Results | University of Saint Joseph Research Output

In this study, components of the food-web in Macao wetlands were quantified using stable isotope ratio techniques based on carbon and nitrogen values. The δ13C and δ15N values of particulate organic matter (δ13CPOM and δ15NPOM, respectively) ranged from −30.64 ± 1.0 to −28.1 ± 0.7 ‰, and from −1.11 ± 0.8 to 3.98 ± 0.7 ‰, respectively. The δ13C values of consumer species ranged from −33.94 to −16.92 ‰, showing a wide range from lower values in a freshwater lake and inner bay to higher values in a mangrove forest. The distinct dietary habits of consumer species and the location-specific food source composition were the main factors affecting the δ13C values. The consumer 15N-isotope enrichment values suggested that there were three trophic levels; primary, secondary, and tertiary. The primary consumer trophic level was represented by freshwater herbivorous gastropods, filter-feeding bivalves, and plankton-feeding fish, with a mean δ15N value of 5.052 ‰. The secondary consumer level included four deposit-feeding fish species distributed in Fai Chi Kei Bay and deposit-feeding gastropods in the Lotus Flower Bridge flat, with a mean δ15N value of 6.794 ‰. The tertiary consumers group consisted of four crab species, one shrimp species, and four fish species in the Lotus Flower Bridge Flat, with a mean δ15N value of 13.473 ‰. Their diet mainly comprised organic debris, bottom fauna, and rotten animal tissues. This study confirms the applicability of the isotopic approach in food web studies.

Chen, Q., Liang, Z., Yue, Q., Wang, X., Siu, S. W. I., Pui-Man Hoi, M., & Lee, S. M.-Y. (2022). A Neuropeptide Y/F-like Polypeptide Derived from the Transcriptome of Turbinaria peltata Suppresses LPS-Induced Astrocytic Inflammation. Journal of Natural Products, 85(6), 1569–1580. https://doi.org/10.1021/acs.jnatprod.2c00158

Neuropeptides are a group of neuronal signaling molecules that regulate physiological and behavioral processes in animals. Here, we used in silico mining to predict the polypeptide composition of available transcriptomic data of Turbinaria peltata. In total, 118 transcripts encoding putative peptide precursors were discovered. One neuropeptide Y/F-like peptide, named TpNPY, was identified and selected for in silico structural, in silico binding, and pharmacological studies. In our study, the anti-inflammation effect of TpNPY was evaluated using an LPS-stimulated C8-D1A astrocyte cell model. Our results demonstrated that TpNPY, at 0.75–3 μM, inhibited LPS-induced NO production and reduced the expression of iNOS in a dose-dependent manner. Furthermore, TpNPY reduced the secretion of proinflammatory cytokines. Additionally, treatment with TpNPY reduced LPS-mediated elevation of ROS production and the intracellular calcium concentration. Further investigation revealed that TpNPY downregulated the IKK/IκB/NF-κB signaling pathway and inhibited expression of the NLRP3 inflammasome. Through molecular docking and using an NPY receptor antagonist, TpNPY was shown to have the ability to interact with the NPY Y1 receptor. On the basis of these findings, we concluded that TpNPY might prevent LPS-induced injury in astrocytes through activation of the NPY-Y1R.

Chen, J., Si, Y.-W., Un, C.-W., & Siu, S. W. I. (2021). Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network. Journal of Cheminformatics, 13(1), 93. https://doi.org/10.1186/s13321-021-00570-8

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home.

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