Search Results (522)

Oxidative stress overwhelms cellular antioxidant defenses, causing DNA damage and pro-tumorigenic signaling that accelerate cancer initiation and progression. Electron shuttles (ESs) from phytocompounds offer precise redox control but lack quantitative benchmarks. This study aims to give a clearer definition to electron shuttles by characterizing mulberry’s electrochemical capabilities via the three defined ES criteria and deciphering its mechanism against oxidative stress-related cancer. Using double-chambered microbial-fuel-cell power metrics, cyclic voltammetry, and compartmental fermentation modeling, we show that anthocyanin shows a significant difference (p < 0.05) in power density at ≥500 µg/mL (maximum of 2.06-fold power-density increase) and reversible redox cycling (ratio = 1.65), retaining >90% activity over four fermentation cycles. Molecular docking implicates meta-dihydroxyl motifs within the core scaffold in receptor binding, overturning the view that only ortho- and para-substituents participate in bioactivity. In vitro, anthocyanins both inhibit nitric oxide release and reduce DU-145 cell viability dose-dependently. Overall, our findings establish mulberry anthocyanins as robust electron shuttles with potential for integration into large-scale bio-electrochemical platforms and targeted redox-based cancer therapies. Full article

Background: Asparaginase (ASPase) plays an important role in the therapy of acute lymphoblastic leukemia (ALL). Serum ASPase activity (SAA) can be modified and even abolished by host immune responses; therefore, current treatment guidelines recommend to monitor SAA during treatment administration. The SAA monitoring schedule needs to be carefully planned to reduce the number of samples without hampering the possibility of measuring pharmacokinetics (PK) parameters in individual patients. Complex modelling approaches, not easily applicable in common practice, have been applied in previous studies to estimate ASPase PK parameters. This study aimed to estimate PK parameters by using a simplified approach suitable for real-world settings with limited sampling. Methods: Our study was based on 434 patients treated in Italy within the AIEOP-BFM ALL 2009 trial. During the induction phase, patients received two doses of pegylated ASPase and were monitored with blood sampling at five time points, including time 0. PK parameters were estimated by using the individually available SAA measurements with simple modifications of the classical non-compartmental PK analysis. We also took the opportunity to develop and validate a series of limited sampling models to predict ASPase exposure. Results: During the induction phase, average ASPase activity at day 7 was 1380 IU/L after the first dose and 1948 IU/L after the second dose; therapeutic SAA levels (>100 IU/L) were maintained until day 33 in 90.1% of patients. The average AUC and clearance were 46,937 IU/L × day and 0.114 L/day/m², respectively. The database was analyzed for possible associations of PK parameters with biological characteristics of the patients, finding only a limited dependence on sex, age and risk score; however, these differences were not sufficient to allow any dose or schedule adjustments. Thereafter the possibility of further sampling reduction by using simple linear models to estimate the AUC was also explored. The most simple model required only two samplings 7 days after each ASPase dose, with the AUC being proportional to the sum of the two measured activities A(7) and A(21), calculated by the formula AUC = 14.1 × [A(7) + A(21)]. This model predicts the AUC with 6% average error and 35% maximum error compared to the AUC estimated with all available measures. Conclusions: Our study demonstrates the feasibility of a direct estimation of PK parameters in a real-life situation with limited and variable blood sampling schedules and also offers a simplified method and formulae easily applicable in clinical practice while maintaining a reliable pharmacokinetic monitoring. Full article

Background/Objectives: The COVID-19 pandemic highlighted the critical need for accurate predictive models to guide public health interventions and optimize healthcare resource allocation. This study evaluates how the complexity of compartmental infectious disease models influences their forecasting accuracy and utility for pandemic resource planning. Methods: We analyzed a range of compartmental models, including simple susceptible-infected-recovered (SIR) models and more complex frameworks incorporating asymptomatic carriers and deaths. These models were calibrated and tested using real-world COVID-19 data from the United States to assess their performance in predicting symptomatic and asymptomatic infection counts, peak infection timing, and resource demands. Both adaptive models (updating parameters with real-time data) and non-adaptive models were evaluated. Results: Numerical results show that while more complex models capture detailed disease dynamics, simpler models often yield better forecast accuracy, especially during early pandemic stages or when predicting peak infection periods. Adaptive models provided the most accurate short-term forecasts but required substantial computational resources, making them less practical for long-term planning. Non-adaptive models produced stable long-term forecasts useful for strategic resource allocation, such as hospital bed and ICU planning. Conclusions: Model selection should align with the pandemic stage and decision-making horizon. Simpler models are effective for rapid early-stage interventions, adaptive models excel in short-term operational forecasting, and non-adaptive models remain valuable for long-term resource planning. These findings can inform policymakers on selecting appropriate modeling approaches to improve pandemic response effectiveness. Full article

Bariatric surgery involves major changes in the anatomy and physiology of the gastrointestinal tract, which may alter oral drug bioavailability and efficacy. Phosphodiesterase-5 inhibitor (PDE5i) drugs are the first-line treatment of erectile dysfunction, a condition associated with a higher BMI. In this paper, we examine the PDE5i vardenafil for possible post-bariatric changes in solubility/dissolution and absorption. Vardenafil solubility was determined in vitro, as well as ex vivo using aspirated gastric contents from patients prior to vs. following bariatric procedures. Dissolution was tested in vitro under unoperated stomach vs. post-gastric sleeve/bypass conditions. Lastly, the gathered solubility/dissolution data were used to produce an in silico physiologically based pharmacokinetic (PBPK) model (GastroPlus^®), where gastric volume, pH, and transit time, as well as proximal GI bypass (when relevant) were all adjusted for, evaluating vardenafil dissolution, gastrointestinal compartmental absorption, and pharmacokinetics before vs. after different bariatric procedures. pH-dependent solubility was demonstrated for vardenafil with low (pH 7) vs. high solubility (pH 1–5), which was confirmed ex vivo. The impaired dissolution of all vardenafil doses under post-gastric bypass conditions was demonstrated, contrary to complete (100%) dissolution under pre-surgery and post-sleeve gastrectomy conditions. Compared to unoperated individuals, PBPK simulations revealed altered pharmacokinetics post-gastric bypass (but not after sleeve gastrectomy), with 30% lower peak plasma concentration (C_max) and 40% longer time to C_max (T_max). Complete absorption after gastric bypass is predicted for vardenafil, which is attributable to significant absorption from the large intestine. The biopharmaceutics and PBPK analysis indicate that vardenafil may be similarly effective after sleeve gastrectomy as before the procedure. However, results after gastric bypass question the effectiveness of this PDE5i. Specifically, vardenafil’s onset of action might be delayed and unpredictable, negatively affecting the practicality of the intended use. Full article

Face recognition systems often falter when deployed in uncontrolled settings, grappling with low light, unexpected occlusions, motion blur, and the degradation of sensor signals. Most contemporary algorithms chase raw accuracy yet overlook the pragmatic need for uncertainty estimation and multispectral reasoning rolled into a single framework. This study introduces HUE-Net—a Human-centric, Uncertainty-aware, Event-fused Network—designed specifically to thrive under severe environmental stress. HUE-Net marries the visible RGB band with near-infrared (NIR) imagery and high-temporal-event data through an early-fusion pipeline, proven more responsive than serial approaches. A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. Central to the architecture is the perturbed multi-branch variational module, which distills probabilistic identity embeddings while delivering calibrated confidence scores. Complementing this, an Adaptive Spectral Attention mechanism dynamically reweights each stream to amplify the most reliable facial features in real time. Unlike previous efforts that compartmentalize uncertainty handling, spectral blending, or computational thrift, HUE-Net unites all three in a lightweight package. Benchmarks on the IJB-C and N-SpectralFace datasets illustrate that the system not only secures state-of-the-art accuracy but also exhibits unmatched spectral robustness and reliable probability calibration. The results indicate that HUE-Net is well-positioned for forensic missions and humanitarian scenarios where trustworthy identification cannot be deferred. Full article

Poverty is a multifaceted phenomenon impacting millions globally, defined by a deficiency in both material and immaterial resources, which consequently restricts access to satisfactory living conditions. Comprehensive poverty analysis can be accomplished through the application of mathematical and modeling techniques, which are useful in understanding and predicting poverty trends. These models, which often incorporate principles from economics, stochastic processes, and dynamic systems, enable the assessment of the factors influencing poverty and the effectiveness of public policies in alleviating it. This paper introduces a mathematical compartmental model to investigate poverty within a population ($\psi \left(t\right)$), considering the effects of immigration, crime, and incarceration. The model aims to elucidate the interconnections between these factors and their combined impact on poverty levels. We begin the study by ensuring the mathematical validity of the model by demonstrating the uniqueness of a positive solution. Next, it is shown that under specific conditions, the probability of poverty persistence approaches certainty. Conversely, conditions leading to an exponential reduction in poverty are identified. Additionally, the semigroup associated with our model is proven to possess the Feller property, and its distribution has a unique invariant measure. To confirm and validate these theoretical results, interesting numerical simulations are performed. Full article

The Shu-Sarysu Basin in central-southern Kazakhstan remains one of the underexplored gas-prone provinces, with 12 discovered gas fields including Amangeldy (884 Bcf) and Pridorozhnoye (225 Bcf). In the context of global energy transition, such basins require integrated geological assessment to constrain exploration potential. Historical studies within the region were spatially limited and prematurely discontinued, resulting in fragmented datasets and a lack of modern interpretation. This review reassesses published geological data within a petroleum systems framework, applying contemporary geodynamic and stratigraphic concepts. Analysis shows that tectonostratigraphic evolution of the basin during Devonian–Permian time (390–250 Ma) favored formation of mature, gas-prone systems within structurally compartmentalized troughs, with effective source, reservoir, and seal configurations. Building on these findings, a three-tier classification of exploration zones is proposed based on system maturity, trap integrity, and gas shows, reflecting geological success probability. This provides a basis for prioritizing future exploration despite limited seismic and drilling coverage in many areas. Recommended priorities include digitization of archival data, structural modeling, modern geochemical and diagenetic analysis, and focused evaluation of promising areas to support future exploration. Full article

Coacervate is a form of liquid–liquid phase separation (LLPS) in which a solution containing one or more charged components spontaneously separates into two immiscible liquid phases. Due to their ability to mimic membraneless cellular environments and their high biocompatibility, coacervates have found broad applications across various fields of life sciences. This review provides a comprehensive overview of recent advances in biomolecule-based coacervation for biotechnological and biomedical applications. Encapsulation via biomolecule-based coacervation enables high encapsulation efficiency, enhanced stability, and the sustained release of cargos. In the field of tissue engineering, coacervates not only support cell adhesion and proliferation but also serve as printable bioinks with tunable rheological properties for 3D bioprinting. Moreover, biomolecule-based coacervates have been utilized to mimic membraneless organelles, serving as experimental models to understand the origin of life or investigate the mechanisms of biochemical compartmentalization. This review discusses the mechanisms of coacervation induced by various types of biomolecules, evaluates their respective advantages and limitations in applied contexts, and outlines future research directions. Given their modularity and biocompatibility, biomolecule-based coacervates are expected to play a pivotal role in next-generation therapeutic development and the construction of controlled tissue microenvironments, especially when integrated with emerging technologies. Full article

Background/Objectives: Stroke survivors often develop asymmetric gait patterns that may lead to abnormal knee joint loading and potentially increased risk of osteoarthritis. This study aimed to investigate differences in knee joint loading between paretic and non-paretic limbs during walking in individuals post-stroke. Methods: Twenty-one chronic stroke survivors underwent three-dimensional gait analysis. A modified musculoskeletal model with a specialized knee mechanism was used to estimate medial and lateral tibiofemoral contact forces during the stance phase. Statistical parametric mapping was used to identify significant differences in joint kinematics, kinetics, and contact forces between limbs. Stepwise regression analyses examined relationships between knee moments and compartmental contact forces. Results: Significant differences in knee loading were observed between limbs, with the non-paretic limb experiencing higher medial compartment forces during early stance (6.7–15.1%, p = 0.001; 21.9–30.7%, p = 0.001) and late stance (72.3–93.7%, p < 0.001), and higher lateral compartment forces were recorded during pre-swing (86.2–99.0%, p < 0.001). In the non-paretic limb, knee extensor moment was the primary predictor of first peak medial contact force (R² = 0.573), while knee abductor moment was the primary predictor in the paretic limb (R² = 0.559). Conclusions: Musculoskeletal modeling revealed distinct asymmetries in knee joint loading between paretic and non-paretic limbs post-stroke, with the non-paretic limb experiencing consistently higher loads, particularly during late stance. These findings suggest that rehabilitation strategies should address not only paretic limb function but also potentially harmful compensatory mechanisms in the non-paretic limb to prevent long-term joint degeneration. Full article

Background/Objectives: Pneumococcal conjugate vaccines (PCVs) were first introduced in the pediatric UK National Immunization Programme (NIP) in 2006 and subsequently led to a significant decline in invasive pneumococcal disease (IPD). In 2020, the UK NIP reduced the pediatric PCV dosing schedule from two infant doses and one toddler dose (2 + 1) to one infant dose and one toddler dose (1 + 1). This analysis evaluated the public health impact of pediatric vaccination with PCV15 versus PCV13 under a 1 + 1 schedule. Methods: A population-level compartmental model was previously adapted to the UK setting. The impact on the IPD incidence of vaccination with PCV15 versus PCV13 under a 1 + 1 schedule was evaluated over a 20-year time horizon. The uncertainty regarding the vaccine efficacy (VE) of PCV13 and PCV15 under a 1 + 1 schedule was investigated through a probabilistic sensitivity analysis, i.e., the PCV VE under a 1 + 1 schedule was assumed to be 0–24% lower than the PCV VE under a 2 + 1 schedule. Results: Relative to the initial IPD incidence, vaccination with PCV13 and PCV15 under a 1 + 1 schedule resulted in the IPD incidence in children <2 years old increasing by 11.1% (95% region: 8.4–14.5%) and 3.5% (0.2–7.7%), respectively, over the time horizon. At the end of the time horizon, in the overall population, PCV15 would lead to a 6.0% lower IPD incidence than PCV13 (10.70 IPD cases per 100,000 versus 11.38 per 100,000, respectively). Conclusions: Switching from PCV13 to PCV15 for routine pediatric vaccinations under the 1 + 1 dosing schedule in the UK led to a lower IPD incidence in both the pediatric and overall populations. Full article

This study investigates the use of Neural Ordinary Differential Equations (NODEs) as an alternative to traditional compartmental models and Nonlinear Mixed-Effects (NLME) models for drug concentration prediction in pharmacokinetics. Unlike standard models that rely on strong assumptions and often struggle with high-dimensional covariate relationships, NODEs offer a data-driven approach, learning differential equations directly from data while integrating covariates. To evaluate their performance, NODEs were applied to a real-world Dalbavancin pharmacokinetic dataset comprising 218 patients and compared against a two-compartment model and an NLME within a cross-validation framework, which ensures an evaluation of robustness. Given the challenge of limited data availability, a data augmentation strategy was employed to pre-train NODEs. Their predictive performance was assessed both with and without covariates, while model explainability was analyzed using Shapley additive explanations (SHAP) values. Results show that, in the absence of covariates, NODEs performed comparably to state-of-the-art NLME models. However, when covariates were incorporated, NODEs demonstrated superior predictive accuracy. SHAP analyses further revealed how NODEs leverage covariates in their predictions. These results establish NODEs as a promising alternative for pharmacokinetic modeling, particularly in capturing complex covariate interactions, even when dealing with sparse and small datasets, thus paving the way for improved drug concentration predictions and personalized treatment strategies in precision medicine. Full article

Three-dimensional (3D) cell culture models are widely used to provide a more physiologically relevant microenvironment in which to host and study desired cell types. These models vary in complexity and cost, ranging from simple and inexpensive to highly sophisticated and costly systems. In this study, we introduce a novel translucent multi-compartmentalized stacked multilayered nanocellulose scaffold and describe its fabrication, characterization, and potential application for co-culturing multiple cell types. The scaffold consists of bacterial nanocellulose (BNC) layers separated by interlayers of a lower density of nanocellulose fibers. Using this system, we co-cultured the MDA-MB-231 cell line with two tumor-associated cell types, namely BC-CAFs and M2 macrophages, to simulate the tumor microenvironment (TME). Cells remained viable and metabolically active for up to 15 days. Confocal microscopy showed no signs of cell invasion. However, BC-CAFs and MDA-MB-231 cells were frequently observed within the same layer. The expression of breast cancer-related genes was analyzed to assess the downstream functionality of the cells. We found that the E-cadherin expression was 20% lower in cancer cells co-cultured in the multi-compartmentalized scaffold than in those cultured in 2D plates. Since E-cadherin plays a critical role in preventing the initial dissociation of epithelial cells from the primary tumor mass and is often downregulated in the tumor microenvironment in vivo, this finding suggests that our scaffold more effectively recapitulates the complexity of a tumor microenvironment. Full article

Huanglongbing (HLB), a globally devastating citrus disease, demands sophisticated mathematical modeling to decipher its complex transmission dynamics and inform optimized disease management protocols. This investigation develops an innovative compartmental framework that simultaneously incorporates two critical factors in HLB epidemiology: saturated removal rates of infected citrus trees and behavioral bias in vector movement patterns. Our study delves into the dynamics of non-spatial systems by analyzing the basic reproduction numbers, equilibria, bifurcation phenomena, and the stability of these equilibria. Additionally, we explore the impact of spatial factors on system stability. Results indicate that when the basic reproduction number $R_0<1$, the system may exhibit bistable behavior, while $R_0>1$ leads to a unique stable equilibrium. Notably, vector bias significantly enhances the likelihood of forward bifurcation, and the delay in the removal of diseased trees increases the risk of backward bifurcation. However, reaction–diffusion processes do not alter the stability of the system’s equilibria, and the spatial system lacks complex dynamic properties. This research offers valuable insights into the mechanisms driving HLB transmission and provides a foundation for developing effective control strategies. Full article

Campylobacteriosis has been described as an ever-changing disease and health issue that is rather dangerous for different population groups all over the globe. The World Health Organization (WHO) reports that 33 million years of healthy living are lost annually, and nearly one in ten persons have foodborne illnesses, including Campylobacteriosis. This explains why there is a need to develop new policies and strategies in the management of diseases at the intergovernmental level. Within this framework, an advanced stochastic fractional delayed model for Campylobacteriosis includes new stochastic, memory, and time delay factors. This model adopts a numerical computational technique called the Grunwald–Letnikov-based Nonstandard Finite Difference (GL-NSFD) scheme, which yields an exponential fitted solution that is non-negative and uniformly bounded, which are essential characteristics when working with compartmental models in epidemic research. Two equilibrium states are identified: the first is an infectious Campylobacteriosis-free state, and the second is a Campylobacteriosis-present state. When stability analysis with the help of the basic reproduction number $R_0$ is performed, the stability of both equilibrium points depends on the $R_0$ value. This is in concordance with the actual epidemiological data and the research conducted by the WHO in recent years, with a focus on the tendency to increase the rate of infections and the necessity to intervene in time. The model goes further to analyze how a delay in response affects the band of Campylobacteriosis spread, and also agrees that a delay in response is a significant factor. The first simulations of the current state of the system suggest that certain conditions can be achieved, and the eradication of the disease is possible if specific precautions are taken. The outcomes also indicate that enhancing the levels of compliance with the WHO-endorsed SOPs by a significant margin can lower infection rates significantly, which can serve as a roadmap to respond to this public health threat. Unlike most analytical papers, this research contributes actual findings and provides useful recommendations for disease management approaches and policies. Full article

Background: Inhalational antibiotics have been used effectively to treat chronic diseases such as Pseudomonas aeruginosa infections associated with cystic fibrosis. This approach may enhance treatment options for difficult-to-treat, acute pneumonic diseases. Liposomal encapsulated ciprofloxacin (Lipoquin and/or Apulmiq) has provided protection in murine models of plague, anthrax, Q fever and tularemia. Development of the ability to deliver these drugs to nonhuman primates (NHPs) would enable further extrapolation of the data observed in small animal models of infection to humans. Methods: In this study, the methodology was established to deliver Apulmiq to common marmosets (Callithrix jacchus). Marmosets were anaesthetised with a novel, reversible anaesthetic comprising fentanyl, medetomidine and midazolam (FMM). They were placed into plethysmography tubes with their heads in an exposure chamber. The LC Sprint jet nebuliser or Pari eFlow Rapid nebuliser were used to aerosolise Apulmiq into the exposure chamber. Animals were euthanised after dosing and the concentration of ciprofloxacin was assessed in the plasma and lungs of the animals. Results: Non-compartmental pharmacokinetic analysis determined that a 30 min exposure of drug was required to reach a human-equivalent target dose of 0.8 mg/kg body weight in the lungs. Conclusions: This approach can now be used to assess the efficacy of inhalational liposomal ciprofloxacin in NHP infection models. Full article