Fang Yan

Impaired wound healing in diabetics usually leads to life-threatening complications. To develop a system for fastening skin wound healing efficiently and safely in diabetics, thermos-sensitive hydrogel containing the nanodrug, loaded in the form of gelatin microspheres (GMs), was designed to deliver curcumin (Cur) as a therapeutic drug. Cur is a naturally existing polyphenolic compound with a broad range of biological functions useful for potential therapies. Because Cur molecule has weakness in both bioavailability and in vivo stability, delivery of Cur requires assistance from other molecules to act as carrier vehicles in a sustained manner for therapeutic use. At first, self-assembly of Cur nanoparticles (CNPs) was done to improve bioavailability. The CNPs were further enclosed into GMs for responding to the matrix metalloproteinases (MMPs) that usually overexpress at diabetic nonhealing wound sites. The GMs containing CNPs were loaded into the thermos-sensitive hydrogel and were finally proved for the capacity of specially induced drug release at the wound bed, which promoted the efficacy in healing the standardized skin wounds in streptozotocin-induced diabetic mice. Our results indicated that the successfully developed CNP delivery system had the capacity to significantly promote skin wound healing, which suggested that it could have the potential to become a wound dressing with the properties of antioxidants and promotions of cell migration.

Purpose: Gouty arthritis could be triggered by the deposition of monosodium uric acid (MSU) crystals. Palmatine (PAL), a protoberberine alkaloid, has been proven to possess compelling health-beneficial activities. In this study, we aimed to explore the effect of PAL on LPS plus MSU crystal-stimulated gouty arthritis in vitro and in vivo. Methods: PMA-differentiated THP-1 macrophages were primed with LPS and then stimulated with MSU crystal in the presence or absence of PAL. The expression of pro-inflammatory cytokines and oxidative stress-related biomarkers and signal pathway key targets were determined by ELISA kit, Western blot, immunohistochemistry and qRT-PCR, respectively. In addition, the anti-inflammatory and antioxidant activities of PAL on MSU-induced arthritis mice were also evaluated. Results: The results indicated that PAL (20, 40 and 80 μM) dose-dependently decreased the mRNA expression and levels of pro-inflammatory cytokines (interleukin-1beta (IL-1β), IL-6, IL-18 and tumor necrosis factor alpha (TNF-α)). The levels of superoxide dismutase (SOD) and glutathione (GSH) were remarkably enhanced, while the level of malondialdehyde (MDA) was reduced. Western blot analysis revealed that PAL appreciably inhibited NF-κB/NLRP3 signaling pathways through inhibiting the phosphorylation of p-65 and IκBα, blocking the expression of NLRP3, ASC, IL-1β and Caspase-1, as well as enhancing the antioxidant protein expression of Nrf2 and HO-1. In vivo, PAL attenuated MSU-induced inflammation in gouty arthritis, as evidenced by mitigating the joint swelling, and decreasing the productions of IL-1β, IL-6, IL-18, TNF-α and MDA, while enhancing the levels of SOD and GSH. Moreover, PAL further attenuated the infiltration of neutrophils into joint synovitis. Conclusion: PAL protected against MSU-induced inflammation and oxidative stress via regulating the NF-κB/NLRP3 and Nrf2 pathways. PAL may represent a potential candidate for the treatment of gouty arthritis.

BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. METHODS: In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. RESULTS: Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. CONCLUSIONS: This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.