DataSheet1_Efficacy and safety of total glucosides of paeony as an add-on treatment in adolesents and adults with chronic urticaria: A systematic review and meta-analysis.PDF

Background: Total glycosides of paeony (TGP), an active compound extracted from the dried roots of Paenoia lactiflora Pall., has been widely used to treat chronic urticaria (CU) in China. This study aims to systematically evaluate the efficacy and safety of TGP as an add-on treatment for the treatment of CU in adolescents and adults.Methods: Eight literature databases and two clinical trial registries were searched from their inception to 31 May 2022. Randomized controlled trials on TGP as an add-on treatment for CU in adolescents and adults were included. The Cochrane Collaboration’s risk of bias tool was used for the methodological quality assessment, and RevMan 5.3 software and Stata 12.0 software were used for data analyses.Results: A total of 30 studies with 2,973 participants were included in this meta-analysis. The methodological qualities of all included studies were suboptimal. The pooled results showed that TGP combined with H1-antihistamine was superior to H1-antihistamine alone in the cure rate (risk ratio (RR) = 1.54, 95% confidence interval (CI) = 1.39 to 1.71, p Conclusions: TGP as an add-on treatment could provide a good effect for CU in adolescents and adults with mild and tolerable adverse events. However, in view of poor methodological quality, high-quality and long-term clinical trials are needed in the future to confirm and update the evidence.</p

Supplemental Material - An investigation on the use of topical antibiotics for treating eczema and dermatitis in China

Supplemental Material for An investigation on the use of topical antibiotics for treating eczema and dermatitis in China by Juan Shao, Xin Wang, Zhongwen Zhang and Linfeng Li in European Journal of Inflammation.</p

Hamming distance of the descriptor between four sets of experimental data.

Hamming distance of the descriptor between four sets of experimental data.</p

The sub-Hamming distance between different biometric fingerprints.

The sub-Hamming distance between different biometric fingerprints.</p

Distribution of feature and control points, with feature points in blue and calculated control points in red.

Distribution of feature and control points, with feature points in blue and calculated control points in red.</p

Private key security management scheme.

The fundamental technology behind bitcoin, known as blockchain, has been studied and used in a variety of industries especially in finance. The security of blockchain is extremely important as it will affects the assets of the clients as well as it is the lifeline feature of the entire system that needs to be guaranteed. Currently, there is a lack of a methodical approach to guarantee the security and dependability of the private key during its whole life. Furthermore, there is no quick, easy, or secure way to create the encryption key. A biometric-based private key encryption and management framework (BPKEM) for blockchain is proposed not only to solve the private key lifecycle manag- ement problem, but also it maintains compatibility with existing blockchain systems. For the problem of private key encryption, a biometric-based stable key generation method is proposed. By using the relative invariance between facial and fingerprint feature points, this measure can convert feature points into stable and distinguishable descriptors, then using a reusable fuzzy extractor to create a stable key. The correct- ness and efficiency of the newly proposed biometric-based blockchain encryption tech- nique in this paper has been validated in the experiments.</div

Fingerprint description sub-Hamming distance before and after screening feature points.

Fingerprint description sub-Hamming distance before and after screening feature points.</p

Hamming distance of the descriptor between four sets of experimental data.

Hamming distance of the descriptor between four sets of experimental data.</p

Hamming distance error before and after screening feature points.

Hamming distance error before and after screening feature points.</p

Distribution of pixel coordinates of facial (left) and fingerprint (right) feature points.

Distribution of pixel coordinates of facial (left) and fingerprint (right) feature points.</p