Introduction: The effectiveness of pharmaceutical treatment methods is vital in cancer treatment. In this context, various targeted drug delivery systems are being developed to minimize or eliminate existing deficiencies and harms. This study aimed to model the interaction of MEN-based drug-targeting systems with cancer cells and determine the properties of interacting MENs.
Methods: Magnetoelectric Nanostructures (MENs) have both targeting and nano-electroporation effects due to their ferroic properties. Among these structures, the most used nanoparticles as targeting mechanisms are CoFe2O4-BaTiO3 structures. For this purpose, the electrical field produced by MENs was modeled using MATLAB R2023b, and a theoretical data pool of appropriate physical properties was created. Testing and applying other magnetoelectric materials defined in the literature may be costly and time-consuming.
Results: The problems with MENs can be eliminated by performing theoretical simulations of each material before proceeding with laboratory tests.
Conclusion: By simulating the interaction of CoFe2O4-BaTiO3 MENs with cancer cells, physical properties affecting drug targeting were theoretically identified and a data pool of MENs with these properties was created.
Ovarian cancer (OC) ranks as the fifth leading cause of cancer-related deaths in the United States, posing a significant threat to female health. Late-stage diagnoses, driven by elusive symptoms often masquerading as gastrointestinal issues, contribute to a concerning 70% of cases being identified in advanced stages. While early-stage OC brags a 90% cure rate, progression involving pelvic organs or extending beyond the peritoneal cavity drastically diminishes it. Overcoming chemoresistance and metastasis requires a deep understanding of the associated progression mechanisms for innovative therapies. Extracellular vesicles (EVs), containing proteins, RNAs, DNAs, and metabolites, have surged in recent years, significantly impacting tumor progression, recurrence, immune evasion, and metastasis associated with the ovarian tumor microenvironment. Recent research unveils organ-specific metastatic patterns in OC, providing insights into tumor cell interactions and signaling crosstalk with stromal cells. The review explores the role of EVs behind OC cell metastasis and chemoresistance. Furthermore, the article delves into the role of EVs in the tumor microenvironment, immune evasion, and as biomarkers in context to OC, offering promising therapeutic strategies to enhance survival rates for OC patients. Lastly, the article focuses on an overview of PI3K/AKT/mTOR, MAPK/ERK, and VEGFR signaling pathways in the pathophysiology of ovarian cancer.
Prostaglandin E2 (PGE2) plays a crucial role in inflammation. Non-steroidal anti-inflammatory medications are commonly utilized to alleviate pain and address inflammation by blocking the production of PGE2 and cyclooxygenase (COX). However, selective inhibition of COX can easily lead to a series of risks for cardiovascular diseases. Hence, it is imperative to discover safer and more efficient targets for reducing inflammation. Research has demonstrated that mPGES-1 serves as the final enzyme that controls the rate of prostaglandin E2 synthesis. Moreover, it is only triggered by inflammation and could serve as a possible treatment target instead of COX in cases of inflammation. 2,5-dimethylcelecoxib (DMC) can effectively inhibit mPGES-1 expression, maintain the overall balance of prostaglandins, reduce the secretion of PGE2, and, most importantly, avoid the side effects of COX inhibitors. DMC has the ability to address illnesses through the stimulation of autophagy and apoptosis, as well as the regulation of the immune microenvironment and intestinal flora. This study provides a comprehensive overview of the advancements in DMC within experimental research and offers suggestions for potential avenues of future investigation.
A planktonic population of bacteria can form a biofilm by adhesion and colonization. Proteins known as "adhesins" can bind to certain environmental structures, such as sugars, which will cause the bacteria to attach to the substrate. Quorum sensing is used to establish the population is dense enough to form a biofilm. This paper presents a comprehensive overview of our investigation into these processes, specifically focusing on Mycobacterium fortuitum, an emerging pathogen of increasing clinical relevance. In our study, we detailed the methodology employed for the proteomic analysis of M. fortuitum, as well as our innovative application of Generative Adversarial Networks (GANs). These advanced computational tools allow us to analyze complex data sets and identify patterns that might otherwise remain obscured. With a particular focus on the effectiveness of GAN, the identified proteins and their potential roles in the context of M. fortuitum's pathogenesis were discussed. The insights gained from this study can significantly contribute to our understanding of this emerging pathogen and pave the way for developing targeted interventions, potentially leading to improved diagnostic tools and more effective therapeutic strategies against M. fortuitum infection. The authors can achieve 95.43% accuracy for the generator and 87.89% for the discriminator. The model was validated by considering different Machine learning algorithms, reinforcing that integrating computational techniques with microbiological investigations can significantly enhance our understanding of emerging pathogens. Overall, this study emphasizes the importance of exploring the molecular mechanisms behind biofilm formation and pathogenicity, providing a foundation for future research that could lead to innovative solutions in combating infections caused by M. fortuitum and other similar pathogens.
Background: Breast cancer is a frequently diagnosed malignant disease and the primary cause of mortality among women with cancer worldwide. The therapy options are influenced by the molecular subtype due to the intricate nature of the condition, which consists of various subtypes. By focusing on the activation of receptors, Epidermal Growth Factor Receptor (EGFR) tyrosine kinase can be utilized as an effective drug target for therapeutic purposes of breast cancer.
Objectives: The objective of this study is to compare the underlying pharmacological properties of several modified agents to the parental Cordycepin to target and inhibit the EGFR tyrosine kinase high expression, and to discover the inhibitor with the highest affinity for this drug target to treat the breast cancer patients.
Methods: The Maestro Application of Schrödinger Suite Paid Software was initially employed for conducting extra precision (XP) structure-based virtual screening to evaluate the binding affinity of the Cordycepin and its 500 structural derivatives with the EGFR tyrosine kinase protein structure. In addition, the anti-breast cancer activity of the chosen compounds was assessed by looking at their drug-likeness and ADMET characteristics using Lipinski's rule of five along with Quantitative structure- activity relationship (QSAR) validation, the prediction of cell line anti-cancer, as well as anti- breast cancer activity of top docked scored compounds. Subsequently, the Desmond paid software- based molecular dynamics simulations (MDS) were conducted for a duration of 100 nanoseconds on the promising candidates followed by the binding free energy estimation was performed utilizing MM-GBSA analysis. To determine the stability of the protein-ligand complex, root-meansquare deviation (RMSD), root-mean-square fluctuation (RMSF), protein-ligand interactions, and other necessary parameters were evaluated from the 100 ns MDS Trajectory.
Results: Based on the overall analysis of our study, N (6)-octylamine adenosine (CID-194932) reported the optimum inhibitory potential against the EGFR tyrosine kinase protein, followed by Adenosine 5-monophosphate (CID-83862) and Cordycepin (CID-6303), which compared favorably to the control drug Vandetanib (CID-3081361).
Conclusion: Consequently, these derivative compounds Cordycepin have the potential to be utilized as lead molecules in the development of highly effective and potent EGFR tyrosine kinase inhibitors for the treatment of breast cancer patients.
Introduction: Diabetes mellitus is associated with an increased risk of atherosclerosis related to dyslipidemia. Although the terms hyperlipidemia and Diabetes Mellitus [DM] or diabetic dyslipidemia are interrelated to each other, these two conditions have some differences.
Aim: This study aimed to highlight possible mechanisms of hyperlipidemia and/or dyslipidemia in diabetic patients, which can be treated with available and newer hypolipidemic drugs. We also re-checked current specific guidelines and their recommendations on the management of patients with diabetic dyslipidemia.
Method: Comprehensive search of peer-reviewed journals was performed based on a wide range of keywords, including diabetes mellitus, dyslipidemia, hyperlipidemia, insulin resistance, free-fatty acids, cardiovascular disease, SCORE-2 calculation, statins, PCSK-9 inhibitors, and fibrates.
Discussion: Diabetic patients with dyslipidemia, including decreased HDL cholesterol, a predominance of small dense LDL particles, and increased triglyceride levels, are more prone to suffering from micro and macrovascular complications regardless of plasma fasting glucose levels. Recent guidelines suggested using the validated scoring system called SCORE2-Diabetes. Moderate to high dosages of statins, aiming for LDL cholesterol reduction, is still the cornerstone in the management of diabetic patients with dyslipidemia. Nowadays, other recommended non-statin drugs, including proprotein convertase subtilisin/kexin type 9 [PCSK9] inhibitors or other novel therapeutic agents [bempedoic acid, inclisiran], are particularly important and given place in recently published guidelines.
Conclusion: The risk of developing atherosclerotic cardiovascular diseases in people with DM is relatively higher than in patients' without DM. Optimal management of lipid parameters and achieving desired target values in lipid parameters are still a challenging issue for clinicians.
Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, but no drugs can cure this disease. Chalcones possess good antioxidant activity, anti-neuroinflammatory activity, neuroprotective effects, inhibitory effects on Aβ aggregation, and Aβ disaggregation ability. Therefore, chalcones are ideal lead compounds, and the discovery of novel anti-AD agent-based chalcones is necessary.
Methods: Hydroxy groups and aryl benzyl ether groups were introduced into chalcone scaffolds to obtain a series of 2-hydroxyl-4-benzyloxy chalcone derivatives. These derivatives were further synthesized, biologically evaluated, and docked.
Results: Most target derivatives exhibited good anti-AD activities. In particular, compound 11d had excellent inhibitory effects on self-induced Aβ1-42 aggregation (90.8% inhibition rate at 25 μM) and Cu2+ induced Aβ1-42 aggregation (93.4% inhibition rate at 25 μM). In addition, it also exhibited good Aβ1-42 fibril disaggregation ability (64.7% at 25 μM), significant antioxidative activity (ORAC = 2.03 Trolox equivalent), moderate MAO-B inhibition (IC50 = 4.81 μM), selective metal chelation, appropriate BBB permeation, and dramatic anti-neuroinflammatory ability. In addition, compound 11d relieved AD symptoms and protected hippocampal neurons in vivo.
Conclusion: Compound 11d is a promising multifunctional anti-Aβ agent.
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