Prediction of miRNA-disease associations based on Weighted [Formula: see text]-Nearest known neighbors and network consistency projection.

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted [Formula: see text]-Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.

Multi-targeted anti-leukemic drug design with the incorporation of silicon into Nelarabine: How silicon increases bioactivity.

Acute Lymphoblastic Leukemia (ALL) represents 30% of all childhood cancers and children younger than 5 years old have the highest risk for developing ALL. Existing ALL drugs do not respond in approximately 20% of treatment. Therefore, drug development studies against ALL must be continued with either developing existing drugs or discovering new ones. In this study, we evaluated the U.S Food and Drug Administration (FDA) approved ALL drugs according to their physicochemical and pharmaceutical properties, and Nelarabine was found to have the highest bioactivity score. Using the key strategy of bioisosterism commonly accepted by medicinal chemists, we investigated in silico ADME properties, drug-likeness, and biological activity of new designed twenty-four compounds including Nelarabine. The results were evaluated in terms of two classifications: broad spectrum biological activity and filtering of five different drug likeness criteria of the literature including Lipinski's rule of five. We interestingly observed that silicon incorporated compounds exhibited better performance on both criteria by targeting broader spectrum of drug receptors including G-protein coupled receptor (GPCR), ion channel modulator, kinase inhibitor, protease and enzyme inhibitor and by satisfying all of five different drug-likeness criteria reported in the literature. Design compound C19 appeared as a potential drug candidate for further pharmacological research.

In Vitro Analysis of the Co-Assembly of Type-I and Type-III Collagen.

An important step towards achieving functional diversity of biomimetic surfaces is to better understand the co-assembly of the extracellular matrix components. For this, we study type-I and type-III collagen, the two major collagen types in the extracellular matrix. By using atomic force microscopy, custom image analysis, and kinetic modeling, we study their homotypic and heterotypic assembly. We find that the growth rate and thickness of heterotypic fibrils decrease as the fraction of type-III collagen increases, but the fibril nucleation rate is maximal at an intermediate fraction of type-III. This is because the more hydrophobic type-I collagen nucleates fast and grows in both longitudinal and lateral directions, whereas more hydrophilic type-III limits lateral growth of fibrils, driving more monomers to nucleate additional fibrils. This demonstrates that subtle differences in physico-chemical properties of similar molecules can be used to fine-tune their assembly behavior.

Causative pathogens and antibiotic resistance in diabetic foot infections: A prospective multi-center study.

AIM: Clinical practice guidelines for the management of diabetic foot infections developed by the Infectious Diseases Society of America (IDSA) are commonly used worldwide. The issue of whether or not these guidelines need to be adjusted for local circumstances, however, has seldom been assessed in large prospective trials. METHODS: The Turk-DAY trial was a prospective, multi-center study in which infectious disease specialists from centers across Turkey were invited to participate (NCT02026830). RESULTS: A total of 35 centers throughout Turkey enrolled patients in the trial. Overall, investigators collected a total of 522 specimens from infected diabetic foot wounds for culture from 447 individual patients. Among all isolates, 36.4% were gram-positive organisms, with Staphylococcus aureus the most common among these (11.4%). Gram-negative organisms constituted 60.2% of all the isolates, and the most commonly isolated gram-negative was Escherichia coli (15%). The sensitivity rates of the isolated species were remarkably low for several antimicrobials used in the mild infection group. CONCLUSIONS: Based on our findings, several of the antimicrobials frequently used for empirical treatment, including some also recommended in the IDSA guidelines, would not be optimal for treating diabetic foot infections in Turkey. Although the IDSA guideline recommendations may be helpful to guide empiric antimicrobial therapy of DFIs, they should be adjusted to local conditions.

Kinetic signature of fractal-like filament networks formed by orientational linear epitaxy.

We study a broad class of epitaxial assembly of filament networks on lattice surfaces. Over time, a scale-free behavior emerges with a 2.5-3 power-law exponent in filament length distribution. Partitioning between the power-law and exponential behaviors in a network can be used to find the stage and kinetic parameters of the assembly process. To analyze real-world networks, we develop a computer program that measures the network architecture in experimental images. Application to triaxial networks of collagen fibrils shows quantitative agreement with our model. Our unifying approach can be used for characterizing and controlling the network formation that is observed across biological and nonbiological systems.