Engineering Bio-MOF/polydopamine as a biocompatible targeted theranostic system for synergistic multi-drug chemo-photothermal therapy.

In this study, a biodegradable multifunctional photothermal drug delivery nanoparticles (MPH NPs) using curcumin (Cur) as the ligand coated with hyaluronic acid (HA) was successfully prepared, which could simultaneously deliver Cur and doxorubicin hydrochloride (DOX·HCl) to overcome the common drug resistance in cancer cells. Polydopamine (PDA) as a protective shell prevents premature degradation of Cur in physiological environment and enables it to play effective medicinal value. MPH NPs can specifically recognize CD44 receptors on the surface of cancer cells for tumor targeting, with the damage of the partially released DOX to the superficial tumor cells, and then the positively charged Cur released may gradually penetrate into the cells through electron interaction to improve the problem of low permeability. In vitro cell experiments showed that hydrophobic/hydrophilic drugs co-loaded MPDH (MPH loaded with DOX·HCl) could enter the cancer cells through the endocytosis mediated by clathrin / caveolin, and the inhibition rate of MPDH on HeLa cells reached 79.28 % irradiation under 808 nm laser. MPH were composed of safe materials that have been proven to be biodegradable in human body, which avoided the disadvantages that NPs were difficult to discharge and caused damage to normal organs during long-term use.

Intensity analysis of chromium cycling in south Jiangsu region of China.

Suzhou (SZ), Wuxi (WX) and Changzhou (CZ) (collectively called the SXC area) in southern Jiangsu Province surround Tai Lake on three sides and have an important impact on its ecology. The emission and circulation of Cr in the three cities were quantified according to the six categories (including industry production, agricultural livestock, vehicle exhaust, solid waste, atmospheric subsidence and runoff) to analyze its regional characteristics and source category characteristics and to build a Cr cycle diagram to evaluate the pollution situation. The results showed that the Cr emissions from solid waste were the highest and mostly came from industrial sludge, accounting for 76.4% of the total circulation. The Cr emissions from SZ and WX were significantly higher than those of CZ, accounting for 47.0% and 42.9% of the regional total. The Cr in the excrement of pigs and poultry, dry sedimentation and surface runoff exceeded 100 tons every year, which needed to be valued. The Cr concentration in the surface water, soil and atmosphere in SXC area all met with the highest national standards. Studies have shown that the sediments and benthic organisms in the west and north of Tai Lake were already in a low-pollution state, but which was overall acceptable. Through this study, Cr circulation was clarified in typical areas, which was convenient for the monitoring and management of heavy metal pollution in the areas surrounding Tai Lake.

Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis.

Deep multitask learning for face analysis has received increasing attentions. From literature, most existing methods focus on optimizing a main task by jointly learning several auxiliary tasks. It is challenging to consider the performance of each task in a multitask framework due to the following reasons: 1) different face tasks usually rely on different levels of semantic features; 2) each task has different learning convergence rate, which could affect the whole performance when joint training; and 3) multitask model needs rich label information for efficient training, but existing facial datasets provide limited annotations. To address these issues, we propose a task-oriented feature-fused network (TFN) for simultaneously solving face detection, landmark localization, and attribute analysis. In this network, a task-oriented feature-fused block is designed to learn task-specific feature combinations; then, an alternative multitask training scheme is presented to optimize each task with considering of their different learning capacities. We also present a large-scale face dataset called JFA in support of proposed method, which provides multivariate labels, including face bounding box, 68 facial landmarks, and 3 attribute labels (i.e., apparent age, gender, and ethnicity). The experimental results suggest that the TFN outperforms several multitask models on the JFA dataset. Furthermore, our approach achieves competitive performances on WIDER FACE and 300W dataset, and obtains state-of-the-art results for gender recognition on the MORPH II dataset.