Complete 1H and 13C NMR signal assignments and chemical shift calculations of four 1,2,4-oxadiazole-based light-emitting liquid crystals

DFT investigations on the influence of stacking on the electronic structure, absorption, and non-linear optical properties of 3,5-bis[4-(4-methylphenylcarbonyloxy)phenyl]-1,2,4-oxadiazole

CONTEXT

1,2,4-Oxadiazole serves as a fundamental building block driving advancements across diverse scientific and technological arenas, contributing to the creation of innovative materials for various applications including devices, sensors, medications, agrochemicals, and biomedical instruments. Employing density functional theory (DFT) methods, we investigate the impact of different conformers of an oxadiazole substituted derivative, specifically 3,5-bis[4-(4-methylphenylcarbonyloxy)phenyl]-1,2,4-oxadiazole, in both monomeric and stacked configurations (dimeric and tetrameric). We analyze the electronic structures of various conformers, including assessment of HOMO-LUMO energy gaps, to detect the influence of diverse substituents and stacking arrangements. We have also explored the stability of stacked structure in explicit solvent environment. Additionally, we examine absorption spectra, non-linear optical properties, and electronic circular dichroism to evaluate the potential applications of these molecules in optoelectronic devices. Our calculations showed that all the conformers were thermodynamically stable within an energy difference of 2.64 kcal mol-1. The study also suggests possible application of the material in optical and electronic devices.

METHODS

DFT calculations were carried out using the CAM-B3LYP and wB97XD functionals with a 6-31 + G* all-electron basis set, paired with the SCRF/PCM solvation model, implemented in the Gaussian 09 package. Equilibrium structure was achieved by performing NPT and NVT simulations using the Gromacs package.

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

INTRODUCTION

In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue.

OBJECTIVES

Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis.

METHODS

We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled.

RESULTS

Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy.

CONCLUSION

The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

A comprehensive survey upon diverse and prolific applications of chitosan-based catalytic systems in one-pot multi-component synthesis of heterocyclic rings

Heterocyclic compounds are among the most prestigious and valuable chemical molecules with diverse and magnificent applications in various sciences. Due to the remarkable and numerous properties of the heterocyclic frameworks, the development of efficient and convenient synthetic methods for the preparation of such outstanding compounds is of great importance. Undoubtedly, catalysis has a conspicuous role in modern chemical synthesis and green chemistry. Therefore, when designing a chemical reaction, choosing and or preparing powerful and environmentally benign simple catalysts or complicated catalytic systems for an acceleration of the chemical reaction is a pivotal part of work for synthetic chemists. Chitosan, as a biocompatible and biodegradable pseudo-natural polysaccharide is one of the excellent choices for the preparation of suitable catalytic systems due to its unique properties. In this review paper, every effort has been made to cover all research articles in the field of one-pot synthesis of heterocyclic frameworks in the presence of chitosan-based catalytic systems, which were published roughly by the first quarter of 2020. It is hoped that this review paper can be a little help to synthetic scientists, methodologists, and catalyst designers, both on the laboratory and industrial scales.

1H NMR-based metabonomics for infertility diagnosis in men with varicocele

PURPOSE

"Omics" techniques have been used to understand and to identify biomarkers of male infertility. We report on the first metabonomics models created to diagnose varicocele and infertility among men with varicocele.

METHODS

We recruited 35 infertile men with varicocele (VI group), 21 fertile men with varicocele (VF group) and 24 fertile men without varicocele (C group). All men underwent standard semen analysis, scrotal duplex ultrasonography, and sexual hormone level measurement. Hydrogen-1 nuclear magnetic resonance (1H NMR) spectra of seminal plasma were used to create metabonomics models to discriminate between men with and without varicocele, and between fertile and infertile men with varicocele.

RESULTS

Using the statistical formalisms partial least square discriminants analysis and genetic algorithm-based linear discriminant analysis (GA-LDA), we created two models that discriminated the three groups from each other with accuracy of 92.17%. We also created metabonomics models using orthogonal partial least square discriminants analysis and GA-LDA that discriminated VF group from VI group, with an accuracy of 94.64% and 100% respectively. We identified 19 metabolites that were important in group segregation: caprate, 2-hydroxy-3-methylvalerate, leucine, valine, 3-hydroxybutyrate, lactate, alanine, 4-aminobutyrate, isoleucine, citrate, methanol, glucose, glycosides, glycerol-3-phosphocoline, n-acetyltyrosine, glutamine, tyrosine, arginine, and uridine.

CONCLUSIONS

1HNMR-based metabonomics of seminal plasma can be used to create metabonomics models to discriminate between men with varicocele from those without varicocele, and between fertile men with varicocele from those infertile with varicocele. Furthermore, the most important metabolites for group segregation are involved in the oxidative stress caused by varicocele.

Characterization of a biosurfactant produced by Pseudomonas cepacia CCT6659 in the presence of industrial wastes and its application in the biodegradation of hydrophobic compounds in soil

The bacterium Pseudomonas cepacia CCT6659 cultivated with 2% soybean waste frying oil and 2% corn steep liquor as substrates produced a biosurfactant with potential application in the bioremediation of soils. The biosurfactant was classified as an anionic biomolecule composed of 75% lipids and 25% carbohydrates. Characterization by proton nuclear magnetic resonance ((1)H and (13)C NMR) revealed the presence of carbonyl, olefinic and aliphatic groups, with typical spectra of lipids. Four sets of biodegradation experiments were carried out with soil contaminated by hydrophobic organic compounds amended with molasses in the presence of an indigenous consortium, as follows: Set 1-soil+bacterial cells; Set 2-soil+biosurfactant; Set 3-soil+bacterial cells+biosurfactant; and Set 4-soil without bacterial cells or biosurfactant (control). Significant oil biodegradation activity (83%) occurred in the first 10 days of the experiments when the biosurfactant and bacterial cells were used together (Set 3), while maximum degradation of the organic compounds (above 95%) was found in Sets 1-3 between 35 and 60 days. It is evident from the results that the biosurfactant alone and its producer species are both capable of promoting biodegradation to a large extent.