Differential component analysis between Fructus Tritici Levis and Triticum aestivum based on qualitative and quantitative methods / 中国药房

OBJECTIVE To analyze the compositional differences between Fructus Tritici Levis and Triticum aestivum， and to provide reference for identification and quality control of both. METHODS Twenty batches of Fructus Tritici Levis and three batches of T. aestivum were collected， and their fingerprints were acquired by high-performance liquid chromatography and the similarities were evaluated by the Evaluation System of Similarity of Chromatographic Fingerprints of Traditional Chinese Medicine （2012 version）. Cluster analysis （CA）， principal component analysis （PCA） and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed to analyze the difference of Fructus Tritici Levis and T. aestivum from different regions， and the differential components were screened. The contents of the six identified components in Fructus Tritici Levis and T. aestivum were determined. RESULTS The similarities of the fingerprints of Fructus Tritici Levis ranged from 0.928 to 0.996， and the relative similarities of T. aestivum with Fructus Tritici Levis ranged from 0.761 to 0.773. A total of 19 common peaks were calibrated， and six components including linolenic acid， linoleic acid， 5-heptadecylresorcinol， 5-nonadodecylresorcinol， 5- heneicosylresorcinol， and 5-tricosylresorcinol were identified. The results of CA and PCA showed that Fructus Tritici Levis and T. aestivum could be clearly distinguished； the distribution of Fructus Tritici Levis from Anhui province was relatively concentrated. The results of OPLS-DA showed that linolenic acid， linoleic acid， and other six unknown compounds were the differential components between Fructus Tritici Levis and T. aestivum. The average contents of the six identified components in Fructus Tritici Levis were 0.100 9， 1.094 0， 0.005 1， 0.030 9， 0.098 2，and 0.024 8 mg/g， respectively； the contents of linolenic acid and linoleic acid in Fructus Tritici Levis were significantly higher than those in T. aestivum （P＜0.05）.CONCLUSIONS The established qualitative and quantitative methods are simple and reliable， and can be used for the identification and quality evaluation of Fructus Tritici Levis and T. aestivum. The identified differential components， such as linolenic acid and linoleic acid， can also provide clues for the differentiation and pharmacological study of Fructus Tritici Levis and T. aestivum.

Differential component analysis between Fructus Tritici Levis and Triticum aestivum based on qualitative and quantitative methods / 中国药房

OBJECTIVE To analyze the compositional differences between Fructus Tritici Levis and Triticum aestivum， and to provide reference for identification and quality control of both. METHODS Twenty batches of Fructus Tritici Levis and three batches of T. aestivum were collected， and their fingerprints were acquired by high-performance liquid chromatography and the similarities were evaluated by the Evaluation System of Similarity of Chromatographic Fingerprints of Traditional Chinese Medicine （2012 version）. Cluster analysis （CA）， principal component analysis （PCA） and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed to analyze the difference of Fructus Tritici Levis and T. aestivum from different regions， and the differential components were screened. The contents of the six identified components in Fructus Tritici Levis and T. aestivum were determined. RESULTS The similarities of the fingerprints of Fructus Tritici Levis ranged from 0.928 to 0.996， and the relative similarities of T. aestivum with Fructus Tritici Levis ranged from 0.761 to 0.773. A total of 19 common peaks were calibrated， and six components including linolenic acid， linoleic acid， 5-heptadecylresorcinol， 5-nonadodecylresorcinol， 5- heneicosylresorcinol， and 5-tricosylresorcinol were identified. The results of CA and PCA showed that Fructus Tritici Levis and T. aestivum could be clearly distinguished； the distribution of Fructus Tritici Levis from Anhui province was relatively concentrated. The results of OPLS-DA showed that linolenic acid， linoleic acid， and other six unknown compounds were the differential components between Fructus Tritici Levis and T. aestivum. The average contents of the six identified components in Fructus Tritici Levis were 0.100 9， 1.094 0， 0.005 1， 0.030 9， 0.098 2，and 0.024 8 mg/g， respectively； the contents of linolenic acid and linoleic acid in Fructus Tritici Levis were significantly higher than those in T. aestivum （P＜0.05）.CONCLUSIONS The established qualitative and quantitative methods are simple and reliable， and can be used for the identification and quality evaluation of Fructus Tritici Levis and T. aestivum. The identified differential components， such as linolenic acid and linoleic acid， can also provide clues for the differentiation and pharmacological study of Fructus Tritici Levis and T. aestivum.

Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model / 生物医学工程学杂志

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease / 生物医学工程学杂志

The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer's diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer's disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer's disease.

Protein Interaction Network Construction and Biological Pathway Analysis Related to Atherosclerosis / 生物医学工程学杂志

Atherosclerosis is a complex disease characterized by lipid accumulation in the vascular wall and influenced by multiple genetic and environmental factors. To understand the mechanisms of molecular regulation related to atherosclerosis better, a protein interaction network was constructed in the present study. Genes were collected in nucleotide database and interactions were downloaded from Biomolecular Object Network Database (BOND). The interactional data were imported into the software Cytoscape to construct the interaction network, and then the degree characteristics of the network were analyzed for Hub proteins. Statistical significance pathways and diseases were figured out by inputting Hub proteins to KOBAS2. 0. The complete pathway network related to atherosclerosis was constructed. The results identified a series of key genes related to atherosclerosis, which would be the potential promising drug targets for effective prevention.

Nucleobindin 2 and its role in tumorigenesis and progression / 国际肿瘤学杂志

Nucleobindin2protein(NUCB2)isanewlydiscoveredneuropeptideprecursorprotein, which has a comprehensive cytology function and is expressed in the hypothalamus nucleus and many peripheral tissues.There aren′t many studies about its signaling pathway,where neuroendocrine regulation,cell survival growth,tumor suppressor,cytokine secretion were found to involve in it.Besides,it has also been confirmed that breast cancer,lung cancer,ovarian cancer and prostate cancer are closely related to NUCB2.Therefore, several downstream pathways of NUCB2 may be related to the formation and progression of tumor.Further stud-ies are still needed to clarify the signal pathways of NUCB2 to provide a reliable basis for clinical cancer preven-tion.