On the analysis and deeper properties of the fractional complex physical models pertaining to nonsingular kernels.

This study solves the coupled fractional differential equations defining the massive Thirring model and the Kundu Eckhaus equation using the Natural transform decomposition method. The massive Thirring model is a dynamic component of quantum field theory, consisting of a coupled nonlinear complex differential equations. Initially, we study the suggested equations under the fractional derivative of Caputo-Fabrizio.

Stochastic delayed analysis of coronavirus model through efficient computational method.

Stochastic delayed modeling has a significant non-pharmaceutical intervention to control transmission dynamics of infectious diseases and its results are close to the reality of nature. The covid-19 has been controlled globally but there is still a threat and appears in different variants like omicron and SARS-CoV-2 etc. globally.

A Comprehensive Analysis of the PI3K/AKT Pathway: Unveiling Key Proteins and Therapeutic Targets for Cancer Treatment.

Background: Cancer development and progression involve a complex network of pathways among which certain pathways play a pivotal role in promoting tumor growth and survival. An important pathway in this context is the PI3K/AKT pathway, which regulates crucial cellular processes including proliferation, viability, and metabolic regulation. Dysregulation of this pathway has been strongly linked to the development of various types of cancers.

QTRAP LC/MS/MS of Garlic Nanoparticles and Improving Sunflower Oil Stabilization during Accelerated Shelf Life Storage.

The purpose of this research was to assess and utilize the bioactive compounds of garlic nanoparticles (Ga-NPs) as a natural antioxidant in sunflower oil (SFO) stored at 65 ± 1 °C for 24 days. The garlic nanoparticles (Ga-NPs) from the Balady cultivar were prepared, characterized, and added to SFO at three concentrations: 200, 600, and 1000 ppm (/), and they were compared with 600 ppm garlic lyophilized powder extract (Ga-LPE), 200 ppm BHT, 200 ppm α-tocopherol, and SFO without Ga-NPs (control). The QTRAP LC/MS/MS profile of Ga-NPs revealed the presence of four organosulfur compounds.

Self-similarity of human protein interaction networks: a novel strategy of distinguishing proteins.

The successful determination of reliable protein interaction networks (PINs) in several species in the post-genomic era has hitherto facilitated the quest to understanding systems and structural properties of such networks. It is envisaged that a clearer understanding of their intrinsic topological properties would elucidate evolutionary and biological topography of organisms. This, in turn, may inform the understanding of diseases' aetiology.

Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery.

Background: We have recently shown by formally modelling human protein interaction networks (PINs) as metric spaces and classified proteins into zones based on their distance from the topological centre that hub proteins are primarily centrally located. We also showed that zones closest to the network centre are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping' functions. We proposed that proteins closest to the network centre may present good therapeutic targets.

Protein interaction networks as metric spaces: a novel perspective on distribution of hubs.

Background: In the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.