Computational Studies on Diverse Characterizations of Molecular Descriptors for Graphyne Nanoribbon Structures.

Materials made of graphyne, graphyne oxide, and graphyne quantum dots have drawn a lot of interest due to their potential uses in medicinal nanotechnology. Their remarkable physical, chemical, and mechanical qualities, which make them very desirable for a variety of prospective purposes in this area, are mostly to blame for this. In the subject of mathematical chemistry, molecular topology deals with the algebraic characterization of molecules. Molecular descriptors can examine a compound's properties and describe its molecular topology. By evaluating these indices, researchers can predict a molecule's behavior including its reactivity, solubility, and toxicity. Amidst the captivating realm of carbon allotropes, γ-graphyne has emerged as a mesmerizing tool, with exquisite attention due to its extraordinary electronic, optical, and mechanical attributes. Research into its possible applications across numerous scientific and technological fields has increased due to this motivated attention. The exploration of molecular descriptors for characterizing γ-graphyne is very attractive. As a result, it is crucial to investigate and predict γ-graphyne's molecular topology in order to comprehend its physicochemical characteristics fully. In this regard, various characterizations of γ-graphyne and zigzag γ-graphyne nanoribbons, by computing and comparing distance-degree-based topological indices, leap Zagreb indices, hyper leap Zagreb indices, leap gourava indices, and hyper leap gourava indices, are investigated.

Improved anti-inflammatory effect of curcumin by designing self-emulsifying drug delivery system.

The purpose of the present study was to prepare and evaluate self-emulsifying drug delivery system (SEDDS) of curcumin (Cur) to enhance its solubility and percentage release for the evaluation of anti-inflammatory effect. Curcumin loaded SEDDS formulation was prepared, and zones of self-emulsification were recognized by dilution method for the construction of phase diagram. Lauroglycol FCC, tween 80 (surfactant), and transcutol HP (co-surfactant) were selected based on their solubility and highest emulsion region in phase diagram. Thermodynamic stability of Cur-SEDDS was calculated through globule size, zeta potential, polydispersity index (PDI), viscosity, and pH. Cur-SEDDS were also characterized by encapsulation efficiency (EE %), FT-IR, in vitro release, and in vivo anti-inflammatory effect. Results revealed that droplet size of Cur-SEDDS was 19.77 ± 0.03 nm with their PDI 0.22 ± 0.19, zeta potential -19.33 ± 0.94 and viscosity 25.68 ± 0.86 cp. EE % of Cur-SEDDS was found to be 94.99 ± 0.38%, percentage release 65.83% compared with pure Cur powder. The designed formulation possesses significant anti-inflammatory activity in paw edema when compared with positive control in carrageenan induced rat paw edema assay. Newly developed Cur-SEDDS with enhanced Cur solubility, percentage release, and better anti-inflammatory action may be an alternative source of oral delivery of Cur.

iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique.

INTRODUCTION: Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification. METHODS: The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called "iHyd-LysSite (EPSV)" (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins. RESULTS: The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold cross-validation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%. CONCLUSION: The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.

On m-polar Diophantine Fuzzy N-soft Set with Applications.

INTRODUCTION: In this paper, we present a novel hybrid model m-polar Diophantine fuzzy N-soft set and define its operations. METHODS: We generalize the concepts of fuzzy sets, soft sets, N-soft sets, fuzzy soft sets, intuitionistic fuzzy sets, intuitionistic fuzzy soft sets, Pythagorean fuzzy sets, Pythagorean fuzzy soft sets and Pythagorean fuzzy N-soft sets by incorporating our proposed model. Additionally, we define three different sorts of complements for Pythagorean fuzzy N-soft sets and examine few outcomes, which do not hold in Pythagorean fuzzy N-soft sets complements unlike to crisp set. We further discuss (α, ß, γ) -cut of m-polar Diophantine fuzzy N-soft sets and their properties. Lastly, we prove our claim that the defined model is a generalization of the soft set, N-soft set, fuzzy Nsoft set, intuitionistic fuzzy N soft set, and Pythagorean fuzzy N-soft set. RESULTS: m-polar Diophantine fuzzy N-soft set is more efficient and an adaptable model to manage uncertainties as it also overcomes drawbacks of existing models, which are to be generalized. CONCLUSION: We introduced the novel concept of m-polar Diophantine fuzzy N-soft sets (MPDFNS sets).