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Showing papers by "University of Macau published in 2019"


Journal ArticleDOI
TL;DR: A cosine-transform-based chaotic system (CTBCS) that can produce chaotic maps with complex dynamical behaviors and an image encryption scheme that provides a higher level of security than several advanced image encryption schemes.

463 citations


Journal ArticleDOI
TL;DR: A mathematical proof of the universal approximation property of BLS is provided and the framework of several BLS variants with their mathematical modeling is given, which include cascade, recurrent, and broad–deep combination structures.
Abstract: After a very fast and efficient discriminative broad learning system (BLS) that takes advantage of flatted structure and incremental learning has been developed, here, a mathematical proof of the universal approximation property of BLS is provided In addition, the framework of several BLS variants with their mathematical modeling is given The variations include cascade, recurrent, and broad–deep combination structures From the experimental results, the BLS and its variations outperform several exist learning algorithms on regression performance over function approximation, time series prediction, and face recognition databases In addition, experiments on the extremely challenging data set, such as MS-Celeb-1M, are given Compared with other convolutional networks, the effectiveness and efficiency of the variants of BLS are demonstrated

327 citations


Journal ArticleDOI
TL;DR: Five key trends were observed: miRNA identification and target prediction have been hot spots in the past decade; manual curation and TM are the main methods for collecting miRNA knowledge from literature; most early tools are well maintained and widely used; however, novel ones have begun to emerge and disease-associated miRNA tools are emerging.
Abstract: MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

315 citations


Proceedings ArticleDOI
01 Jul 2019
TL;DR: This paper showed that a deep Transformer model can surpass the Transformer-Big counterpart by proper use of layer normalization and a novel way of passing the combination of previous layers to the next.
Abstract: Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT’16 English-German and NIST OpenMT’12 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.

312 citations


Journal ArticleDOI
TL;DR: This study provides detailed information on the principles, effective parameters, advantages, disadvantages and applications of microencapsulation techniques.

288 citations


Journal ArticleDOI
Qun Luo1, Jianding Li2, Bo Li2, Bin Liu1, Huaiyu Shao2, Qian Li1 
TL;DR: In this article, a review on the enhancement method of kinetics in Mg-based hydrogen storage materials and introduces the new kinetic models is presented, which is an efficient way to reveal the hydriding/dehydriding (H/D) kinetic mechanism.

256 citations


Journal ArticleDOI
TL;DR: This review is based on the principles of cancer immunotherapy and the combined treatment design reflected by advances in materials science, including the structures of nanoplatforms and their underlying mechanisms towards cancer.
Abstract: In recent years, conventional treatments including surgery, chemotherapy and radiotherapy have been the main approaches in tumour therapy. Cancer immunotherapy is a new therapeutic modality to fight cancer by harnessing the power of patients’ own immune system. Ongoing research related to these therapies has demonstrated their advantages and intrinsic limitations. Nanomaterial-based platforms are utilized in these emerging fields. In particular, a combination of other treatment methods with cancer immunotherapy to achieve precision medicine and prevent recurrence and metastasis, could improve patients’ outcome. The combined multiple treatments have superior efficacy to any monotherapy alone in producing improved anti-cancer activity. Therefore, it's necessary to summarise research advances in nanomaterial-based combination cancer immunotherapy contributing to clinical transformation. This review is based on the principles of cancer immunotherapy and the combined treatment design reflected by advances in materials science, including the structures of nanoplatforms and their underlying mechanisms towards cancer. The ultimate goals are to stimulate the design of better strategies for versatile use in the future based on biomaterial engineering methods to enhance the efficacy of combined cancer treatments, and to provide new ideas for the prospects of a synergistic cancer combination immunotherapy for clinical application transformation.

251 citations


Posted Content
TL;DR: It is claimed that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next.
Abstract: Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for the development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT'16 English- German, NIST OpenMT'12 Chinese-English and larger WMT'18 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.

248 citations


Journal ArticleDOI
TL;DR: An appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay.
Abstract: This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.

241 citations


Journal ArticleDOI
TL;DR: This paper solves the stochastically finite-time control problem for uncertain stochastic nonlinear systems in nontriangular form by combining the novel criterion and backstepping technique, and an adaptive fuzzy stochorian control method is proposed.
Abstract: This paper solves the stochastically finite-time control problem for uncertain stochastic nonlinear systems in nontriangular form. The considered controlled plants are different from the previous results of finite-time control systems, which are the multiple-input and multiple-output (MIMO) stochastic systems with the unknown functions consisting of all states, stochastic disturbance, and immeasurable states. Fuzzy logic systems and a state filter are used to model the uncertain systems and estimate the immeasurable states, respectively. Based on the finite-time theory and It $\hat{o}$ differential equation, a novel stochastically finite-time stability theorem is first raised. By combining the novel criterion and backstepping technique, an adaptive fuzzy stochastically finite-time control method is proposed. It is testified that all signals in the closed-loop signals are semiglobal finite-time stable in probability, and the tracking performances are well. Simulation example results further show the effectiveness of the proposed approach.

236 citations


Journal ArticleDOI
TL;DR: The construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes.

Journal ArticleDOI
11 Apr 2019-Chem
TL;DR: In this article, an alternative environmentally friendly room-temperature molten salt, methylammonium acetate (MAAc), was used for facile fabrication of perovskite solar cells (PSCs) in ambient air.

Journal ArticleDOI
TL;DR: The present review has extended to describe other promising compounds including dihydroartemisinin, ginsenoside Rh2, compound K, cucurbitacins D, E, I, tanshinone IIA and cryptotanshin one in view of their potentials in cancer therapy.
Abstract: Numerous natural products originated from Chinese herbal medicine exhibit anti-cancer activities, including anti-proliferative, pro-apoptotic, anti-metastatic, anti-angiogenic effects, as well as regulate autophagy, reverse multidrug resistance, balance immunity, and enhance chemotherapy in vitro and in vivo. To provide new insights into the critical path ahead, we systemically reviewed the most recent advances (reported since 2011) on the key compounds with anti-cancer effects derived from Chinese herbal medicine (curcumin, epigallocatechin gallate, berberine, artemisinin, ginsenoside Rg3, ursolic acid, silibinin, emodin, triptolide, cucurbitacin B, tanshinone I, oridonin, shikonin, gambogic acid, artesunate, wogonin, β-elemene, and cepharanthine) in scientific databases (PubMed, Web of Science, Medline, Scopus, and Clinical Trials). With a broader perspective, we focused on their recently discovered and/or investigated pharmacological effects, novel mechanism of action, relevant clinical studies, and their innovative applications in combined therapy and immunomodulation. In addition, the present review has extended to describe other promising compounds including dihydroartemisinin, ginsenoside Rh2, compound K, cucurbitacins D, E, I, tanshinone IIA and cryptotanshinone in view of their potentials in cancer therapy. Up to now, the evidence about the immunomodulatory effects and clinical trials of natural anti-cancer compounds from Chinese herbal medicine is very limited, and further research is needed to monitor their immunoregulatory effects and explore their mechanisms of action as modulators of immune checkpoints.

Journal ArticleDOI
TL;DR: The experimental results on three real-life HSI data sets show that the proposed semisupervised learning framework, $\text{S}^{2}$ GCN can significantly improve the classification accuracy.
Abstract: Collecting labeled samples is quite costly and time-consuming for hyperspectral image (HSI) classification task. Semisupervised learning framework, which combines the intrinsic information of labeled and unlabeled samples, can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this letter, we propose a novel semisupervised learning framework that is based on spectral–spatial graph convolutional networks ( $\text{S}^{2}$ GCNs). It explicitly utilizes the adjacency nodes in graph to approximate the convolution. In the process of approximate convolution on graph, the proposed method makes full use of the spatial information of the current pixel. The experimental results on three real-life HSI data sets, i.e., Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed $\text{S}^{2}$ GCN can significantly improve the classification accuracy. For instance, the overall accuracy on Indian data is increased from 66.8% (GCN) to 91.6%.

Journal ArticleDOI
30 Dec 2019
TL;DR: A synopsis of the recent literature exploring the relationship between quercetin and cognitive performance in Alzheimer’s disease and its potential as a lead compound in clinical applications is provided.
Abstract: Quercetin is a flavonoid with notable pharmacological effects and promising therapeutic potential. It is widely distributed among plants and found commonly in daily diets predominantly in fruits and vegetables. Neuroprotection by quercetin has been reported in several in vitro studies. It has been shown to protect neurons from oxidative damage while reducing lipid peroxidation. In addition to its antioxidant properties, it inhibits the fibril formation of amyloid-β proteins, counteracting cell lyses and inflammatory cascade pathways. In this review, we provide a synopsis of the recent literature exploring the relationship between quercetin and cognitive performance in Alzheimer’s disease and its potential as a lead compound in clinical applications.

Journal ArticleDOI
TL;DR: Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods.
Abstract: Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder–decoder structure based on recurrent neural networks (RNNs) with attention SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods

Journal ArticleDOI
TL;DR: An adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body and it can prove the stability of the closed-loop system.
Abstract: In this paper, an adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body. The NNs are used to approximate the unknown mass of car body. It is commonly known that the stability and security of the ASSs will be weakened when the constraints are violated. Thus, the control problem of the time-varying vertical displacement and speed constraints for the quarter-car ASSs is a very important task because of the demand of the handing safety. The time-varying barrier Lyapunov functions are used to guarantee the constraints of the vertical displacement not violated, and it can prove the stability of the closed-loop system. Finally, a simulation example for the ASSs is employed to show the feasibility and rationality of the proposed approach.

Journal ArticleDOI
TL;DR: This review summarizes human studies and clinical trials of polyphenols as anti-diabetic agents and indicates that individual flavonoid or isoflavonoid compounds appear to have no therapeutic effect on diabetes, based on the limited clinical data.
Abstract: Significant evidence from epidemiological investigations showed that dietary polyphenols might manage and prevent type 2 diabetes (T2D). This review summarizes human studies and clinical trials of polyphenols as anti-diabetic agents. Polyphenols from coffee, guava tea, whortleberry, olive oil, propolis, chocolate, red wine, grape seed, and cocoa have been reported to show anti-diabetic effects in T2D patients through increasing glucose metabolism, improving vascular function as well as reducing insulin resistance and HbA1c level. However, individual flavonoid or isoflavonoid compounds appear to have no therapeutic effect on diabetes, based on the limited clinical data. Preliminary clinical trials provided evidence that resveratrol had anti-diabetic activity in humans by improving glycemic control in subjects with insulin resistance. Besides, anthocyanins exhibited anti-diabetic properties by reducing blood glucose and HbA1c levels or the improvement of insulin secretion and resistance. The structure-activity relationship of polyphenols as anti-diabetic agents in humans has been rarely reported.

Journal ArticleDOI
TL;DR: The current progresses of cancer related exosomes are described, including their biogenesis, molecular contents, biological functions, sources where they are derived from, and methods for their detection.
Abstract: Cancer related exosomes are nano-size membrane vesicles that play important roles in tumor microenvironment. Emerging evidence indicates that exosomes can load unique cargoes, including proteins and nucleic acids that reflect the condition of tumor. Therefore, exosomes are being used as diagnostic and prognostic biomarkers for various cancers. In this review, we describe the current progresses of cancer related exosomes, including their biogenesis, molecular contents, biological functions, sources where they are derived from, and methods for their detection. We will also discuss the current exosomal biomarkers and the utilization of them for early diagnosis and prognostics in cancer.

Journal ArticleDOI
TL;DR: In this paper, the effect of fluorine dopant and oxygen vacancy on electrochemical performance of a fluorine-doped oxygen-deficient Co2MnO4 nanowires grown on carbon fiber (CF) as advanced electrode materials for supercapacitor was investigated.

Journal ArticleDOI
TL;DR: This review provides a comprehensive overview of the theory of cell membrane coating technology, followed by a summary of the existing preparation and characterization techniques, and focuses on the functions and applications of various cell membrane types.
Abstract: Cell membrane coating technology is an approach to the biomimetic replication of cell membrane properties, and is an active area of ongoing research readily applicable to nanoscale biomedicine. Nanoparticles (NPs) coated with cell membranes offer an opportunity to unite natural cell membrane properties with those of the artificial inner core material. The coated NPs not only increase their biocompatibility but also achieve effective and extended circulation in vivo, allowing for the execution of targeted functions. Although cell membrane-coated NPs offer clear advantages, much work remains before they can be applied in clinical practice. In this review, we first provide a comprehensive overview of the theory of cell membrane coating technology, followed by a summary of the existing preparation and characterization techniques. Next, we focus on the functions and applications of various cell membrane types. In addition, we collate model drugs used in cell membrane coating technology, and review the patent applications related to this technology from the past 10 years. Finally, we survey future challenges and trends pertaining to this technology in an effort to provide a comprehensive overview of the future development of cell membrane coating technology.

Journal ArticleDOI
TL;DR: The literature studying relations between problematic smartphone use (PSU) and anxiety symptom severity is examined, and an own theoretical model of how PSU is specifically related to anxiety is presented.

Journal ArticleDOI
TL;DR: Chen et al. show that by fine tuning the alkaline environment in precursor solution, they can greatly suppress defects density and obtain high certified efficiency of 20.87% in the planar heterojunction perovskite solar cell.
Abstract: Further minimizing the defect state density in the semiconducting absorber is vital to boost the power conversion efficiency of solar cells approaching Shockley-Queisser limit. However, it lacks a general strategy to control the precursor chemistry for defects density reduction in the family of iodine based perovskite. Here the alkaline environment in precursor solution is carefully investigated as an effective parameter to suppress the incident iodine and affects the crystallization kinetics during film fabrication, via rationale adjustment of the alkalinity of additives. Especially, a ‘residual free’ weak alkaline is proposed not only to shrink the bandgap of the absorber by modulating the stoichiometry of organic cation, but also to improve the open circuit voltage in the resultant device. Consequently, the certified efficiency of 20.87% (Newport) is achieved with one of the smallest voltage deficits of 413 mV in the planar heterojunction perovskite solar cell.

Journal ArticleDOI
TL;DR: This paper proposes novel container migration algorithms and architecture to support mobility tasks with various application requirements and demonstrates that the strategy outperforms the existing baseline approaches in terms of delay, power consumption, and migration cost.
Abstract: Fog Computing (FC) is a flexible architecture to support distributed domain-specific applications with cloud-like quality of service. However, current FC still lacks the mobility support mechanism when facing many mobile users with diversified application quality requirements. Such mobility support mechanism can be critical such as in the industrial internet where human, products, and devices are moveable. To fill in such gaps, in this paper we propose novel container migration algorithms and architecture to support mobility tasks with various application requirements. Our algorithms are realized from three aspects: 1) We consider mobile application tasks can be hosted in a container of a corresponding fog node that can be migrated, taking the communication delay and computational power consumption into consideration; 2) We further model such container migration strategy as multiple dimensional Markov Decision Process (MDP) spaces. To effectively reduce the large MDP spaces, efficient deep reinforcement learning algorithms are devised to achieve fast decision-making and 3) We implement the model and algorithms as a container migration prototype system and test its feasibility and performance. Extensive experiments show that our strategy outperforms the existing baseline approaches 2.9, 48.5 and 58.4 percent on average in terms of delay, power consumption, and migration cost, respectively.

Journal ArticleDOI
Roy Burstein1, Nathaniel J Henry1, Michael Collison1, Laurie B. Marczak1  +663 moreInstitutions (290)
16 Oct 2019-Nature
TL;DR: A high-resolution, global atlas of mortality of children under five years of age between 2000 and 2017 highlights subnational geographical inequalities in the distribution, rates and absolute counts of child deaths by age.
Abstract: Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

Journal ArticleDOI
01 Nov 2019-Carbon
TL;DR: In this article, the authors successfully designed and synthesized a lightweight (31.5 mg/cm3) and porous NiCo2O4@carbon nanotubes (CNTs) hybrid sponge based on N-doped and Fe-filled CNTs.

Journal ArticleDOI
TL;DR: Based on the backstepping recursive technique and the common Lyapunov function method, a finite-time switching control method is presented and the effectiveness of the proposed method is given through its application to a mass-spring-damper system.
Abstract: This paper solves the finite-time switching control issue for the nonstrict-feedback nonlinear switched systems. The controlled plants contain immeasurable states, arbitrarily switchings, and the unknown functions which are constructed with the whole states. Neural network is used to simulate the uncertain systems and a filter-based state observer is designed to estimate the immeasurable states in this paper, respectively. Based on the backstepping recursive technique and the common Lyapunov function method, a finite-time switching control method is presented. Due to the developed finite-time control strategy, the closed-loop signals can be ensured to be bounded under arbitrarily switchings, and the outputs of systems can quickly track the desired reference signals in finite time. The effectiveness of the proposed method is given through its application to a mass-spring-damper system.

Journal ArticleDOI
Yan Dai1, Shao-Ru Chen1, Ling Chai, Jing Zhao1, Yitao Wang1, Ying Wang1 
TL;DR: Despite the various pharmacological activities and formula of A. paniculata, further development of more structural derivatives of andrographolide with reduced toxicity and increased therapeutic efficacy is still needed for the clinical application of this ancient mighty herb and its major component.
Abstract: Andrographis paniculata (A. paniculata) is a medicinal plant traditionally used as anti-inflammation and anti-bacteria herb. Andrographolide, the major active component of A. paniculata, exhibits diverse pharmacological activities, including anti-inflammation, anti-cancer, anti-obesity, anti-diabetes, and other activities. In this article, we comprehensively review the therapeutic potential of A. paniculata and andrographolide focusing on the mechanisms of action and clinical application. We systemically discuss the structure-activity relationship of andrographolide and derivatives. Despite the various pharmacological activities and formula of A. paniculata and andrographolide, we propose further development of more structural derivatives of andrographolide with reduced toxicity and increased therapeutic efficacy is still needed for the clinical application of this ancient mighty herb and its major component.

Journal ArticleDOI
TL;DR: An adaptive neural control method is investigated for the first time to address the problems of the time-varying full-state constraints and time-Varying delays in a unified framework with Lyapunov–Krasovskii functions.
Abstract: This paper proposes an adaptive neural control method for a class of nonlinear time-varying delayed systems with time-varying full-state constraints. To address the problems of the time-varying full-state constraints and time-varying delays in a unified framework, an adaptive neural control method is investigated for the first time. The problems of time delay and constraint are the main factors of limiting the system performance severely and even cause system instability. The effect of unknown time-varying delays is eliminated by using appropriate Lyapunov–Krasovskii functionals. In addition, the constant constraint is the only special case of time-varying constraint which leads to more complex and difficult tasks. To guarantee the full state always within the time-varying constrained interval, the time-varying asymmetric barrier Lyapunov function is employed. Finally, two simulation examples are given to confirm the effectiveness of the presented control scheme.

Journal ArticleDOI
TL;DR: In’situ catalytic oxygenation strategy in tumor using porphyrinic metal-organic framework (MOF)-gold nanoparticles (AuNPs) nanohybrid as a therapeutic platform to achieve O2 -evolving chemoradiotherapy is proposed.
Abstract: Tumor hypoxia, the "Achilles' heel" of current cancer therapies, is indispensable to drug resistance and poor therapeutic outcomes especially for radiotherapy. Here we propose an in situ catalytic oxygenation strategy in tumor using porphyrinic metal-organic framework (MOF)-gold nanoparticles (AuNPs) nanohybrid as a therapeutic platform to achieve O2 -evolving chemoradiotherapy. The AuNPs decorated on the surface of MOF effectively stabilize the nanocomposite and serve as radiosensitizers, whereas the MOF scaffold acts as a container to encapsulate chemotherapeutic drug doxorubicin. In vitro and in vivo studies verify that the catalase-like nanohybrid significantly enhances the radiotherapy effect, alleviating tumor hypoxia and achieving synergistic anticancer efficacy. This hybrid nanomaterial remarkably suppresses the tumor growth with minimized systemic toxicity, opening new horizons for the next generation of theranostic nanomedicines.