Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Quality evaluation of commercial products of Ganoderma lucidum made from its fruiting body and spore

Yeung Steven, Chen Quanlan, Yu Yongbang, Zhou Bingsen, Wu Wei, Li Xia, Huang Ying, Wang Zhijun

Acta Chromatographica

2022

Vol. 34, no. 1

100--113

Ganoderma lucidum (GL), also known as Reishi or Lingzhi, is a medicinal mushroom widely used in traditional and folk medicines. The extracts made from the fruiting body and spore of naturally grown GL are the most frequently used in commercial products. More than 400 compounds have been identified in GL with the triterpenoids considered to be the major active components. Large variations in the chemical components were reported in previous studies and there is no comprehensive study of the content of multiple major triterpenoids in the GL product. In addition, there is no report in the comparison of chemical profiles in different parts of GL (i.e., fruiting body and spore). Determining the chemical composition and comparing the differences between fruiting body and spore are essential for the identity, efficacy and safety of various GL products. In this study, 13 compounds (ganoderenic Acid C, ganoderic Acid C2, ganoderic Acid G, ganoderic Acid B, ganoderenic Acid B, ganoderic Acid A, ganoderic Acid H, ganoderenic Acid D, ganoderic Acid D, ganoderic Acid F, ganoderic Acid DM, ganoderol A, and ergosterol) were selected as the chemical markers. The purpose of this study is to develop an HPLC-DAD fingerprint method for quantification of these active components in GL (spore and fruiting body) and test the feasibility of using the HPLC-DAD fingerprint for quality control or identity determination of GL products. The results showed that this method could determine the levels of the major components accurately and precisely. Among the 13 components, 11 ganoderma acids were identified to be proper chemical markers for quality control of GL products, while ganoderal A was in a very low amount and ergosterol was not a specific marker in GL. The extracts of fruiting body contained more chemical compounds than those of spore, indicating that these 11 compounds could be a better chemical marker for the fruiting body than the spore. The HPLC chemical fingerprint analysis showed higher variability in the quality of GL harvest in different years, while lesser variation in batches harvested in the same year. In conclusion, an HPLC assay detecting 11 major active components and a fingerprinting method was successfully established and validated to be feasible for quality control of most commercial GL products.

Multi-path convolutional neural network in fundus segmentation of blood vessels

Tian Chun, Fang Tao, Fan Yingle, Wu Wei

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 2

583--595

There is a close correlation between retinal vascular status and physical diseases such as eye lesions. Retinal fundus images are an important basis for diagnosing diseases such as diabetes, glaucoma, hypertension, coronary heart disease, etc. Because the thickness of the retinal blood vessels is different, the minimum diameter is only one or two pixels wide, so obtaining accurate measurement results becomes critical and challenging. In this paper, we propose a new method of retinal blood vessel segmentation that is based on a multi-path convolutional neural network, which can be used for computer-based clinical medical image analysis. First, a low-frequency image characterizing the overall characteristics of the retinal blood vessel image and a high-frequency image characterizing the local detailed features are respectively obtained by using a Gaussian low-pass filter and a Gaussian high-pass filter. Then a feature extraction path is constructed for the characteristics of the low- and high-frequency images, respectively. Finally, according to the response results of the low-frequency feature extraction path and the high-frequency feature extraction path, the whole blood vessel perception and local feature information fusion coding are realized, and the final blood vessel segmentation map is obtained. The performance of this method is evaluated and tested by DRIVE and CHASE_DB1. In the experimental results of the DRIVE database, the evaluation indexes accuracy (Acc), sensitivity (SE), and specificity (SP) are 0.9580, 0.8639, and 0.9665, respectively, and the evaluation indexes Acc, SE, and SP of the CHASE_DB1 database are 0.9601, 0.8778, and 0.9680, respectively. In addition, the method proposed in this paper could effectively suppress noise, ensure continuity after blood vessel segmentation, and provide a feasible new idea for intelligent visual perception of medical images.