Search Results (618)

This study focused on optimizing ultrasound-assisted extraction (UAE) conditions (X_PC: pulse cycle of 1:1, 2:1, and 3:1 s on/off; X_UP: ultrasound power of 80, 90, and 100%; and X_ET: extraction time of 2, 4, and 6 min) for maximizing the content of soluble phenols (TSPs) and flavonoids (FLAs) from a native Mexican pigmented corn kernel powder through response surface methodology (RSM). Under the Box–Behnken design conditions, the UAE of TSPs ranged from 27.72 to 34.87 mg/g, while FLA content ranged from 16.59 to 27.28 mg/g. The highest content for TSPs was under 4 min X_ET, 1:1 s on/off X_PC, and 100% X_UP, while for flavonoids it was under 6 min X_ET, 2:1 s on/off X_PC, and 80% X_UP. According to RSM analysis, the optimal UAE conditions for TSPs were found to be X_ET 3.15 min, 1.58 s on/off X_PC, and 100% X_UP, and an X_ET of 4.18 min, 3 s on/off X_PC, and 80% X_UP were the best experimental conditions for FLAs with a predictive TSP of 35.07 mg/g and FLA of 27.51 mg/g. These data were adjusted in a second-order polynomial model and experimentally validated (TSP = 34.06 mg/g and 27.04 mg/g). Furthermore, the extracts demonstrated antioxidant activity (ABTS, FRAP, and DPPH methods) for optimal UAE for TSPs and FLAs. The antioxidant extract from the native Mexican pigmented corn kernel powder can be used for diverse industrial applications. Thus, the UAE is an effective and sustainable technology for recovering bioactive compounds from maize-based materials. Full article

As global population growth and urbanisation intensify energy demands, the quest for sustainable energy sources gains paramount importance. Hydrogen (H₂) emerges as a versatile energy carrier, contributing to diverse processes in energy systems, industrial applications, and scientific research. To harness the H₂ potential effectively, a profound grasp of its thermodynamic properties across varied conditions is essential. While field and laboratory measurements offer accuracy, they are resource-intensive. Experimentation involving high-pressure and high-temperature conditions poses risks, rendering precise H₂ solubility determination crucial. This study evaluates the application of Deep Neural Networks (DNNs) for predicting H₂ solubility in n-alkanes. Three DNNs are developed, focusing on model structure and overfitting mitigation. The investigation utilises a comprehensive dataset, employing distinct model structures. Our study successfully demonstrates that the incorporation of dropout layers and batch normalisation within DNNs significantly mitigates overfitting, resulting in robust and accurate predictions of H₂ solubility in n-alkanes. The DNN models developed not only perform comparably to traditional ensemble methods but also offer greater stability across varying training conditions. These advancements are crucial for the safe and efficient design of H₂-based systems, contributing directly to cleaner energy technologies. Understanding H₂ solubility in hydrocarbons can enhance the efficiency of H₂ storage and transportation, facilitating its integration into existing energy systems. This advancement supports the development of cleaner fuels and improves the overall sustainability of energy production, ultimately contributing to a reduction in reliance on fossil fuels and minimising the environmental impact of energy generation. Full article

Soluble interleukin 1 receptor-like 1 (ST2) is a circulating protein demonstrated to be associated with cardiovascular diseases; however, it has not been studied as a biomarker for peripheral artery disease (PAD). Using a prospectively recruited cohort of 476 patients (312 with PAD and 164 without PAD), we conducted a prognostic study of PAD using clinical/biomarker data. Plasma concentrations of three circulating proteins [ST2, cytokine-responsive gene-2 (CRG-2), vascular endothelial growth factor (VEGF)] were measured at baseline and the cohort was followed for 2 years. The outcome of interest was a 2-year major adverse limb event (MALE; composite of major amputation, vascular intervention, or acute limb ischemia). Using 10-fold cross-validation, a random forest model was trained using clinical characteristics and plasma ST2 levels. The primary model evaluation metric was the F1 score. Out of the three circulating proteins analyzed, ST2 was the only one that was statistically significantly higher in individuals with PAD compared to patients without PAD (mean concentration in plasma of 9.57 [SD 5.86] vs. 11.39 [SD 6.43] pg/mL, p < 0.001). Over a 2-year period, 28 (9%) patients with PAD experienced MALE. Our predictive model, incorporating clinical features and plasma ST2 levels, achieved an F1 score of 0.713 for forecasting 2-year MALE outcomes. Patients identified as high-risk by this model showed a significantly increased likelihood of developing MALE (HR 1.06, 95% CI 1.02–1.13, p = 0.003). By combining clinical characteristics and plasma ST2 levels, our proposed predictive model offers accurate risk assessment for 2-year MALE in PAD patients. This algorithm supports risk stratification in PAD, guiding clinical decisions regarding further vascular evaluation, specialist referrals, and appropriate medical or surgical interventions, thereby potentially enhancing patient outcomes. Full article

Underground CO₂ storage is crucial for sustainability as it reduces greenhouse gas (GHG) emissions, helping mitigate climate change and protect the environment. This research explores the use of Explainable Artificial Intelligence (XAI) to enhance the predictive modelling of CO₂ solubility in brine solutions. Employing Random Forest (RF) models, the study integrates Shapley Additive exPlanations (SHAP) analysis to uncover the complex relationships between key variables, including pressure (P), temperature (T), salinity, and ionic composition. Our findings indicate that while P and T are primary factors, the contributions of salinity and specific ions, notably chloride ions (Cl⁻), are essential for accurate predictions. The RF model exhibited high accuracy, precision, and stability, effectively predicting CO₂ solubility even for brines not included during the model training as evidenced by R² values greater than 0.96 for the validation and testing samples. Additionally, the stability assessment showed that the Root Mean Squared Error (RMSE) spans between 8.4 and 9.0 for 100 different randomness, which shows good stability. SHAP analysis provided valuable insights into feature contributions and interactions, revealing complex dependencies, particularly between P and ionic strength. These insights offer practical guidelines for optimising CO₂ storage and mitigating associated risks. By improving the accuracy and transparency of CO₂ solubility predictions, this research supports more effective and sustainable CO₂ storage strategies, contributing to the overall goal of reducing greenhouse gas emissions and combating climate change. Full article

Understanding the impact of variables involved in soluble-extracellular polymeric substance (S-EPS) production processes is crucial for reducing production costs and enhancing sustainability. Response surface methodology (RSM) provides essential tools that assist in developing predicted interactions among process variables for both industrial and non-industrial applications. The present study offers a simple and systematic demonstration of RSM capabilities, focusing on maximizing efficiency and minimizing production costs of S-EPS produced by Cyanocohniella rudolphia. RSM was employed to (1) design the production setup; (2) fit the collected data into a second-order polynomial model; (3) statistically evaluate the model’s validity and the significance of the involved variables; and (4) identify and optimize production variables to enhance output and reduce costs. Focused on four key variables, each at three levels, RSM designed 25 distinct S-EPS production conditions, each with three replicates. Statistical analysis identified the most significant variables affecting S-EPS production as the culture medium/wet biomass ratio, production days, and nitrogen concentration. The model’s validation demonstrated a strong correlation between the predicted and experimental values, with S-EPS production ranging from 70.46 to 228.65 mg/L and a maximum variation of 11.6%. This study demonstrates the effectiveness of RSM in optimizing S-EPS production, with the developed model showing a strong correlation between the variables and the response. The RSM model offers a promising approach for the bioprocessing industry, enhancing productivity and efficiency, minimizing costs, and leading to sustainable, cost-effective practices. Full article

Despite decreasing anthropogenic mercury (Hg) emissions in Europe and the banning and restriction of many persistent organic pollutants (POPs) under the Stockholm Convention, Mediterranean marine mammals still have one of the highest body burdens of persistent pollutants in the world. Moreover, the Mediterranean basin is one of the most sensitive to climate change, with likely changes in the biogeochemical cycle and bioavailability of Hg, primary productivity, and the length and composition of pelagic food webs. The availability of food resources for marine mammals is also affected by widespread overfishing and the increasing number of alien species colonizing the basin. After reporting the most recent findings on the biogeochemical cycle of Hg in the Mediterranean Sea and the physico-chemical and bio-ecological factors determining its exceptional bioaccumulation in odontocetes, this review discusses possible future changes in the bioavailability of the metal. Recent ocean–atmosphere–land models predict that in mid-latitude seas, water warming (which in the Mediterranean is 20% faster than the global average) is likely to decrease the solubility of Hg and favor the escape of the metal to the atmosphere. However, the basin has been affected for thousands of years by natural and anthropogenic inputs of metals and climate change with sea level rise (3.6 ± 0.3 mm year⁻¹ in the last two decades), and the frequency of extreme weather events will likely remobilize a large amount of legacy Hg from soils, riverine, and coastal sediments. Moreover, possible changes in pelagic food webs and food availability could determine dietary shifts and lower growth rates in Mediterranean cetaceans, increasing their Hg body burden. Although, in adulthood, many marine mammals have evolved the ability to detoxify monomethylmercury (MMHg) and store the metal in the liver and other organs as insoluble HgSe crystals, in Mediterranean populations more exposed to the metal, this process can deplete the biological pool of Se, increasing their susceptibility to infectious diseases and autoimmune disorders. Mediterranean mammals are also among the most exposed in the world to legacy POPs, micro- and nanoplastics, and contaminants of emerging interest. Concomitant exposure to these synthetic chemicals may pose a much more serious threat than the Se depletion. Unfortunately, as shown by the literature data summarized in this review, the most exposed populations are those living in the NW basin, the main feeding and reproductive area for most Mediterranean cetaceans, declared a sanctuary for their protection since 2002. Thus, while emphasizing the adoption of all available approaches to mitigate anthropogenic pressure with fishing and maritime traffic, it is recommended to direct future research efforts towards the assessment of possible biological effects, at the individual and population levels, of chronic and simultaneous exposure to Hg, legacy POPs, contaminants of emerging interest, and microplastics. Full article

Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the $C_2^8$ type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems. Full article

The adaptability of bermudagrass genotypes to high-pH saline–alkali conditions was investigated through a comprehensive evaluation of 38 genotypes during the seedling stage. For this purpose, two distinct treatments were established: exposure to saline–alkali solution composed of 45% NaCl, 5% Na₂SO₄, 5% NaHCO₃, and 45% Na₂CO₃ (pH 10.0), and exposure to distilled water as control. On 6th day of treatment, eight physiological indicators were measured. Compared with the control, the net photosynthetic rates, leaf water content, and chlorophyll content of the test genotypes decreased under stress. In contrast, the soluble protein content, proline levels, malondialdehyde concentration, and conductivity exhibited an increase. The salt–alkali tolerance coefficients of each indicator ranged from 0.24 to 8.54, and the variable coefficient was from 9.77% to 62.82%. Based on the salt–alkali tolerance coefficients, the comprehensive evaluation value (D) and resistance coefficient (CSAC) for each genotype were calculated. Subsequently, 38 genotypes were classified into three salt–alkali tolerance clusters by hierarchical clustering analysis, with Cluster I consisting of 10 genotypes with the most salt–alkali tolerance, and Cluster II with intermediate tolerance. Cluster III was comprised of 18 genotypes showing the lowest tolerance. The predictive model for assessing salt–alkali tolerance in bermudagrass is (D) = −0.238 + 0.106 × SAC_Chlb + 0.209 × SAC_RWC + 0.015 × SAC_Pro + 0.284 × SAC_Protein + 0.051 × SAC_Pn. Notably, Cluster I genotypes were more vigorous and showed lower damage under saline stress compared to Cluster III. Moreover, stepwise regression analysis pinpointed Chlb, RWC, and Pro as crucial indicators for evaluating salt–alkali tolerance in bermudagrass genotypes. Full article

Laser surface remelting of aluminum alloys has emerged as a promising technique to enhance mechanical properties through refined microstructures. This process involves rapid cooling rates ranging from 10³ to 10⁸ °C/s, which increase solid solubility within aluminum alloys, shifting their eutectic composition to a larger value of silicon content. Consequently, the resulting microstructure combines a strengthened aluminum matrix with silicon fibers. This study focuses on the laser scanning of Al-Si aluminum alloy to reduce the size of aluminum matrix spacings and transform fibrous silicon particles from micrometer to nanometer dimensions. Analysis revealed that the eutectic structure contained 17.55% silicon by weight, surpassing the equilibrium eutectic composition of 12.6% silicon. Microstructure dimensions within the molten zones, termed ‘melt pools’, were extensively examined using Scanning Electron Microscopy (SEM) at intervals of approximately 20 μm from the surface. A notable increase in hardness, exceeding 50% compared to the base plate, was observed in the melt pool regions. Thus, it is exemplified that laser surface remelting introduces a novel strengthening mechanism in the alloy. Moreover, this study develops an in situ method for predicting melt pool properties and dimensions. A predictive model is proposed, correlating energy density and spectral signals emitted during laser remelting with mechanical properties and melt pool dimensions. This method significantly reduces characterization time from days to seconds, offering a streamlined approach for future studies in additive manufacturing. Full article

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible–near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500–978 nm) and NIR (908–1676 nm) data collected from 180 samples of “Tochigi iW1 go” white strawberries and 150 samples of “Tochigi i27 go” red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R²_p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R²_p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system. Full article

A technology for the hydrochemical processing of the kaolinite fraction of bauxite has been developed, and it involves preliminary chemical activation in a sodium bicarbonate solution and alkaline leaching in a recycled high-modulus solution with the addition of an active form of calcium oxide. Chemical activation allows for the transformation of the difficult-to-explore kaolinite phase to form easily soluble phases of dawsonite, sodium hydroaluminosilicate and bemite. An active, finely dispersed form of calcium oxide was obtained as a result of CaO quenching in Na₂SO₄ solution at elevated temperature and pressure. Using a central composite design (CCD) via response surface methodology (RSM), the technological leaching mode was achieved. The influence on the leaching process was studied by adjusting the CaO/SiO₂ ratio, temperature, alkaline solution concentration and duration. It was found that the determining factors are the concentration of the leaching solution and the temperature. At a stable CaO/SiO₂ ratio, a combination of these two factors determines the duration of the process to achieve the predicted degree of recovery. The results of experiments carried out using the developed model of the leaching process confirmed the validity of the calculated indicators, with an error of 2.01%. In an optimal technological mode at a Na₂O leaching solution concentration of 260 g/L, a temperature of 260 °C, a CaO/SiO₂ ratio of 1.5 and a leaching time of 5 h, the extraction of Al₂O₃ into the solution was 89.7%, which is close to the value of 87.9% predicted by the model. Full article

Tadalafil (TD) has poor water solubility but is well absorbed without affecting food intake when administered orally. Owing to patient adherence and therapeutic characteristics, a TD-loaded orodispersible film (TDF) is preferable. However, the mechanistic role of dietary status on the clinical pharmacokinetic analysis of TDF in human volunteers should be investigated because the gastrointestinal environment varies periodically according to meal intervals, although commercial 20 mg TD-loaded tablets (TD-TAB, Cialis^® tablet) may be taken with or without food. TDF was prepared by dispersing TD in an aqueous solution and polyethylene glycol 400 to ensure good dispersibility of the TD particles. In the fasting state, each T/R of Cmax and AUC between TD-TAB and TDF showed bioequivalence with 0.936–1.105 and 1.012–1.153, respectively, and dissolution rates in 1000 mL water containing 0.5% SLS were equivalent. In contrast, TDF was not bioequivalent to TD-TAB under the fed conditions by the C_max T/R of 0.610–0.798. The increased dissolution rate of TDF via the micronization of drug particles and the reduced viscosity of the second meal content did not significantly affect the bioequivalence. Interestingly, an increase in second meal intake time from 4 h to 6 h resulted in the bioequivalence by the Cmax T/R of 0.851–0.998 of TD-TAB and TDF. The predictive diffusion direction model for physical digestion of TD-TAB and TDF in the stomach after the first and second meal intake was successfully simulated using computational fluid dynamics modeling, accounting for the delayed drug diffusion of TDF caused by prolonged digestion of stomach contents under postprandial conditions. Full article

In a study aimed at developing a rapid and nondestructive method for testing the quality of strawberries, spectral data from four strawberry varieties at different ripening stages were collected using a geophysical spectrometer, primarily focusing on the 350–1800 nm band. The spectra were preprocessed using Savitzky–Golay (SG) filtering, and characteristic bands were extracted using Pearson correlation coefficient (PCC) analysis. Models for predicting strawberry quality were built using random forest (RF), support vector machine (SVM), partial least squares (PLS), and Gaussian regression (GPR). The results indicated that the SVM model exhibited relatively high accuracy in predicting anthocyanin, hardness, and soluble solids content in strawberries. For the test set, the SVM model achieved R² and RMSE values of 0.81, 0.87, and 0.89, and 0.04 mg/g, 0.33 kg/cm², and 0.72%, respectively. Additionally, the PLS model demonstrated relatively high accuracy in predicting the titratable acid content of strawberries, achieving R² and RMSE values of 0.85 and 0.03%, respectively, for the test set. These findings provided a solid foundation for strawberry quality modeling and a veritable guide for non-destructive assessment of strawberry quality. Full article

Background/Objectives: Hepatitis E virus (HEV) is an RNA virus recognized to be spread mainly by fecal-contaminated water. Its infection is known to be a serious threat to public health globally, mostly in developing countries, in which Africa is one of the regions sternly affected. An African-based vaccine is necessary to actively prevent HEV infection. Methods: This study developed an in silico epitope-based subunit vaccine, incorporating CTL, HTL, and BL epitopes with suitable linkers and adjuvants. Results: The in silico-designed vaccine construct proved immunogenic, non-allergenic, and non-toxic and displayed appropriate physicochemical properties with high solubility. The 3D structure was modeled and subjected to protein docking with Toll-like receptors 2, 3, 4, 6, 8, and 9, which showed a stable binding efficacy, and the dynamics simulation indicated steady interaction. Furthermore, the immune simulation predicted that the designed vaccine would instigate immune responses when administered to humans. Lastly, using a codon adaptation for the E. coli K12 bacterium produced optimum GC content and a high CAI value, which was followed by in silico integration into a pET28 b (+) cloning vector. Conclusions: Generally, these results propose that the design of an epitope-based subunit vaccine can function as an outstanding preventive vaccine candidate against HEV, although validation techniques via in vitro and in vivo approaches are required to justify this statement. Full article

Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline. Full article