In-Situ Growth of One-Dimensional Blocking Layer to Mitigate Deficient Surface of Single-Crystal Perovskites.

Organic-inorganic halide perovskite (OIHP) single crystals are promising for optoelectronic application, but their high surface trap density and associated ion migration hinders device performance and stability. Herein, a one-dimensional (1D) perovskites are designed and proposed as blocking layer at the crystal/electrode interface to mitigate the surface issues. As a model system, the interface ion migration in Cs0.05FA0.95PbI3 (FA=formamidinium) single-crystal perovskite solar cells (PSCs) is obviously suppressed, leading to increase of T90 lifetime from 260 to 1000 hours, five times better than previously reported results. Besides, the reduction of surface iodide ion vacancies inhibits nonradiative recombination, thus increasing the efficiency from 22.1% to 23.8%, which is one of the highest values for single-crystal PSCs. Since the deficient crystal surface is a universal and open issue, our strategy is instructive for optimizing diverse single-crystal perovskite devices.

Facile Solvothermal Synthesis of Hollow BiOBr Submicrospheres with Enhanced Visible-Light-Responsive Photocatalytic Performance.

In this work, hierarchical hollow BiOBr submicrospheres (HBSMs) were successfully prepared via a facile yet efficient solvothermal strategy. Remarkable effects of solvents upon the crystallinities, morphologies, and microstructures of the BiOBr products were systematically investigated, which revealed that the glycerol/isopropanol volumetric ratio played a significant role in the formation of hollow architecture. Accordingly, the underlying formation mechanism of the hollow submicrospheres was tentatively put forward here. Furthermore, the photocatalytic activities of the resulting HBSMs were evaluated in detail with photocatalytic degradation of the organic methyl orange under visible light irradiation. Encouragingly, the as-obtained HBSMs with striking recyclability demonstrated excellent visible-light-responsive photocatalytic performance, which benefits from their large surface area, effective visible light absorption, and unique hollow feature, highlighting their promising commercial application in waste water treatment.

RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction.

Based on advancements in deep sequencing technology and microbiology, increasing evidence indicates that microbes inhabiting humans modulate various host physiological phenomena, thus participating in various disease pathogeneses. Owing to increasing availability of biological data, further studies on the establishment of efficient computational models for predicting potential associations are required. In particular, computational approaches can also reduce the discovery cycle of novel microbe-disease associations and further facilitate disease treatment, drug design, and other scientific activities. This study aimed to develop a model based on the random walk on hypergraph for microbe-disease association prediction (RWHMDA). As a class of higher-order data representation, hypergraph could effectively recover information loss occurring in the normal graph methodology, thus exclusively illustrating multiple pair-wise associations. Integrating known microbe-disease associations in the Human Microbe-Disease Association Database (HMDAD) and the Gaussian interaction profile kernel similarity for microbes, random walk was then implemented for the constructed hypergraph. Consequently, RWHMDA performed optimally in predicting the underlying disease-associated microbes. More specifically, our model displayed AUC values of 0.8898 and 0.8524 in global and local leave-one-out cross-validation (LOOCV), respectively. Furthermore, three human diseases (asthma, Crohn's disease, and type 2 diabetes) were studied to further illustrate prediction performance. Moreover, 8, 10, and 8 of the 10 highest ranked microbes were confirmed through recent experimental or clinical studies. In conclusion, RWHMDA is expected to display promising potential to predict disease-microbe associations for follow-up experimental studies and facilitate the prevention, diagnosis, treatment, and prognosis of complex human diseases.

Integrating random walk and binary regression to identify novel miRNA-disease association.

BACKGROUND: In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly. RESULTS: In this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database. CONCLUSIONS: We anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works.

Maximal entropy random walk on heterogenous network for MIRNA-disease Association prediction.

The last few decades have verified the vital roles of microRNAs in the development of human diseases and witnessed the increasing interest in the prediction of potential disease-miRNA associations. Owning to the open access of many miRNA-related databases, up until recently, kinds of feasible in silico models have been proposed. In this work, we developed a computational model of Maximal Entropy Random Walk on heterogenous network for MiRNA-disease Association prediction (MERWMDA). MERWMDA integrated known disease-miRNA association, pair-wise functional relation of miRNAs and pair-wise semantic relation of diseases into a heterogenous network comprised of disease and miRNA nodes full of information. As a kind of widely-applied biased walk process with more randomness, MERW was then implemented on the heterogenous network to reveal potential disease-miRNA associations. Cross validation was further performed to evaluate the performance of MERWMDA. As a result, MERWMDA obtained AUCs of 0.8966 and 0.8491 respectively in the aspect of global and local leave-one-out cross validation. What' more, three different case study strategies on four human complex diseases were conducted to comprehensively assess the quality of the model. Specifically, one kind of case study on Esophageal cancer and Prostate cancer were conducted based on HMDD v2.0 database. 94% and 88% out of the top 50 ranked miRNAs were confirmed by recent literature, respectively. To simulate new disease without known related miRNAs, Lung cancer (confirmed ratio 94%) associated miRNAs were removed for case study. Lymphoma (verified ratio 88%) was adopted to assess the prediction robustness of MERWMDA based on HMDD v1.0 database. We anticipated that MERWMDA could offer valuable candidates for in vitro biomedical experiments in future.

Combination of Phospholipid Complex and Submicron Emulsion Techniques for Improving Oral Bioavailability and Therapeutic Efficacy of Water-Insoluble Drug.

Water-insoluble drugs cannot be absorbed effectively through the gastrointestinal tract due to insufficient solubility and often face the problems of low bioavailability and poor therapeutic efficacy. To overcome these biopharmaceutical challenges, lipid-based formulations were suggested and have been researched in recent years. In this study, we used atorvastatin as a model drug to prepare a phospholipid complex prodrug system to upgrade its lipophilicity and further developed a drug loaded submicron emulsion to improve its in vivo bioavailability. The mean particle size and zeta potential of submicron emulsion were 122.7 nm and -22.7 mV. Intestinal absorption of atorvastatin from submicron emulsion was significantly improved compared with free drug, and the absorption rate constant ( Ka) and apparent permeability coefficients ( Papp) increase 2.88-fold and 2.45-fold, respectively. After oral administration, the atorvastatin plasma concentration of the emulsion group was much higher than that of free drug and the area under the curve (AUC) reached to 4.033 mg/L·h (2.58-fold). In vivo pharmacodynamics results revealed that atorvastatin submicron emulsion showed excellent antihyperlipidemia efficacy by reducing the total cholesterol, triglyceride, and low density lipoprotein cholesterol (LDL-cholesterol) levels and simultaneously increasing the high density lipoprotein cholesterol (HDL-cholesterol) level in comparison with Lipitor. In conclusion, drug-phospholipid complex loaded submicron emulsion was a promising oral delivery system for improving in vivo absorption behavior and therapeutic efficacy for water-insoluble drugs.

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

BACKGROUND: Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. METHODS: In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. RESULTS: MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. CONCLUSIONS: All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ±â€¯0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA.

Formulation and Drug Loading Features of Nano-Erythrocytes.

Nano erythrocyte ghosts have recently been used as drug carriers of water-soluble APIs due to inherit biological characteristics of good compatibility, low toxicity, and small side-effect. In this study, we developed a novel drug delivery system based on nano erythrocyte ghosts (STS-Nano-RBCs) to transport Sodium Tanshinone IIA sulfonate (STS) for intravenous use in rat. STS-Nano-RBCs were prepared by hypotonic lysis and by extrusion methods, and its biological properties were investigated compared with STS injection. The results revealed that STS-Nano-RBCs have narrow particle size distribution, good drug loading efficiency, and good stability within 21 days. Compared with STS injection, STS-Nano-RBCs extended the drug release time in vitro and in vivo with better repairing effect on oxidative stress-impaired endothelial cells. These results suggest that the nano erythrocyte ghosts system could be used to deliver STS.