A responsive drug delivery system (DDS) for oxaliplatin (OX) has been designed with a view to overcoming several common drawbacks associated with this widely used anticancer agent, including fast degradation/deactivation in the blood stream, lack of tumor selectivity, and low bioavailability.

Herein, two new classes of macrocyclic compounds, terphen[n]arenes (TPns) (n=3–6) and quaterphen[n]arenes (QPns) (n=3–6), were designed and synthesized by a one‐step condensation reaction in relatively high yields. They comprise 2,2′′‐dimethoxy terphenyl and 2,2′′′‐dimethoxy quaterphenyl monomers, respectively, linked by methylene bridges. Given their long and rigid monomers, TPns and QPns have much larger cavities and better self‐assembly properties than classic macrocycles. More interestingly, the cyclic pentamers and hexamers TP5, TP6, QP5, and QP6 formed supramolecular organogels, which were composed of interwoven fibers, nanosheets, or entangled macropore networks formed by multiple face‐to‐face and edge‐to‐face π⋅⋅⋅π stacking interactions. The xerogel materials effectively captured volatile iodine, not only in aqueous media but also in the gaseous state, and could be recycled multiple times without obvious loss in performance.

Polyamines are essential for the growth of eukaryotic cells and can be dysregulated in tumors. Here we describe a strategy to deplete polyamines through host–guest encapsulation using a peptide-pillar[5]arene conjugate (P1P5A, P1 = RGDSK(N 3 )EEEE) as a supramolecular trap. The RGD in the peptide sequence allows the molecule to bind to integrin α v β 3 -overexpressing tumor cells. The negative charged glutamic acid residues enhance the inclusion affinities between the pillar[5]arene and cationic polyamines via electrostatic interactions and facilitate the solubility of the conjugate in aqueous media. The trap P1P5A efficiently encapsulates polyamines with association constants of 10 5 –10 6 M −1 . We show that P1P5A has a wide spectrum of antitumor activities, and induces apoptosis via affecting the polyamine biosynthetic pathway. Experiments in vivo show that P1P5A effectively inhibits the growth of breast adenocarcinoma xenografts in female nude mice. This work reveals an approach for suppressing tumor growth by using supramolecular macrocycles to trap polyamines in tumor cells.

Herein, two new classes of macrocyclic compounds, terphen[n]arenes (TPns) (n=3–6) and quaterphen[n]arenes (QPns) (n=3–6), were designed and synthesized by a one‐step condensation reaction in relatively high yields. They comprise 2,2′′‐dimethoxy terphenyl and 2,2′′′‐dimethoxy quaterphenyl monomers, respectively, linked by methylene bridges. Given their long and rigid monomers, TPns and QPns have much larger cavities and better self‐assembly properties than classic macrocycles. More interestingly, the cyclic pentamers and hexamers TP5, TP6, QP5, and QP6 formed supramolecular organogels, which were composed of interwoven fibers, nanosheets, or entangled macropore networks formed by multiple face‐to‐face and edge‐to‐face π⋅⋅⋅π stacking interactions. The xerogel materials effectively captured volatile iodine, not only in aqueous media but also in the gaseous state, and could be recycled multiple times without obvious loss in performance.

A water-soluble 2,2′-biphen[4]arene (2,2’-CBP4) containing eight carboxylato moieties was synthesized and characterized. Its complexation behavior towards two alkaloids, palmatine (P) and berberine (B), was investigated by means of fluorescence and 1H NMR spectroscopy in aqueous phosphate buffer solution (pH 7.4). In the presence of 2,2’-CBP4, 1H NMR signals of P and B displayed very large upfield shifts, indicating the formation of inclusion complexes with strong binding affinities. Fluorescence titration experiments showed that P and B exhibited dramatic fluorescence enhancement of more than 600 times upon complexation with 2,2’-CBP4. Particularly, the fluorescence intensity is strong enough to be readily distinguished by the naked eye. Although the two guests have similar structures, the association constant of B with 2,2’-CBP4 (K a = (2.29 ± 0.27) × 106 M−1) is 3.9 times larger than that of P (K a = (5.87 ± 0.24) × 105 M−1).

Rheumatoid arthritis (RA) is a chronic immune disease characterized by synovitis and bone destruction. The osteoclasts play a critical role in pathologic bone loss during inflammatory arthritis. In this paper, we report that Interleukin (IL)-6, IL-6Rα/gp130, IL-11, IL-27, and Matrix Metallo Proteinases (MMP)-9 expression results in serum of the RA group were significantly higher than that of the control group. The gp130 positive cells in peripheral blood mononuclear cell (PBMC) and osteoclast-like cells (OLC) which had been induced with receptor activator of nuclear factor κB ligand (RANKL) in RA group were also higher than that in the control group. In addition, after OLC in RA group is cultured with anti -gp130 Monoclonal antibody (McAb), the IL-6 and MMP-9 expression in osteoclast supernatant insignificantly decreased. Meanwhile, the expression results of Tartrate Resistant Acid Phosphatase (TRAP)-positive cells and osteoclasts were also decreased significantly. Our study suggests that regulating gp130 receptor can be used to control the differentiation and formation of osteoclasts, which provides a new clinical strategy for RA patients in the future.

No abstract

Efficient infrared dim object detection has been challenged by low signal-to-noise ratios (SNRs). Traditional methods rely on the gradient difference and fixed-parameter model. These methods fail to adapt to sophisticated and variable situations in the real world. To tackle the issue, a deep learning method based on the spatio-temporal network is proposed in this paper. The model is established by the Convolutional Long Short-Term Memory cell (Conv-LSTM) and the 3D Convolution cell (3D-Conv). It is trained to learn the motion constraint of moving targets (spatio-temporal constraint module, called STM) and to fuse the multiscale local feature between the target and background (deep spatial features module, called DFM). In addition, a variable interval search module (state-aware module, called STAM) is added to the inference. The submodule decides to conduct a global search for images only if the target is lost due to fast motion, uncertain obstruction, and frame loss. Comprehensive experiments indicate that the proposed method achieves better performance over all baseline methods. On the mid-wave infrared datasets collected by the authors, the proposed method achieves a 95.87% detection rate. The SNR of the dataset is around 1–3 dB, and the background of the sequence includes sky, asphalt road, and buildings.