linked Poly(methyl vinyl ether--maleic acid) and Alginate Double-Network Hydrogels.

ACS Biomater Sci Eng 2020 06 21;6(6):3310-3326. Epub 2020 Apr 21.

State Key Lab of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

A better understanding of cancer stem cells (CSCs) is essential for research on cancer therapy and drug resistance. Currently, increasingly more investigations are focused on obtaining CSCs to study the mechanism of their enhanced malignancy. In this work, three kinds of double-network hydrogels (PEMM/alginate), consisting of poly(ethylene glycol) (PEG) covalently cross-linked poly(methyl vinyl ether--maleic acid) (P(MVE--MA)) (network 1, denoted as PEMM) and Sr (or Ca, Fe) ionically cross-linked alginates (network 2, denoted as SrAlg, CaAlg, or FeAlg), were prepared. The stiffness, morphology, and components of the PEMM/alginate hydrogels were systematically investigated to understand their effects on CSC enrichment. Only the PEMM/FeAlg hydrogels could support the long-term growth, proliferation, and spheroid formation of SK-OV-3 cells. The expression of CSC-related markers was evaluated with the levels of protein and gene at different stages. The cell spheroids cultured in the PEMM/FeAlg hydrogels acquired certain CSC-like properties, thus drug resistance was enhanced, especially in the PEMM-1/FeAlg hydrogel. tumorigenicity experiments also confirmed the presence of more CSCs in the PEMM-1/FeAlg hydrogel. The results suggest that matrix stiffness, morphology, and cations act synergistically on the regulation of the epithelial-mesenchymal transition (EMT), interleukin-6 (IL-6), and Wnt pathways, affecting the invasiveness of ovarian cancer and the conversion of the non-CSCs into CSCs. The PEMM-1/FeAlg hydrogel with lower elastic modulus, a more macroporous morphology, and higher swelling rate can significantly enhance the stemness, malignancy, and tumorigenicity of SK-OV-3 cells.
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http://dx.doi.org/10.1021/acsbiomaterials.9b01967DOI Listing
June 2020

Combination of Polypropylene Mesh and in Situ Injectable Mussel-Inspired Hydrogel in Laparoscopic Hernia Repair for Preventing Post-Surgical Adhesions in the Piglet Model.

ACS Biomater Sci Eng 2020 03 12;6(3):1735-1743. Epub 2020 Feb 12.

Department of General Surgery, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China.

Polypropylene (PP) mesh has been used successfully for a long time in clinical practice as an impressive prosthesis for ventral hernia repair. To utilize a physical barrier for separating mesh from viscera is a general approach for preventing adhesions in clinical practice. However, a serious abdominal adhesion between the mesh and viscera can possibly occur post-hernia, especially with the small intestine; this can lead to a series of complications, such as chronic pain, intestinal obstruction, and fistula. Thus, determining how to prevent abdominal adhesions between the mesh and viscera is still an urgent clinical problem. In this study, a dopamine-functionalized polysaccharide derivative (oxidized-carboxymethylcellulose--dopamine, OCMC-DA) was synthesized; this was blended with carboxymethylchitosan (CMCS) to form a hydrogel (OCMC-DA/CMCS) in situ at the appropriate time. The physical and chemical properties of the hydrogel were characterized successfully, and its excellent biocompatibility was presented by the in vitro cell test. The combination of this hydrogel and PP mesh was used in laparoscopic surgery for repairing the abdominal wall defect, where the hydrogel could become fixed in situ on the PP mesh to form an anti-adhesion gel-mesh. The results showed that the gel-mesh could prevent abdominal adhesions effectively in the piglet model. Moreover, the histology and immunohistochemical staining proved that the gel-mesh could effectively alleviate the inflammation reaction and deposition of collagen around the mesh, and it did not disturb the integration between mesh and abdominal wall. Thus, the gel-mesh has superior tissue compatibility.
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http://dx.doi.org/10.1021/acsbiomaterials.9b01333DOI Listing
March 2020

Effect of RGD content in poly(ethylene glycol)-crosslinked poly(methyl vinyl ether-alt-maleic acid) hydrogels on the expansion of ovarian cancer stem-like cells.

Mater Sci Eng C Mater Biol Appl 2021 Jan 3;118:111477. Epub 2020 Sep 3.

State Key Lab of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China. Electronic address:

The extracellular matrix (ECM) affects cell behaviors, such as survival, proliferation, motility, invasion, and differentiation. The arginine-glycine-aspartic acid (RGD) sequence is present in several ECM proteins, such as fibronectin, collagen type I, fibrinogen, laminin, vitronectin, and osteopontin. It is very critical to develop ECM-like substrates with well-controlled features for the investigation of influence of RGD on the behavior of tumor cells. In this study, poly(ethylene glycol) (PEG)-crosslinked poly(methyl vinyl ether-alt-maleic acid) (P(MVE-alt-MA)) hydrogels (PEMM) with different RGD contents were synthesized, fully characterized, and established as in vitro culture platforms to investigate the effects of RGD content on cancer stem cell (CSC) enrichment. The morphology, proliferation, and viability of SK-OV-3 ovarian cancer cells cultured on hydrogels with different RGD contents, the expression of CSC markers and malignant signaling pathway-related genes, and drug resistance were systematically evaluated. The cell aggregates formed on the hydrogel surface with a lower RGD content acquired certain CSC-like properties, thus drug resistance was enhanced. In contrast, the drug sensitivity of cells on the higher RGD content surface increased because of less CSC-like properties. However, the presence of RGD in the stiff hydrogels (PEMM2) had less effect on the stemness expression than did its presence in the soft hydrogels (PEMM1). The results suggest that RGD content and matrix stiffness can lead to synergetic effects on the expression of cancer cell stemness and the epithelial-mesenchymal transition (EMT), interleukin-6 (IL-6), and Wnt pathways.
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http://dx.doi.org/10.1016/j.msec.2020.111477DOI Listing
January 2021

How Human Activity Has Changed the Regional Habitat Quality in an Eco-Economic Zone: Evidence from Poyang Lake Eco-Economic Zone, China.

Int J Environ Res Public Health 2020 08 27;17(17). Epub 2020 Aug 27.

School of Economics and Management, Tianjin Polytechnic University, Tianjin 300387, China.

Human activities such as deforestation and urbanization have affected the regional habitat quality of the Poyang Lake area. To evaluate the evolution of habitat quality and its influencing factors in the area, we used Classification and Regression Trees (CART) to interpret the land-use status and used the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model to analyze the characteristics of changes in habitat quality in the Poyang Lake Eco-Economic Zone (PLEEZ) from 1988 to 2018. The results show that, from 1988 to 2018, land use in the PLEEZ underwent significant changes. The changes in land use led to a gradual increase in habitat degradation and a gradual decrease in habitat quality in the study area. Rapid urbanization notably decreased the habitat quality in the study area. However, at the same time, the ecological protection projects such as returning farmland to forests slowed the decline in habitat quality. Driven by these two factors, habitat quality in the PLEEZ gradually declined but the rate of its decline was suppressed. The findings of this study are of great significance for the coordinated development of social, economic, and ecological development in the PLEEZ and similar areas.
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http://dx.doi.org/10.3390/ijerph17176253DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503520PMC
August 2020

Plasmon catalytic PATP coupling reaction on Ag-NPs/graphite studied electrochemical surface-enhanced Raman spectroscopy.

Phys Chem Chem Phys 2020 Oct;22(41):23482-23490

Institute of Materials, China Academy of Engineering Physics, Mianyang 621907, China.

The p-aminothiophenol (PATP) coupling reaction on plasmon substrates such as Ag and Au nanoparticles has received extensive attention since the catalytic effect of the surface plasmon was found. Currently, in situ kinetic studies of this reaction are rare, especially those focusing on the specific role of the hot electron-hole carriers. Here, in situ electrochemical surface-enhanced Raman spectroscopy (SERS) is developed to study the plasmon catalytic reaction of PATP in a controlled aqueous environment involving the factors of O2, electron and hole carriers, and solution pH. Ag nanoparticles supported on graphite serve as a SERS substrate, which could separate hot electron-hole pairs effectively and is beneficial to study the effects of hot carriers on plasmon-driven reactions. In situ electrochemical SERS measurements reveal two reaction paths for the PATP coupling reaction. One is that plasmon-induced hot holes activate the dehydrogenation of PATP and then the radical coupling reaction to form p,p'-dimercaptoazobenzene (DMAB) under O2-free conditions. Another is likely to be that the surface Ag2O/AgOH, which is generated from Ag and 1O2/O2-, catalyzes the oxidation of PATP and then the coupling process under O2-rich conditions. Benefitting from the potential/atmosphere controlled measurements in situ, the intermediate species of PATP(NH)/PATP(N) are observed with vibrational bands at around 1056, 1202, 1253, 1395, 1514 and 1540 cm-1.
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http://dx.doi.org/10.1039/d0cp01733gDOI Listing
October 2020

Self-supervised Agent Learning for Unsupervised Cross-Domain Person Re-identification.

IEEE Trans Image Process 2020 Aug 20;PP. Epub 2020 Aug 20.

Unsupervised person re-identification (Re-ID) has better scalability and practicability than supervised Re-ID in the actual deployment. However, it is difficult to learn a discriminative Re-ID model without annotations. To address the above issue, we propose an end-to-end Self-supervised Agent Learning (SAL) algorithm by exploiting a set of agents as a bridge to reduce domain gaps for unsupervised cross-domain person Re- ID. The proposed SAL model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit selfsupervised learning for unsupervised person Re-ID. Second, our model has designed three effective learning mechanisms including supervised label learning in source domain, similarity consistency learning in target domain, and self-supervised learning in cross domain, which can learn domain-invariant yet discriminative representations through the principled lens of agent learning by reducing domain discrepancy adaptively. Extensive experimental results on three standard benchmarks demonstrate that the proposed SAL performs favorably against state-of-the-art unsupervised person Re-ID methods.
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http://dx.doi.org/10.1109/TIP.2020.3016869DOI Listing
August 2020

A high-strength double network polydopamine nanocomposite hydrogel for adhesion under seawater.

J Mater Chem B 2020 09;8(36):8232-8241

State Key Lab of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

Mussel-inspired catechol-based strategy has been widely used in the development of underwater adhesives. Nonetheless, the properties of the adhesives were still severely limited under harsh environments. A facile approach was proposed herein to prepare a double network hydrogel adhesive with low swelling rate and high strength in seawater, where the first network was polyacrylamide (PAM) and the second network was alginate (Alg). Meanwhile, polydopamine (PDA) nanoparticles, which were formed through self-polymerization as adhesion anchoring sites, distributed evenly throughout the double network hydrogel and effectively enhanced the adhesion capability of the hydrogel. The properties of the resulting hydrogel have been fully characterized. The optimal adhesion strength of the hydrogel adhesive in seawater was as high as 146.84 ± 7.78 kPa. Furthermore, the hydrogel also has excellent ability to promote the growth of zooxanthellae. Our studies provide useful insights into the rational design of underwater adhesives with high performances even beyond nature.
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http://dx.doi.org/10.1039/d0tb00513dDOI Listing
September 2020

Cross-modality paired-images generation and augmentation for RGB-infrared person re-identification.

Neural Netw 2020 Aug 19;128:294-304. Epub 2020 May 19.

Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom. Electronic address:

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. Considering no correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing marginal distribution divergence between the entire RGB and IR sets. However, this set-level alignment strategy may lead to misalignment of some instances, which limit the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged features. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Third, our method learns a latent manifold space. In the space, we can random sample and generate lots of images of unseen classes. Training with those images, the learned identity feature space is more smooth can generalize better when test. Finally, extensive experimental results on two standard benchmarks demonstrate that the proposed model favorably against state-of-the-art methods.
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http://dx.doi.org/10.1016/j.neunet.2020.05.008DOI Listing
August 2020

A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment.

IEEE Trans Image Process 2020 May 8. Epub 2020 May 8.

Facial expression recognition, face synthesis, and face alignment are three coherently related tasks and can be solved in a joint framework. To achieve this goal, in this paper, we propose a novel end-to-end deep learning model by exploiting the expression code, geometry code and generated data jointly for simultaneous pose-invariant facial expression recognition, face image synthesis, and face alignment. The proposed deep model enjoys several merits. First, to the best of our knowledge, this is the first work to address these three tasks jointly in a unified deep model to complement and enhance each other. Second, the proposed model can effectively disentangle the global and local identity representation from different expression and geometry codes. As a result, it can automatically generate facial images with different expressions under arbitrary geometry codes. Third, these three tasks can further boost their performance for each other via our model. Extensive experimental results on three standard benchmarks demonstrate that the proposed deep model performs favorably against state-of-the-art methods on the three tasks.
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http://dx.doi.org/10.1109/TIP.2020.2991549DOI Listing
May 2020

Clinical Manifestation and Laboratory Characteristics of SARS-CoV-2 Infection in Pregnant Women.

Virol Sin 2020 Jun 20;35(3):305-310. Epub 2020 Apr 20.

Department of Laboratory Medicine, Maternal and Child Health Hospital of Hubei Province, Wuhan, 430070, China.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic has become a major challenge to public health in China and other countries, considering its pathogenicity across all age groups. Pregnancy is a unique physiological condition, and is characterized by altered immunity and elevated hormone levels to actively tolerate the semi-allogeneic fetus, which undergoes a sudden and substantial fluctuation during the immediate postpartum period. Changes in clinical features, laboratory characteristics, and imaging features of pregnant women during the pre-partum and post-partum periods require further elucidation. Here, we retrospectively analyzed the clinical features, laboratory characteristics, and imaging features of eight pregnant cases of SARS-CoV-2 infection during the pre-partum and post-partum periods. Our results showed that four of the eight pregnant women were asymptomatic before delivery but became symptomatic post-partum. Correspondingly, white blood cell (WBC) counts increased and lymphocyte (LYMPH) counts decreased. C-reactive protein (CRP) levels in the serum also increased to a higher level than those in general pregnancy. Therefore, it is imperative to closely monitor laboratory parameters including the WBC count, LYMPH count, and CRP, along with other imaging features in chest CT scans, to promptly prevent, diagnose, and treat a SARS-CoV-2 infection during pregnancy.
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http://dx.doi.org/10.1007/s12250-020-00227-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7167538PMC
June 2020

Learning to Model Relationships for Zero-Shot Video Classification.

IEEE Trans Pattern Anal Mach Intell 2020 Apr 14. Epub 2020 Apr 14.

Zero-Shot Learning (ZSL) has become a promising research direction in pattern analysis and machine learning. Based on auxiliary information, the key to a robust ZSL method is to transfer the learned knowledge from seen classes to unseen classes, which requires relationship modeling between these concepts. However, most existing approaches ignore to model the explicit relationships in an end-to-end manner, resulting in low effectiveness of knowledge transfer. To tackle this problem, we reconsider the video ZSL task as a task-driven message passing process to jointly enjoy several merits including alleviated heterogeneity gap, low domain shift, and robust temporal modeling. Specifically, we propose a Prototype-Sample GNN (PS-GNN) consisting of a prototype branch and a sample branch to directly and adaptively model all the relationships between category-attribute, category-category, and attribute-attribute. The prototype branch aims to learn robust representations of video categories, which takes as input a set of word embedding vectors corresponding to the concepts. The sample branch is designed to generate features of a video sample by leveraging its object semantics. With the co-adaption and cooperation between both branches, a unified and robust ZSL framework is achieved. Extensive experiments strongly evidence that PS-GNN obtains favorable performance on five popular video benchmarks consistently.
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http://dx.doi.org/10.1109/TPAMI.2020.2985708DOI Listing
April 2020

Mussel-inspired hybrid network hydrogel for continuous adhesion in water.

J Mater Chem B 2020 03;8(10):2148-2154

State Key Lab of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

The mussel-inspired catechol-based strategy has been widely used in the development of adhesives. However, the properties of the obtained adhesives were still severely limited in a humid environment, particularly in water. In this study, a facile and versatile approach was proposed to prepare an underwater adhesion hydrogel. First, dopamine (DA) was grafted on oxidized carboxymethylcellulose (OCMC) to obtain dopamine-grafted oxidized carboxymethylcellulose (OCMC-DA). Second, the acrylamide (AM) monomer was conjugated with OCMC-DA by a Schiff base reaction, and then polymerized to form an OCMC-DA/PAM hydrogel. The properties of the resulting hydrogel have been fully characterized. The underwater adhesion strength of the hydrogel can reach as high as 86.3 ± 7.2 kPa and reduced to 43 ± 3.4 kPa after being immersed in water for 9 days. More remarkably, we found that the maximal adhesion strength was shown when the G' and G'' of the hydrogel were very close. Moreover, we demonstrated the mechanical properties of our fabricated hydrogel by compressive tests and rheological analysis. The adhesive hydrogel also exhibits excellent biocompatibility.
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http://dx.doi.org/10.1039/c9tb02863cDOI Listing
March 2020

Geometry Guided Pose-invariant Facial Expression Recognition.

IEEE Trans Image Process 2020 Feb 12. Epub 2020 Feb 12.

Driven by recent advances in human-centered computing, Facial Expression Recognition (FER) has attracted significant attention in many applications. However, most conventional approaches either perform face frontalization on a non-frontal facial image or learn separate classifier for each pose. Different from existing methods, this paper proposes an end-to-end deep learning model that allows to simultaneous facial image synthesis and pose-invariant facial expression recognition by exploiting shape geometry of the face image. The proposed model is based on generative adversarial network (GAN) and enjoys several merits. First, given an input face and a target pose and expression designated by a set of facial landmarks, an identity-preserving face can be generated through guiding by the target pose and expression. Second, the identity representation is explicitly disentangled from both expression and pose variations through the shape geometry delivered by facial landmarks. Third, our model can automatically generate face images with different expressions and poses in a continuous way to enlarge and enrich the training set for the FER task. Our approach is demonstrated to perform well when compared with state-of-the-art algorithms on both controlled and in-the-wild benchmark datasets including Multi-PIE, BU-3DFE, and SFEW.
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http://dx.doi.org/10.1109/TIP.2020.2972114DOI Listing
February 2020

A newly designed intensive caregiver education program reduces cognitive impairment, anxiety, and depression in patients with acute ischemic stroke.

Braz J Med Biol Res 2019 2;52(9):e8533. Epub 2019 Sep 2.

Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.

This study aimed to evaluate the effect of a newly designed intensive caregiver education program (ICEP) on reducing cognitive impairment, anxiety, and depression in acute ischemic stroke (AIS) patients. One hundred and ninety-six AIS patients were divided into ICEP group and Control group in a 1:1 ratio using blocked randomization method. In the ICEP group, the caregivers received ICEP, while in the Control group caregivers received usual education and guidance. All patients received conventional rehabilitation treatment. Cognitive impairment (assessed by Mini Mental State Examination (MMSE) score and Montreal Cognitive Assessment (MoCA) score), anxiety (assessed by Hospital Anxiety and Depression Scale (HADS)-A score and Self-rating Anxiety Scale (SAS) score), and depression (assessed by HADS-D score and Self-rating Depression Scale (SDS) score) were assessed at baseline (M0), 3 months (M3), 6 months (M6), and 12 months (M12). Cognitive impairment score at M12 and cognitive impairment score change (M12-M0) were increased, while cognitive impairment rate at M12 was reduced in the ICEP group compared with the Control group. Anxiety score change (M12-M0), anxiety score at M12, and anxiety rate at M12 were decreased in the ICEP group compared with the Control group. Depression score change (M12-M0), depression score at M12, and depression rate at M12 were lower in the ICEP group compared with the Control group. Further subgroup analysis based on baseline features also provided similar results. In conclusion, ICEP effectively reduced cognitive impairment, anxiety, and depression in AIS patients.
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http://dx.doi.org/10.1590/1414-431X20198533DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720023PMC
September 2019

An improved procedure to implement NSGA-III in coordinate waste management for urban agglomeration.

Waste Manag Res 2019 Nov 23;37(11):1161-1169. Epub 2019 Aug 23.

School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, People's Republic of China.

With the growth of urbanization in countries globally, large cities have often formed clusters of urban agglomerations in metropolitan areas. The coordinated management of regional solid waste produced by such urban agglomeration poses a typical high-dimensional, multi-objective optimization issue. This paper aims to introduce a procedure to implement the third-generation genetic algorithm (NSGA-III), an established multi-objective genetic algorithm based on non-dominated sorting mechanisms, for the purpose of evaluating environmental and economic benefits simultaneously while seeking the optimal solutions for coordinated management among multiple recycling centres. In this study, two series of scenarios were abstracted from scrap tire recycling, representing linear calculation and nonlinear calculation cases separately. Several improvements were made to the originally published NSGA-III procedure that solve the problem of non-convergence for hypervolumes of the output. Through comparisons of calculation results, an improved procedure is suggested and shown to have improved performance.
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http://dx.doi.org/10.1177/0734242X19865341DOI Listing
November 2019

Online Multi-expert Learning for Visual Tracking.

IEEE Trans Image Process 2019 Aug 16. Epub 2019 Aug 16.

The correlation filters based trackers have achieved an excellent performance for object tracking in recent years. However, most existing methods use only one filter but ignore the information of the previous filters. In this paper, we propose a novel online multi-expert learning algorithm for visual tracking. In our proposed scheme, there are former trackers which retain the previous filters, and those trackers will give their predictions in each frame. The current tracker represents the filter of current frame, and both the current tracker and the former trackers constitute our expert ensemble. We use an adaptive Second-order Quantile strategy to learn the weights of each expert, which can take full advantage of all the experts. To simplify our model and remove some bad experts, we prune our models via a minimum entropy criterion. Finally, we propose a new update strategy to avoid the model corruption problem. Extensive experimental results on both OTB2013 and OTB2015 benchmarks demonstrate that our proposed tracker performs favorably against state-of-the-art methods.
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http://dx.doi.org/10.1109/TIP.2019.2931082DOI Listing
August 2019

miR-25 Promotes Cell Proliferation, Migration, and Invasion of Non-Small-Cell Lung Cancer by Targeting the LATS2/YAP Signaling Pathway.

Oxid Med Cell Longev 2019 18;2019:9719723. Epub 2019 Jun 18.

Department of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.

Metastasis is the leading cause of high mortality in lung cancer patients, and metastatic lung cancer is difficult to treat. miRNAs are involved in various biological processes of cancer, including metastasis. Our previous studies revealed that miR-25 promoted non-small-cell lung cancer (NSCLC) cell proliferation and suppressed cell apoptosis by directly targeting and . In this work, we further explored the miR-25 expression in NSCLC patients in the Cancer Genome Atlas (TCGA) database and measured the miR-25 expression levels in the tissues of NSCLC patients and cell lines. miR-25 was overexpressed in both NSCLC tissues and cell lines. NSCLC patients who expressed a higher level of miR-25 exhibited worse overall survival than those with a lower level of miR-25. Overexpression of miR-25 enhanced NSCLC cell migration and invasion, while the inhibition of miR-25 exhibited the opposite effects. We identified the large tumor suppressor homology 2 () as a new target gene of miR-25 in lung cancer. The effects of miR-25 on promoting NSCLC cell migration and invasion were at least partially due to activation of the Hippo signaling pathway. Additionally, miR-25 antagomir inhibited xenograft tumor growth and metastasis by the upregulation of LATS2. Taken together, our findings demonstrate that miR-25 contribute to lung cancer cell proliferation and metastasis by targeting the LATS2/YAP signaling pathway, which implicate miR-25 as a promising therapeutic target for lung cancer metastasis. Given that oxidative stress induces the overexpression of miR-25 and plays a critical role in cancer progression, this study establishes miR-25 as an intermediate between oxidative stress and lung cancer metastasis.
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http://dx.doi.org/10.1155/2019/9719723DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604298PMC
January 2020

MicroRNA-301a promotes pancreatic cancer invasion and metastasis through the JAK/STAT3 signaling pathway by targeting SOCS5.

Carcinogenesis 2020 06;41(4):502-514

Department of Medical Laboratory, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Pancreatic cancer is one of the most lethal digestive malignant tumors. We had previously found that microRNA-301a (miR-301a) is a oncogenic microRNA whose recognized conduce to nuclear factor-kappa B (NF-κB) activation in pancreatic cancer, yet the underlying mechanisms of miR-301a in promoting pancreatic cancer invasion and migration is obscure. In this work we found that high expression of miR-301a in human pancreatic cancer patients is related to poor survival. Overexpression of miR-301a enhances pancreatic cancer cell invasion, angiogenesis and migration, whereas inhibition of miR-301a suppresses pancreatic cancer cell invasion and reduces orthotopic pancreatic tumor growth and metastasis. Furthermore, suppressor of cytokine signaling 5 (SOCS5) is identified as a target gene of miR-301a. We found that miR-301a suppressed the expression of SOCS5 leads to janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3) activation and is related to poor overall survival of pancreatic cancer patients. Taken together, our data show for the first time that the feedback loop between miR-301a and JAK/STAT3 pathway may play a significant role in pancreatic cancer invasion and metastasis. Targeting the loop may prove beneficial to prevent metastasis and provide a more effective therapeutic strategy for pancreatic cancer.
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http://dx.doi.org/10.1093/carcin/bgz121DOI Listing
June 2020

Harmine induces anticancer activity in breast cancer cells via targeting TAZ.

Int J Oncol 2019 Jun 9;54(6):1995-2004. Epub 2019 Apr 9.

Key Laboratory for Molecular Diagnosis of Hubei Province, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, P.R. China.

Harmine (HM) is a β‑carboline alkaloid found in multiple medicinal plants. It has been used in folk medicine for anticancer therapy; however, the molecular mechanism of HM on human breast cancer remains unclear. Transcriptional co‑activator with PDZ‑binding motif (TAZ), also known as WW domain‑containing transcription regulator 1, serves an important role in the carcinogenesis and progression of breast cancer. The aim of the present study was to elucidate the potential anticancer activity and mechanism of HM in breast cancer, in vitro and in vivo. Cell proliferation was measured using a CCK‑8 assay, apoptotic activity was detected by flow cytometry and DAPI staining, and cell migration was examined using a wound healing assay. The expression of proteins, including extracellular signal‑regulate kinase (Erk), phosphorylated (p‑) Erk, protein kinase B (Akt), p‑Akt, B‑cell lymphoma 2 (Bcl‑2) and Bcl‑2‑associated X protein (Bax), were determined by western blotting. The mRNA expression of TAZ was detected using reverse transcription‑quantitative polymerase chain reaction analysis. The expression of proteins in mouse tumor tissues were examined by immunohistochemistry. HM significantly suppressed cellular proliferation and migration, promoted apoptosis in vitro and inhibited tumor growth in vivo. In addition, HM significantly decreased the expression of TAZ, p‑Erk, p‑Akt and Bcl‑2, but increased that of Bax. The overexpression of TAZ in breast cancer cells inhibited the antitumor effect of HM. In conclusion, HM was found to induce apoptosis and prevent the proliferation and migration of human breast cancer cell lines, possibly via the downregulation of TAZ.
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http://dx.doi.org/10.3892/ijo.2019.4777DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521938PMC
June 2019

Long Non-Coding RNA and Breast Cancer.

Technol Cancer Res Treat 2019 01;18:1533033819843889

1 Department of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Breast cancer, one of the most common diseases among women, is regarded as a heterogeneous and complicated disease that remains a major public health concern. Recently, owing to the development of next-generation sequencing technologies, long non-coding RNAs have received extensive attention. Numerous studies reveal that long non-coding RNAs are playing important roles in tumor development. Although the biological function and molecular mechanisms of long non-coding RNAs remain enigmatic, recent researchers have demonstrated that an array of long non-coding RNAs express abnormally in cancers, including breast cancer. Herein, we summarized the latest literature about long non-coding RNAs in breast cancer, with a particular focus on the multiple molecular roles of regulatory long non-coding RNAs that regulate cell proliferation, invasion, metastasis, and apoptosis.
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http://dx.doi.org/10.1177/1533033819843889DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466467PMC
January 2019

Aqueous extracts of se-enriched Auricularia auricular attenuates D-galactose-induced cognitive deficits, oxidative stress and neuroinflammation via suppressing RAGE/MAPK/NF-κB pathway.

Neurosci Lett 2019 06 3;704:106-111. Epub 2019 Apr 3.

Institute of Agricultural Quality Standards and Testing Technology, Jilin Academy of Agricultural Sciences, Changchun, 130033, China. Electronic address:

Aging is a natural process that accompanied with progressive cognitive deficits and functional decline in organisms. Selenium (Se), an essential trace element, exhibits antioxidative and anti-inflammatory abilities. Here, our study aimed to investigate the protective effects of aqueous extracts of Se-enriched Auricularia auricular (AESAA) on aging mice induced by d-galactose (D-gal) and explore its potential mechanism. d-gal was administered (100 mg/kg) subcutaneously for 12 weeks to establish an aging mouse model. Morris water maze (MWM) test was conducted to assess the cognitive deficits of mice. Superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT) activities and malondialdehyde (MDA) level in hippocampus were measured to evaluate oxidative stress. The contents of pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β) and interleukin-6 (IL-6) in hippocampus were determined by ELISA method. Further, hippocampal levels of RAGE, p-Erk, p-JNK, p-P38 and p-NF-κB were detected by western blot and the RAGE expression was confirmed by immunohistochemistry. We found that AESAA supplementation significantly decreased d-gal-induced cognitive deficits, as evidenced by better performance in the MWM test. Furthermore, AESAA treatment attenuated oxidative stress and decreased the contents of pro-inflammatory cytokines in hippocampus. Importantly, AESAA inhibited the up-regulation of RAGE, p-Erk, p-JNK, p-P38 in the hippocampus of d-gal treated mice. Moreover, the results also indicated that AESAA inhibited p-NF-κB and p-IκBα expression. In conclusion, our findings suggest that AESAA effectively decreases cognitive impairment, alleviates oxidative damage and neuroinflammation in mice through s RAGE/MAPK/NF-κB signaling pathway, which provides a potential therapy for delaying the aging process.
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http://dx.doi.org/10.1016/j.neulet.2019.04.002DOI Listing
June 2019

SMART: Joint Sampling and Regression for Visual Tracking.

IEEE Trans Image Process 2019 Aug 12;28(8):3923-3935. Epub 2019 Mar 12.

Most existing trackers are either sampling-based or regression-based methods. Sampling-based methods estimate the target state by sampling many target candidates. Although these methods achieve significant performance, they often suffer from a high computational burden. Regression-based methods often learn a computationally efficient regression function to directly predict the geometric distortion between frames. However, most of these methods require large-scale external training videos and are still not very impressive in terms of accuracy. To make both types of methods enhance and complement each other, in this paper, we propose a joint sampling and regression scheme for visual tracking, which leverages the region proposal network by a novel design. Specifically, our method can jointly exploit discriminative target proposal generation and structural target regression to predict target location in a simple feedforward propagation. We evaluate the proposed method on five challenging benchmarks, and extensive experimental results demonstrate that our method performs favorably compared with state-of-the-art trackers with respect to both accuracy and speed.
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http://dx.doi.org/10.1109/TIP.2019.2904434DOI Listing
August 2019

Mussel-inspired copolymer-coated polypropylene mesh with anti-adhesion efficiency for abdominal wall defect repair.

Biomater Sci 2019 Mar;7(4):1323-1334

State Key Lab of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

Polypropylene (PP) meshes are one of the most commonly used prosthesis materials in repairing abdominal wall defects. However, their application is usually limited due to possible serious abdominal adhesions between the mesh and the viscera. Instilling PP meshes with excellent anti-adhesion characteristics is still a formidable challenge. In this work, in order to prevent intestinal adhesion to the PP mesh, an effective method was developed to prepare anti-adhesive PP meshes, which was inspired by mussel adhesive proteins. A functional monomer, namely, dopamine methacrylamide, was first synthesized. Then, it was copolymerized with poly(ethylene glycol) methacrylate on the surface of O2-plasma-treated PP (OPP) meshes to form comb-like copolymer poly[poly(ethylene glycol) methacrylate-co-dopamine methacrylamide] (PEDMA), which was simultaneously grafted in situ on the OPP mesh surface through the catechol group of PEDMA, subsequently yielding an anti-adhesive PP mesh (OPP-g-PEDMA). The properties of PEDMA and OPP-g-PEDMA meshes were characterized by NMR, GPC, TGA, FTIR, XPS, SEM, and water contact angle measurements. NIH-3T3 cells were employed to assess the cytocompatibility of OPP-g-PEDMA in vitro. Furthermore, the rat abdominal wall defect model was used to evaluate the efficacy of adhesion prevention. The results show that OPP-g-PEDMA not only possesses fantastic biocompatibility but also satisfactory anti-adhesion property involving minimal chronic inflammation, as well as lower adhesion formation rate and adhesion tenacity scores (less than 1.0). This type of OPP-g-PEDMA mesh is a promising candidate in effectively preventing peritoneal adhesion during abdominal wall defect repair.
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http://dx.doi.org/10.1039/c8bm01198bDOI Listing
March 2019

Assembled anti-adhesion polypropylene mesh with self-fixable and degradable in situ mussel-inspired hydrogel coating for abdominal wall defect repair.

Biomater Sci 2018 Oct;6(11):3030-3041

State Key Lab of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

Abdominal adhesion to polypropylene (PP) mesh remains one of the major complications in hernia repair. Thus, a challenge exists to endow PP mesh with powerful anti-adhesion properties in hernia repair. To investigate potential options, the assembled PP mesh was developed with effective anti-adhesion properties through an in situ coating of the mesh surface with self-fixable and biodegradable mussel-inspired hydrogels. Through mixing oxidized-carboxymethylcellulose functionalized with dopamine (OCMC-DA) with carboxymethylchitosan (CMCS), a layer of hydrogel (OCMC-DA/CMCS) can be formed in situ on the PP mesh without the addition of crosslinking agents; the dopamine then acts as an immobilization group to fix these hydrogels to the PP mesh and the tissue surface. In this way, the assembled PP mesh (OCMC-DA/CMCS/PP) was obtained. The properties of the OCMC-DA/CMCS hydrogels were optimized, and the OCMC-DA4/CMCS hydrogel was selected to construct the assembled PP mesh. The lap-shear test revealed that OCMC-DA4/CMCS has tissue-adhesive properties. In vitro cell tests proved the excellent biocompatibility of the hydrogel. An optimized bioabsorption time and significant anti-adhesion properties were demonstrated through an in vivo test with a rat model. The adhesion area and tenacity of the OCMC-DA4/CMCS/PP group were more than 80% lower than those of the native PP mesh group and created a slightly inflammatory reaction.
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http://dx.doi.org/10.1039/c8bm00824hDOI Listing
October 2018

H727 Multicellular Spheroids and Its Resistance to Antitumor Drugs Sunitinib and Axitinib.

J Nanosci Nanotechnol 2018 12;18(12):8078-8084

Department of General Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China.

The three-dimensional (3D) culture model of neuroendocrine tumor H727 cells was established by using the agarose gel as culture matrix, which provided a new method for drug screening of neuroendocrine tumors. As VEGFR inhibitor, sunitinib and axitinib were applied to inhibit human neuroendocrine H727 cell line in two-dimensional (2D) and 3D culture models. The inhibitory rate of H727 cells with different drug concentration were assessed by CCK-8 assay method and combined with using the FDA/PI double staining and the digital microscope analysis system. When the concentration of sunitinib ≥4.0 μmol/L, the H727 spheroids began to split, and the apoptosis of H727 cells occurred, the sizes of multicellular spheroids was significantly reduced in the groups of high-dose axitinib. These results illustrated that sunitinib and axitinib can effectively inhibit the growth and proliferation of neuroendocrine tumor H727 cells. Sunitinib and axitinib can also promote apoptosis of H727 cells.
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http://dx.doi.org/10.1166/jnn.2018.16030DOI Listing
December 2018

Robust Structural Sparse Tracking.

IEEE Trans Pattern Anal Mach Intell 2019 Feb 23;41(2):473-486. Epub 2018 Jan 23.

Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
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http://dx.doi.org/10.1109/TPAMI.2018.2797082DOI Listing
February 2019

Learning Multi-Task Correlation Particle Filters for Visual Tracking.

IEEE Trans Pattern Anal Mach Intell 2019 Feb 23;41(2):365-378. Epub 2018 Jan 23.

In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different object parts and features into account to learn the correlation filters jointly. Next, the proposed MCPF is introduced to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed MCPF enjoys several merits. First, it exploits the interdependencies among different features to derive the correlation filters jointly, and makes the learned filters complement and enhance each other to obtain consistent responses. Second, it handles partial occlusion via a part-based representation, and exploits the intrinsic relationship among local parts via spatial constraints to preserve object structure and learn the correlation filters jointly. Third, it effectively handles large scale variation via a sampling scheme by drawing particles at different scales for target object state estimation. Fourth, it shepherds the sampled particles toward the modes of the target state distribution via the MCF, and effectively covers object states well using fewer particles than conventional particle filters, thereby resulting in robust tracking performance and low computational cost. Extensive experimental results on four challenging benchmark datasets demonstrate that the proposed MCPF tracking algorithm performs favorably against the state-of-the-art methods.
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http://dx.doi.org/10.1109/TPAMI.2018.2797062DOI Listing
February 2019

P2T: Part-to-Target Tracking via Deep Regression Learning.

IEEE Trans Image Process 2018 Mar 9. Epub 2018 Mar 9.

Most existing part based tracking methods are part-to-part trackers, which usually have two separated steps including part matching and target localization. Different from existing methods, in this paper, we propose a novel part-totarget (P2T) tracker in a unified fashion by inferring target location from parts directly. To achieve this goal, we propose a novel deep regression model for part to target regression in an end-to-end framework via Convolutional Neural Networks. The proposed model is able to not only exploit part context information to preserve object spatial layout structure, but also learn part reliability to emphasize part importance for robust part to target regression. We evaluate the proposed tracker on 4 challenging benchmark sequences, and extensive experimental results demonstrate that our method performs favorably against state-of-the-art trackers because of the powerful capacity of the proposed deep regression model.
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http://dx.doi.org/10.1109/TIP.2018.2813166DOI Listing
March 2018