Studying the Influence of Mica Particle Size on the Properties of Epoxy Acrylate/Mica Composite Coatings through Reducing Mica Particle Size by the Ball-Milled Method

In this work, a series of epoxy acrylate (EA)/mica composite coatings were synthesized through introducing mica powders of different particle size into epoxy acrylate coatings and using an ultraviolet (UV) curing technique to investigate the influence of mica particle size on the coatings. Mica powders of different particle sizes were obtained by ball-milling for 4, 8, 12, 16, and 20 h with a planetary high-energy ball mill. The particle size and morphologies of ball-milled mica powders were characterized by laser particle size analyzer and scanning electron microscopy (SEM). The results indicated that planetary ball-milling reduced the particle size of mica powders effectively. Mica powders that were un-ball-milled and ball-milled were added into the epoxy acrylate matrix by a blending method to synthesize the organic-inorganic UV curable coatings. The optical photographs of the coatings showed greater stability of liquid mixtures with smaller particle size fillers. The chemical structures of EA/mica composite coatings were investigated by Fourier transform infrared spectroscopy (FTIR), and the conversion rate of C=C bonds was calculated. The results indicated that the C=C conversion of coatings with mica powders of smaller particle sizes was higher. Tests of mechanical properties and tests using electrochemical impedance spectroscopy (EIS) showed that pencil hardness, impact resistance, and coating resistance were improved due to the reduction of mica powders particle size.

FDMine: a graph mining approach to predict and evaluate food-drug interactions

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

Tenacibactins K–M, cytotoxic siderophores from a coral-associated gliding bacterium of the genus Tenacibaculum

HPLC/DAD-based chemical investigation of a coral-associated gliding bacterium of the genus Tenacibaculum yielded three desferrioxamine-class siderophores, designated tenacibactins K (1), L (2), and M (3). Their chemical structures, comprising repeated cadaverine–succinic acid motifs terminated by a hydroxamic acid functionality, were elucidated by NMR and negative MS/MS experiments. Compounds 1–3 were inactive against bacteria and a yeast but displayed cytotoxicity against 3Y1 rat embryonic fibroblasts and P388 murine leukemia cells at GI50 in submicromolar to micromolar ranges. Their iron-chelating activity was comparable to deferoxamine mesylate.

Mini-review on the phyto-chemistry, pharmacology and toxicology of Cola nitida (Vent.) Schott & Endl. (Malvaceae): A medically interesting bio-resource of multiple purposes in Africa

The purpose of this mini-review was to summarize and update knowledge on the phytochemistry, pharmacology, and toxicity of <i>Cola nitida</i>, with the view of providing baseline data for herbal drug formulation. In January 2021, a non-exhaustive online search of relevant articles was carried out on the phytochemistry, pharmacology, and toxicology of <i>C. nitida</i> from scientifically well-established databases such as Science Direct, PubMed, Web of Science, Scopus, Google Scholar, and SciELO. The plant's scientific name as well as phytochemistry, pharmacology, pharmacognosy, bioactivity and toxicology were used as keywords. The chemical structures of the compounds isolated from this plant were drawn using ChemBioDraw Ultra 12.0 software. A literature survey has revealed that <i>C. nitida</i> is highly appreciated by African populations in various cultures, especially in West Africa. Phytochemical analyses showed that <i>C. nitida</i> contains interesting compounds like catechin, caffeine, epicatechin, polyphenols, alkaloids, tannins, saponins, bromelain, cardenolides, proanthocyanidins, triterpenes, glycosides, flavonoids, anthraquinones, steroids, anthocyanins, glycosides, alkaloids, etc. The presence of these phyto-compounds in the investigated plant species justifies its used as an antimicrobial, anti-malarial, anti-inflammatory, anti-diabetic, anti-coagulant agent. Thus, <i>C. nitida</i> could be used as a raw material for manufacturing efficient medication against various diseases, including sickle cell disease.

Secondary Metabolites from Marine-Derived Fungi and Actinobacteria as Potential Sources of Novel Colorectal Cancer Drugs

Colorectal cancer is one of the most common cancers diagnosed in the world. Chemotheraphy is one of the most common methods used for the pharmacological treatment of this cancer patients. Nevertheless, the adverse effect of chemotherapy is not optimized for improving the quality of life of people who are older, who are the most vulnerable subpopulation. This review presents recent updates regarding secondary metabolites derived from marine fungi and actinobacteria as novel alternatives for cytotoxic agents against colorectal cancer cell lines HCT116, HT29, HCT15, RKO, Caco-2, and SW480. The observed marine-derived fungi were from the species Aspergillus sp., Penicillium sp., Neosartorya sp., Dichotomomyces sp., Paradendryphiella sp., and Westerdykella sp. Additionally, Streptomyces sp. and Nocardiopsis sp. are actinobacteria discussed in this study. Seventy one compounds reviewed in this study were grouped on the basis of their chemical structures. Indole alkaloids and diketopiperazines made up most compounds with higher potencies when compared with other groups. The potency of indole alkaloids and diketopiperazines was most probably due to halogen-based functional groups and sulfide groups, respectively.

Mono-/Bis-Alkenoic Acid Derivatives From an Endophytic Fungus Scopulariopsis candelabrum and Their Antifungal Activity

During a screening for antifungal secondary metabolites, six new mono-/bis-alkenoic acid derivatives (2–7) and one known alkenoic acid derivative (1) were isolated from an endophytic fungi Scopulariopsis candelabrum. Their chemical structures were identified by 1H-NMR, 13C-NMR, 2D NMR, and high-resolution mass spectrometry, as well as comparisons with previously reported literatures. Among them, fusariumesters C‒F (2–5) are bis-alkenoic acid derivatives dimerized by an ester bond, while acetylfusaridioic acid A (6) and fusaridioic acid D (7) are alkenoic acid monomers. All the isolates were submitted to an antifungal assay against Candida albicans and the corn pathogen Exserohilum turcicum using the filter paper agar diffusion method. As a result, only compound 1 decorating with β-lactone ring turned out to be active against these two tested fungi. The broth microdilution assay against Candida albicans showed the minimum inhibitory concentration (MIC) value of 1 to be 20 μg/ml, while the minimum inhibitory concentration value of the positive control (naystatin) was 10 μg/ml. And the half maximal inhibitory concentration (IC50) value (21.23 μg/ml) of 1 against Exserohilum turcicum was determined by analyzing its inhibition effect on the mycelial growth, using cycloheximide (IC50 = 46.70 μg/ml) as the positive control.

Automated Recognition of Chemical Molecule Images Based on an Improved TNT Model

The automated recognition of optical chemical structures, with the help of machine learning, could speed up research and development efforts. However, historical sources often have some level of image corruption, which reduces the performance to near zero. To solve this downside, we need a dependable algorithmic program to help chemists to further expand their research. This paper reports the results of research conducted for the Bristol-Myers Squibb-Molecular Translation competition, which was held on Kaggle and which invited participants to convert old chemical images to their underlying chemical structures, annotated as InChI text; we define this work as molecular translation. We proposed a model based on a transformer, which can be utilized in molecular translation. To better capture the details of the chemical structure, the image features we want to extract need to be accurate at the pixel level. TNT is one of the existing transformer models that can meet this requirement. This model was originally used for image classification, and is essentially a transformer-encoder, which cannot be utilized for generation tasks. On the other hand, we believe that TNT cannot integrate the local information of images well, so we improve the core module of TNT—TNT block—and propose a novel module—Deep TNT block—by stacking the module to form an encoder structure, and then use the vanilla transformer-decoder as a decoder, forming a chemical formula generation model based on the encoder–decoder structure. Since molecular translation is an image-captioning task, we named it the Image Captioning Model based on Deep TNT (ICMDT). A comparison with different models shows that our model has benefits in each convergence speed and final description accuracy. We have designed a complete process in the model inference and fusion phase to further enhance the final results.

Synthesis of New Hybrid Structured Magnetite Crosslinked Poly Ionic Liquid for Efficient Removal of Coomassie Brilliant Blue R-250 Dye in Aqueous Medium

In this work, new crosslinked pyridinium poly ionic liquid and its magnetite hybrid structured composite were prepared and applied to remove the toxic dye Coomassie Brilliant Blue (CBB-R250) from aqueous solutions. In this respect, vinyl pyridine, maleic anhydride, and dibromo nonane were used to prepare crosslinked quaternized vinyl pyridinium/maleic anhydride ionic liquid (CQVP-MA). Furthermore, a linear copolymer was prepared by the reaction of vinyl pyridine with bromo nonane followed by its copolymerization with maleic anhydride in order to use it as a capping agent for magnetite nanoparticles. The monodisperse MNPs were incorporated into the crosslinked PIL (CQVP-MA) by ultrasonication to prepare CQVP-MA/Fe3O4 composite to facilitate its recovery using an external magnetic field and enhance its adsorption capacity. The chemical structures, thermal stabilities, zeta potential, particle size, EDS, and SEM of the prepared CQVP-MA and CQVP-MA/Fe3O4 were investigated. Adsorption kinetics, isotherms, and mechanisms of CB-R250 elimination from aqueous solutions using CQVP-MA and CQVP-MA/Fe3O4 were also studied, and the results revealed that the pseudo second-order kinetic model and the Langmuir isotherm model were the most suitable to describe the CBB adsorption from an aqueous solution. The adsorption capacities of CQVP-MA and CQVP-MA/Fe3O4 were found to be 1040 and 1198, respectively, which are more than those for previously reported material in the literature with reasonable stability for five cycles.

Photocatalytic degradation of tetracycline in aqueous systems under visible light irridiation using needle-like SnO2 nanoparticles anchored on exfoliated g-C3N4

Background Pharmaceuticals is one of the groups of contaminants of emerging concern that are resistant to decomposition or removal by most of the existing water and wastewater treatment procedures, hence the need to develop techniques to facilitate the removals of this group of organic contaminants from water systems. In this study, needle-like SnO2 nanoparticles was synthesised and loaded on exfoliated g-C3N4 nanosheet through a hydrothermal method, for use as sensitive visible light induce-photocatalyst for the decomposition of tetracycline in aqueous systems. The synthesised composites was characterized and analysed for the nature of the heterojunction between the SnO2 nanoparticle and g-C3N4 nanosheet using microscopic and spectroscopic techniques. Results The composites were of improved surface properties and enhanced visible-light absorption. The synthesised SnO2/g-C3N4 nanocomposites with various amounts of SnO2 (10–50 mg), employed in the degradation of tetracycline under visible light irradiation, were of good degradation efficiency. The degradation efficiencies of tetracycline by 1 wt.%, 2 wt.%, 3 wt.% and 5 wt.% SnO2/g-C3N4 photocatalyst were 81.54%, 90.57%, 95.90% and 92.15% as compared to g-C3N4 and SnO2 with 40.92% and 51.32% degradation efficiencies. The synergistic interaction between the needle-like SnO2 and exfoliated g-C3N4 nanosheet promoted the separation of photogenerated electron holes pairs, which enhanced their migration rate between SnO2 and g-C3N4 heterojunction, thereby facilitating the degradation of tetracycline. The ·O2− was noted to be the major reactive species in the photocatalytic of the 3 wt.% SnO2/g-C3N4 nanocomposite. Conclusion The fabricated SnO2 nanoparticles anchored on exfoliated g-C3N4 showed good performance for the decomposition of tetracycline in water, with possible application on other pharmaceuticals having same moiety (similar chemical structures).