We obtain new results regarding the existence, uniqueness, and stability of solutions for a class of fractional Caputo sequential coupled hybrid system which are investigated by applying fixed-point theorems such as Banach’s contraction mapping principle and Leray–Schauder’s alternative. Also, the stability of the mentioned fractional sequential hybrid system is defined and studied. Lastly, we demonstrate some numerical examples in order to confirm our results.
Breast cancer is the major cause of cancer-related deaths globally. This public health emergency is further worsened by challenges of drug resistance and toxicities of the current medications. Hence, the search for novel drugs has become very necessary.
In this study, some cytotoxic 1, 3-diphenyl-1H-pyrazole derivatives were virtually screened against estrogen receptor alpha (ERα) of breast cancer to identify promising ligands from the dataset of molecules. Afterward, the bioactive ligands were subjected to QSAR modeling and the validated model was used to design more potent analogs of the most active member of the dataset selected as lead/template molecule (Tm). Subsequently, the binding affinity of the designed ligands against the active sites of ERα was investigated with the aid of molecular docking and molecular mechanics generalized born surface area (MM/GBSA) calculations. Furthermore, density functional theory (DFT) calculation and molecular dynamics simulations were used to study the electronic properties and thermodynamic stability of the most promising ligands and their complexes with the receptor, respectively. Finally, the pharmacokinetic and toxicity profiles of the ligands were also investigated.
The validated penta-parametric QSAR model (R2train = 0.896; R2adj = 0.875; Q2CV = 0.816; and R2test = 0.703) used for designing new bioactive ligands hinted the predominant influence of molecular size, shape, and symmetry on the observed cytotoxic effects of the investigated 1, 3-diphenyl-1H-pyrazole derivatives on breast cancer cells (MCF-7). The designed ligands; DP-1, DP-2, DP-3, DP-4, and DP-5 which bind to the ERα target with Gibbs free energy change (∆GTotal) of − 41.57, − 42.06, − 42.16, − 41.64, and − 41.91 kcal/mol, respectively, appear more potent than Tm with ∆GTotal value of − 34.89 kcal/mol and tamoxifen (∆GTotal = − 34.89 kcal/mol), an approved drug used herein as positive control. Moreover, the excellent cytotoxic potentials of the new drug candidates against MCF-7 cells were supported by both quantum mechanical calculations and molecular dynamic simulations. In addition, the ligands display sound pharmacokinetic profiles.
The designed ligands could be excellent sources of novel drug candidates against estrogen receptor positive breast cancer. Hence, further in vitro and in vivo investigations on the bioactive molecules are recommended.
The occurrence of malaria, filariasis, yellow fever, West Nile virus, chikungunya, Zika, and dengue diseases causes significant morbidity and mortality in underdeveloped countries due to the emergence of virulent strains and the development of drug resistance. Nanotechnology, a potent tool, can mitigate the mosquito’s emergence and larvae breeding sources effectively. Nanocomposites, an amalgamation of metals/metal oxide/polymer, will target the specific mosquitoes and can suppress their growth and development. Therefore, our objective is to explore the larvicidal potency of nanocomposites in the larvae of Aedes aegypti, Culex quinquefasciatus, Anopheles stephensi, and Culex pipiens and to address the possible use of nanocomposites as insecticides in Integrated Pest Management.
Nanocomposites have diverse applications in biological sectors due to the distinctive optical and physical chemical properties. These tailored composites can tackle virulent strains, and mosquitoes can’t develop resistance. Nanocomposites act against larvae of various developmental stages (1st–4th instar) in Ae.aegypti, Cx. quinquefasciatus, An.stephensi, and Cx. pipiens with significant LC50 values. The larvicidal effect of the nanocomposite is influenced by species-specific, size and shape, surface coating, and others. The nanocomposites induce reactive oxygen species (ROS), triggering oxidative stress and activating apoptosis mechanisms in the larvae. From the available literature studies, nanocomposites are effective against Ae. aegypti > Cx. quinquefasciatus > Cx. pipiens > An. stephensi mosquitoes.
The nanocomposites are effective against all stages of mosquito larvae and can be further formulated as an insecticide to control the mosquito menace. Thus, the nanocomposites are a novel material for the eradication of juvenile mosquitoes with higher efficacy and efficiency.
Epilepsy is a chronic neurological disorder affecting millions globally, with a significant portion of patients remaining pharmacoresistant. Acupuncture, particularly transcutaneous auricular vagus nerve stimulation (taVNS), has gained attention for its potential in epilepsy management. This study conducts a comprehensive bibliometric analysis of global research trends in acupuncture for epilepsy from 2000 to 2024 to assess publication trends, key research themes, influential articles, and research hotspots.
A systematic search was conducted using the Web of Science Core Collection (WoSCC) database for articles published between 2000 and 2025. Bibliometrix and VOSviewer were used for bibliometric and thematic analysis, mapping research trends, co-occurrence networks, and thematic evolution.
A steady increase in publications was observed, with notable contributions from China and institutions like Fudan University. Key research themes include vagus nerve stimulation (VNS), electroacupuncture, and neuroinflammation, highlighting the shift toward neuromodulation. Citation impact was highest in journals such as Evidence-Based Complementary and Alternative Medicine. Emerging trends point to taVNS and neuroprotection as significant research areas, with clinical studies increasingly focusing on the biological mechanisms behind acupuncture’s anticonvulsant effects.
Acupuncture for epilepsy has grown in global recognition, especially in VNS and electroacupuncture. However, further high-quality clinical trials and standardized protocols are required to establish acupuncture’s efficacy in clinical epilepsy treatment, particularly in pharmacoresistant cases.
Clostridium perfringens, a bacterium associated with various animal and human diseases, could produce several toxins, such as epsilon toxin (ETX). The economic importance of ETX is related to its ability to cause enterotoxemia in domestic ruminants, which causes sudden death. This neurotoxin is also a unique environmental triggering agent for multiple sclerosis (MS). Antibodies against the TGVSLTTSYSFANTN peptide of ETX had been found in clinically definite multiple sclerosis (CDMS). However, no similarity with human proteins was found by the conducted basic local alignment search tool (BLAST) search. Several studies revealed that similar epitopes shared between the infectious microorganisms and the human proteome could trigger autoimmune responses. Although several ETX-based antigens had been designed, this issue was not considered in the designs.
In the current study, the ETX sequence was analyzed to find any shared peptides with more than 5 residues in length in the human proteome. Then, a vaccine construct was designed based on specific peptides of ETX with no similarity to the human proteome. The obtained construct was analyzed regarding its antigenic and structural properties.
No match was discovered for peptides longer than 7-meric. Hepta- and 6-meric peptides matched to a total of 4 and 83 identical peptides in the human proteome, respectively. A construct with a length of 110 amino acids (approximately 13 kDa with the estimated pI of 8.75) was obtained. This construct contains both alpha helical and extended regions linked by coiled regions. The extended and coiled regions were more frequent than the helical regions. The AlphaFold 3D model was consistent with the results obtained from the secondary structure prediction. The molecular dynamic (MD) simulation demonstrated that the designed construct maintains its structural compactness during the simulation, and after 50 ns of MD, the designed construct achieves an equilibrium and stabilized state.
A novel antigen was designed based on safe epitopes of epsilon toxin by which potential molecular mimicry involved in autoimmune responses could be avoided. The current study results require experimental verification in future investigations.
This study investigates the antiviral potential of turmeric-derived compounds, particularly curcuminoids, against the Egyptian strain of Potato Virus Y (PVYN-Egypt) using in silico molecular docking simulations. The binding interactions of five key compounds—curcumin, bisdemethoxycurcumin, demethoxycurcumin, isorhamnetin, and ribavirin (as a control)—were evaluated against three essential viral proteins: P1 protease, helper component proteinase (HCPro), and coat protein, to assess their therapeutic viability.
Molecular docking results revealed that isorhamnetin exhibited the strongest binding affinity toward P1 protease. Curcumin and bisdemethoxycurcumin showed favorable binding to both HCPro and CP. ADMET profiling demonstrated that most tested ligands, except for curcuminol and ribavirin, had good oral bioavailability and favorable gastrointestinal absorption. Polar surface area (PSA), a key factor in membrane permeability and drug-likeness, was also considered—compounds with lower PSA values generally show better bioavailability. However, potential toxicity concerns were identified for curcuminol and ribavirin. Among the compounds, curcumin and its derivatives—particularly isorhamnetin—emerged as promising antiviral candidates, while bisdemethoxycurcumin showed potential to inhibit viral replication. Ribavirin displayed moderate binding but fewer favorable interactions compared to curcumin-based ligands.
This study provides new insights into the development of antiviral agents targeting PVY. The findings support the potential of curcumin derivatives, especially isorhamnetin and bisdemethoxycurcumin, as effective antiviral agents. Further experimental validation is recommended to explore their applications in agriculture and pharmaceutical biotechnology.
Luteolin is a natural polyphenolic flavonoid (C6–C3–C6 structure) found in various medicinal herbs. It exhibits significant antioxidant, anti-inflammatory, and anti-fibrotic properties, making it a promising therapeutic compound for fibrotic illnesses, including pulmonary fibrosis. The condition is distinguished by excessive extracellular matrix formation in lung tissue, resulting in stiffness and reduced respiratory performance. Current treatments, limited to nintedanib and pirfenidone, merely slow disease progression, highlighting the need for more effective therapeutic options.
Luteolin exerts its effects by modulating key signaling pathways, including transforming growth factor-beta/small mothers against decapentaplegic (TGF-β/SMAD), phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), nuclear factor erythroid 2–related factor 2 (NRF2), signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase (MAPK), and NOD-like receptor family pyrin domain-containing 3 (NLRP3), thereby reducing oxidative stress, inflammation, and fibrosis. In vitro and pre-clinical studies also support its therapeutic potential. Furthermore, network pharmacology has identified multiple hub targets of luteolin and highlighted its synergistic interactions with other bioactive compounds. These interactions contribute to enhanced biological outcomes such as cell cycle deceleration, apoptosis induction, and angiogenesis inhibition in inflammatory conditions and cancers. However, poor bioavailability and targeted delivery challenges limit luteolin’s clinical utility. To overcome these issues, nanoparticle-based delivery systems, including nanosuspensions, solid lipid nanoparticles, and polymeric nanoparticles, have been developed to enhance their lung-targeted delivery and therapeutic efficacy.
This narrative review outlines luteolin’s multiple molecular targets, pathways, and mechanisms in modulating fibrotic diseases, especially lung fibrosis, and emphasizes the gap between current formulation strategies and clinical translation. Advanced delivery technologies hold promise for enhancing luteolin’s therapeutic value. Hence, additional investigation is needed to establish its effectiveness as well as its safety in clinical settings and develop luteolin as a viable treatment option for chronic respiratory diseases, particularly pulmonary fibrosis.
The Zaremaoghaddam model studies internal waves, fluid dynamics, and nonlinear wave equations. In shallow water, internal solitons, or stratified fluids, the procedure may involve modifying or applying nonlinear wave models like Korteweg–de Vries equation, Boussinesq, or Gear–Grimshaw. The Gear–Grimshaw system simulates internal waves in a two-layer stratified fluid, such as an ocean with two density layers, using interconnected nonlinear evolution equations. The Korteweg–de Vries equation is extended to include wave mode interactions.
The paper recovers solitary waves and shock waves for double-layered fluid flow that is modeled whose basic platform is the Korteweg–de Vries equation. This retrieval is made possible with the usage of the generalized exponential differential rational function approach. Two models for the double-layered flow are taken into consideration, namely the Zaremaoghaddam model and the Gear–Grimshaw model. The parameter restrictions for the existence of such solutions are also enumerated.
This paper has many implications and opens up many future opportunities. Since double-layered fluid flow never addresses rogue wave features, the paper’s results would be the foundation for studying them. Additionally, viscosity can be considered in the two models in this paper. A practical perspective would result since viscosity is inevitable in any fluid or airflow. Additionally, these models are new. Thus, such models must be investigated by identifying conservation laws and studying soliton perturbation theory.
Plant-based compounds have gained significant attention as therapeutic agents for managing infections and facilitating tissue repair. RHRet is a plant-derived product composed of bioactive compounds from the extract of different plant species, like Phyllanthus emblica, Curcuma longa, Terminalia chebula, Swertia chirayita, Azadirachta indica, Pterocarpus santalinus, Adhatoda vasica, and Terminalia bellirica. These medicinal plants are known for their potent antibacterial and immune-modulatory properties. In this study, we evaluated the molecular interactions between major bioactive compounds present in RHRet and bacterial proteins, followed by evaluation of the antibacterial properties of RHRet, its impact on biofilm formation, and oxidative stress responses.
Molecular docking was studied to evaluate the interactions of different bioactive compounds potentially present in RHRet with PerR, SodA, and KatG proteins. The antibacterial activity was evaluated using disc diffusion, growth curve analysis, minimum inhibitory concentration analysis, and biofilm formation. Hyaluronidase inhibitory effect was also evaluated to study the potential of RHRet in restricting bacterial growth. The oxidative stress response was analyzed through lipid peroxidation, superoxide dismutase and catalase activity, and measurement of total thiol content.
Docking analysis revealed strong binding interactions between compounds potentially present in RHRet and PerR, SodA, and KatG proteins. RHRet inhibits Staphylococcus aureus growth, biofilm formation, and hyaluronidase activity in a concentration-dependent manner. Additionally, RHRet increases lipid peroxidation levels, thiol content, and reducing superoxide dismutase activity. However, no significant changes have been found in catalase activity.
RHRet inhibits Staphylococcus aureus growth and biofilm formation and inhibits hyaluronidase activity while modulating oxidative stress and interacting with bacterial proteins. Although RHRet showed promising antibacterial potential, further in vivo studies are necessary to thoroughly evaluate its efficacy and safety profile.
The persistent resurgence of influenza and influenza-like illness despite concerted vaccination interventions is a global health burden, thus necessitating accurate tools for early intervention and preparedness. This scoping review aims to map the currently available literature on artificial intelligence (AI)-based forecasting models for seasonal influenza and to identify trends in those published models, approaches, and research gaps.
A detailed search was conducted in PubMed, Scopus, and IEEE Xplore to find relevant studies published between 2014 and 2025. The AI techniques (such as machine learning and deep learning) applied in predicting seasonal influenza activity are considered eligible studies. Model types, data inputs, performance metrics, and validation approaches were summarized on data that were extracted and charted.
Nine studies met the inclusion criteria and were included. Owing to their effectiveness in solving temporal sequence models, many deep learning models have been applied, including the long short-term memory (LSTM) model and the CNN LSTM hybrid model. The data sources are epidemiological records, meteorological variables and social media signals. Most of the models achieved excellent predictive accuracy, but shortcomings in model interpretability, external validation or consistency across performance reporting became issues.
Although AI-based models show promising capabilities for predicting influenza, there are still issues related to standardization and deployment in the real world. Future work should focus on real-time data integration, external validation and interpretable transferable models appropriate for a wide variety of health settings.
This graphical abstract encapsulates AI-based forecasting models for seasonal influenza, depicted as a navigational chart through the research terrain. A central magnifying glass over a globe anchors the global health challenge, guiding the viewer through a flowchart-like journey. A funnel filters literature from PubMed, Scopus, and IEEE Xplore (2014–2025), yielding 9 pivotal studies. Layered icons delineate machine learning and deep learning models, with LSTM and CNN-LSTM hybrids highlighted. Interconnected circles symbolize diverse data inputs—epidemiological, meteorological, and social media—converging into a data integration hub. The bar chart connotes high predictive accuracy, tempered by a warning sign flagging interpretability, validation, and reporting challenges. A roadmap at the journey’s end points to future horizons: real-time data integration, external validation, and interpretable models, charting the course for advancing global influenza preparedness.
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