The objective of the present investigation was to preparesolid lipid nanoparticles of the BCS class II drug Glimepiride (GP) comprising coconut oil and Poloxamer 407, for improving the in vitro dissolution profile of the drug.Additionally, formulations were statistically optimized (32 full factorial experimental design) for focusing on the impact of independent variables on dependent factors of the nanoformulation (particle size, encapsulation efficiency, and drug content) through response surface methodology. Orally given lipophilic drug moieties, especially those in the BCS class II and IV categories, may have a number of issues that lead to poor absorption, bioavailability, and significant intra-and inter-subject variance.A biocompatible colloidal lipidic nanocarrier, solid lipid nanoparticles, are considered as potent substitute against traditional polymeric nanocarriers for delivering BCS class II and IV categories of drugs. Glimepiride solid lipid nanoparticles was prepared by solvent evaporation method and then physico-chemical parameters, thermal analysis, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analysis, and stability profile of the formulations were assessed. The optimized Glimepiride SLNsdemonstrated particle size of 329.58 ± 0.63 nm, significant encapsulation efficiency (91.41 ± 0.13

Targeted therapies for BRAF-mutant melanoma have extended median survival but often lead to drug resistance. Preclinical studies revealed that some resistant melanoma cells exhibit “drug addiction”, growing poorly without treatment, suggesting intermittent dosing could exploit this vulnerability. However, clinical trials of fixed intermittent schedules showed continuous therapy outperformed cycling regimens. We address this apparent contradiction using optimal control theory to determine treatment schedules that minimize both tumor burden and drug-related side effects. Building on a bilinear control model of susceptible and treatment-addicted resistant cells, we apply the Pontryagin maximum principle to analytically characterize optimal regimens and implement a sequential quadratic Hamiltonian (SQH) numerical method with proven finite-step convergence. Our results demonstrate that optimal schedules never cycle: they begin with full-dose treatment, potentially transition to a singular control (intermediate effective dose), then cease treatment permanently, never returning to active dosing. This monotone structure, rather than periodic on-off cycling, aligns with clinical trial findings showing continuous therapy superiority. The treatment cost weight parameter in the objective functional determines whether singular regimens emerge. These findings provide mathematical insight into why fixed cycling fails and suggest that monotone dose-reduction strategies may better exploit resistance vulnerabilities than predetermined intermittent schedules.

AIMS:This study focused on developing an effective Rheumatoid Arthritis (RA) treatment by creating diacerein-loaded nanoparticles. The goal was to create a more efficient RA treatment with fewer side effects, something conventional treatments fail to achieve. MATERIALS AND METHODS:Using the solvent evaporation technique, the researchers produced nanoparticles and formulated them, which they optimized using DOE (Design of Experiments). They also analyzed their nanoparticle formulations through three methods: particle size, entrapment efficiency, and in vitro drug release. RESULTS:Their results indicate controlled drug release and desirable physical-chemical properties that should lead to greater bioavailability, which is a good thing because it leads to less waste. The in-vitro studies show that when using diacerein delivered through nanoparticles, there will be a greater anti-inflammatory response from chondrocytes than if diacerein were administered alone. CONCLUSIONS:Therefore, this study provides evidence that nanoparticles can be used to treat RA, improving drug effectiveness, decreasing systemic toxicity, and improving patient compliance.

BACKGROUND:Cancer pain is a complex, debilitating symptom involving both nociceptive mechanisms from tissue damage, tumour acidification, and pro-inflammatory mediators, and neuropathic components from tumour invasion, chemo- or radiation-induced nerve injury, altered ion channels, and glial activation. This peripheral and central sensitisation makes conventional analgesics inadequate and toxic. OBJECTIVES AND METHODOLOGY:Engineered small extracellular vesicles (EVs) can offer a promising nanomedicine solution. These biocompatible, nanoscale vesicles cross biological barriers, including the blood-brain barrier, and can be loaded with therapeutic cargo like anti-inflammatory miRNAs, neurotrophic factors, or analgesics via parent cell engineering or direct loading and functionalization. CONCLUSION:Targeted EV delivery can enable localised suppression of pro-nociceptive inflammation, nerve repair, precise analgesic release, and tumour burden reduction, addressing cancer pain's molecular mechanisms for more effective pain relief.

This paper presents a novel methodological framework for deriving personalized and optimal treatment strategies for esophageal cancer (EC). The proposed approach models the interaction dynamics between epidermal growth factor ligands and epidermal growth factor receptor (EGFR) binding in EC cells as an It & ocirc; stochastic process. The evolution of this stochastic system is described by a Fokker-Planck (FP) equation governing the associated probability density function. Patient-specific parameters, which appear as coefficients in the FP equation, are inferred from observed data by solving a nonlinear constrained optimization problem. To facilitate robust and accurate parameter estimation, we employ physics-informed neural networks (PINNs), which allow highfidelity solutions of the FP equation even under conditions of sparse or noisy data. PINNs are also used to approximate the solution of the FP equation as part of the parameter estimation pipeline. To compute optimal treatment strategies, we formulate and solve an optimal control problem aimed at determining the best open-loop dosage protocol. This is accomplished using a gradient-free sequential quadratic Hamiltonian (SQH) method, which is based on the Pontryagin's maximum principle. Numerical simulations demonstrate that the proposed framework enables a robust simulation of optimal dosage strategies tailored to individual EC patient profiles undergoing monoclonal antibody (mAb) therapy.

Liquid-Liquid Phase Separation (LLPS) plays a crucial role in the assembly and regulation of inflammatory signalling ensembles. LLPS is a phenomenon where biomacromolecules, such as proteins and RNA, separate from a homogeneous solution, and form distinct liquid droplets or condensates. These condensates can orchestrate various cellular processes. LLPS can modulate signalling pathways by creating distinct microenvironments that facilitate or inhibit protein-protein interactions. Misfolding and aggregation of specific proteins associated with LLPS can interfere with normal cellular functions, and lead to various forms of cell death. Aberrant LLPS has now been linked to various diseases, including hypersensitivity reactions, and autoimmune disorders. Researchers are nowadays exploring ways to disrupt LLPS, which could potentially treat such diseases associated with aberrant phase separation. Pharmacological targeting of LLPS is thus becoming a promising area of research, with LLPS based therapies modulating immunological crosstalk and signal transduction, having potential applications in treating various disorders. Pathological condensates can be disrupted by reducing multivalency with selective inhibitors or using small molecule drugs to reverse solidification. To overcome the high concentration requirements for dissolution, nanomedicine is essential. Nanoparticle encapsulated drug can ensure precise delivery and high local payloads, effectively dissolving these condensates. This review highlights the biophysical and biochemical aspects of phase separation and biomolecular condensate formation, and how understanding of their components and mechanisms can be leveraged to develop therapeutic strategies against pathological immunological condensates.

We propose an improved estimation method for the destructive cure rate model by introducing a generic maximum likelihood algorithm, the sequential quadratic Hamiltonian (SQH) scheme, which employs a gradient-free optimization approach. The SQH algorithm is applied to the destructive cure model with exponentially weighted Poisson competing risks, and its performance is evaluated through a comprehensive simulation study. Specifically, we compare the model-fitting accuracy of SQH with the recently developed conjugate gradient line search (CGLS) algorithm. Given that the CGLS method has been shown to outperform the widely used expectation-maximization algorithm and other optimization routines available in R (e.g. optim, nlm, and Rcgmin), our focus is on assessing whether the SQH algorithm can offer further improvements. Simulation results show that the SQH algorithm yields parameter estimates with consistently lower bias and root mean square error, resulting in more accurate and precise cure rate estimation. Furthermore, due to its gradient-free nature, the SQH algorithm requires less CPU time than CGLS. These advantages position the SQH algorithm as a preferred estimation method over CGLS for the destructive cure rate model. To demonstrate its practical utility, we apply the SQH algorithm to a well-known melanoma dataset and present the analysis results.

Lung cancer remains foremost cause of cancer-related deaths worldwide. Over the past decade, conventional monotherapies frequently limited by therapeutic resistance and dose-limiting toxicity. To address these limitations, the present study aimed to design and evaluate anti-PD-L1 peptide-conjugated temozolomide self-assembled prodrug nanoparticles (PD-NPs) in lung cancer chemoimmunotherapy. PD-NPs were synthesized and extensively characterized by different analytical techniques. Molecular docking and molecular dynamic simulation studies further validated the protein-ligand interactions and demonstrated the structural stability of PD-NPs in physiological conditions. In vitro studies, including cellular viability, clonogenicity, cellular uptake, scratch assay, apoptosis, flow cytometry, and western blotting were performed in A549 and NCI-H460 cell lines. Subsequently in vivo toxicity studies were performed and established a safe and tolerable therapeutic range for further investigation. Chemoimmunotherapeutic evaluation were conducted in B16-F10-induced lung tumor bearing C57BL/6 allograft mice model. Biodistribution study, histopathology, immunohistochemistry, qRT-PCR, ELISA, microarray analysis, cell proliferation study, and apoptosis assay were performed to elucidate therapeutic efficacy of PD-NPs. PD-NPs exhibited favorable physicochemical profile with a zeta potential of +32.2 mV, and uniform nanoscale size (DLS: 208.1 ± 3.7 nm and TEM: 206.2 ± 3.5 nm), facilitating effective tumor microenvironment penetration. In vitro studies demonstrated that PD-NPs significantly reduced cancer cell viability. In vivo studies revealed significant tumor regression, restoration of typical lung tissue architecture, downregulation of oncogenic and immunosuppressive markers (KRAS, MEK, FOXM1, and PD-L1), and enhanced CD8 T-cell activation. Overall, PD-NPs demonstrated potent chemoimmunotherapeutic efficacy by modulating both carcinogenic and immune-regulatory pathways, highlighting their potential as a next-generation therapeutic platform for lung cancer chemoimmunotherapy.

This study aimed to design and optimize anti-PD-L1 peptide-conjugated mocetinostat prodrug nanoparticles (PD-NPs) as a targeted chemoimmunotherapeutic strategy. Rationale design and bioinformatic analysis were performed to determine the binding affinity of PD-NPs with targeted proteins through molecular docking and molecular dynamic simulation. PD-NPs were synthesized by self-assembling EDC/NHS coupling reaction, characterized by different spectroscopical techniques. Enzyme-responsive drug release was assessed through in-vitro drug release study in presence and absence of cathepsin B, and quantified by HPLC and UV–visible spectroscopy. In-vitro studies were performed in A549, NCI-H1975 and NCI-H460 cells to assess HDAC activity, PD-1/PD-L1 binding affinity, intracellular cathepsin B levels, and immune activation. In-vivo toxicity study, hemolysis and pharmacokinetic study were performed to evaluate biological safety profile. Biodistribution, and histopathological evaluations were performed in B16-F10-induced lung tumor-bearing mice to assess therapeutic efficacy of PD-NPs. The optimized zeta potential value was + 23.26 mV and Polydispersity Index (PDI) 0.21, indicating colloidal stability. TEM analysis demonstrated that PD-NPs has uniform spherical morphology. High drug loading efficiency was estimated to be 83.23 ± 2.5

Gastric cancer remains the fifth most common cause of cancer-related deaths worldwide, underscoring the critical need for continued research to improve therapeutic outcomes. Coordination of flavonoids with metal ions results in the creation of distinctive organometallic complexes that often demonstrate improved stability and therapeutic efficacy. In the present study, a novel Isoliquiritigenin ruthenium-p-cymene complex was synthesized and optimized by utilizing various analytical techniques. The chemotherapeutic effects of complex were assessed in AGS and MKN-45 human gastric cancer cell lines through cytotoxicity, clonogenic, wound-healing, apoptosis, cell cycle, and western blot assays. In vivo safety was evaluated by acute and subacute toxicity studies, while pharmacokinetic profiling was performed through HPLC in rats. Chemotherapeutic efficacy was further investigated in MNNG-induced gastric cancer rat model over 24 weeks, followed by histopathological, immunohistochemical, qRT-PCR, Ki-67 proliferation, and TUNEL apoptosis analyses. The complex demonstrated dose-dependent cytotoxicity, reduced colony formation and cell migration, induced cell cycle arrest at S/G2 phases, and triggered apoptosis in cancer cells. Western blot analysis revealed significant modulation of β-catenin, FAK, PI3K, DNMT1, and p53 expression. Toxicological evaluation confirmed that doses of 5, 10, and 20 mg/kg were well tolerated. Pharmacokinetic analysis indicated moderate oral absorption (Tmax ≈ 3 h), AUC₀–₂₄ ≈ 25 ± 3.0 µg·h/mL, and half-life 16 ± 2.0 h. In vivo treatment demonstrated that the anticancer efficacy of the complex in MNNG-induced gastric cancer through downregulation of AKT, mTOR, β-catenin, c-Myc, GRB2, and Ki-67 expression while enhancing caspase-3-mediated apoptosis. These findings suggest that Isoliquiritigenin ruthenium-p-cymene complex exerts promising chemotherapeutic effects by modulating proliferative and apoptotic signaling pathways in gastric cancer.

Lung cancer continues to be a major contributor to global cancer mortality due to extensive tumor heterogeneity, drug resistance, and limited efficacy of conventional therapies. To address these limitations, the present study aimed on rational design and development of anti-CTLA4 peptide-conjugated gemcitabine prodrug nanoparticles (PD-NPs) for targeted lung cancer chemoimmunotherapy. PD-NPs was synthesized by conjugating an anti-CTLA4 peptide (ARHPSWYRPFEGCG), a cathepsin B cleavable linker (FRRG) to the free amino group of gemcitabine via a single step amide bond formation, enabling the formation of stable self-assemble PD-NPs. In-silico evaluation was carried out by molecular docking study, while physicochemical properties were confirmed through different spectroscopical analyses. In-vitro studies, including cell viability, clonogenic, scratch, and apoptosis assay by flow cytometry were performed in A549 and NCI-H460 cell lines. In-vivo toxicity and biodistribution study were conducted to determine therapeutic doses. Subsequently chemoimmunotherapeutic efficacy of PD-NPs was evaluated in B16-F10 induced lung tumor mice model. PD-NPs exhibited a zeta potential of ± 34.4 mV, an average particle size of 229.8 ± 2.1 nm, and polydispersity index 0.2, suggesting a stable and monodisperse formulation. In vitro studies demonstrated potent cytotoxic effect, suppression of clonogenicity, inhibition of cellular migration, apoptosis induction, and inhibition of carcinogenic protein expression. In vivo treatment resulted in significant tumor regression, restoration of lung tissue architecture, activating CD8 expression, and altering TNF-α expression following PD-NPs treatment. Overall, these findings highlight the therapeutic potential of enzyme responsive, anti-CTLA4 peptide conjugated gemcitabine prodrug nanoparticles as a promising strategy for targeted lung cancer chemoimmunotherapy.

In this article, we introduce and study a set of generalized Gaussian beam Radon transforms (GbRt) acting on tensor fields in ℝ^2. The operators considered include longitudinal, transverse, mixed GbRts, along with their integral moments. These operators extend the corresponding notions of the classical generalized Radon transforms for tensor fields. We establish reconstruction results for vector and symmetric 2-tensor fields using appropriate combinations of the defined transforms. This work extends a recent study on the recovery of scalar functions from their GbRt to the recovery of vector and tensor fields from analogously defined generalized GbRts.

The inflammasome, a supramolecular complex, plays important role in the gut-brain axis (GBA), acting as a sensor of cellular stress and danger signals. It activates inflammatory responses, and its dysregulation has been implicated in neurological diseases. Various inflammasomes, such as NLRP3, NLRC4, and AIM2, contribute to disease progression by promoting inflammation, tissue damage, and oxidative stress. These inflammasomes recognize and get activated by different stimuli, such as ATP, fluctuations in ion fluxes, nucleic acids, and molecular signatures associated with pathogens, eventually releasing inflammatory cytokines, and triggering inflammatory responses. Although the inflammatory response is orchestrated, sometimes it might happen that its dysregulation causes excessive and sustained inflammation leading to cellular damage and tissue injuries. Inhibiting or modulating these inflammasomes can therefore, provide therapeutic benefits. Targeting NLRP3, like MCC950, has shown promise in reducing inflammation. Some natural compounds have also been found to inhibit NLRP3 and other inflammasomes. Interestingly, regulating the gut microbiome can impact inflammasome activation, and reduce unsolicited inflammation. This review explores the bidirectional communication network between the gut and brain, and emphasizes the importance of understanding the role of inflammasomes in the GBA which can lead to novel therapeutic strategies for neurological diseases like Multiple Sclerosis, Parkinson's, and Alzheimer's.

We propose an enhanced estimation method for the Box-Cox transformation (BCT) cure rate model parameters by introducing a generic maximum likelihood estimation algorithm, the sequential quadratic Hamiltonian (SQH) scheme, which is based on a gradient-free approach. We apply the SQH algorithm to the BCT cure model and, through an extensive simulation study, compare its model fitting results with those obtained using the recently developed non-linear conjugate gradient (NCG) algorithm. Since the NCG method has already been shown to outperform the well-known expectation maximization algorithm, our focus is on demonstrating the superiority of the SQH algorithm over NCG. First, we show that the SQH algorithm produces estimates with smaller bias and root mean square error for all BCT cure model parameters, resulting in more accurate and precise cure rate estimates. We then demonstrate that, being gradient-free, the SQH algorithm requires less CPU time to generate estimates compared to the NCG algorithm, which only computes the gradient and not the Hessian. These advantages indicate that the SQH algorithm performs favorably compared with the NCG method for estimation in the BCT cure model. Finally, we apply the SQH algorithm to analyze a well-known melanoma dataset and present the results.

The hair growth is a highly controlled biological process, governed by the cycles of the hairs in anagen (growth), catagen (regression) and telogen (rest period). Breaks in it could lead to hair thinning and loss, that is why a response there's certainly a need for effective treatment. Current methods such as topical minoxidil, oral finasteride and modern techniques including platelet-rich plasma therapy or hair transplantation work by improving the functioning of hair follicles to prolong their growth phase. In this instance, the aim of this article is to mainly review about emerging mechanobiological strategies such as electrical stimulation, microneedling, microcurrent therapy conjugated with nanotechnology, low-frequency techniques that provide a context for futuristic non-invasive approaches.

One of the most effective strategies for treating cancer is the discovery of molecular medicines that block particular processes in the growth of cancer cells.Since many malignancies exhibit over-activation of the epidermal growth factor receptor (EGFR), targeting EGFR and its downstream signaling cascades is thought to be a sensible and beneficial strategy for cancer treatment. Here, five novel indanone-pyrazole hybridized Schiff base scaffolds were designed and subjected to computational evaluation to identify potential EGFR inhibitors. In silico docking studies were conducted to evaluate the binding affinities and interactions of the designed congeners with the 1M17 protein active site. Furthermore, Lipinski analysis, ADMET profiling, toxicity profiling, molecular simulation study werecarried out of these novel compounds. According to the docking study, all lead compounds demonstrated strong binding energy (ranging from − 7.6 to − 8.6 kcal/mol) than native and standard drug (Neratinib). These Schiff base congeners revealed favorable physicochemical properties, high gastrointestinal absorption, low toxicity, and compliance with the Lipinski rule of five, suggesting their potential as effective therapeutic agents.The results of this study are of great interest for the rational design of new EGFR-targeted anticancer drugs and point to the need for additional in vitro and in vivo studies.

Lung cancer is the leading cause of life lost in recent years and is associated with the highest economic burden relative to other cancer types. Research remains at the cornerstone of achieving the improved treatment outcomes of lung cancer. The flavonoid-metal compound, fisetin ruthenium-p-cymene complex, illustrates significant less toxic profile which was assessed using acute and subacute toxicological studies and geno-toxicological study. In this regard, this study investigated the chemotherapeutic effects of fisetin ruthenium-p-cymene compound on lung cancer through in vivo and in vitro approaches. Cytotoxicity study, apoptotic assay and caspase-3 estimation by flow cytometry, and western blot study has been implemented on A549 and NCI-H460 cell lines. Subsequently, the benzo[α]pyrene-induced lung cancer in mice model has been evaluated in in vivo approach to demonstrate chemotherapeutic activity. As a result, downregulation of GRB2/RAS/MEK/DNMT1-mediated carcinogenic signaling pathway with upregulation of tumor suppressor gene PTEN (phosphatase and tensin homolog) expression has been depicted in western blot analysis. Cell cycle arrest and caspase-3 levels has been estimated by flow cytometry study. Followed by in vivo chemotherapeutic study, significant restoration of normal cellular architecture has been shown due to the treatment with complex. Immunohistochemical analysis has been represented the downregulation of EGFR, Ras, MEK, and DNMT1 genes expression and increase the expression of PTEN in association with the initiation of apoptosis. Consequently, the fisetin ruthenium-p-cymene complex has been established as a potent chemotherapeutic drug through the alteration of GRB2/RAS/MEK/DNMT1 transduction mechanism in tumor microenvironment linked to induce apoptotic events.

Optimal control theory has long been a cornerstone of mathematical modeling and decision-making across disciplines such as engineering, economics, and the physical sciences. Yet, as the complexity of control systems continues to grow, so does the demand for more robust and efficient computational techniques to solve these problems. Alfio Borzì’s The Sequential Quadratic Hamiltonian Method: Solving Optimal Control Problems addresses this challenge head-on, introducing a groundbreaking numerical optimization procedure, the sequential quadratic Hamiltonian (SQH) method. This book not only builds upon the theoretical framework established by the Pontryagin maximum principle (PMP), but also offers a practical computational tool that is both versatile and robust. With applications ranging from differential Nash games to deep learning via residual neural networks, the book is as much a testament to the SQH method’s adaptability as it is to its computational power. In this review, we describe the book’s structure, its significant contributions to the field of applied and computational mathematics, and its interdisciplinary relevance. We explore how the SQH method redefines the landscape of optimal control, offering new pathways for both theoretical investigation and practical implementation.