graph-theoretical-analysis-modeled-protein-protein-interaction

Graph-theoretical Analysis of a Modeled Protein-protein Interaction Network in the RAS Signaling Pathway

The RAS signaling pathway is a fundamental regulator of cellular growth, proliferation, and survival. Dysregulation of this pathway is strongly implicated ...

king faisal university

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. Ga-NPs exhibited the highest capacity for phenolic, flavonoid, and antioxidant compounds. In Ga-NP SFO samples, the values of peroxide, -anisidine, totox, conjugated dienes, and conjugated trienes were significantly lower than the control. The antioxidant indices of SFO samples containing Ga-NPs were higher than the control. The Ga-NPs enhanced the sensory acceptability of SFO treatments up to day 24 of storage. The shelf life of SFO treated with Ga-NPs was substantially increased (presuming a Q amount). The results show that Ga-NPs are a powerful antioxidant that improves SFO stability and extends the shelf life (~384 days at 25 °C).

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

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. Consequently, it is imperative to identify the key proteins within this pathway as potential targets for impeding cancer cell proliferation and survival.

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

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. This article, considered pattern a mathematical model based on Susceptible, Infected, and recovered populations with highly nonlinear incidence rates. we studied the dynamics of the coronavirus model; a newly proposed version is a stochastic delayed model that is based on nonlinear stochastic delayed differential equations (SDDEs). Transition probabilities and parametric perturbation methods were used for the construction of the stochastic delayed model. The fundamental properties like positivity, boundedness, existence and uniqueness, and stability results of equilibria of the model with certain conditions of reproduction number are studied regularly. Also, the extinction and persistence of disease are studied with the help of well-known theorems. The numerical methods used to find a visualization of results due to the complexity of stochastic delayed differential equations. Furthermore, for computational analysis, we implemented existing methods in the literature and compared their results with the proposed method like nonstandard finite difference for stochastic delayed model. The proposed method restores all dynamical properties of the model with a free choice of time steps.

Stochastic delayed analysis of coronavirus model through efficient computational method.

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. The Atangana-Baleanu derivative is then used to evaluate the comparable equations. The results are significant and necessary for exploring a range of physical processes. This paper uses modern approach and the fractional operators in this situation to develop satisfactory approximations to the offered problems. The proposed approach combines the natural transform technique with the efficient Adomian decomposition scheme. Obtaining numerical findings in the form of a fast-converge series significantly improves the scheme's accuracy. Some graphical plot distributions are presented to show that the present approach is very simple and straightforward. We performed a fractional order analysis of assumed phenomena to demonstrate and validate the effectiveness of the future technique. The behaviour of the approximate series solution for several fractional orders is shown visually. Additionally, the nature of the derived outcome has been observed for various fractional orders. The derived results demonstrate how simple and efficient the proposed method is to apply for analysing the behaviour of fractionally-order complex nonlinear differential equations that arise in related fields of engineering and science.

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

According to the Centers for Disease Control and Prevention (CDC) estimates that 576 to 740 million people globally are infected with hookworms. It remains a significant public health threat in tropical and subtropical regions. Especially in low-income countries, hookworm infection continues to affect millions, even with the availability of modern medical advancements. The present study is based on the transmission dynamics of hookworm infection in a population by using the strategy of mathematical modeling with computational methods. The population has been categorized into the following subpopulations such as susceptible humans, infectious humans, infectious humans with heavy infection, humans recovered, worm eggs, non-infective larvae, and infectious larvae and exposed humans. Firstly, the fundamental properties like positivity and boundness are studied. The equilibrium points like hookworm-endemic equilibrium (HEE), hookworm-free equilibrium (HFE), and basic reproduction numbers for the model were computed. Secondly, the stochastic formation of the model was studied with well-known properties like positivity, and the boundedness of the hookworm model. The model has no analytical solution due to the highly complex nonlinearity of the stochastic delay differential equation (SDDEs) of the model. Methods like Euler Maruyama, stochastic Euler, stochastic Runge Kutta, and stochastic nonstandard finite difference are used for its solution and visualization of results. Also, the comparison of standard with nonstandard methods is presented to verify the efficiency of the computational method. Furthermore, the stochastic nonstandard finite difference approximation is a good agreement to restore the dynamical properties of the model like positivity, boundedness, and dynamical consistency. Also, it is shown as efficient, low-cost, and independent of the time step size. In conclusion, the theoretical and numerical results support understanding the transmission dynamics of hookworm infection in the population.

Computational analysis of a mathematical model of hookworm infection.

Scabies is a highly transmitted skin disease that can affect people of all ages, especially children. According to the World Health Organization (WHO), South Asia and sub-Saharan Africa are the regions most affected. For the study of the dynamics of scabies in the population, the mathematical model is designed with delay differential equations (DDEs) for four subpopulations: unvaccinated individuals, vaccinated individuals, infected individuals, and recovered individuals. The fundamental properties of the model, such as positivity, boundedness, existence, and uniqueness, are proved. The equilibria, reproduction number, sensitivity analysis, and (Local and Global) stabilities for the second order are studied rigorously. The numerical simulations were performed to confirm the validity of their theoretical results. The study's findings suggest delay-based modeling of scabies and the advanced stability analysis provides a better understanding of epidemic management and disease dynamics over time.

Dynamical analysis of scabies delayed epidemic model with second-order global stability.

Rice, the world's most important food crop, requires an early and accurate identification of the diseases that infect rice panicles and leaves to increase production and reduce losses. Most conventional methods of diagnosing diseases involve the use of manual instruments, which are ineffective, imprecise, and time-consuming. In light of such drawbacks, this article introduces an improved deep learning and transfer learning method for diagnosing and categorizing rice leaf diseases proficiently. First, all input images are preprocessed; the images are resized to a fixed size before applying a sophisticated contrast enhanced adaptive histogram equalization procedure. Diseased regions are then segmented through the developed gravity weighted kernelised density clustering algorithm. In terms of feature extraction, EfficientNetB0 is fine-tuned by subtracting the last fully connected layers, and the classification is conducted with the new fully connected layers. Also, the tent chaotic particle snow ablation optimizer is added into the learning process in order to improve the learning process and shorten the time of convergence. The performance of the proposed framework was tested on two benchmark datasets and presented accuracy results of 98.87% and 97.54%, respectively. Comparisons of the proposed method with six fine-tuned models show the performance advantage and validity of the proposed method.

Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification.

The PI3K/Akt pathway plays a central role in cancer progression by regulating cell proliferation, survival, and metabolism. Dysregulation of this pathway, often due to mutations in genes such as PIK3CA, PTPN11, EGFR, and AKT1, contributes to tumorigenesis and therapy resistance. Using a network metric space approach, we systematically analyzed the human protein-protein interaction network to identify key hub proteins. Our findings suggest that signaling proteins dominate the PI3K/Akt pathway (100%), with significant overlaps in MAPK cascades (29.1%) and essential oncogenic drivers (70.8%), indicating potential co-targeting strategies to overcome resistance. Functional enrichment analysis highlights the therapeutic relevance of these hubs, while 5.8% of identified proteins are oncogenes, reinforcing their candidacy for therapies. This study provides a systematic, network-based framework for identifying and prioritizing hub proteins in the PI3K/Akt pathway, with potential implications for rational multi-target drug design in precision oncology.

Network centric identification of PI3K/Akt hub proteins as key oncogenic drivers and therapeutic targets.

Respiratory syncytial virus (RSV) is a single-stranded RNA virus responsible for a wide range of respiratory tract infections, including those affecting the lungs, airways, and middle ear. Understanding its transmission dynamics remains essential for effective disease control. A bio-inspired stochastic delay model for RSV transmission is proposed and analyzed. The model's qualitative properties including positivity, boundedness, equilibrium states, and the basic reproduction number are rigorously established through well-posedness theorems. Parameter sensitivity is also examined. To investigate the system's stochastic behavior, numerical schemes such as Stochastic Euler, Runge-Kutta, and Euler-Maruyama methods are applied. However, these traditional approaches fail to fully preserve the dynamic characteristics of the model. To address these limitations, a stochastic nonstandard finite difference (NSFD) scheme with delay is developed. This approach ensures non-negativity, boundedness, consistency, and unconditional convergence, overcoming issues of instability and divergence often observed in standard stochastic numerical methods. Comparative simulations demonstrate that the NSFD method reliably reproduces the true dynamic states of the model. The proposed stochastic delayed modeling framework enhances our understanding of RSV dynamics and provides a stable computational tool for analyzing complex biological systems. The findings open new avenues for exploring nonlinear stochastic processes in epidemiological and neurobiological modeling.

Article	Year
QTRAP LC/MS/MS of Garlic Nanoparticles and Improving Sunflower Oil Stabilization during Accelerated Shelf Life Storage. Foods (Basel, Switzerland)\| PubMed ID: 36553704	2022
A Comprehensive Analysis of the PI3K/AKT Pathway: Unveiling Key Proteins and Therapeutic Targets for Cancer Treatment. Cancer informatics\| PubMed ID: 37649725	2023
Stochastic delayed analysis of coronavirus model through efficient computational method. Scientific reports\| PubMed ID: 39256433	2024
On the analysis and deeper properties of the fractional complex physical models pertaining to nonsingular kernels. Scientific reports\| PubMed ID: 39333163	2024
Computational analysis of a mathematical model of hookworm infection. Scientific reports\| PubMed ID: 39994253	2025
Dynamical analysis of scabies delayed epidemic model with second-order global stability. PloS one\| PubMed ID: 40258081	2025
Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification. PeerJ. Computer science\| PubMed ID: 40989405	2025
Network centric identification of PI3K/Akt hub proteins as key oncogenic drivers and therapeutic targets. Scientific reports\| PubMed ID: 41258151	2025
Mathematical analysis of a stochastic delay model for respiratory syncytial virus dynamics. Scientific reports\| PubMed ID: 41714680	2026

Emad Fadhal

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