CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network.

Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence "images". Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks.

Mussel-inspired antimicrobial hydrogel with cellulose nanocrystals/tannic acid modified silver nanoparticles for enhanced calvarial bone regeneration.

Bacterial infection is a serious challenge in the treatment of open bone defects, and reliance on antibiotic therapy may contribute to the emergence of drug-resistant bacteria. To solve this problem, this study developed a mineralized hydrogel (PVA-Ag-PHA) with excellent antibacterial properties and osteogenic capabilities. Silver nanoparticles (CNC/TA@AgNPs) were greenly synthesized using natural macromolecular cellulose nanocrystals (CNC) and plant polyphenolic tannins (TA) as stabilizers and reducing agents respectively, and then introduced into polyvinyl alcohol (PVA) and polydopamine-modified hydroxyapatite (PDA@HAP) hydrogel. The experimental results indicate that the PVA-Ag-PHA hydrogel, benefiting from the excellent antibacterial properties of CNC/TA@AgNPs, can not only eliminate Staphylococcus aureus and Escherichia coli, but also maintain a sustained sterile environment. At the same time, the HAP modified by PDA is uniformly dispersed within the hydrogel, thus releasing and maintaining stable concentrations of Ca2+ and PO43- ions in the local environment. The porous structure of the hydrogel with excellent biocompatibility creates a suitable bioactive environment that facilitates cell adhesion and bone regeneration. The experimental results in the rat critical-sized calvarial defect model indicate that the PVA-Ag-PHA hydrogel can effectively accelerate the bone healing process. Thus, this mussel-inspired hydrogel with antibacterial properties provides a feasible solution for the repair of open bone defects, demonstrating the considerable potential for diverse applications in bone repair.

Antidiabetic Potential of Tea and Its Active Compounds: From Molecular Mechanism to Clinical Evidence.

Diabetes mellitus (DM) is a chronic endocrine disorder that poses a long-term risk to human health accompanied by serious complications. Common antidiabetic drugs are usually accompanied by side effects such as hepatotoxicity and nephrotoxicity. There is an urgent need for natural dietary alternatives for diabetic treatment. Tea (Camellia sinensis) consumption has been widely investigated to lower the risk of diabetes and its complications through restoring glucose metabolism homeostasis, safeguarding pancreatic ß-cells, ameliorating insulin resistance, ameliorating oxidative stresses, inhibiting inflammatory response, and regulating intestinal microbiota. It is indispensable to develop effective strategies to improve the absorption of tea active compounds and exert combinational effects with other natural compounds to broaden its hypoglycemic potential. The advances in clinical trials and population-based investigations are also discussed. This review primarily delves into the antidiabetic potential and underlying mechanisms of tea active compounds, providing a theoretical basis for the practical application of tea and its active compounds against diabetes.

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.