Three unprecedented thioether-linked dimeric pyrimidines isolated from the medicinal-edible herb Ligusticum striatum DC.

Three unprecedented thioether-linked dimeric pyrimidines, namely ligusticumines A-C, together with twelve known compounds were isolated and identified from the traditional Chinese medicinal-edible herb, Ligusticum striatum DC. The structures of all the isolated compounds were determined from NMR, HRESIMS and X-ray diffraction spectroscopies. Additionally, a novel 3-step synthetic route was developed to synthesize ligusticumine C by substitution, thiolation and coupling, with an overall yield of 5.4%. The inhibitory activities of the isolated compounds against phosphatidylinositol 3-kinase (PI3K) were tested, of which, (3S)-butylphthalide, a characteristic component of L. striatum, showed a potent inhibitory effect on PI3Kα (IC50: 3.6 µg/mL).

Stephtetrandrine A-D, bisbenzylisoquinoline alkaloids from Stephania tetrandra.

Four undescribed bisbenzylisoquinoline alkaloids, designated as Stephtetrandrine A-D, were isolated from the roots of Stephania tetrandra. Their structures were elucidated by IR, HRESIMS, ECD spectra, 1 D and 2 D NMR spectra and comparison with the literature data. Additional five known compounds (limacine, tetrandrine, N-trans-Feruloyltyramine, 2'-N-chloromethyltetrandrine, 2,2'-N-N-dichloromethyltetrandrine) were also isolated. N-trans-Feruloyltyramine was isolated from Stephania tetrandra for the first time. The isolated compounds were tested for monoamine oxidase, acetylcholinesterase, phosphoinositide 3-kinase α and human hepatoma cell HepG2 inhibitory activities. Stephtetrandrine C showed obvious inhibitory effect on human hepatoma HepG2, with IC50 value of 16.2 µM. Limacine and 2'-N-chloromethyltetrandrine showed moderate monoamine oxidase inhibitory effect with the IC50 values of 37.7 and 29.2 µM, respectively.

Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation / 法医学杂志

Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo. It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.

Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation.

Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo. It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.

Two New Eremophilane-Type Sesquiterpenoids from Ligularia sagitta.

Two new eremophilane-type sesquiterpenoids, sagittacins F and G (1 and 2), together with one known isomer of sagittacin F (3) were isolated from the leaves and stems of Ligularia sagitta. Their structures were elucidated by interpretation of spectroscopic data and the absolute configurations of 1 and 3 were determined by X-ray spectroscopy. Compound 1 belongs to a rare class of eremophilane-type sesquiterpenoid featuring an α-oriented hydroxy group at C-1. A nitric oxide (NO) production inhibitory assay was applied to evaluate their anti-inflammatory activities by using LPS-induced RAW 264.7 cells. Compounds 2 and 3 exhibited modest NO production inhibitions with IC50 values of 45.15±2.72 and 49.83±2.34 µM, respectively.

Inferring Postmortem Submersion Interval in Rats Found in Water Based on Vitreous Humor Metabolites.

OBJECTIVES: The metabolomics technique of LC-MS/MS combined with data analysis was used to detect changes and differences in metabolic profiles in the vitreous humor of early rat carcasses found in water, and to explore the feasibility of its use for early postmortem submersion interval (PMSI) estimation and the cause of death determination. METHODS: The experimental model was established in natural lake water with 100 SD rats were randomly divided into a drowning group (n=50) and a postmortem (CO2 suffocation) immediately submersion group (n=50). Vitreous humor was extracted from 10 rats in each group at 0, 6, 12, 18 and 24 h postmortem for metabolomics analyses, of which 8 were used as the training set to build the model, and 2 were used as test set. PCA and PLS multivariate statistical analysis were performed to explore the differences in metabolic profiles among PMSI and causes of death in the training set samples. Then random forest (RF) algorithm was used to screen several biomarkers to establish a model. RESULTS: PCA and PLS analysis showed that the metabolic profiles had time regularity, but no differences were found among different causes of death. Thirteen small molecule biomarkers with good temporal correlation were selected by RF algorithm. A simple PMSI estimation model was constructed based on this indicator set, and the data of the test samples showed the mean absolute error (MAE) of the model was 0.847 h. CONCLUSIONS: The 13 metabolic markers screened in the vitreous humor of rat corpses in water had good correlations with the early PMSI. The simplified PMSI estimation model constructed by RF can be used to estimate the PMSI. Additionally, the metabolic profiles of vitreous humor cannot be used for early identification of cause of death in water carcasses.

GNPI: Graph normalization to integrate phylogenetic information for metagenomic host phenotype prediction.

Microorganisms play important roles in our lives especially on metabolism and diseases. Determining the probability of human suffering from specific diseases and the severity of the disease based on microbial genes is the crucial research for understanding the relationship between microbes and diseases. Previous could extract the topological information of phylogenetic trees and integrate them to metagenomic datasets, thus enable classifiers to learn more information in limited datasets and thus improve the performance of the models. In this paper, we proposed a GNPI model to better learn the structure of phylogenetic trees. GNPI maintained the original vector format of metagenomic datasets, while previous research had to change the input form to matrices. The vector-like form of the input data can be easily adopted in the baseline machine learning models and is available for deep learning models. The datasets processed with GNPI help enhance the accuracy of machine learning and deep learning models in three different datasets. GNPI is an interpretable data processing method for host phenotype prediction and other bioinformatics tasks.

Inferring Postmortem Submersion Interval in Rats Found in Water Based on Vitreous Humor Metabolites / 法医学杂志

OBJECTIVES@#The metabolomics technique of LC-MS/MS combined with data analysis was used to detect changes and differences in metabolic profiles in the vitreous humor of early rat carcasses found in water, and to explore the feasibility of its use for early postmortem submersion interval (PMSI) estimation and the cause of death determination.@*METHODS@#The experimental model was established in natural lake water with 100 SD rats were randomly divided into a drowning group (n=50) and a postmortem (CO2 suffocation) immediately submersion group (n=50). Vitreous humor was extracted from 10 rats in each group at 0, 6, 12, 18 and 24 h postmortem for metabolomics analyses, of which 8 were used as the training set to build the model, and 2 were used as test set. PCA and PLS multivariate statistical analysis were performed to explore the differences in metabolic profiles among PMSI and causes of death in the training set samples. Then random forest (RF) algorithm was used to screen several biomarkers to establish a model.@*RESULTS@#PCA and PLS analysis showed that the metabolic profiles had time regularity, but no differences were found among different causes of death. Thirteen small molecule biomarkers with good temporal correlation were selected by RF algorithm. A simple PMSI estimation model was constructed based on this indicator set, and the data of the test samples showed the mean absolute error (MAE) of the model was 0.847 h.@*CONCLUSIONS@#The 13 metabolic markers screened in the vitreous humor of rat corpses in water had good correlations with the early PMSI. The simplified PMSI estimation model constructed by RF can be used to estimate the PMSI. Additionally, the metabolic profiles of vitreous humor cannot be used for early identification of cause of death in water carcasses.

A norbisabolane and an arabitol benzoate from Talaromyces marneffei, an endophytic fungus of Epilobium angustifolium.

A norbisabolane and an arabitol benzoate, Talaromarnine A (1), Talaromarnine B (2), together with eight known compounds were obtained from cultures of Talaromyces marneffei, an endophytic fungus of Epilobium angustifolium. Their structures were elucidated by IR, MS, 1D and 2D NMR spectra, and their absolute configuration was determined by single-crystal X-ray diffraction and molecular computation. These compounds were tested for monoamine oxidase, acetylcholinesterase and PI3K inhibitory activity, but no compounds exhibited significant activities.

Wortmannine H, a phenylpentenol isolated from an endophytic fungus, Talaromyces wortmannii LGT-4.

A new phenylpentenol, wortmannine H (1) was isolated from Talaromyces wortmannii LGT-4, an endophytic fungus of Tripterygium wilfordii. The structure of 1 was elucidated by IR, MS, 1D and 2D NMR spectra and comparison of the experimental and calculated optical rotatory dispersion (ORD). Monoamine oxidase (MAO), acetylcholinesterase (AChE) and phosphoinositide 3-kinase (PI3Kα) inhibitory activities of 1 was also tested. The compound did not show good biological activity.

Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module.

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI.

Chemical Composition and Biological Activities of Endophytic Fungus Talaromyces Wortmannii LGT-4, Cultured in CYM Medium.

In the present study, nine compounds (1-9) were isolated from Talaromyces wortmannii LGT-4 (an endophytic fungus from Tripterygium wilfordi) which was cultured in CYM Medium. Their structures were determined as 4-hydroxyphthalide (1), Fumitremorgin C (2), Ergosterol (3), 3-(2-hydroxypropyl)-8-hydroxy-3,4- dihydroisocoumarin (4), Cis-cyclo(L-Ala-L-Pro) (5), 6-Amino-3-(4-hydroxybenzyl)- 1,4-diazonane-2,5-dione (6), Aspergillumarin B (7), Deacetylisowortmin B (8), and Entonaemin A (9) based on NMR spectral data, as well as comparing with previous literature data. This is the first report of the isolation of compounds 1-2 and 4-7 from Talaromyces genus. All compounds were tested for their monoamine oxidase and phosphoinositide 3-kinase (PI3Kα) inhibitory activities. Compound 1, 5 showed moderate anti-monoamine oxidase activity with IC50 value of 35 µg/mL, 28 µg/mL, respectively. Compound 9 showed PI3Kα inhibitory activity with IC50 value of 10.3 µg/mL.

Two new compounds, Talaromycin A and B, isolated from an endophytic fungus, Talaromyces aurantiacus.

Two new compounds Talaromycin A (1) and Talaromycin B (2) were isolated from a liquid culture of Talaromyces aurantiacus. The structures of 1 and 2 were elucidated by IR, MS, 1D and 2D NMR spectra and comparison of the experimental and calculated electronic circular dichroism spectra. Additional known compounds (3-6) were also isolated. These compounds were tested for monoamine oxidase, acetylcholinesterase and PI3K inhibitory activity, but showed only weak activity.

A hybrid deep learning framework for bacterial named entity recognition with domain features.

BACKGROUND: Microbes have been shown to play a crucial role in various ecosystems. Many human diseases have been proved to be associated with bacteria, so it is essential to extract the interaction between bacteria for medical research and application. At the same time, many bacterial interactions with certain experimental evidences have been reported in biomedical literature. Integrating this knowledge into a database or knowledge graph could accelerate the progress of biomedical research. A crucial and necessary step in interaction extraction (IE) is named entity recognition (NER). However, due to the specificity of bacterial naming, there are still challenges in bacterial named entity recognition. RESULTS: In this paper, we propose a novel method for bacterial named entity recognition, which integrates domain features into a deep learning framework combining bidirectional long short-term memory network and convolutional neural network. When domain features are not added, F1-measure of the model achieves 89.14%. After part-of-speech (POS) features and dictionary features are added, F1-measure of the model achieves 89.7%. Hence, our model achieves an advanced performance in bacterial NER with the domain features. CONCLUSIONS: We propose an efficient method for bacterial named entity recognition which combines domain features and deep learning models. Compared with the previous methods, the effect of our model has been improved. At the same time, the process of complex manual extraction and feature design are significantly reduced.

Secondary Metabolites and PI3K Inhibitory Activity of Colletotrichum gloeosporioides, a Fungal Endophyte of Uncaria rhynchophylla.

In the present study, nine compounds (1-9) were isolated from Colletotrichum gloeosporioides (an endophytic fungus from Uncaria rhynchophylla) which was cultured in wheat bran medium. Their structures were elucidated as 4-Epi-14-hydroxy-10, 23-dihydro-24, 25-dehydroaflavinine (1), 10, 23-Dihydro-24,25 -dehydro-21-oxoaflavinine (2), Ergosterol (3), Ergosterol peroxide (4), Mellein (5), 4, 5-dihydroblumenol A (6), Colletotrichine A (7), Cyclo(L-leucyl-L-leucyl) (8), and Brevianamide F (9) based on NMR spectral data, as well as comparing with previous literature data. This is the first report about the isolation of compounds 1-2, 6, and 8-9 from Colletotrichum genus. All compounds were tested for their phosphoinositide 3-kinase (PI3Kα) inhibitory activity. Compounds 8 and 9 showed potent PI3K α inhibitory activity with IC50 values of 38.1 and 4.8 µM, respectively, while the other compounds showed very weak activity at a concentration of 20 µg/mL.

Colletotrichine B, a new sesquiterpenoid from Colletotrichum gloeosporioides GT-7, a fungal endophyte of Uncaria rhynchophylla.

One new compound, colletotrichine B (1), was produced by the fungal Colletotrichum gloeosporioides GT-7. The structure of 1 was elucidated on the basis of spectroscopic analysis and X-ray crystallographic analysis. Monoamine oxidase (MAO), acetylcholinesterase (AChE) and phosphoinositide 3-kinase (PI3Kα) inhibitory activity of 1 was also evaluated. Compound 1 showed only AChE inhibiting activity with IC50 value of 38.0 ± 2.67 µg/mL.

Three-Layer Heterogeneous Network Combined With Unbalanced Random Walk for miRNA-Disease Association Prediction.

miRNA plays an important role in many biological processes, and increasing evidence shows that miRNAs are closely related to human diseases. Most existing miRNA-disease association prediction methods were only based on data related to miRNAs and diseases and failed to effectively use other existing biological data. However, experimentally verified miRNA-disease associations are limited, there are complex correlations between biological data. Therefore, we propose a novel Three-layer heterogeneous network Combined with unbalanced Random Walk for MiRNA-Disease Association prediction algorithm (TCRWMDA), which can effectively integrate multi-source association data. TCRWMDA based not only on the known miRNA-disease associations, also add the new priori information (lncRNA-miRNA and lncRNA-disease associations) to build a three-layer heterogeneous network, lncRNA was added as the transition path of the intermediate point to mine more effective information between networks. The AUC value obtained by the TCRWMDA algorithm on 5-fold cross validation is 0.9209, compared with other models based on the same similarity calculation method, TCRWMDA obtained better results. TCRWMDA was applied to the analysis of four types of cancer, the results proved that TCRWMDA is an effective tool to predict the potential miRNA-disease association. The source code and dataset of TCRWMDA are available at: https://github.com/ylm0505/TCRWMDA.

Relationship between Expression Changes of CB2R and Wound Age of Brain Contusion in Mice / 法医学杂志

Objective To investigate the expression of cannabinoid type 2 receptor （CB2R） at different time points after brain contusion and its relationship with wound age of mice. Methods A mouse brain contusion model was established with PCI3000 Precision Cortical Impactor. Expression changes of CB2R around the injured area were detected with immunohistochemical staining, immunofluorescent staining and Western blotting at different time points. Results Immunohistochemical staining results showed that only a few cells in the cerebral cortex of the sham operated group had CB2R positive expression. The ratio of CB2R positive cells gradually increased after injury and reached the peak twice at 12 h and 7 d post-injury, followed by a decrease to the normal level 28 d post-injury. The results of Western blotting were consistent with the immunohistochemical staining results. Immunofluorescent staining demonstrated that the changes of the ratio of CB2R positive cells in neurons, CB2R positive cells in monocytes and CB2R positive cells in astrocytes to the total cell number showed a single peak pattern, which peaked at 12 h, 1 d and 7 d post-injury, respectively. Conclusion The expression of CB2R after brain contusion in neurons, monocytes and astrocytes in mice suggests that it is likely to be involved in the regulation of the biological functions of those cells. The changes in CB2R are time-dependent, which suggests its potential applicability as a biological indicator for wound age estimation of brain contusion in forensic practice.

Significant genetic differentiation of Gobiopterus lacustris, a newly recorded transparent goby in China.

In our recent survey, the transparent small Lacustrine goby, Gobiopterus lacustris had reported as the endemic species of Luzon, Philippines, was identified as an abundant species in mangroves of Leizhou Peninsula, China. Here, high diversity and significant differentiation of five sites of samples representing the west and east populations were revealed by mitochondrial DNA sequences. Five haplotypes of 56 cytochrome oxidase subunit I (Cox1) with the lengths of 623 base pairs (bp) have the high pairwise identity (>98.8%). Moreover, a total of 31 haplotypes for 129 partial D-loop regions were clustered into two clades corresponding to the east and west sampling sites. The strong population structure was confirmed (ΦST = 0.43017, p < .0001) with high haplotype diversity (h = 0.880 ± 0.017) and low nucleotide diversity (p=.00484). Moreover, both the mismatch distribution analysis and neutral test of D-loop revealed that the west group might experience a recent demographic expansion. Lastly, the isolation-with-migration analysis supported the expansion and indicated that the east-west split happened at approximately 7.1 kyr ago. Given the distribution and diversity, G. lacustris could be a good model for the study of the sea-level fluctuations and coast evolution of the South China Sea.