Search Results (289)

A thermodynamically consistent model framework to describe ion transport in nanopores is presented. The continuum model unifies electro-diffusion and selective ion transport and extends the classical Poisson–Nernst–Planck (PNP) system for an idealized incompressible mixture by including finite ion size and solvation effects. Special emphasis is placed on the consistent modeling of the selectivity filter within the pore. It is treated as an embedded domain in which the constituents can change their chemical properties and mobility. Using this framework, we achieve good agreement with an experimentally observed current–voltage (IV) characteristic for an L-type selective calcium ion channel for a range of ion concentrations. In particular, we show that the model captures the experimentally observed anomalous mole fraction effect (AMFE). As a result, we find that calcium and sodium currents depend on the surface charge in the selectivity filter, the mobility of ions and the available space in the channel. Our results show that negative charges within the pore have a decisive influence on the selectivity of divalent over monovalent ions, supporting the view that AMFE can emerge from competition and binding effects in a multi-ion environment. Furthermore, the flexibility of the model allows its application in a wide range of channel types and environmental conditions, including both biological ion channels and synthetic nanopores, such as engineered membrane systems with selective ion transport. Full article

Background/Objectives: Rutin (RT), a promising bioflavonoid, faces clinical limitations due to its poor solubility and bioavailability. In this study, we formulated RT-loaded phytosome nanoparticles (RT-PNPs) via thin-layer hydration and characterized their morphology, size distribution, and zeta potential. Methods: We established a mouse model of Ehrlich ascites carcinoma (EAC), randomly allocating ninety female Swiss albino mice into six groups: untreated controls, RT-treated, RT-PNP-treated, EAC, EAC + RT, and EAC + RT-PNPs. Tumor induction and treatment protocols were controlled, with the oral administration of 25 mg/kg/day of RT or RT-PNPs for 20 days. We comprehensively assessed survival, body weight, ascitic fluid/tumor volume, and cell viability and performed detailed hematological, serum biochemical, and tumor marker analyses. Multiorgan (liver and kidney) function and redox homeostasis were evaluated through enzymatic assays for SOD, CAT, GSH-Px, and GSH, as well as lipid peroxidation assessment. Proinflammatory cytokines and tumor markers (AFP, CEA, CA19-9, CA125, and CA15-3) were quantified via ELISA. Results: Gene expression profiling (TP53, Bax, and Bcl-2) and flow cytometry (p53 and Ki-67) elucidated the modulation of apoptosis. Histopathological scoring documented organ protection, while advanced multivariate (heatmap and principal component) analyses revealed distinct treatment clusterings. The RT-PNPs demonstrated potent anti-tumor, antioxidant, anti-inflammatory, and apoptosis-inducing effects, outperforming free RT in restoring physiological markers and tissue integrity. Conclusions: The current results underscore the potential of RT-PNPs as a multifaceted therapeutic approach to EAC, leveraging nanoparticle technology to optimize efficacy and systemic protection. Full article

Modeling ion transport through membrane channels is crucial for understanding cellular processes, and Poisson–Nernst–Planck (PNP) equations provide a fundamental continuum framework for such ionic fluxes. We investigate a quasi-one-dimensional steady-state PNP system for two oppositely charged ion species, focusing on how large permanent charges within the channel and realistic boundary conditions impact ion transport. In contrast to classical models that impose ideal electroneutrality at the channel ends (a simplification that eliminates boundary layers near the membrane interfaces), we adopt relaxed neutral boundary conditions that allow small charge imbalances at the boundaries. Using asymptotic analysis treating the large permanent charge as a singular perturbation, we derive explicit first-order expansions for each ionic flux, incorporating boundary layer parameters $(\sigma ,\rho )$ to quantify slight deviations from electroneutrality. This analysis enables a qualitative characterization of individual cation and anion flux behaviors. Notably, we identify two critical transmembrane potentials, $V_{1c}$ and $V_{2c}$, at which the cation and anion fluxes, respectively, vanish, signifying flux-reversal thresholds that delineate distinct monotonic regimes in the flux-voltage response; these critical values depend on the permanent charge magnitude and the boundary layer parameters. We further show that both ionic fluxes exhibit saturation: as the applied voltage becomes extreme, each flux approaches a finite limiting value, with the saturation level modulated by the degree of boundary charge imbalance. Moreover, allowing even small boundary charge deviations reveals non-intuitive discrepancies in flux behavior relative to the ideal electroneutral case. For example, in certain parameter regimes, a large permanent charge that enhances an ionic current under strict electroneutral conditions will instead suppress that current under relaxed-neutral conditions (and vice versa). This new analytical framework exposes subtle yet essential nonlinear dynamics that classical electroneutral assumptions would otherwise obscure. It provides deeper insight into the interplay between large fixed charges and boundary-layer effects, emphasizing the importance of incorporating such realistic boundary conditions to ensure accurate modeling of ion transport through membrane channels. Numerical simulations are performed to provide more intuitive illustrations of our analytical results. Full article

Lanthanide rare earth elements possess significant promise for material applications owing to their distinctive optical and magnetic characteristics, as well as their versatile coordination capabilities. This study introduced a lanthanide-functionalized magnetic nanocellulose composite (NNC@Fe₃O₄@La(OH)₃) for effective phosphorus/nitrogen (P/N) ligand separation. The hybrid material employs the adaptable coordination geometry and strong affinity for oxygen of La³⁺ ions to show enhanced DNA-binding capacity via multi-site coordination with phosphate backbones and bases. This study utilized cellulose as a carrier, which was modified through carboxylation and amination processes employing deep eutectic solvents (DES) and polyethyleneimine. Magnetic nanoparticles and La(OH)₃ were subsequently incorporated into the cellulose via in situ growth. NNC@Fe₃O₄@La(OH)₃ showed a specific surface area of 36.2 m²·g⁻¹ and a magnetic saturation intensity of 37 emu/g, facilitating the formation of ligands with accessible La³⁺ active sites, hence creating mesoporous interfaces that allow for fast separation. NNC@Fe₃O₄@La(OH)₃ showed a significant affinity for DNA, with adsorption capacities reaching 243 mg/g, mostly due to the multistage coordination binding of La³⁺ to the phosphate groups and bases of DNA. Simultaneously, kinetic experiments indicated that the binding process adhered to a pseudo-secondary kinetic model, predominantly dependent on chemisorption. This study developed a unique rare-earth coordination-driven functional hybrid material, which is highly significant for constructing selective separation platforms for P/N-containing ligands. Full article

Background/Objectives: Infants with high-output enterostomies often require prolonged parenteral nutrition (PN), increasing risks of infections, liver dysfunction, and impaired growth. Mucous fistula refeeding (MFR) is proposed to enhance intestinal adaptation, weight gain, and distal bowel maturation. This systematic review and meta-analysis assessed its effectiveness, safety, and technical aspects. Methods: Following PRISMA guidelines, studies reporting MFR-related outcomes were included without data or language restrictions. Data sources included PubMed, EMBASE, CINAHL, Scopus, Web of Science, Cochrane Library, and UpToDate. Bias risk was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Meta-analysis employed random- and fixed-effects models, with outcomes reported as odds ratios (ORs) and 95% confidence interval (CI). Primary outcomes assessed were weight gain, PN duration, and complications and statistical comparisons were made between MFR and non-MFR groups. Results: Seventeen studies involving 631 infants were included; 482 received MFR and 149 did not. MFR started at 31 postoperative days and lasted for 50 days on average, using varied reinfusion methods, catheter types, and fixation strategies. MFR significantly improved weight gain (4.7 vs. 24.2 g/day, p < 0.05) and reduced PN duration (60.3 vs. 95 days, p < 0.05). Hospital and NICU stays were also shorter (160 vs. 263 days, p < 0.05; 122 vs. 200 days, p < 0.05). Cholestasis risk was lower (OR 0.151, 95% CI 0.071–0.319, p < 0.0001), while effects on bilirubin levels were inconsistent. Complications included sepsis (3.5%), intestinal perforation (0.83%), hemorrhage (0.62%), with one MFR-related death (0.22%). Conclusions: Despite MFR benefits neonatal care, its practices remain heterogeneous. Standardized protocols are required to ensure MFR safety and efficacy. Full article

High-frequency (h. f.) harmonic distortion (HD) of advanced SiGe heterojunction bipolar transistor (HBT)-based power cells (PwCs), featuring optimized metallization interconnections between individual HBTs, was investigated. Single tone input power (P_in) excitations at 1, 2, 5, and 10 GHz frequencies were employed. The output power (P_out) of the fundamental tone and its harmonics were analyzed in both the frequency and time domains. A rapid increase in the third harmonic of P_out was observed at input powers exceeding −8 dBm for a fundamental frequency of 10 GHz in two different PwC technologies. This increase in the third harmonic was analyzed in terms of nonlinear current waveforms, the nonlinearity of the HBT p-n junction diffusion capacitances, substrate current behavior versus P_in, and avalanche multiplication current. To assess the RF power performance of the PwCs, scalar and vectorial load-pull (LP) measurements were conducted and analyzed. Under matched conditions, the SiGe PwCs demonstrated good linearity, particularly at high frequencies. The key power performance of the PwCs was measured and simulated as follows: input power 1 dB compression point (P_{in_1dB}) of −3 dBm, transducer power gain (G_T) of 15 dB, and power added efficiency (PAE) of 50% at 30 GHz. All measured data were corroborated with simulations using the compact model HiCuM L2. Full article

Background: Despite advances in treatment, oral squamous cell carcinoma (OSCC) remains associated with high recurrence and mortality rates. Traditional TNM staging, while foundational, may not fully capture tumor aggressiveness. This study aimed to identify clinical and histopathological predictors of survival to enhance risk stratification and guide treatment planning in OSCC patients. Methods: A retrospective study of 100 patients with confirmed OSCC treated surgically with curative intent between January 2019 and January 2024 was analyzed. Clinicopathologic variables—including tumor volume, angioinvasion, perineural invasion, lymphatic invasion, and nodal status—were evaluated. Disease-specific survival (DSS) was assessed using Kaplan–Meier estimates, Cox regression, and logistic regression models. Results: The cohort had a mean age of 62.1 years, with a 46% OS rate and 43% DSS at study end. Perineural invasion (44%) and lymphatic invasion (42%) were the most common invasive features. Kaplan–Meier analysis revealed significantly reduced DSS in patients with angioinvasion, perineural invasion, and pN+ status. Multivariate logistic regression identified perineural invasion (OR = 3.93, p = 0.0023) and pN+ status (OR = 2.74, p = 0.0284) as independent predictors of cancer-specific mortality. Tumor volume was significantly associated with lymphatic invasion but not directly with DSS. Conclusions: Perineural invasion, angioinvasion, lymph node involvement, and tumor volume are important prognostic markers in OSCC, offering critical information beyond TNM staging. Incorporating these features into risk assessment models could improve prognostic accuracy and inform more individualized treatment strategies for high-risk OSCC patients. Full article

Background/Objectives: Obesity, a major risk factor for cardiometabolic traits, is influenced by both genetic and environmental factors. Genetic studies have identified multiple single-nucleotide polymorphisms (SNPs) associated with obesity and related traits. This study aimed to examine the association between genetic risk score (GRS) and obesity-associated traits, while incorporating SNPs with established gene–diet interactions to explore their potential role in precision nutrition (PN) strategies. Methods: A total of 4279 participants were stratified into low- and intermediate-/high-GRS groups based on 18 SNPs linked to obesity and cardiometabolic traits. This study followed a case–control design, where cases included individuals with overweight/obesity, T2DM-positive (+), or CVD-positive (+) individuals and controls, which comprised individuals free of these traits. Logistic regression area under the curve (AUC) models were used to assess the predictive power of the GRS and traditional risk factors on BMI, T2DM and CVD. Results: Individuals in the intermediate-/high-GRS group had higher odds of being overweight or obese (OR = 1.23, CI: 1.03–1.48, p = 0.02), presenting as T2DM+ (OR = 1.56, CI: 1.03–2.49, p = 0.03) and exhibiting CVD-related traits (OR = 1.56, CI: 1.25–1.95, p < 0.0001), compared to the low-GRS group. The GRS was the second most predictive factor after age for BMI (AUC = 0.515; 95% CI: 0.462–0.538). The GRS also demonstrated a predictive power of 0.528 (95% CI: 0.508–0.564) for CVD and 0.548 (95% CI: 0.440–0.605) for T2DM. Conclusions: This study supports the potential utility of the GRS in assessing obesity and cardiometabolic risk, while emphasizing the potential of PN approaches in modulating genetic susceptibility. Incorporating gene–diet interactions provides actionable insights for personalized dietary strategies. Future research should integrate multiple gene–diet and gene–gene interactions to enhance risk prediction and targeted interventions. Full article

Background: MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. Results: Sixteen studies (n = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63–0.72), 0.73 (95% CI, 0.68–0.78), and 0.70 (95% CI, 0.65–0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75–0.80) and 6.03 (95% CI, 4.65–7.82). Funnel plot asymmetry and Egger’s test (p = 0.025) indicated potential publication bias. Conclusions: Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice. Full article

Background/Objectives: Lymphovascular invasion (LVI) has been consistently linked to poor outcomes in patients with bladder cancer (BC), yet its independent prognostic value, especially after adjusting for established pathological features, remains debated. This study aimed to evaluate the prognostic value of LVI in the context of other pathological features of patients undergoing radical cystectomy. Methods: We conducted a retrospective cohort study including 200 patients treated at the Municipal Clinical Hospital in Cluj-Napoca, Romania. Associations between LVI and overall survival (OS) were assessed using univariable and multivariable Cox proportional hazards models, with Kaplan–Meier curves used for visualizing survival distributions. Results: In univariable analysis, increasing age, presence of LVI, advanced pathological tumor stage (pT ≥ 2), and nodal involvement (pN ≥ 1) were significantly associated with worse OS. LVI was a strong predictor of poor survival (HR 3.13; 95% CI: 2.09; 4.69; p < 0.001). However, in multivariable analysis, only tumor stage (HR 4.85; 95% CI: 2.19; 10.77; p < 0.001) and nodal involvement (HR 1.87; 95% CI: 1.13; 3.09; p = 0.015) remained independently associated with OS. In patients with incomplete nodal staging (Nx), LVI was significantly associated with OS (p = 0.028). Conclusions: Our findings reinforce the prognostic relevance of LVI in bladder cancer and support its role as a marker of aggressive tumor biology, highlighting its value in clinical risk assessment, especially in patients with incomplete nodal staging. Routine reporting of LVI in pathology and consideration in treatment planning are warranted. Full article

Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and compromised resilience. To address this critical limitation, we propose a novel empirical likelihood (EL)-based variable selection framework that integrates a Bayesian LASSO penalty within the composite quantile regression framework. By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. This innovative design enables simultaneous optimization of regression coefficients and penalty parameters, circumventing the need for ad hoc selection of optimal penalty parameters—a long-standing challenge in conventional LASSO estimation. Moreover, the proposed method imposes no restrictive assumptions on the distribution of random errors in the model. Through Monte Carlo simulations under outlier interference and empirical analysis of two U.S. house price datasets, we demonstrate that the new approach significantly enhances variable selection accuracy, reduces estimation bias for key regression coefficients, and exhibits robust resistance to data outlier contamination. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Pulmonary dynamic contrast-enhanced (DCE) MRI is clinically useful for assessing pulmonary perfusion, but its signal-to-noise ratio (SNR) is limited. A self-supervised learning network-based plug-and-play (PnP) denoising model was developed to improve the image quality of pulmonary perfusion MRI. A dataset of patients with suspected pulmonary diseases was used. Asymmetric pixel-shuffle downsampling blind-spot network (AP-BSN) training inputs were two-dimensional background-subtracted perfusion images without clean ground truth. The AP-BSN is incorporated into a PnP model (PnP-BSN) for balancing noise control and image fidelity. Model performance was evaluated by SNR, sharpness, and overall image quality from two radiologists. The fractal dimension and k-means segmentation of the pulmonary perfusion images were calculated for comparing denoising performance. The model was trained on 29 patients and tested on 8 patients. The performance of PnP-BSN was compared to denoising convolutional neural network (DnCNN) and a Gaussian filter. PnP-BSN showed the highest reader scores in terms of SNR, sharpness, and overall image quality as scored by two radiologists. The expert scoring results for DnCNN, Gaussian, and PnP-BSN were 2.25 ± 0.65, 2.44 ± 0.73, and 3.56 ± 0.73 for SNR; 2.62 ± 0.52, 2.62 ± 0.52, and 3.38 ± 0.64 for sharpness; and 2.16 ± 0.33, 2.34 ± 0.42, and 3.53 ± 0.51 for overall image quality (p < 0.05 for all). PnP-BSN outperformed DnCNN and a Gaussian filter for denoising pulmonary perfusion MRI, which led to improved quantitative fractal analysis. Full article

Wearable sensors have rapidly advanced, enabling applications such as human activity monitoring, electronic skin, and biomimetic robotics. To meet the growing demands of these applications, multifunctional sensing has become essential for wearable devices. However, most existing studies predominantly focus on enhancing single-function sensing capabilities. This study introduces a multifunctional sensor that combines high stretchability for strain and pressure detection with ultraviolet (UV) sensing capability. To achieve simultaneous detection of strain, pressure, and UV light, a multi-sensing approach was employed: a capacitive method for strain and pressure detections and a resistive method utilizing a pn-heterojunction diode for UV detection. In the capacitive method, polyaniline (PANI) served as parallel-plate electrodes, while silicon-based elastomer acted as the dielectric layer. This configuration enabled up to 100% elongation and enhanced operational stability through encapsulation. The sensor demonstrated a strong linear relationship between capacitance value changes reasonably based on the area of PANI, and showed a good linearity with an R-squared value of 0.9918. It also detected pressure across a wide range, from low (0.4 kPa) to high (9.4 kPa). Furthermore, for wearable applications, the sensor reliably captured capacitance variations during finger bending at different angles. For UV detection, a pn-heterojunction diode composed of p-type silicon and n-type zinc oxide nanorods exhibited a rapid response time of 6.1 s and an on/off ratio of 13.8 at −10 V. Durability under 100% tensile strain was confirmed through Von Mises stress calculations using finite element modeling. Overall, this multifunctional sensor offers significant potential for a variety of applications, including human motion detection, wearable technology, and robotics. Full article

Traumatic injuries to the peripheral (PNS) and central nervous systems (CNS) trigger distinct regenerative responses, with the PNS displaying limited regenerative capacity and the CNS remaining largely refractory. Recent research highlights the role of epigenetic modifications, particularly histone acetylation, in modulating the gene expression programs that drive axonal regeneration. This review synthesizes current findings on post-translational histone modifications, focusing on histone acetyltransferases (HATs), histone deacetylases (HDACs), and epigenetic readers, in addition to their impact on neuronal and non-neuronal cells following injury. While HATs like p300/CBP and PCAF promote the expression of regeneration-associated genes, HDAC inhibition has been shown to facilitate neurite outgrowth, neuroprotection, and functional recovery in both PNS and CNS models. However, HDAC3, HDAC5, and HDAC6 demonstrate context- and cell-type-specific roles in both promoting and limiting regenerative processes. The review also highlights cell-specific findings that have been scarcely covered in the previous literature. Thus, the immunomodulatory roles of epigenetic regulators in microglia and macrophages, their involvement in remyelination via Schwann cells and oligodendrocytes, and their impact on astrocyte function are within the scope of this review. Closely considering cell-context specificity is critical, as some targets can exert opposite effects depending on the cell type involved. This represents a major challenge for current pharmacological therapies, which often lack precision. This complexity underscores the need to develop strategies that allow for cell-specific delivery or target regulators with converging beneficial effects across cell types. Such approaches may enhance regenerative outcomes after CNS or PNS injury. Full article