Constructing sequence-controlled heterolayered dendritic lanthanide chelates via a one-pot strategy using orthogonal chemistry.

The construction of sequence-controlled heterometallic lanthanide complexes is challenging despite their intriguing physical/chemical properties and enormous potential applications. Here we report a one-pot strategy that exploits orthogonal chemical reactions for modular assembly, which allows for rapid preparation of sequence-controlled heterolayered lanthanide-complex dendritic structures.

A dual-responsive doxorubicin-indoximod conjugate for programmed chemoimmunotherapy.

Herein we report a dual-responsive doxorubicin-indoximod conjugate (DOXIND) for programmed chemoimmunotherapy. This conjugate is able to release doxorubicin and indoximod upon exposure to appropriate stimuli for synergistic chemotherapy and immunotherapy, respectively. We demonstrate its promoting effects on immune response and inhibiting effects on tumor growth through a series of in vitro and in vivo experiments.

Tandem Chemoimmunotherapy by a Cascade-Responsive Molecular Prodrug.

The limited therapeutic effects of immunotherapy for most types of cancer stimulates the pursuit for efficient methods to improve its response rate. Herein we report the design and synthesis of a cascade-responsive molecular prodrug for tandem chemoimmunotherapy. This molecular prodrug first releases doxorubicin (DOX) in the mildly acidic tumor microenvironment (TME) to induce immunogenic cell death (ICD) of tumor cells. Caspase 3/7 released during tumor cell apoptosis liberates NLG919 from the prodrug, which inhibits the activity of indoleamine 2,3-dioxygenase (IDO) and results in relief of TME immunosuppression. Meanwhile, tumor-associated antigens and immune stimulatory cytokines released during ICD activate the immune response against the tumor, leading to synergistic chemoimmunotherapy. The efficacy of this prodrug is validated by in vitro and in vivo experiments, demonstrating the success of this strategy for cancer treatment.

Activatable Multiplexed 19F Magnetic Resonance Imaging Visualizes Reactive Oxygen and Nitrogen Species in Drug-Induced Acute Kidney Injury.

In vivo levels of reactive oxygen species (ROS) and reactive nitrogen species (RNS) are critical to many physiological and pathological processes. Because of the distinct differences in their biological generation and effects, simultaneously visualizing both of them could help deepen our insights into the mechanistic details of these processes. However, real-time and deep-tissue imaging and differentiation of ROS- and RNS-related molecular events in living subjects still remain a challenge. Here, we report the development of two activatable 19F magnetic resonance imaging (MRI) molecular probes with different 19F chemical shifts and specific responsive behaviors for simultaneous in vivo detection and deep-tissue imaging of O2•- and ONOO-. These probes are capable of real-time visualization and differentiation of O2•- and ONOO- in living mice with drug-induced acute kidney injury by interference-free multiplexed hot-spot 19F MRI, illustrating the potential of this technique for background-free real-time imaging of diverse biological processes, accurate diagnosis of various diseases in deep tissues, and rapid toxicity evaluation of assorted drugs.

Redox-Activated Contrast-Enhanced T1-Weighted Imaging Visualizes Glutathione-Mediated Biotransformation Dynamics in the Liver.

GSH-mediated liver biotransformation is a crucial physiological process demanding efficient research tools. Here, we report a type of amorphous FexMnyO nanoparticles (AFMO-ZDS NPs) as redox-activated probes for in vivo visualization of the dynamics of GSH-mediated biotransformation in liver with T1-weighted magnetic resonance imaging (MRI). This imaging technique reveals the periodic variations in GSH concentration during the degradation of AFMO-ZDS NPs due to the limited transportation capacity of GSH carriers in the course of GSH efflux from hepatocytes to perisinusoidal space, providing direct imaging evidence for this important carrier-mediated process during GSH-mediated biotransformation. Therefore, this technique offers an effective method for in-depth investigations of GSH-related biological processes in liver under various conditions as well as a feasible means for the real-time assessment of liver functions, which is highly desirable for early diagnosis of liver diseases and prompt a toxicity evaluation of pharmaceuticals.

A camptothecin prodrug induces mitochondria-mediated apoptosis in cancer cells with cascade activations.

Mitochondria are crucial regulators of the intrinsic pathway of apoptosis. Herein, we report a photosensitizer-conjugated camptothecin (CPT)-based prodrug for combinative chemo-photodynamic treatment of solid tumors with cascade activations. Upon light irradiation, our prodrug can effectively target the mitochondria of cancer cells, generate singlet oxygen to increase the level of reactive oxygen species (ROS) and trigger ROS-responsive release of CPT, which synergistically induce mitochondrial damage and cause the apoptosis of cancer cells, therefore achieving high therapeutic efficacy for solid tumors and minimized adverse effects to normal tissues. Our prodrug holds great promise as a potent and inspiring means for cancer treatment.

Activatable Mitochondria-Targeting Organoarsenic Prodrugs for Bioenergetic Cancer Therapy.

Despite widespread applications for cancer treatment, chemotherapy is restricted by several limitations, including low targeting specificity, acquired drug resistance, and concomitant adverse side effects. It remains challenging to overcome these drawbacks. Herein, we report a new bioenergetic approach for treating cancer efficiently. As a proof-of-concept, we construct activatable mitochondria-targeting organoarsenic prodrugs from organoarsenic compounds and traditional chemotherapeutics. These prodrugs could accomplish selective delivery and controlled release of both therapeutic agents to mitochondria, which synergistically promote mitochondrial ROS production and induce mitochondrial DNA damage, finally leading to mitochondria-mediated apoptosis of cancer cells. Our in vitro and in vivo experiments reveal the excellent anticancer efficacy of these prodrugs, underscoring the encouraging outlook of this strategy for effective cancer therapy.

DOTA-Branched Organic Frameworks as Giant and Potent Metal Chelators.

Multinuclear complexes as metallo-agents for clinical use have caught extensive attention. In this paper, using 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) as both a functioning unit and a constructing junction, we build a series of DOTA-branched organic frameworks with multiple chelating holes by organizing DOTA layer by layer. These giant chelators are well characterized, which reveals their nanosized and soft structures. Further experiments demonstrate that they could efficiently hold abundant metal ions with much higher kinetic stabilities than the conventional small DOTA chelator. Their corresponding polynuclear complexes containing Gd3+, Tb3+, or both show superior imaging properties, excellent feasibility for peripheral modification, and unusual kinetic stability. This work can be easily extended to the fabrication of diverse homomultinuclear complexes and core/shell heteromultinuclear complexes with multifunctional properties. We expect that this new type of giant molecules and the ligand-branching strategy would open up a new avenue for the design and construction of next-generation polymetallic agents with high performance and stabilities for biomedical applications.

A gadolinium-complex-based theranostic prodrug for in vivo tumour-targeted magnetic resonance imaging and therapy.

Here we report a target-specific theranostic prodrug (1a) containing Gd-DOTA, biotin, and camptothecin (CPT) along with a disulfide self-immolative linker. This prodrug exhibits selective targeting towards tumour cells and tissues, stimuli-responsive controlled release, enhanced anticancer efficacy, and accurate diagnosis and real-time monitoring via contrast-enhanced magnetic resonance imaging (MRI).

Facile synthesis of aquo-cisplatin arsenite multidrug nanocomposites for overcoming drug resistance and efficient combination therapy.

Cisplatin (CDDP) and arsenic trioxide (ATO), two representative inorganic anticancer drugs, have been successful in the treatment against several kinds of malignancies. However, combination therapy with these two drugs in clinical application suffers from poor pharmacokinetics, serious side effects, and drug resistance of the tumor. Herein, we report a carrier-free aquo-cisplatin arsenite multidrug nanocomposite loaded with cisplatin and arsenic trioxide prodrugs simultaneously. This nanocomposite achieves a high loading capacity and pH-dependent controlled release of the drugs. Because of these features, this nanocomposite shows better in vitro toxicity against various carcinoma cell lines than either the single drug or free drug combination, promotes the synergistic effect of cisplatin and arsenic trioxide, and significantly inhibits the growth of tumors in vivo. Furthermore, cisplatin and arsenic trioxide in this nanocomposite can realize a coordination of both enhanced DNA damage and DNA repair interference within cisplatin-resistant cells, which results in overcoming the drug resistance effectively. Gene expression profiles demonstrate the reduced expression of proto-oncogenes and DNA damage repair related genes MYC, MET, and MSH2, along with the increase of tumor suppressor genes PTEN, VHL, and FAS after the nanocomposite treatment. This type of multidrug nanocomposite offers an alternative and promising strategy for combination therapy and overcoming drug resistance.

Jointly Learning Multiple Sequential Dynamics for Human Action Recognition.

Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the part-induced spatiotemporal action unit sequence to represent each action sample with multiple partwise sequential feature subspaces. For model learning, we propose the multi-task conditional random fields (MTCRFs) model to discover the sequence-specific structure and the sequence-shared relationship. Specifically, the multi-chain graph structure and the corresponding probabilistic model are designed to represent the interaction among multiple part-induced action unit sequences. Moreover we propose the model learning and inference methods to discover temporal context within individual action unit sequence and the latent correlation among different body parts. Extensive experiments are implemented to demonstrate the superiority of the proposed method on two popular RGB human action datasets, KTH & TJU, and the depth dataset in MSR Daily Activity 3D.

Multipe/single-view human action recognition via part-induced multitask structural learning.

This paper proposes a unified framework for multiple/single-view human action recognition. First, we propose the hierarchical partwise bag-of-words representation which encodes both local and global visual saliency based on the body structure cue. Then, we formulate the multiple/single-view human action recognition as a part-regularized multitask structural learning (MTSL) problem which has two advantages on both model learning and feature selection: 1) preserving the consistence between the body-based action classification and the part-based action classification with the complementary information among different action categories and multiple views and 2) discovering both action-specific and action-shared feature subspaces to strengthen the generalization ability of model learning. Moreover, we contribute two novel human action recognition datasets, TJU (a single-view multimodal dataset) and MV-TJU (a multiview multimodal dataset). The proposed method is validated on three kinds of challenging datasets, including two single-view RGB datasets (KTH and TJU), two well-known depth dataset (MSR action 3-D and MSR daily activity 3-D), and one novel multiview multimodal dataset (MV-TJU). The extensive experimental results show that this method can outperform the popular 2-D/3-D part model-based methods and several other competing methods for multiple/single-view human action recognition in both RGB and depth modalities. To our knowledge, this paper is the first to demonstrate the applicability of MTSL with part-based regularization on multiple/single-view human action recognition in both RGB and depth modalities.

Sparse coding induced transfer learning for HEp-2 cell classification.

Automated human larynx carcinoma (HEp-2) cell classification is critical for medical diagnosis. In this paper, we propose a sparse coding-based unsupervised transfer learning method for HEp-2 cell classification. First, the low level image feature is extracted for visual representation. Second, a sparse coding scheme with the Elastic Net penalized convex objective function is proposed for unsupervised feature learning. At last, a Support Vector Machine classifier is utilized for model learning and predicting. To our knowledge, this work is the first to transfer the human-crafted visual feature, sensitive to the variation of appearance and shape during cell movement, to the high level representation which directly denotes the correlation of one sample and the bases in the learnt dictionary. Therefore, the proposed method can overcome the difficulty in discriminative feature formulation for different kinds of cells with irregular and changing visual patterns. Large scale comparison experiments will be conducted to show the superiority of this method.

Nonnegative mixed-norm convex optimization for mitotic cell detection in phase contrast microscopy.

This paper proposes a nonnegative mix-norm convex optimization method for mitotic cell detection. First, we apply an imaging model-based microscopy image segmentation method that exploits phase contrast optics to extract mitotic candidates in the input images. Then, a convex objective function regularized by mix-norm with nonnegative constraint is proposed to induce sparsity and consistence for discriminative representation of deformable objects in a sparse representation scheme. At last, a Support Vector Machine classifier is utilized for mitotic cell modeling and detection. This method can overcome the difficulty in feature formulation for deformable objects and is independent of tracking or temporal inference model. The comparison experiments demonstrate that the proposed method can produce competing results with the state-of-the-art methods.