Impact of −C₂H₅ and −OH Functionalizations on the Water Flow Blockage in Carbon Nanotubes

Carbon nanotube (CNT) filter membranes are excellent promising materials for efficient desalination. In our previous studies (<i>Phys. Rev. Lett.</i> <b>2015,</b> <i>115</i>, 164502) we showed that Na⁺ cations in seawater would easily bind at the entrance of the pristine CNT due to cation−π interaction, resulting in the blocking of water flow through the nanotube. Here, we systematically investigate the binding behavior of ions and blockage effects of water flow in much more chemically realistic CNTs that are functionalized at the ends with various density of hydrophilic −OH or hydrophobic −C₂H₅ groups. Our findings show that hydrophobic −C₂H₅ groups will weaken the cation−π interaction between Na⁺ ions and CNTs, and accordingly, water flows through the CNTs fluently. CNTs functionalized with −C₂H₅ groups in moderate density are expected to work excellently in desalination application, whereas functionalization with hydrophilic −OH groups cannot prevent the blockage of water. This finding brings insights in designing efficient desalination filter materials based on CNT

Examples of extracted gene-disease relationships and protein-protein interactions with their support evidences.

<p>Examples of extracted gene-disease relationships and protein-protein interactions with their support evidences.</p

Logic forms for the interactions involved in the drug mechanism domain.

<p>Logic forms for the interactions involved in the drug mechanism domain.</p

Logic forms for the classes and entities involved in the drug mechanism domain.

<p>Logic forms for the classes and entities involved in the drug mechanism domain.</p

Different types of knowledge used in our approach and their sources.

<p>Different types of knowledge used in our approach and their sources.</p

A diagrammatic view of (a) direct and (b) indirect inferences for dipyridamole and tazarotene as novel cancer indications.

<p>A diagrammatic view of (a) direct and (b) indirect inferences for dipyridamole and tazarotene as novel cancer indications.</p

Performance of the extraction of gene-disease relations (GDRs) and protein-protein interactions (PPIs).

<p>Performance of the extraction of gene-disease relations (GDRs) and protein-protein interactions (PPIs).</p

Treatment distribution for the 296 inferred drugs that neither have cancer as the original indication nor in clinical trials for cancer.

<p>Treatment distribution for the 296 inferred drugs that neither have cancer as the original indication nor in clinical trials for cancer.</p

Identifying Novel Drug Indications through Automated Reasoning

<div><h3>Background</h3><p>With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using <em>in silico</em> approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.</p> <h3>Methodology</h3><p>In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.</p> <h3>Conclusion/Significance</h3><p>To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.</p> </div

Chemical composition and biological activities of essential oil from <i>Filifolium sibiricum</i> (L.) Kitam

<p>The essential oil from <i>Filifolium sibiricum</i> (L.) Kitam were extracted using hydrodistillation and GC-MS was used to analyse the essential oil. The main components were espatulenol (8.55%), geranyl acetate (8.03%), caryophyllene oxide (5.47%), calamenene (4.79%), geraniol (4.28%), calamenene (4.53%), geraniol (4.06%), cedrene epoxide (3.23%), myrtenol (3.18%), transgeranylgeranio (3.13%), etc. The essential oil showed intensive inhibitory effects against MCF-7 with IC₅₀ level of 0.78 mg/mL, HepG-2 with IC₅₀ level of 0.44 mg/mL, SKOV-3 with IC₅₀ level of 0.27 mg/mL, BGC-823 with IC₅₀ level of 0.34 mg/mL. In the antibacterial test, the essential oil showed the significant antibacterial activities. The MIC and MBC values were 5.20 and 5.20 mg/mL against <i>Staphylococcus aureus</i>.</p