Search Results (48,948)

Colorectal cancer (CRC) is one of the leading causes of cancer-related morbidity and mortality worldwide, with most patients, especially those with microsatellite-stable disease, having limited treatment options. Oncolytic viruses (OVs) have emerged as a promising therapeutic modality due to their ability to selectively replicate in malignant cells and mediate antitumor effects through direct oncolysis, immune activation, and modulation of tumor angiogenesis. This review analyzed 101 primary studies that reported the use of OV in CRC. The extracted data, including virus type, study design, model system, mechanistic pathways, and therapeutic strategies, were organized as standalone therapy, combination therapy, or enhancer-based approaches. Across studies, OV monotherapy consistently induced selective tumor cell lysis and, in some models, also exhibited additional immunogenic and anti-angiogenic effects. Combination strategies, particularly those with immune checkpoint inhibitors, demonstrated synergistic activity, enhancing T-cell infiltration, cytokine production, and tumor control even in resistant CRC settings. Enhancer approaches, including mesenchymal stem cell delivery systems and tumor-specific promoters, have improved viral selectivity, tumor penetration, and reduced immune clearance. Despite promising findings, progress is hindered by heterogeneous models and the scarcity of advanced clinical trials. Translation into well-designed clinical studies is now warranted to optimize therapeutic outcomes. Full article

Conventional particleboards often exhibit limited mechanical strength, which restricts their use in load-bearing and high-performance applications; reinforcing these boards with carbon fibers offers a potential solution to overcome these limitations. This study investigated the effect of carbon fiber (CF) content on the mechanical performance of single-layer particleboards bonded with polymeric methylene diphenyl diisocyanate (pMDI) adhesive. Carbon fibers were examined as a reinforcement to improve the mechanical properties of particleboards. Experimental boards were produced with 0, 10, 20, 30, 40, and 50% CF (based on the oven-dry mass of wood particles). The analysis included density profile distribution, modulus of rupture (MOR), modulus of elasticity (MOE), and screw withdrawal resistance (SWR). The results showed that mechanical performance improved only at lower CF contents. The most pronounced effect was observed at 10% CF, where MOR increased from 15.2 MPa (control) to 19.2 MPa, and MOE increased from 2.45 GPa to 2.91 GPa. Higher CF additions (≥20%) did not yield further improvements, and at elevated levels (≥30%), bending performance decreased (MOR dropped to 14.1–13.5 MPa) due to poor fiber dispersion and weakened interfacial bonding between fibers and wood particles. Screw withdrawal resistance increased gradually with CF content, from 156 N in the control boards to 182 N at 50% CF, although the improvement was limited by adhesion quality and mat heterogeneity. Overall, the study demonstrates that small CF additions can enhance selected mechanical properties of particleboards, whereas higher loadings negatively affect performance due to microstructural incompatibilities. Full article

Integrating perennial grain crops into agricultural systems can become a key milestone for increasing the provision of ecosystem services of food production systems. Intermediate wheatgrass is a novel perennial grain and forage crop that is undergoing domestication. Potential trade-offs between resource allocation and reproductive and vegetative plant structures can challenge the response to selection for both grain and forage production under dual-purpose use. Our goal was to understand the genetic relationship between grain and forage yield components, quantify potential trade-offs between vegetative and reproductive allocation, and optimize the response to selection under dual-purpose management. Phenological, grain, and forage traits were evaluated in 30 half-sib families across two field experiments conducted over three years. No trade-offs were detected between grain and forage yield traits, indicating that the simultaneous improvement of both traits is feasible. Grain yield per spike and spikes per plant are promising secondary traits for indirect selection, given their moderate-to-high heritability (h² = 0.58 and 0.41) and strong Pearson correlation coefficients with grain yield per plant (0.68 and 0.82). These traits could be assessed in the first year, increasing genetic gain per unit time. Intermediate wheatgrass germplasm could therefore be efficiently developed by shortening the time to first evaluation, using secondary traits, and performing selection under dual-purpose management. Full article

CO₂-enhanced oil recovery (CO₂-EOR) with water-alternating-gas (WAG) injection offers the dual benefit of boosted oil production and CO₂ storage, addressing both energy needs and climate goals. However, designing CO₂-WAG schemes is challenging; maximizing oil recovery, CO₂ storage, and economic returns (net present value, NPV) simultaneously under a limited simulation budget leads to conflicting trade-offs. We propose a novel closed-loop multi-objective framework that integrates high-fidelity reservoir simulation with stacking surrogate modeling and active learning for multi-objective CO₂-WAG optimization. A high-diversity stacking ensemble surrogate is constructed to approximate the reservoir simulator. It fuses six heterogeneous models (gradient boosting, Gaussian process regression, polynomial ridge regression, k-nearest neighbors, generalized additive model, and radial basis SVR) via a ridge-regression meta-learner, with original control variables included to improve robustness. This ensemble surrogate significantly reduces per-evaluation cost while maintaining accuracy across the parameter space. During optimization, an NSGA-II genetic algorithm searches for Pareto-optimal CO₂-WAG designs by varying key control parameters (water and CO₂ injection rates, slug length, and project duration). Crucially, a decision-space diversity-controlled active learning scheme (DCAF) iteratively refines the surrogate: it filters candidate designs by distance to existing samples and selects the most informative points for high-fidelity simulation. This closed-loop cycle of “surrogate prediction → high-fidelity correction → model update” improves surrogate fidelity and drives convergence toward the true Pareto front. We validate the framework of the SPE5 benchmark reservoir under CO₂-WAG conditions. Results show that the integrated “stacking + NSGA-II + DCAF” approach closely recovers the true tri-objective Pareto front (oil recovery, CO₂ storage, NPV) while greatly reducing the number of expensive simulator runs. The method’s novelty lies in combining diverse stacking ensembles, NSGA-II, and active learning into a unified CO₂-EOR optimization workflow. It provides practical guidance for economically aware, low-carbon reservoir management, demonstrating a data-efficient paradigm for coordinated production, storage, and value optimization in CO₂-WAG EOR. Full article

Socio-ecological systems (SESs) exhibit nonlinear feedback across environmental, social, and economic processes, requiring integrative analytical tools capable of representing such coupled dynamics. This study presents a quantitative framework that integrates a compartmental model of a global human–ecosystem with two complementary optimization approaches (Fisher Information (FI) and Multi-Objective Optimization (MOO)) to evaluate policy strategies for sustainability. The model represents biophysical and socio-economic interactions across 15 compartments, incorporating feedback loops between greenhouse gas (GHG) accumulation, temperature anomalies, and trophic–economic dynamics. Six policy-relevant decision variables were selected (wild plant mortality, sectoral prices (agriculture, livestock, and industry), base wages, and resource productivity) and optimized under temporal (25-year) and magnitude (±10%) constraints to ensure policy realism. FI-based optimization enhances system stability, whereas the MOO framework balances environmental, social, and economic objectives using the Ideal Point Method. Both approaches prevent the systemic collapse observed in the baseline scenario. The FI and MOO strategies reduce terminal global temperature by 11.4% and 15.0%, respectively, relative to the baseline (35 °C → 31.0 °C under FI; 35 °C → 29.7 °C under MOO). Resource-use efficiency, measured through the resource requirement coefficient (λ), improves by 8–10% under MOO (0.6767 → 0.6090) and by 6–7% under FI (0.6668 → 0.6262). These outcomes offer actionable guidance for long-term climate policy at national and international scales. The MOO framework provided the most balanced outcomes, enhancing environmental and social performance while maintaining economic viability. Overall, the integration of optimization and information-theoretic approaches within SES models can support evidence-based public policy design, offering actionable pathways toward resilient, efficient, and equitable sustainability transitions. Full article

Skin scars represent a complex therapeutic challenge, with significant functional, aesthetic, and psychological implications. Despite advances in laser therapy, monotherapy has significant limitations, particularly for patients with complex scars with atrophic, hypertrophic, vascular, and pigmentary components. The combined use of multiple laser sources, in sequential or simultaneous mode, allows for the selective targeting of specific tissue components and improves clinical efficacy while maintaining a good safety profile. This narrative review critically analyses the available evidence on combination therapies for atrophic, hypertrophic, keloid, and post-surgical and burn scars. Protocols combining ablative lasers (CO₂, Er:YAG), non-ablative lasers (1540–1550 nm), vascular lasers (PDL, Nd:YAG) and intense pulsed light (IPL) are reported. Possible integrations with adjuvant techniques, such as radiofrequency, platelet-rich plasma (PRP), and laser-assisted drug delivery, are also mentioned as areas for future development. The available data suggest a promising role for multimodal strategies, but the literature remains limited by small cohorts, heterogeneous protocols, and short follow-up periods. Although adverse events are generally mild and transient, typically involving erythema, oedema, or temporary dyschromia, an awareness of safety considerations remains essential, particularly in higher phototypes and when using ablative modalities. Further prospective and multicentre studies are needed to define standardised protocols and consolidate the role of combination therapies in the management of scars. Full article

High-quality test cases are vital for ensuring software reliability and security. However, existing symbolic execution tools generally rely on single-path search strategies, have limited feature extraction capability, and exhibit unstable model predictions. These limitations make them prone to local optima in complex or cross-scenario tasks and hinder their ability to balance testing quality with execution efficiency. To address these challenges, this paper proposes a Deep Active Ensemble Learning Framework for symbolic execution path exploration. During training, the framework integrates active learning with ensemble learning to reduce annotation costs and improve model robustness, while constructing a heterogeneous model pool to leverage complementary model strengths. In the testing stage, a dynamic ensemble mechanism based on sample similarity adaptively selects the optimal predictive model to guide symbolic path exploration. In addition, a gated graph neural network is employed to extract structural and semantic features from the control flow graph, improving program behavior understanding. To balance efficiency and coverage, a dynamic sliding window mechanism based on branch density enables real-time window adjustment under path complexity awareness. Experimental results on multiple real-world benchmark programs show that the proposed framework detects up to 16 vulnerabilities and achieves a cumulative 27.5% increase in discovered execution paths in hybrid fuzzing. Furthermore, the dynamic sliding window mechanism raises the F1 score to 93%. Full article

The study of cotton stalk extraction resistance provides important parameters for the design of cotton stalk harvesting machinery. To investigate the effects of soil moisture content, cotton stalk diameter, and extraction angle on the extraction force of densely planted cotton stalks, this paper designs a real-time measurement system based on virtual instrument technology and conducts field tests. The tests were carried out in cotton fields at the First Farm in Aral City, Xinjiang, using the cotton variety “Xiulu Zhong 70”. Single-factor experiments were conducted with extraction angle and stalk diameter as influencing factors. A combined three-factor experiment was performed under the following conditions: soil moisture contents of 21.87% and 26.32%; extraction angles of 25°, 30°, and 35°; and cotton stalk diameters of 8.50–9.00 mm, 10.00–10.50 mm, and 11.50–12.00 mm. The results show that the minimum extraction force is required when the extraction angle is 30°. Soil moisture content significantly affects the extraction force, which increases with stalk diameter. The combined test results indicate that the order of significance of the three factors is as follows: cotton stalk diameter (A), extraction angle (B), and soil moisture content (C). The optimal combination is A₁B₁C₂, corresponding to a diameter of 8.50–9.00 mm, an extraction angle of 35°, and a soil moisture content of 26.32%. Based on comprehensive analysis, the recommended extraction angle range is 30–35°. The proposed system can efficiently complete cotton stalk extraction force tests, and the collected data provide valuable references for the design of cotton stalk harvesting machinery. By appropriately selecting the extraction angle and conducting harvesting under suitable soil moisture conditions, it is possible to reduce power consumption and improve production efficiency. Full article

Shots are becoming increasingly popular due to their convenience and concentrated nutrient content. In this study, innovative shots were developed as herbal-enriched formulations designed to improve bioaccessibility, bioavailability, and health-promoting properties. To achieve this, pear–flowering quince juice was enriched with a mixture of herbal infusions and evaluated for its physicochemical characteristics, including bioactive compounds, as well as its functional and sensory properties. Additionally, the products were subjected to a three-stage in vitro digestion model (oral–gastric–small intestine) to assess bioaccessibility and bioavailability. The results revealed that the shot containing mint and nettle had the highest polyphenolic content (579 mg/100 mL), while the shot enriched with white mulberry and common yarrow had the highest mineral content (28 mg/100 mL). The developed formulations also exhibited strong inhibitory effects on pancreatic lipase and lipoxygenase. It was demonstrated that the addition of selected herbs, particularly those rich in rosmarinic acid, can enhance both bioaccessibility and bioavailability, and that menthol may further potentiate these effects. In conclusion, the study showed that incorporating different types of herbs into pear–flowering quince juice enables the development of novel products with tailored health-promoting and sensory properties, primarily through the synergistic action of the individual ingredients. Full article

In this work, we introduce a new four-parameter distribution, called the integrated linear–Weibull (ILW) model, constructed by embedding a dynamic linear component within the Weibull framework. The ILW distribution is capable of capturing a wide variety of symmetric and asymmetric density shapes and accommodates diverse failure-rate behaviors. We derive several of its key mathematical and statistical properties, including moments, extropy, cumulative residual entropy, order statistics, and their asymptotic forms. The mean residual life function and its reciprocal relationship with the failure rate are also obtained. An algorithm for generating random samples from the ILW distribution is provided, and model identifiability is examined numerically through the Kullback–Leibler divergence. Parameter estimation is carried out via maximum likelihood, and a simulation study is conducted to assess the accuracy of the estimators; the results show improvements in estimator performance as sample size increases. Finally, three real datasets involving failure-time observations and one describing hydrological and epidemiological data are analyzed to demonstrate the practical usefulness of the ILW model. In these applications, the proposed model exhibits competitive or superior performance relative to several existing lifetime distributions based on standard model selection criteria and goodness-of-fit measures. Full article

Background: The Fc region of immunoglobulin G (IgG) is a key target in therapeutic and analytical applications, such as antibody purification and site-specific bioconjugation. Although Protein A exhibits strong Fc-binding affinity, its large molecular weight and limited chemical flexibility pose challenges for use in compact or chemically defined systems. To address these limitations, we designed two α-helical peptides, SpA h1 and SpA h2, based on the Fc-binding helices of the Z34C domain from Staphylococcus aureus Protein A. Method: To enhance the structural stability and Fc-binding capability of these peptides, a lactam-based stapling strategy was employed by introducing lysine and glutamic acid residues at positions i and i + 4. Result: The resulting stapled peptides, (s)SpA h1 and (s)SpA h2, exhibited significantly improved α-helical content and IgG-binding performance, as demonstrated by circular dichroism (CD) spectroscopy and fluorescence-based IgG capture assays. Surface plasmon resonance (SPR) analysis confirmed specific, concentration-dependent interactions with the Fc region of human IgG, with (s)SpA h1 consistently showing the binding affinity and stability. Proteolytic resistance assays using α-chymotrypsin revealed that (s)SpA h1 maintained its structural integrity over time, exhibiting markedly enhanced resistance to enzymatic degradation compared to its linear counterpart. Furthermore, (s)SpA h1 exhibited strong Fc selectivity with minimal Fab affinity, confirming its suitability as a compact and Fc-specific binding ligand. Conclusions: These results confirm the successful design and development of structurally reinforced Fc-binding peptides that overcome the inherent limitations of short linear sequences through both high-affinity sequence optimization and lactam-based stapling. Among them, (s)SpA h1 demonstrates the most promising characteristics as a compact yet stable Fc-binding ligand, suitable for applications such as antibody purification and site-specific bioconjugation. Full article

Non-contact heart rate estimation technology based on frequency-modulated continuous wave (FMCW) radar has garnered extensive attention in single-target scenarios, yet it remains underexplored in multi-target environments. Accurate discrimination of multiple targets and precise estimation of their heart rates constitute key challenges in the multi-target domain. To address these issues, we propose a novel scheme for multi-target heart rate estimation. First, a high-precision distance-bin selection (HDBS) method is proposed for target localization in the range domain. Next, multiple-input multiple-output (MIMO) array processing is combined with the Root-multiple signal classification (Root-MUSIC) algorithm for angular domain estimation, enabling accurate discrimination of multiple targets. Subsequently, we propose an efficient method for interference suppression and vital sign extraction that cascades variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) termed as VLW, which enables high-quality heartbeat signal extraction. Finally, to achieve high-precision and super-resolution heart rate estimation with low computational burden, an improved fast iterative interpolated beamforming (FIIB) algorithm is proposed. Specifically, by leveraging the conjugate symmetry of real-valued signals, the improved FIIB algorithm reduces the execution time by approximately 60% compared to the standard version. In addition, the proposed scheme provides sufficient signal-to-noise ratio (SNR) gain through low-complexity accumulation in both distance and angle estimation. Six experimental scenarios are designed, incorporating densely arranged targets and front-back occlusion, and extensive experiments are conducted. Results show this scheme effectively discriminates multiple targets in all tested scenarios with a mean absolute error (MAE) below 2.6 beats per minute (bpm), demonstrating its viability as a robust multi-target heart rate estimation scheme in various engineering fields. Full article

Alopecia is a multifactorial disorder in which immune, endocrine, metabolic, and microbial systems converge within the follicular microenvironment. In alopecia areata (AA), loss of immune privilege, together with interferon-γ- and interleukin-15-driven activation of the JAK/STAT cascade, promotes cytotoxic infiltration, whereas selective inhibitors, including baricitinib, ritlecitinib, and durvalumab, restore immune balance and permit anagen reentry. In androgenetic alopecia (AGA), excess dihydrotestosterone and androgen receptor signaling increase DKK1 and prostaglandin D2, suppress Wnt and β-catenin activity, and drive follicular miniaturization. Combination approaches utilizing low-dose oral minoxidil, platelet-rich plasma, exosome formulations, and low-level light therapy enhance vascularization, improve mitochondrial function, and reactivate metabolism, collectively supporting sustained regrowth. Elucidation of intracellular axes such as JAK/STAT, Wnt/BMP, AMPK/mTOR, and mitochondrial redox regulation provides a mechanistic basis for rational, multimodal intervention. Advances in stem cell organoids, biomaterial scaffolds, and exosome-based therapeutics extend treatment from suppression toward structural follicle reconstruction. Recognition of microbiome and mitochondria crosstalk underscores the need to maintain microbial homeostasis and redox stability for durable regeneration. This review synthesizes molecular and preclinical advances in AA and AGA, outlining intersecting signaling networks and regenerative interfaces that define a framework for precision and sustained follicular regeneration. Full article

Financial forecasting is a challenging task due to the complexity and nonlinear volatility that characterize modern financial markets. Machine learning algorithms are very effective at increasing prediction accuracy, thereby supporting data-driven decision making, optimizing pricing strategies, and improving financial risk management. In particular, combining machine learning techniques with metaheuristic algorithms often leads to significant performance improvements across various domains. This study proposes a hybrid framework for cryptocurrency price prediction, where Support Vector Regression (SVR) with radial basis function kernel is used to perform the prediction, while a Firefly algorithm is employed for correlation-based feature selection and hyperparameter tuning. To improve search performance, the parameters of the Firefly algorithm are optimized using the Fully Informed Search Algorithm (FISA) which is an improved version of the parameterless Rao algorithm. The model is applied to hourly data of Bitcoin, Ethereum, Binance, Solana and Ripple, separately. The model’s performance is evaluated by comparison with Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), and SVR methods using MSE, MAE, and MAPE metrics, along with statistical validation by Wilcoxon’s signed-rank test. The results show that the proposed model achieves a superior accuracy and demonstrate the critical importance of feature selection and hyperparameter tuning for achieving accurate predictions in volatile markets. Moreover, customizing both feature sets and model configurations for each cryptocurrency allows the model to capture distinct market characteristics and provides deeper insights into intra-day market dynamics. Full article

Managing bacterial infections and the spread of microbial resistance is one of the most critical and complex tasks of modern healthcare infrastructures. Antiseptics and disinfectants such as biocides play a significant role in controlling microbial resistance by reducing the microbial load on surfaces, skin, and environments, thereby limiting the opportunity for pathogens to proliferate and develop resistance. Herein, we tested the different interactions of quaternary ammonium compound (QAC)-based biocide compositions in pursuit of a better antimicrobial performance. An extensive microbiological analysis was conducted for 12 selected compositions of various combinations of mono-QACs, bis-QACs, and alcohols on 17 strains of bacteria of the ESKAPEE group and fungi, including 11 clinical highly resistant varieties, highlighting synergistic or additive dynamics. The evaluation showed noticeable improvements in activity, with up to 16-fold MBC and 32-fold MBEC reductions for alcohol-based compositions of lead QAC. Moreover, synergistic interactions were detected and confirmed via an optimized checkerboard assay for pyridinium QAC combinations against planktonic Gram-positive S. aureus with a fractional inhibitory concentration index (FICI) and fractional bactericidal concentration index (FBCI) of 0.39–0.5 and Gram-negative A. baumannii biofilms. The studied biocides demonstrated the long-term preservation of antimicrobial efficiency without resistance development during a 40-day period and do not induce QAC-associated cross-resistance for four commercially available antibiotics with similar mechanisms of action. Full article