Search Results (3,916)

Green synthesis of KGM-capped CeO₂ nanoparticles was successfully achieved through a simple coprecipitation method using Konjac Glucomannan (KGM) as a biopolymeric capping and stabilizing agent. The reaction conditions were optimized by varying pH (9–11) and temperature (30–70 °C) to evaluate their influence on nanoparticle formation and photocatalytic performance. The synthesized KGM–CeO₂ nanoparticles were comprehensively characterized using FTIR, UV–Vis spectroscopy, XRD, SEM–EDS, TEM, DLS, and ZP analysis to investigate their structural, optical, morphological, and surface properties. The characterization results confirmed the successful formation of porous sponge-like branched CeO₂ nanostructures with irregular morphology. XRD analysis revealed the crystalline nature of the nanoparticles with an average crystallite size of approximately 7.7 nm, while DLS analysis showed an average hydrodynamic particle size of 29.7 nm with a biomodal particle size distribution. The positive zeta potential value (+16.75 mV) confirmed good colloidal stability and reduced agglomeration due to effective capping by KGM. The synthesized nanoparticles also exhibited favorable optical properties with band gap values suitable for photocatalytic applications. The adsorption and photocatalytic degradation performance of the KGM–CeO₂ nanoparticles was investigated against synthetic textile dyes, including Naphthol Blue Black (NBB), Methyl Orange (MO), and a mixed NBB–MO dye system under acidic conditions. Using an adsorbent dosage of 50 mg and dye concentrations of 100 mg/L, the material achieved degradation efficiencies of approximately 99% for NBB, 91% for MO, and 52% for the mixed dye system under UV irradiation for 120 min. Adsorption kinetic studies indicated that the pseudo-second-order model provided the best fit, suggesting that chemisorption is the dominant adsorption mechanism involving multifunctional surface interactions. These findings are particularly relevant for industrial wastewater treatment, since actual textile effluents typically contain complex mixtures of dyes and organic contaminants rather than single dye pollutants. The mixed dye experiments, therefore, provide a more realistic simulation of industrial wastewater conditions. Overall, the synthesized KGM–CeO₂ nanoparticles demonstrate excellent potential as an eco-friendly, cost-effective, and sustainable multifunctional material for adsorption-assisted photocatalytic treatment of dye-contaminated wastewater. Further optimization of operational conditions and catalyst surface properties may enhance its efficiency in multicomponent wastewater systems. Full article

Efficient recovery of long-chain dicarboxylic acids (LCDAs) from aqueous fermentation broths is a key challenge for the industrial development of bio-based LCDA production. This study evaluates liquid–liquid extraction (LLE) as a downstream recovery strategy, comparing physical extraction (PE) and reactive extraction (RE) for DCA 12, DCA 16, and DCA 18. The novelty of this work lies in demonstrating that LCDA extraction is governed by mechanisms fundamentally different from those of short- and medium-chain dicarboxylic acids. Whereas shorter chain dicarboxylic acids are mainly controlled by dissociation degree, LCDA recovery is strongly influenced by carbon-chain apolarity, low aqueous solubility, and compound losses through agglomeration, precipitation, and/or micellization. PEs enabled the selective recovery of the more hydrophobic DCA 16 and DCA 18 over DCA 12, confirming the dominant role of chain length in LCDA separation. In contrast, RE with Aliquat^®336 maximized total LCDA recovery, achieving extraction efficiencies above 85%, but with reduced selectivity. Validation in autoclaved fermentation broth from UCO feedstock confirmed the potential of Aliquat^®336 in octanol for high LCDA recovery, while revealing lower extraction efficiencies than in model mixtures due to broth matrix complexity. Overall, this study establishes LLE as a promising platform for LCDA recovery and highlights that future downstream process design must balance total recovery, chain-length selectivity, and broth-specific matrix effects. Full article

Considering the therapeutic potential of the Proteus mirabilis PM16 podophage, the interaction between PM16, its host strain, and the mouse immune system was investigated. We evaluated how pre-existing humoral immunity to PM16 influences the immune response against P. mirabilis and the neutralization of the phage itself. Balb/c mice were divided into three groups and immunized two times with (1) 0.9% NaCl, (2) adjuvants, or (3) a mixture of PM16 and an adjuvant. Then, each group was subdivided into three subgroups: mock infection, infection with P. mirabilis, and infection with P. mirabilis followed by model phage therapy with PM16. The obtained results demonstrated that pre-immunization with PM16 enhanced the anti-P. mirabilis IgG antibody response upon bacterial challenge, indicating that the phage potentiates antibacterial immunity. In addition, pre-immunization elicited a significant anti-PM16 antibody response that resulted in in vitro neutralization of phage lytic activity. However, phage-neutralizing antibodies neither decreased the efficacy of phage therapy nor influenced bacteria-specific immune response. Thus, while PM16 can boost the host’s immune response against its bacterial host, the resulting humoral immunity also drives phage clearance through both direct and bacteria-mediated neutralization pathways, revealing a complex immunopharmacological relationship central to phage therapy. Full article

In the myocardium of rats of two phenotypes (low and high resistance to hypoxia), the dependence of the reaction of catalytic subunits of mitochondrial enzyme complexes I–V and the severity of ultrastructural changes in mitochondria upon exposure to repeated hypoxia (20 days—three daily hourly exposures to hypoxic mixtures of −14% O₂, 10.5% O₂ and 8% O₂, equivalent to 3000 m, 5000 m and 7000 m). The dynamics of expression of catalytic subunits of mitochondrial complexes I–V and ultrastructural changes in three subpopulations of mitochondria were analyzed. During the course of exposure to hypoxia (training sessions) each repeated hypoxic exposure under any regimen caused an activation of mitochondrial complex II and mitochondrial complexes III–V. At 14–10.5% O₂, this reaction was repeated with each hypoxic exposure during 8–12 training sessions. After 20 sessions, ATP synthesis returned to its initial level, indicating the completion of adaptation. These changes correlated with optimization of the mitochondrial ultrastructure, which was most pronounced at 14% O₂. On the contrary, at 8% O₂ under conditions of inhibition of succinate dehydrogenase (mitochondrial complex II), ATP synthesis was suppressed; and pronounced structural disorders of mitochondria developed. Thus, we have demonstrated that mitochondrial enzymes and the ultrastructure of subpopulations of myocardial mitochondria are informative indicators of the functional and metabolic state of the heart. Full article

The successful application of extrusion-based 3D-printed geopolymer mortars largely depends on precursor chemistry, activator composition, mixture proportions, and fresh-state behavior, which is highly sensitive to time-dependent structural build-up. This review examines the relationships among mix design, geopolymerization chemistry, rheological properties, and printability requirements for 3D-printed geopolymer mortars. Particular emphasis is placed on the effects of precursor type, alkaline activator characteristics, liquid-to-solid ratio, additives, and fibers on flowability, yield stress, viscosity, extrudability, buildability, shape retention, and interlayer bonding. The review further discusses how geopolymerization kinetics influence the evolution of fresh-state properties, the printable time window, and the transition from extrusion to structural stability. In addition, early-age performance is evaluated in terms of setting behavior, green strength development, and layer-interface integrity. Current challenges, including the lack of standardized test methods, limited comparability among published studies, and the complex coupling between material design and process parameters, are also highlighted. Finally, the review identifies key research gaps and proposes future directions for developing robust, printable, and sustainable geopolymer mortar systems for additive manufacturing in construction. Full article

Background/Objectives: Colistin is a complex polymyxin antibiotic with a narrow therapeutic window and significant interindividual pharmacokinetic variability, necessitating precise concentration monitoring. Current analytical methods often utilize colistin mixtures or require large sample volumes, potentially limiting the precision and resolution of individual component quantification. This study aimed to develop a sensitive and component-specific bioanalytical assay for the simultaneous quantification of colistin A and colistin B in human plasma. Methods: A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was developed using pure, component-specific reference standards to ensure rigorous independent quantification of each component. Analytes were efficiently extracted from a small volume of plasma (50 µL) using solid-phase extraction. Chromatographic separation was achieved on a C18 column with a total runtime of 4 min, and detection was performed using negative-ion multiple reaction monitoring (MRM). Results: Calibration curves showed excellent linearity over a range of 0.1–20 µg/mL for both colistin A and B (R² > 0.99). The precision (%CV ≤ 8.8%) and accuracy (86.4–105.7%) for both components met the predefined regulatory criteria. This method was clinically validated using 60 plasma samples from 15 patients, demonstrating its applicability for capturing individual concentration–time profiles within the clinically relevant range (0.323–19.579 µg/mL for colistin A and 0.065–6.132 µg/mL for colistin B). Conclusions: This validated bioanalytical assay enables precise clinical pharmacokinetic assessments in a high-throughput workflow using a small plasma volume. Therefore, it serves as a practical tool for therapeutic drug monitoring (TDM)-guided dose optimization and further clinical investigations of colistin therapy. Full article

Soil is the primary source of nutrients for plants, mainly through the clay–humus complex derived from mineral components and decomposed organic matter. The decomposition of the litter—composed only of leaves—contributes significantly to the nutrient cycle. This study evaluated the effects of the intact leaves and the leaf powder resulting from three agroforestry species: Daniellia oliveri (DO), Terminalia macroptera (TM), and Piliostigma thonningii (PT) on the germination and growth of okra (Abelmoschus esculentus L.). The experiment was conducted under pot conditions using 10, 20, and 30 g doses of intact or powdered litter. Germination rate was measured over a period of 16 days, and plant height, number of leaves, and collar diameter were measured over a 90-day period. According to the results, all litter treatments maintained germination rates above 50%, indicating the absence of phytotoxic effects. Germination was significantly affected by litter treatment under both intact litter and powdered litter applications (p < 0.001). Several leaf powder mixtures achieved 100% germination at 16 days, particularly the combinations DO + TM at 30 g, DO + PT at 20 g, TM + PT at 30 g, and DO + TM + PT at 10 and 30 g. Intact leaves amendments promoted significantly greater vegetative growth than powders. Mean plant height was higher under intact leaves (18.04 cm) than powders (14.36 cm; p < 0.001). Daniellia oliveri produced the greatest plant height (23.33 cm at 20 g), whereas the three-species powder mixture resulted in the lowest values (9.50–10.23 cm at 90 days). Collar diameter was not significantly affected by treatment type. Overall, intact leaf litter was more effective than powders in promoting okra vegetative growth. Full article

Identification of ligands targeting essential enzymes in Mycobacterium species remains an important strategy for anti-tuberculosis drug discovery. Here, a native mass spectrometry approach was employed using pooled 100-compound mixtures, enabling the direct detection of intact HPPK–ligand complexes in solution. Dual-mode MS acquisitions (low collision energy for complex detection and high collision energy for ligand confirmation), combined with an automated data analysis workflow, ensured robust identification of binding events from these complex samples. This strategy led to the identification of several HPPK-binding small molecules, all belonging to the dammarane triterpene glycoside (ginsenoside) class. Subsequent analysis of the hits revealed clear structure–affinity relationships, highlighting how specific aglycone modifications and glycosylation patterns influence binding to HPPK. Our findings expand the known chemical space of HPPK ligands and demonstrate the utility of native MS-based screening coupled with automated data analysis to uncover new ligand scaffolds for challenging enzyme targets. Full article

Mass concrete design presents a significant challenge due to the inherent conflicts among key performance metrics: high compressive strength, low heat of hydration, and low water absorption (a key durability indicator). Traditional trial-and-error methods are inefficient and fail to systematically navigate these complex trade-offs. To address this, this study proposes a data-driven multi-objective optimization framework for mass concrete mix design. A comprehensive experimental dataset of 64 mixtures was established by varying the water-to-binder ratio (0.40–0.55), fly ash content (0–120 kg/m³), and slag content (0–120 kg/m³), with cement content fixed at 400 kg/m³. Kriging surrogate models were developed to accurately map the nonlinear relationships between these design variables and the three performance responses. These models were then integrated with the NSGA-II algorithm to generate a Pareto-optimal front of solutions. The framework’s predictive accuracy and generalization capability were rigorously validated through out-of-sample experiments, demonstrating prediction errors consistently below 10%. The results provide a quantified map of feasible engineering compromises, enabling engineers to select tailored mixtures for specific project priorities, such as low-heat mixes for dams or high-strength mixes for foundations. Full article

Estimating dependency relationships between variables is an important issue in statistics. Mutual information (MI) is a measure of dependency which quantifies the amount of shared information between two variables. It is free of distribution assumption and captures both linear and non-linear dependencies. MI estimation methods were primarily developed for datasets with exclusively discrete variables, exclusively continuous variables, or a mixture of both. In practice, complex variables containing both discrete and continuous values (discrete-continuous variables), specifically continuous censored variables, are often present in real datasets (e.g., biological measures from analytical tools with lower detection limit). Methods have been developed to handle discrete-continuous data, but their effectiveness on the specific case of continuous censored data has not yet been evaluated. We propose a new estimation method based on the decomposition of the MI formula, with a first part handling the censoring status of the data, and a second part handling its continuous section. This estimation method works as a correction, as it takes in parameter one MI estimator for continuous data, and makes it able to handling censoring. We constructed different simulation scenarios of pairs of correlated censored log-normal variables, by varying the censoring rate, correlation, and sample size. We evaluated our correction on a few existing estimators previously developed for continuous, mixed or discrete-continuous data. We compared the selected estimators, with and without the correction, on these different scenarios. We found that the correction globally enables to reduce bias, and allows convergence towards the true MI value as the number of observations increases. Full article

Military burn pits used during post-9/11 U.S. military deployments functioned as uncontrolled combustion systems and were widely utilized to dispose of large volumes of outdoor waste by burning. Burn pits involved heterogeneous waste materials burned under variable temperature and oxygen conditions. These combustion environments generated complex, toxic, multipollutant airborne emission mixtures that included particulate matter (PM_2.5), polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). This narrative review synthesizes epidemiologic, experimental, and mechanistic evidence linking burn pit emissions to disruption of the lung–brain axis and adverse neurological outcomes. We specifically aim to address a critical gap in understanding how combustion-derived toxicants impact brain health and are associated with unfavorable neuropsychiatric outcomes, including increased risk of post-traumatic stress disorder (PTSD) and depression. Combustion-related exposures promote pulmonary inflammation and system-wide immune signaling that propagate to the central nervous system, contributing to neuroinflammation and dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis. These interconnected mechanisms are associated with toxic encephalopathy and related cognitive and mood disturbances, underscoring the need to integrate fire science with military and environmental health services research to better define the systemic and neurological consequences of acute and chronic fire-derived inhalation exposures. Full article

Spectrophotometry offers the advantage of low cost and less time consumption, making it still attractive as a method of analysis, especially when coupled with multivariate calibration models. This enhancement solves the majority of the drawbacks of UV–VIS spectrophotometry, which have to do with the entangled spectra of complex mixtures. In this study, a multivariate model was developed and validated for the determination of perindopril erbumine, amlodipine besylate and indapamide, addressing previously unresolved challenges by systematically covering three fixed-dose combinations with differing component ratios and by achieving accuracy suitable for the assay determination. The experimental plan involved a Taguchi orthogonal array design with three factors at five levels. In order to create multivariate calibration models, principal component regression, partial least squares and concentration residual augmented least squares regression algorithms were tested. Principal component regression combined with a genetic algorithm for feature selection was chosen as the optimal model based on prediction performance estimated by nested cross-validation with cluster-based sample splitting. The developed method was also evaluated for its environmentally friendly potential while the analytical method validation procedure confirmed its applicability for the assay testing of the fixed-dose drug combination. Full article

Forensic biotechnology is a rapidly evolving interdisciplinary field integrating molecular biology, genomics, and data science to address complex investigative challenges. Its applications span diverse domains, including criminalistics, food authentication, environmental monitoring, and bioterrorism preparedness. Advanced technologies such as Next-Generation Sequencing (NGS), CRISPR-Cas biosensors, and Artificial Intelligence (AI) play pivotal roles in modern diagnostics. NGS and eDNA revolutionize genetic profiling and ecological tracking, while microbiome analysis provides crucial insights into post-mortem intervals, cause of death, and geolocation. Simultaneously, CRISPR-based methods enable ultra-rapid pathogen detection, nanobiotechnology facilitates portable Lab-on-a-Chip (LOC) DNA analysis, and AI-driven algorithms optimize the interpretation of complex genomic mixtures and epigenetic age estimation. Despite these breakthroughs, significant challenges persist, including the strict legal admissibility of novel methodologies, the “black-box” dilemma in AI, ethical concerns regarding genetic privacy, and the critical need for global standardization. This review critically examines current biotechnological progress and future prospects, emphasizing the necessity of interdisciplinary collaboration to ensure reliable, accurate, and ethically sound forensic practices. Full article

As the demand for sustainable construction materials grows, wood ash and nanosilica have emerged as promising components for eco-friendly mortars, whose optimization requires advanced analytical techniques capable of capturing their complex linear and nonlinear interactions, making frameworks such as response surface methodology and artificial neural networks essential for effective mix design. This study examines the mechanical performance of eco-friendly mortar incorporating wood ash (WA) as a partial cement replacement and nanosilica solution (NSS) as a strength-enhancing additive, with the aim of optimizing compressive and flexural behaviour. Wood ash was substituted at levels of 5–25%, while NS (0.265 moL⁻¹) was substituted at levels of 0–1.7%. Twenty-one mortar samples were produced and tested at multiple curing ages. Two modelling techniques, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to evaluate the individual and interactive effects of WA and NSS on strength development at curing ages of 28 and 180 days. While RSM provided insight into factor significance and linear interactions, ANN more effectively captured nonlinear behaviour, achieving superior predictive accuracy (R² = 1.000 for 28-day strength). Experimental results revealed that nanosilica substantially enhanced strength up to an optimal dosage of approximately 2.5 g, beyond which performance declined due to particle agglomeration or matrix over-refinement. In contrast, higher WA contents produced strength reductions attributable to dilution effects. Optimization showed that mixtures containing low WA (≤30 g) combined with moderate NSS (2.0–2.5 g) exhibited the highest mechanical performance. Collectively, the findings confirm that ANN-based models outperform RSM and multilinear regression, underscoring their effectiveness for mix design optimization and performance forecasting in sustainable cementitious systems. Full article

Food waste is a readily digestible and fermentable feedstock for waste to energy bioprocesses. Approximately one third of food is wasted, thus making improvements in food waste valorization is essential for a circular economy. Laboratory results must be reproducible and as representative of scaled performance as possible to facilitate knowledge sharing between research groups. Food waste used in laboratory studies is often collected in situ or overly simplistic synthetic mixtures are used. Food waste collected in situ from any one local source at a single time point (e.g., grab samples from a cafeteria or restaurant) are not reproducible or nationally representative; additionally, overly simple synthetic mixtures are reproducible, but lack the complexity of real food waste and are not nationally representative. Thus, an adequately complex, reproducible, and nationally representative food waste recipe is needed to standardize the feedstocks used in laboratory scale food waste digestion and fermentation studies. In this work, we developed a food waste recipe made from widely and commercially available ingredients which is based on national-scale food wastage data in the United States. The nationally representative food waste mixture was 45.4% carbohydrates, 32.5% lipids, and 13.4% proteins. The biomethane potential was 495 ± 44 mL CH₄/g VS and the food waste mixture was suitable for use in low-pH bench-scale arrested anaerobic digesters. This design approach can be adapted for other regions and countries where food loss data are available. Full article