Deep eutectic solvents and glycerol: a simple,environmentally benign and efficient catalyst/reaction media for synthesis of N-aryl phthalimide derivatives

Abstract

Deep eutectic solvents (DES) and glycerol have been successfully employed as efficient catalysts/reaction media in the synthesis of N-aryl phthalimide derivatives from phthalic anhydride and primary aromatic amines. The DES prepared from choline chloride and malonic acid proved to be an efficient catalyst whereas glycerol and the DES of choline chloride and urea played a dual role of catalyst and solvent. These mixtures are biodegradable, nontoxic, and cost-effective thereby providing a good industrial alternative to conventional methods. These methods gave products in moderate to high yields with good recyclability of catalyst/solvent at least up to five consecutive runs.     

Re-casting traditional organic experiments into green guided-inquiry based experiments: student perceptions

ABSTRACT

Green Chemistry principles can be used to re-cast traditional Organic chemistry experiments into more guided-inquiry based experiments. Inquiry questions related to green chemistry principles and metrics have been incorporated into our laboratory for the development of more guided-inquiry based experiments. Re-casting traditional experiments provides time for guided-inquiry by allowing students to evaluate reaction conditions and wastefulness of reactions. This includes evaluating solvent choices, heating methods, use of renewal materials, and contemplating reactants and products impacts on human health and environment. Students examine the changes as it pertains to green chemistry, the success of the reaction and the potential impacts on the mechanism. Involving students in these discoveries rooted in a guiding question made the Organic experiments guided-inquiry. Students were surveyed about their exposure to green chemistry and guided-inquiry based labs. Examples of some of the re-casted experiments, excerpts from student reports, and student impressions of the theme are presented.     

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.     

Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients

Paraquat (PQ) poisoning seriously harms the health of humanity. An effective diagnostic method for paraquat poisoned patients is a crucial concern. Nevertheless, it's difficult to identify the patients with low intake of PQ or delayed treatment. Here, a new efficient diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry (GC–MS), named GEE, is proposed to identify the PQ poisoned patients. First, GC–MS provides the original data that efficiently identified the paraquat-poisoned patients. According to the high dimensionality of the original data, in the second stage, the chaos enhanced grey wolf optimization (EGWO) is adopted to search the optimal feature sets to improve the accuracy of identification. Finally, the extreme learning machine (ELM) is used to identify the PQ poisoned patients. To efficiently evaluate the proposed method, four measures were used in our experiments and comparisons were made with six other methods. The PQ-poisoned patients and robust volunteers can be well identified by GEE and the values of AUC, accuracy, sensitivity and specificity were 95.14%, 93.89%, 94.44% and 95.83%, respectively. Our experimental results demonstrated that GEE had better performance and might serve as a novel candidate diagnosis of PQ-poisoned patients.     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein’s function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.     

Distance versus Capillary Flow Dynamics-Based Detection Methods on a Microfluidic Paper-Based Analytical Device (μPAD)

In recent years, there has been high interest in paper-based microfluidic sensors or microfluidic paper-based analytical devices (μPADs) towards low-cost, portable, and easy-to-use sensing for chemical and biological targets. μPAD allows spontaneous liquid flow without any external or internal pumping, as well as an innate filtration capability. Although both optical (colorimetric and fluorescent) and electrochemical detection have been demonstrated on μPADs, several limitations still remain, such as the need for additional equipment, vulnerability to ambient lighting perturbation, and inferior sensitivity. Herein, alternative detection methods on μPADs are reviewed to resolve these issues, including relatively well studied distance-based measurements and the newer capillary flow dynamics-based method. Detection principles, assay performance, strengths, and weaknesses are explained for these methods, along with their potential future applications towards point-of-care medical diagnostics and other field-based applications.     

Simple Estimates for Eutectic Behavior

A simple formula is derived for the eutectic point of an A–B system in terms of the monomer melting points and melting enthalpies. This estimate is tested on several non-ionic or ionic systems, with or without common ions, including choline chloride/urea mixtures. The results are compared with the Schröder-van Laar equation.     

Deep eutectic solvent based homogeneous liquid–liquid extraction coupled with in‐syringe dispersive liquid–liquid microextraction performed in narrow tube; application in extraction and preconcentration of some herbicides from tea

A homogeneous liquid‐liquid extraction performed in narrow tube coupled to in–syringe‐dispersive liquid‐liquid microextraction based on deep eutectic solvent has been developed for the extraction of six herbicides from tea samples. In this method, sodium chloride as a separation agent is filled into the narrow tube and the tea sample is placed on top of the salt. Then a mixture of deionized water and deep eutectic solvent (water miscible) is passed through the tube. In this procedure, the deep eutectic solvent is realized as tiny droplets in contact with salt. By passing the droplets from the tea layer placed on the salt layer, the analytes are extracted into them. After collecting the solvent as separated layer, it is mixed with another deep eutectic solvent (choline chloride/butyric acid) and the mixture is dispersed into deionized water placed in a syringe. After adding acetonitrile to break up the cloudy state, the collected organic phase is injected into gas chromatography‐mass spectrometry. Under optimal conditions, limits of detection and quantification in the ranges of 2.6–8.4 and 9.7–29 ng/kg, respectively, were obtained. The extraction recoveries and enrichment factors in the ranges of 70–89% and 350–445 were obtained, respectively.