Network insights on oxaliplatin anti-cancer mechanisms

Abstract Oxaliplatin has been a crucial component of combination therapies since admission into the clinic causing modest gains in survival across multiple malignancies. However, oxaliplatin functions in a non-targeted manner, posing a difficulty in ascertaining precise efficacy mechanisms. While previously thought to only affect DNA repair mechanisms, Platinum-protein adducts (Pt-Protein) far outnumber Pt-DNA adducts leaving a big part of oxaliplatin function unknown. Through preliminary network modeling of high throughput data, this article critically reviews the efficacy of oxaliplatin as well as proposes a better model for enhanced efficacy based on a network approach. In our study, not only oxaliplatin’s function in interrupting DNA-replication was confirmed, but also its role in initiating or intensifying tumorigenesis pathways was uncovered. From our data we present a novel picture of competing signaling networks that collectively provide a plausible explanation of chemotherapeutic resistance, cancer stem cell survival, as well as invasiveness and metastases. Here we highlight oxaliplatin signaling networks, their significance and the clinical implications of these interactions that verifies the importance of network modeling in rational drug design

Large scale study of multiple-molecule queries

<p>Abstract</p> <p>Background</p> <p>In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.</p> <p>Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics.</p> <p>Results</p> <p>Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics.</p> <p>Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (<b>BKD</b>), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data.</p> <p>Conclusion</p> <p>Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from <url>http://cdb.ics.uci.edu/</url>.</p

CORRELATIONS OF DEOXYRIBONUCLEIC ACID METHYLATION, HISTONE ACETYLATION, AND MICRORNA‑320 WITH SORBITOL DEHYDROGENASE IN DIABETIC RETINOPATHY

Objective: Prolonged and persistent hyperglycemia in diabetes mellitus (DM) leads to a variety of vascular complications, including the retinal disorder of diabetic retinopathy (DR). The mechanism of fructose formation of sorbitol assisted by sorbitol dehydrogenase (SDH) causing the loss of pericytes in the blood vessel is affected by epigenetic work comprised of deoxyribonucleic acid (DNA) methylation, histone acetylation, and microRNA‑320. This study aimed to determine the correlation of DNA methylation, histone acetylation, and microRNA‑320 with SDH in DR. Methods: This case–control study was conducted at a tertiary general hospital from July 2014 to June 2016. Study subjects were type 2 DM patients with and without DR, over 40 years old, suffered from DM for &gt; 10 years. DNA methylation, histone acetylation, and microRNA‑320 were examined by real‑time quantitative polymerase chain reaction, while SDH level examination was carried out by enzyme‑linked immunosorbent assay. Analyses were performed with independent t‑test, Mann–Whitney, Spearman correlation, and multiple linear regression. Results: With respect to SDH, DNA methylation showed no significant correlation so as histone acetylation, in contrary to microRNA‑320 with a very strong negative correlation (r=−0.968, P &lt; 0.005). Conclusion: MicroRNA‑320 was correlated to SDH in a manner of protective properties against the occurrence of DR. Involvement of DNA methylation and histone acetylation was perceptible in influencing SDH enzyme despite their insignificance if they took place individually

TiO₂ Nanoparticles Prepared by Sol-Gel Method for Anode Application in Lithium-Ion Batteries

TiO 2 nanoparticles are prepared via sol-gel method using titanium tetraisopropoxide (TTIP) as a precursor under refluxing and controlled pH. It is found that pure anatase phase is obtained with pH 10. Further characterization studies are carried out on pure nanoparticle anatase phase by XRD, SEM, and transmission electron microscope (TEM). Their electrochemical performances as anode material in lithium-ion batteries are investigated by cyclic voltammetry, galvanostatic cycling, and rate capability measurements. A high discharge capacity of 321 mAh/g (vs. 335 mAh/g theoretical) is achieved at 1C rate. After the first galvanostatic charge/discharge cycle, voltage profiles show plateaus at 1.75 and 1.95 V versus Li at discharge and charge, respectively. High Coulombic efficiency (>99%) is maintained after 300 cycles, which makes anatase TiO 2 nanoparticles prepared by sol-gel method, a very promising material for anode application in lithium rechargeable batteries