Growth Factor-Decorated Ti3 C2 MXene/MoS2 2D Bio-Heterojunctions with Quad-Channel Photonic Disinfection for Effective Regeneration of Bacteria-Invaded Cutaneous Tissue.

Phototherapy has recently emerged as a competent alternative for combating bacterial infection without antibiotic-resistance risk. However, owing to the bacterial endogenous antioxidative glutathione (GSH), the exogenous reactive oxygen species (ROS) generated by phototherapy can hardly behave desired antibacterial effect. To address the daunting issue, a quad-channel synergistic antibacterial nano-platform of Ti3 C2 MXene/MoS2 (MM) 2D bio-heterojunctions (2D bio-HJs) are devised and fabricated, which possess photothermal, photodynamic, peroxidase-like (POD-like), and glutathione oxidase-like properties. Under near-infrared (NIR) laser exposure, the 2D bio-HJs both yield localized heating and raise extracellular ROS level, leading to bacterial inactivation. Synchronously, Mo4+ ions can easily invade into ruptured bacterial membrane, arouse intracellular ROS, and deplete intracellular GSH. Squeezed between the "ROS hurricane" from both internal and external sides, the bacteria are hugely slaughtered. After being further loaded with fibroblast growth factor-21 (FGF21), the 2D bio-HJs exhibit benign cytocompatibility and boost cell migration in vitro. Notably, the in vivo evaluations employing a mouse-infected wound model demonstrate the excellent photonic disinfection towards bacterial infection and accelerated wound healing. Overall, this work provides a powerful nano-platform for the effective regeneration of bacteria-invaded cutaneous tissue using 2D bio-HJs.

Photonic double-network hydrogel dressings for antibacterial phototherapy and inflammation regulation in the general management of cutaneous regeneration.

The treatment of festering pathogenic bacteria-induced skin wounds with increased inflammation is an ongoing challenge. The traditional antibacterial photothermal therapy always results in localized hyperthermia (over 50 °C), which inevitably delays tissue recovery. To address this serious issue, we devise a novel photonic hydrogel by integrating urchin-like Bi2S3 nano-heterojunctions (nano-HJs) into double-network hydrogels for infected skin regeneration. The synergy of NIR-triggered heat and ROS enables the hydrogels to achieve a rapid germicidal efficacy against bacteria within 15 min at mild temperature (below 50 °C). In vitro cell analysis results revealed that the photonic hydrogels exhibit superior cytocompatibility even after NIR illumination. More importantly, an in vivo study demonstrated that the photonic hydrogel dressings have a robust ability of accelerating contagious full-thickness wound regeneration through debriding abscesses, eliminating pathogens, improving collagen deposition, promoting angiogenesis, and adjusting the inflammation state. This photonic hydrogel system provides a general management strategy for the remedy of infectious wounds, where the incorporation of nano-HJs endows the hydrogels with the photodisinfection ability; in addition, the multifunctional hydrogels alleviate the damage from overwhelming heat towards surrounding tissues during phototherapy and steer the inflammation during the process of tissue regeneration. Accordingly, this work highlights the promising application of the photonic hydrogels in conquering refractory pathogen-invaded infection.

Large-scale Prediction of Drug-Protein Interactions Based on Network Information.

BACKGROUND: The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and repositioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs. METHODS: In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs. RESULTS: The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test. CONCLUSION: Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure.

BACKGROUND: MicroRNAs (miRNAs) play a key role in regulating various biological processes such as participating in the post-transcriptional pathway and affecting the stability and/or the translation of mRNA. Current methods have extracted feature information at different levels, among which the characteristic stem-loop structure makes the greatest contribution to the prediction of putative miRNA precursor (pre-miRNA). We find that none of these features alone is capable of identifying new pre-miRNA accurately. RESULTS: In the present work, a pre-miRNA stem-loop secondary structure is translated to a network, which provides a novel perspective for its structural analysis. Network parameters are used to construct prediction model, achieving an area under the receiver operating curves (AUC) value of 0.956. Moreover, by repeating the same method on two independent datasets, accuracies of 0.976 and 0.913 are achieved, respectively. CONCLUSIONS: Network parameters effectively characterize pre-miRNA secondary structure, which improves our prediction model in both prediction ability and computation efficiency. Additionally, as a complement to feature extraction methods in previous studies, these multifaceted features can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA.

An electroless-plating-like solution approach for the preparation of PS@TiO2@Ag core-shell spheres.

PS@TiO2@Ag spheres with triple-level core-shell nanostructures were prepared via a versatile coating procedure based on an electroless-plating-like solution deposition (EPLSD) method. A peroxo-titanium-complex (PTC) aqueous solution was used as the precursor to react with an aniline monomer in the EPLSD preparation. Aniline plays an important role in the TiO2 layer anchoring process through the swollen effects of the PS cores. As extended, peroxo-metal-complex (PMC) with the d0 configuration can be introduced onto PS spheres to form varieties of PS@metal oxide core-shell structures by this method under mild conditions. Ag layers were then modified onto the PS@TiO2 spheres via the photocatalytic method. By the extraction of the PS cores, hollow TiO2 and TiO2@Ag spheres could be obtained. The photochemical degradation of methylene blue (MB) under UV light irradiation was performed on the composite nanostructures.

Fe/Zn-modified tricalcium phosphate (TCP) biomaterials: preparation and biological properties.

Bone repairing materials play an essential role in the repair treatment of bone defects. The presence of calcium phosphate invertebrates is of significance for bone repairing processes. However, the mechanical properties and osteogenic activities of many current calcium phosphate materials are not ideal, which limit their biological applications. Therefore, it is an effective alternative strategy to study the modification of calcium phosphate biomaterials to address these limitations. In this research, in order to enhance the biological performance of tricalcium phosphate (ß-TCP), metal species (Fe and Zn) modified ß-TCP materials through the co-precipitation method were successfully developed. The physical, chemical and biological properties of the binary composites were carefully studied for the first time. The bioactivities of the Fe-TCP and Zn-TCP were evaluated by simulating body fluid (SBF) immersion experiments, blood compatibility, and cytotoxicity tests. The findings demonstrated that the metal-TCP with excellent cytocompatibility and osteogenic properties shows good potential in medical applications.

Using ensemble methods to deal with imbalanced data in predicting protein-protein interactions.

In proteins, the number of interacting pairs is usually much smaller than the number of non-interacting ones. So the imbalanced data problem will arise in the field of protein-protein interactions (PPIs) prediction. In this article, we introduce two ensemble methods to solve the imbalanced data problem. These ensemble methods combine the based-cluster under-sampling technique and the fusion classifiers. And then we evaluate the ensemble methods using a dataset from Database of Interacting Proteins (DIP) with 10-fold cross validation. All the prediction models achieve area under the receiver operating characteristic curve (AUC) value about 95%. Our results show that the ensemble classifiers are quite effective in predicting PPIs; we also gain some valuable conclusions on the performance of ensemble methods for PPIs in imbalanced data. The prediction software and all dataset employed in the work can be obtained for free at http://cic.scu.edu.cn/bioinformatics/Ensemble_PPIs/index.html.

Feature importance analysis in guide strand identification of microRNAs.

MicroRNA (miRNA) is the negative regulator of gene expression, also known as guide strand of transient miRNA:miRNA* duplex. It is critical in maintaining the normal physiological processes such as development, differentiation, and apoptosis in many organisms. With increasing miRNA data, it is desirable to design methods to identify guide strand based on machine learning algorithms. In this study, the random forest models based on local sequence-structure features were proposed to identify miRNA in four species. The accuracies achieved were 86.51% for Homo sapiens, 81.66% for Ornithorhynchus anatinus, 82.33% for Mus musculus and 85.71% for Schmidtea mediterranea, respectively. Furthermore, the important analysis of feature elements was carried out by using the conditional feature importance strategy. The analysis results revealed that most of the significant elements were related to guanine-cytosine (GC) base pair. We believed that our method could be beneficial to annotate the function of miRNA and help the further understanding of the RNA interference mechanism.

A novel method to predict protein-protein interactions based on the information of protein-protein interaction networks and protein sequence.

Protein-protein interactions (PPIs) are crucial to most biochemical processes in human beings. Although many human PPIs have been identified by experiments, the number is still limited compared to the available protein sequences of human organisms. Recently, many computational methods have been proposed to facilitate the recognition of novel human PPIs. However the existing methods only concentrated on the information of individual PPI, while the systematic characteristic of protein-protein interaction networks (PINs) was ignored. In this study, a new method was proposed by combining the global information of PINs and protein sequence information. Random forest (RF) algorithm was implemented to develop the prediction model, and a high accuracy of 91.88% was obtained. Furthermore, the RF model was tested using three independent datasets with good performances, suggesting that our method is a useful tool for identification of PPIs and investigation into PINs as well.

A semi-supervised learning based method: Laplacian support vector machine used in diabetes disease diagnosis.

Pattern recognition methods could be of great help to disease diagnosis. In this study, a semi-supervised learning based method, Laplacian support vector machine (LapSVM), was used in diabetes diseases prediction. The diabetes disease dataset used in this article is Pima Indians diabetes dataset obtained from the UCI Repository of Machine Learning Databases and all patients in the dataset are females at least 21 years old of Pima Indian heritage. Firstly, LapSVM was trained as a fully-supervised learning classifier to predict diabetes dataset and 79.17% accuracy was obtained. Then, it was trained as a semi-supervised learning classifier and we got the prediction accuracy 82.29%. The obtained accuracy 82.29% is higher than other previous reports. The experiments led to the finding that LapSVM offers a very promising application, i.e., LapSVM can be used to solve a fully-supervised learning problem by solving a semi-supervised learning problem. The result suggests that LapSVM can be of great help to physicians in the process of diagnosing diabetes disease and it could be a very promising method in the situations where a lot of data are not class-labeled.