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Background/Objectives: Patients with schizophrenia have significant challenges in adhering to and complying with oral medicines, resulting in adverse consequences such as symptom worsening and psychotic relapse. Methods: This study aimed to develop clove oil-based bilosomes using definitive screening design (DSD) to maximize the anti-schizophrenic action of clozapine and promote its nose-to-brain delivery. The target was to optimize the physicochemical properties of bilosomes and incorporate them into mucoadhesive intranasal in situ gels, searching for augmented ex vivo and in vivo clozapine delivery. Results: The bilosomes' particle size was decreased by increasing the span, SDC, and clove oil amounts. In addition to using a high lipid amount, the aforementioned components also helped increase the entrapment efficiency values. Increased zeta potential was only observed by increasing surfactant amount and reducing clozapine concentration. After incorporation of optimized liquid clove oil-based bilosomes, which had a spherical nano-sized vesicular shape, into P 407-dependent gels, an HPMC (2% w/w)/P 407 (20% w/w)-containing formulation (G6) was selected as an optimized gel owing to its acceptable gelation time (13.28 s), gel strength (27.72 s), viscosity (12,766.67 cP), and mucoadhesive strength (4273.93 dyne/cm2). The optimized G6 exhibited higher Jss (50.86 μg/cm2·h−1) through the nasal mucosa compared to the control gel (23.03 μg/cm2·h−1). Compared to the control gel, G6 displayed higher relative bioavailability (491.37%) than a commercial tablet (264.46%). Following ELISA analysis, dopamine and serotonin were significantly reduced, while BDNF was remarkably increased after administration of optimized G6 into schizophrenic rats. Conclusion: Our study indicates the potential of intranasal bilosomal gels in upgrading the anti-schizophrenic and neuroprotective activity of clozapine. [ABSTRACT FROM AUTHOR]

Nickel contamination in water is a critical issue due to its toxicity and persistence. This study presents a novel magnetic resin, developed by modifying Lewatit® MonoPlus TP 207 with magnetite nanoparticles, to enhance adsorption capacity and facilitate efficient separation. A Definitive Screening Design (DSD) was employed to identify and optimize key parameters affecting nickel adsorption, including pH, resin dosage, initial nickel concentration, and the presence of competing ions (calcium and magnesium). The DSD analysis revealed that pH and magnesium concentration were the most significant factors influencing nickel removal. Optimal conditions were determined as pH 7, 270 min contact time, resin dosage of 0.5 mL/L, initial nickel concentration of 110 µg/L, calcium concentration of 275 mg/L, and magnesium concentration of 52.5 mg/L, achieving a maximum removal efficiency of 99.21%. The magnetic resin exhibited enhanced adsorption capacity and faster kinetics compared to the unmodified resin, leading to more efficient nickel removal. Moreover, its magnetic properties facilitated rapid separation from treated water, offering practical advantages for real-world applications. This study demonstrates the effective use of DSD in optimizing adsorption parameters and underscores the potential of magnetic resin as a sustainable and efficient adsorbent for water treatment. [ABSTRACT FROM AUTHOR]

The Analytical Quality by Design (AQbD) concept was adopted to establish a quantitative analysis of multi-components with a single marker (QAMS) method for industrial lanolin alcohol, targeting cholesterol, lanosterol, and 24,25-dihydrolanosterol. The potential critical method parameters (CMPs) were identified as column temperature, flow rate, and gradient. Definitive screening design and statistical modeling were employed to optimize the gradient conditions of the mobile phase, column temperature, and flow rate. The Method Operable Design Region (MODR) was determined using a risk-based quantification approach. The robustness was assessed using a Plackett–Burman experimental design, followed by methodological validation. Optimal analytical conditions were as follows: acetonitrile (B)—water (A) mobile phase system; flow rate of 1.58 mL/min; detection wavelength of 205 nm; injection volume of 10 µL; and column temperature of 37 °C. A gradient elution program was implemented as follows: 0–19.0 min, 90.5% B; 19.0–25.0 min, 90.5–100% B; and 25.0–55.0 min, 100% B. Cholesterol served as an internal standard for quantifying lanosterol and 24,25-dihydrolanosterol, with relative correction factors of 0.4227 and 0.8228, respectively. This analytical method utilized only the cholesterol reference substance as an internal standard to quantify the content of cholesterol, lanosterol, and 24,25-dihydrolanosterol in industrial lanolin alcohol. It reduced the testing costs and enhanced efficiency, making it potentially suitable for widespread adoption in lanolin alcohol processing industries. [ABSTRACT FROM AUTHOR]

Introduction: Toxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness. Methods: The lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process. Results: Five physicochemical variables showed a significant influence (p < 0.05) on the Pb2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO4.7H2O 0.2, resulting in a 96.12% removal rate. The Pb2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb2+ biosorption. The maximum Pb2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir's isotherm and pseudo-second-order model with R² of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively. Discussion: The outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems. [ABSTRACT FROM AUTHOR]

Introduction: Previously reported preparation methods of Ginkgo biloba leaf extract (EGBL) have mainly focused on the enrichment of flavonoid glycosides (FG) and terpene trilactones (TT), which led to the underutilization of G. biloba leaves (GBL). Objectives: To make full use of GBL, in this study, a comprehensive optimization strategy for preparing EGBL by macroporous resin column chromatography was proposed and applied to enrich FG, TT, and shikimic acid (SA) from GBL. Methodology: Initially, the static adsorption and desorption were executed to select suitable resin. Then, the influences of solution pH were investigated by the static and dynamic adsorption. Subsequently, eight process parameters were systematically investigated via a definitive screening design (DSD). After verification experiments, scale‐up enrichment was carried out, investigating the feasibility of the developed strategy for application on an industrial scale. Results: It was found that XDA1 was the most appropriate adsorbent for the preparation of EGBL at solution pH 2.0. Furthermore, based on the constraints of the desired quality attributes, the optimized ranges of operating parameters were successfully acquired, and the verification experiments demonstrated the accuracy and reliability of using DSD to investigate the chromatography process for the preparation of EGBL. Finally, magnified experiments were successfully performed, obtaining the EGBL containing 26.54% FG, 8.96% TT, and 10.70% SA, which reached the SA level of EGB761, an international standard EGBL. Conclusion: The present study not only provided an efficient and convenient approach for the preparation of EGBL enriched in SA but also accelerated efforts to high‐value utilization of GBL. In the present study, a comprehensive optimization strategy for preparing EGBL by macroporous resin column chromatography was proposed and applied to enrich flavonoid glycosides, terpene trilactones, and shikimic acid from Ginkgo biloba leaves. This study not only provided an efficient and convenient approach for the preparation of EGBL enriched in shikimic acid but also accelerated efforts to high‐value utilization of G. biloba leaves. [ABSTRACT FROM AUTHOR]

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This study showcases a promising approach to sustainably unlocking plant biomass residues by combining biodegradation with artificial intelligence to optimize the process. Specifically, we utilized the definitive screening design (DSD) and artificial neural networks (ANNs) to optimize the degradation of common bean biomass by the endophytic fungus Trichoderma asperellum WNZ-21. The optimized process yielded a fungal hydrolysate rich in 12 essential and non-essential amino acids, totaling 18,298.14 μg/g biomass. GC-MS analysis revealed four potential novel components not previously reported in microbial filtrates or plants and seven components exclusive to plant sources but not reported in microbial filtrates. The hydrolysate contained phenolic, flavonoid, and tannin compounds, as confirmed by FT-IR analysis. High-resolution transmission electron microscopy depicted structures resembling amino acid micelles and potential protein aggregates. The hydrolysate exhibited antioxidant, antibacterial, and anticancer properties and innovatively induced apoptotic modulation in the MCF7 cancer cell line. These findings underscore the potential of ANN-optimized fermentation for various applications, particularly in anticancer medicine due to its unique composition and bioactivities. The integration of the DSD and ANNs presents a novel technique for biomass biodegradation, warranting the valorization of plant biomass and suggesting a further exploration of the new components in the fungal hydrolysate. This approach represents the basic concept for exploring other biomass sources and in vivo studies. [ABSTRACT FROM AUTHOR]

The analysis of screening experiments is often done in two stages, starting with factor selection via an analysis under a main effects model. The success of this first stage is influenced by three components: (1) main effect estimators' variances and (2) bias, and (3) the estimate of the noise variance. Component (3) has only recently been given attention with design techniques that ensure an unbiased estimate of the noise variance. In this paper, we propose a design criterion based on expected confidence intervals of the first stage analysis that balances all three components. To address model misspecification, we propose a computationally-efficient all-subsets analysis and a corresponding constrained design criterion based on lack-of-fit. Scenarios found in existing design literature are revisited with our criteria and new designs are provided that improve upon existing methods. [ABSTRACT FROM AUTHOR]

Environmentally friendly and economically viable methods are essential in the selection of materials and techniques for the synthesis of nano--zero-valent iron. Plants, with their high polyphenol content and antioxidant capacity, have found application in eco-friendly synthesis processes. The definitive screening design (DSD) monitored four key process parameters for the concurrent removal of copper and phosphate: copper concentration (ranging from 1 to 9 mg L-1), phosphate concentration (ranging from 1 to 9 mg L-1), initial pH values (ranging from 2 to 10), and the dosage of nano-zero-valent iron (ranging from 2 to 16 mL). The analysis results provide valuable insights into the significant individual factors influencing the process, along with the potential for their interactions. The model also proposes process optimization to attain maximum removal efficiency, and subsequent verification confirmed its superiority among the alternatives. Mechanisms such as sorption, reduction, complexation, electrostatic attraction, and ligand exchange play pivotal roles in the effective removal of copper and phosphate using nano-zero-valent iron. In summary, this research yields several benefits: the utilization of environmentally sustainable materials, a substantial reduction in experimental complexity, coupled with the ease of the entire procedure, simultaneous and highly efficient copper and phosphate removal, favorable pH levels and, notably, no requirement for additional treatment. [ABSTRACT FROM AUTHOR]

Can nano-zero-valent iron, synthesized using oak leaf extract, be the key solution for water preservation, efficiently removing heavy metal ions and phosphate anions simultaneously? This research unveils how this technology not only promises high efficiency in the remediation of water resources, but also sets new standards for environmentally friendly processes. The high antioxidant capacity and high phenol content indicate suggest the possibility of oak-nZVI synthesis using oak leaf extract as a stable material with minimal agglomeration. The simultaneous removal of Cd and phosphates, as well as and Ni and phosphates was optimized by a statistically designed experiment with a definitive screening design approach. By defining the key factors with the most significant impact, a more efficient and faster method is achieved, improving the economic sustainability of the research by minimizing the number of experiments while maximizing precision. In terms of significance, four input parameters affecting process productivity were monitored: initial metal concentration (1–9 mg L−1), initial ion concentration (1–9 mg L−1), pH value (2–10), and oak-nZVI dosage (2–16 mL). The process optimization resulted in the highest simultaneous removal efficiency of 98.99 and 87.30% for cadmium and phosphate ions, respectively. The highest efficiency for the simultaneous removal of nickel and phosphate ions was 93.44 and 96.75%, respectively. The optimization process fits within the confidence intervals, which confirms the assumption that the selected regression model well describes the process. In the context of e of the challenges and problems of environmental protection, this work has shown considerable potential and successful application for the simultaneous removal of Cd(II) and Ni(II) in the presence of phosphates from water. [ABSTRACT FROM AUTHOR]

IntroductionToxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness.MethodsThe lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process.ResultsFive physicochemical variables showed a significant influence (p < 0.05) on the Pb2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO4.7H2O 0.2, resulting in a 96.12% removal rate. The Pb2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb2+ biosorption. The maximum Pb2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir’s isotherm and pseudo-second-order model with R2 of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively.DiscussionThe outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems.

Background/Objectives: Patients with schizophrenia have significant challenges in adhering to and complying with oral medicines, resulting in adverse consequences such as symptom worsening and psychotic relapse. Methods: This study aimed to develop clove oil-based bilosomes using definitive screening design (DSD) to maximize the anti-schizophrenic action of clozapine and promote its nose-to-brain delivery. The target was to optimize the physicochemical properties of bilosomes and incorporate them into mucoadhesive intranasal in situ gels, searching for augmented ex vivo and in vivo clozapine delivery. Results: The bilosomes’ particle size was decreased by increasing the span, SDC, and clove oil amounts. In addition to using a high lipid amount, the aforementioned components also helped increase the entrapment efficiency values. Increased zeta potential was only observed by increasing surfactant amount and reducing clozapine concentration. After incorporation of optimized liquid clove oil-based bilosomes, which had a spherical nano-sized vesicular shape, into P 407-dependent gels, an HPMC (2% w/w)/P 407 (20% w/w)-containing formulation (G6) was selected as an optimized gel owing to its acceptable gelation time (13.28 s), gel strength (27.72 s), viscosity (12,766.67 cP), and mucoadhesive strength (4273.93 dyne/cm2). The optimized G6 exhibited higher Jss (50.86 μg/cm2·h−1) through the nasal mucosa compared to the control gel (23.03 μg/cm2·h−1). Compared to the control gel, G6 displayed higher relative bioavailability (491.37%) than a commercial tablet (264.46%). Following ELISA analysis, dopamine and serotonin were significantly reduced, while BDNF was remarkably increased after administration of optimized G6 into schizophrenic rats. Conclusion: Our study indicates the potential of intranasal bilosomal gels in upgrading the anti-schizophrenic and neuroprotective activity of clozapine.

The Analytical Quality by Design (AQbD) concept was adopted to establish a quantitative analysis of multi-components with a single marker (QAMS) method for industrial lanolin alcohol, targeting cholesterol, lanosterol, and 24,25-dihydrolanosterol. The potential critical method parameters (CMPs) were identified as column temperature, flow rate, and gradient. Definitive screening design and statistical modeling were employed to optimize the gradient conditions of the mobile phase, column temperature, and flow rate. The Method Operable Design Region (MODR) was determined using a risk-based quantification approach. The robustness was assessed using a Plackett–Burman experimental design, followed by methodological validation. Optimal analytical conditions were as follows: acetonitrile (B)—water (A) mobile phase system; flow rate of 1.58 mL/min; detection wavelength of 205 nm; injection volume of 10 µL; and column temperature of 37 °C. A gradient elution program was implemented as follows: 0–19.0 min, 90.5% B; 19.0–25.0 min, 90.5–100% B; and 25.0–55.0 min, 100% B. Cholesterol served as an internal standard for quantifying lanosterol and 24,25-dihydrolanosterol, with relative correction factors of 0.4227 and 0.8228, respectively. This analytical method utilized only the cholesterol reference substance as an internal standard to quantify the content of cholesterol, lanosterol, and 24,25-dihydrolanosterol in industrial lanolin alcohol. It reduced the testing costs and enhanced efficiency, making it potentially suitable for widespread adoption in lanolin alcohol processing industries.

This study showcases a promising approach to sustainably unlocking plant biomass residues by combining biodegradation with artificial intelligence to optimize the process. Specifically, we utilized the definitive screening design (DSD) and artificial neural networks (ANNs) to optimize the degradation of common bean biomass by the endophytic fungus Trichoderma asperellum WNZ-21. The optimized process yielded a fungal hydrolysate rich in 12 essential and non-essential amino acids, totaling 18,298.14 μg/g biomass. GC-MS analysis revealed four potential novel components not previously reported in microbial filtrates or plants and seven components exclusive to plant sources but not reported in microbial filtrates. The hydrolysate contained phenolic, flavonoid, and tannin compounds, as confirmed by FT-IR analysis. High-resolution transmission electron microscopy depicted structures resembling amino acid micelles and potential protein aggregates. The hydrolysate exhibited antioxidant, antibacterial, and anticancer properties and innovatively induced apoptotic modulation in the MCF7 cancer cell line. These findings underscore the potential of ANN-optimized fermentation for various applications, particularly in anticancer medicine due to its unique composition and bioactivities. The integration of the DSD and ANNs presents a novel technique for biomass biodegradation, warranting the valorization of plant biomass and suggesting a further exploration of the new components in the fungal hydrolysate. This approach represents the basic concept for exploring other biomass sources and in vivo studies.

The material extrusion fused deposition modeling (FDM) technique has become a widely used technique that enables the production of complex parts for various applications. To overcome limitations of PLA material such as low impact toughness, commercially available materials such as UltiMaker Tough PLA were produced to improve the parent PLA material that can be widely applied in many engineering applications. In this study, 3D-printed parts (test specimens) considering six different printing parameters (i.e., layer height, wall thickness, infill density, build plate temperature, printing speed, and printing temperature) are experimentally investigated to understand their impact on the mechanical properties of Tough PLA material. Three different standardized tests of tensile, flexural, and compressive properties were conducted to determine the maximum force and Young's modulus. These six properties were used as responses in a design of experiment, definitive screening design (DSD), to build six regression models. Analysis of variance (ANOVA) is performed to evaluate the effects of each of the six printing parameters on Tough PLA mechanical properties. It is shown that all regression models are statistically significant ( p < 0.05 ) with high values of adjusted and predicted R 2 . Conducted confirmation tests resulted in low relative errors between experimental and predicted data, indicating that the developed models are adequately accurate and reliable for the prediction of tensile, flexural, and compressive properties of Tough PLA material. [ABSTRACT FROM AUTHOR]

In recent years, fused deposition modeling (FDM) based on material extrusion additive manufacturing technology has become widely accepted as a cost-effective method for fabricating engineering components with net-shapes. However, the limited exploration of the influence of FDM process parameters on surface roughness parameters, i.e., Ra (average surface roughness), Rq (root mean square surface roughness), and Rz (maximum height of the profile) across different sides (bottom, top, and walls) poses a challenge for the fabrication of functional parts. This research aims to bridge the knowledge gap by analyzing surface roughness under various process parameters and optimizing it for nylon carbon fiber printed parts. A definitive screening design (DSD) was employed for experimental runs. The Pareto chart highlighted the significant effects of layer height, part orientation, and infill density on all surface roughness parameters and respective sides. The surface morphology was analyzed through optical microscopy. Multi-response optimization was performed using an integrated approach of composited desirability function and entropy. The findings of the present study hold significant industrial applications, enhancing the quality and performance of 3D printed parts. From intricate prototypes to durable automotive components, the optimized surfaces contribute to production of functional and visually appealing products across various sectors. [ABSTRACT FROM AUTHOR]

A definitive screening design was used in order to evaluate the effects of starch, glycerol and montmorillonite (MMT) concentrations, as well as the drying temperature, drying tray type and starch species, on packaging film's functional properties. Optimization showed that in order to obtain films with the minimum possible thickness, the maximum elongation at break, the maximum tensile strength, as well as reduced water vapor permeability and low opacity, a combination of factors should be used as follows: 5.5% wt starch concentration, 30% wt glycerol concentration on a dry starch basis, 10.5% wt MMT concentration on a dry starch basis, 45 °C drying temperature, chickpea as the starch species and plexiglass as the drying tray type. Based on these results, starch films were prepared, and fresh minced meat was stored in them for 3 days. It was shown that the incorporation of MMT at 10.5% wt on a dry starch basis in the packaging films led to a decreased mesophilic and psychrotrophic bacteria growth factor compared to commercial packaging. When assessed for their biodegradability, the starch films disintegrated after 10 days of thermophilic incubation under simulated composting conditions. Finally, to prove their handling capability during industrial production, the starch films were rewound in a paper cylinder using an industrial-scale rewinding machine. [ABSTRACT FROM AUTHOR]

Experimental data are often highly structured due to the use of experimental designs. This does not only simplify the analysis, but it allows for tailored methods of analysis that extract more information from the data than generic methods. One group of three-level experimental designs that are suitable for such tailored methods are orthogonal minimally aliased response surface (OMARS) designs (Núñez Ares and Goos 2020), where all main effects are orthogonal to each other and to all second-order effects. The design based analysis method of Jones and Nachtsheim (2017) has shown significant improvement over existing methods in terms of powers to detect active effects. However, the application of their method is limited to only a small subgroup of OMARS designs known as definitive screening designs (DSDs). In our work, we not only improve upon the Jones and Nachtsheim method for DSDs, but we also generalize their analysis framework to the entire family of OMARS designs. Using extensive simulations, we show that our customized method for analyzing data from OMARS designs is highly effective in identifying active effects when compared to other modern (non-design based) analysis methods, especially in cases where the true model is complex and involves many second-order effects. [ABSTRACT FROM AUTHOR]

In fused deposition modeling (FDM), the prediction and optimization of surface roughness distribution by varying the process parameters of the printing is required during the process planning stage. During this stage, the traditional screening design, such as fractional factorial design, is commonly used to identify the process parameters that have the most considerable effect on process outcomes. The screening design is followed by further experiments, including only essential process parameters to develop prediction models and identify the optimal process parameters. Recently developed custom design approaches such as I-optimal design and definitive screening design (DSD) eliminate the need for follow-up experiments by identifying the critical process parameters and optimum process conditions in a single experimental design. Therefore, the present study is intended to compare the performance of I-optimal design and DSD in terms of prediction and optimization of average surface roughness (Ra) of fused deposition modeling (FDM) printed parts from poly lactic acid (PLA). The following process parameters were considered to reduce the prediction error of Ra models: layer thickness, number of contours, infill density, raster angle, printing speed, extrusion temperature, bed temperature, and built orientation. The results revealed that regression models based on the I-optimal design are saturated and complex, with 38 terms (including eight main effects, twenty three interaction effect, and seven quadratic effects) in the final model. However, the DSD model has 12 model terms (including seven main effects, two interaction effects, and three quadratic effects), making it unsaturated and less complex compared to the I-optimal design. Both models have comparable prediction accuracy based on validation tests. Finally, Ra is optimized based on the desirability function. The results of the present study will add knowledge to the existing literature about the performance of I-optimal and DSD methods in predicting and optimizing responses. [ABSTRACT FROM AUTHOR]

In the present work, biodiesel was produced from watermelon waste oil by the transesterification process. A definitive screening design was used to investigate the effect of transesterification parameters such as catalyst concentration (4−10%), methanol-to-oil ratio (2−10mol/mol)and reaction temperature (46-60°C). The quadratic regression analysis was used to predict the yield of biodiesel. It is found that the optimum catalyst concentration, methanol-to-oil molar ratio and reaction temperature are 5.51%, 5.77:1 and 60°C, respectively, resulting in a 96.763% yield of methyl esters, which is in excellent confirmation with the predicted value. According to the results, all variables had a significant effect on the yield of biodiesel. The physicochemical property of the extracted watermelon waste oil and produced biodiesel using optimum process parameters was analyzed. The results revealed that the extracted oil and produced biodiesel met the requirements of ASTM and EN14214 (European committee for standardization) standard methods. Oleic acid and Linoleic acid were detected to be the dominant unsaturated fatty acids; while palmitic acid was found to be the highest amount of saturated fatty acid. It can be concluded that a definitive screening design for optimization of transesterification reaction parameters is an effective way to boost the yield of biodiesel. In addition, this method significantly reduces the number of experiments, which is important for a limited amount of raw materials. In general, the finding of this work proposes that the watermelon waste oils seem to be a potential and alternative feedstock for the production of biodiesel from waste materials.

Conversion of nonfood natural products to value-added products is an important work, which is going on worldwide. In addition, to obtain a product with better quality, a sufficient separation method is helpful to improve its utilization value by using effective, rapid and environmentally friendly techniques. Thus in this work, the microwave-assisted hydrodistillation (MAHD) process of essential oils from important natural plant Rumex Crispus leaves was investigated. The experimental design and extraction conditions were optimized using a definitive screening design. The yield of essential oils of 4.67 ± 0.02 % was investigated under the optimum conditions; 534.89 W of microwave power, 23.48 min of hydrodistillation time and 4.5 mL/g of the volume of water to plant mass ratio. Under these conditions, the essential oils were analyzed using gas chromatography-mass spectrometry (GC–MS). Results revealed that the essential oil extracted by MAHD possessed high quality as proved by its higher percentage of oxygenated compounds. In addition, α-santol (29.63 %) and β-santol (25.60 %) are the primary components of oxygenated compounds in essential oils. In conclusion, the MAHD was successfully employed to obtain mainly Oxygenated compounds-rich essential oil that may be used in several industrial applications. Subsequently, a definitive screening design may be regarded as an alternative and reliable method for the prediction of experimental parameters. It was concluded that the weeds plant (Rumex Crispus) contains a reliable quantity of oils that is extremely feasible to use and recommended as good feedstock for possible use in industrial applications.

The huge amount of solid waste produced during the hydrodistillation of Pelargonium graveolens L’Hér. is a serious environmental hazard. The aim of this research is to reduce this environmental pollution and to valorize this waste by using its extract as a natural dye. Extracts were used for dyeing wool samples. The dyed fabrics showed shades ranging between brown and beige. By using the Minitab 19 Software, a definitive screening design was used to optimize the dye extraction process. The studied extraction process parameters were pH, temperature, and extraction time. The main studied responses were the color strength and the polyphenol, flavonoid, and condensed tannin contents (mg.g−1). Based on the photochemical study and the UV–vis absorption spectra, it was found that the polyphenol content varies between 4.144 and 7.567 mg.g−1. For the tannin content, the highest value was equal to 120.967 mg.g−1. To optimize the colorant extraction process, the K/S was also measured by the spectrocolorimeter. Process optimum conditions were of 11, 100°C, and 65 min, respectively, for pH, temperature, and extraction time. The optimum K/S value was equal to 115.15. Therefore, the solid waste hydrodistillation of Pelargonium graveolens can be an interesting choice for the natural dyeing of textile fabrics.

BACKGROUND: Auricularia auricula (A. auricula) is one of the most abundant sources of plant protein in edible fungi. Problems of low protein yield exist in traditional methods of protein extraction such as alkali extraction and ultrasonic-assisted alkali after pretreatment with enzymes. Thus, the protein extraction process was investigated and optimized using a definitive screening design from A. auricula to improve the protein yield under practical operating conditions of temperature, the concentration of NaCl, meal/water ratio, extraction time and pH. RESULTS: The yield of protein isolates of the isoelectric-ammonium sulfate precipitation (9.34% w/w) was obtained almost three times and the protein content (55.23% w/w) was approximately 1.6 times that of the traditional extraction method of isoelectric precipitation. Next, the optimized method was successfully applied to the analysis of the functional properties of the protein. A. auricula protein isolate (AAPI) had better solubility, emulsification and foaming capacity than soy protein isolate (SPI) and pea protein isolate (PPI), and the oil holding capacity of AAPI exhibited extremely well, which was approximately five times that of SPI and six times that of PPI. The texture properties of AAPI gel were similar to those of PPI gels. CONCLUSION: AAPI extracted by the optimized method had a satisfactory yield and had the potential to substitute plant-originated proteins in food processing. [ABSTRACT FROM AUTHOR]

The huge amount of solid waste produced during the hydrodistillation of Pelargonium graveolens L’Hér. is a serious environmental hazard. The aim of this research is to reduce this environmental pollution and to valorize this waste by using its extract as a natural dye. Extracts were used for dyeing wool samples. The dyed fabrics showed shades ranging between brown and beige. By using the Minitab 19 Software, a definitive screening design was used to optimize the dye extraction process. The studied extraction process parameters were pH, temperature, and extraction time. The main studied responses were the color strength and the polyphenol, flavonoid, and condensed tannin contents (mg.g−1). Based on the photochemical study and the UV–vis absorption spectra, it was found that the polyphenol content varies between 4.144 and 7.567 mg.g−1. For the tannin content, the highest value was equal to 120.967 mg.g−1. To optimize the colorant extraction process, the K/S was also measured by the spectrocolorimeter. Process optimum conditions were of 11, 100°C, and 65 min, respectively, for pH, temperature, and extraction time. The optimum K/S value was equal to 115.15. Therefore, the solid waste hydrodistillation of Pelargonium graveolens can be an interesting choice for the natural dyeing of textile fabrics. [ABSTRACT FROM AUTHOR]

Background: Xiaochaihu capsule is composed of seven traditional Chinese medicines. The pharmacopoeia only focuses on the quantitative detection of baicalin, which cannot fully reflect the quality of the preparation. Some medium polar components were used to establish the fingerprint of Xiaochaihu capsule, but there was no report on the strong polar components. Methods: A high performance liquid chromatography-corona charged aerosol detection technology was used to establish a fingerprint analysis method for Xiaochaihu capsules following an analytical quality by design approach. Definitive screening designed experiments were used to optimize the method parameters. A stepwise regression method was used to build quantitative models. The method operable design region was calculated using the experimental error simulation method. Plackett–Burman designed experiments were carried out to test robustness. Results: The contents of four components were simultaneously determined. There were seven common peaks in the fingerprint. The common peak area accounted for 91.72%. Both fingerprint and quantitative analysis methods were validated as applicable in the methodology study. The quantitative fingerprint analysis method for sugar components can fill the gap in the detection of strong polar components in the existing methods. It provides a new technology for the comprehensive overall evaluation of Xiaochaihu capsule. [ABSTRACT FROM AUTHOR]

Orthogonal minimally aliased response surface (OMARS) designs constitute a new family of three-level experimental designs for studying quantitative factors. Many experiments, however, also involve one or more two-level categorical factors. In this work, we derive necessary conditions for the existence of mixed-level OMARS designs, and present three construction methods based on integer programming. Like the original three-level OMARS designs, the new mixed-level designs are orthogonal main-effect plans in which the main effects are also orthogonal to the second-order effects. These properties distinguish the new designs from definitive screening designs with additional two-level categorical factors and other mixed-level designs recently presented in the literature. To demonstrate the flexibility of our construction methods, we provide 587 mixed-level OMARS designs in the online Supplementary Material. [ABSTRACT FROM AUTHOR]

Definitive screening design (DSD) has become a widely used type of Design of Experiments for chemical, pharmaceutical and biopharmaceutical processes and product development due to its optimization properties with an estimation of main, interaction, and squared variable effects with a minimum number of experiments. These high dimensional DOEs with more variables than samples, and with partly correlated variables, make the statistical interpretation frequently challenging. The purpose of the study was to test bootstrap PLSR using a heredity procedure to select the variable subset to be finally evaluated by MLR. The heredity selection was used on bootstrap T values given by original PLSR coefficients (B) divided on the bootstrap estimated standard deviation. The investigated fractional weighted and non-parametric bootstrap PLSR resulted in same variable selection outcome and final models in this study. A simulation study with 7 main variables and 12 tested literature real data DSDs with 4, 5, 7 and 8 main variables showed improved model performance for small and particularly for large DSDs for the bootstrap PLSR MLR methods compared to two common DSD reference methods; DSD fit definitive screening and AICc forward stepwise regression (AICc FSR). Variable selection accuracy and predictive ability were significantly improved by the investigated method in 6 out of 13 DSDs compared to the best model from either of the two reference methods. The remaining 7 DSDs gave the same model as best reference model. Strong heredity was found to provide the best models for all real data in this study. The use of the heredity procedure on the percent non-zero SVEM FSR variable effects followed by MLR showed promising results. AICc Lasso regression was among other methods partially tested and was found to set almost all variables to zero effect when tested on three large minimum DSDs. While the DSD fit definitive screening method may often be the first choice for DSD, the heredity bootstrap PLSR MLR and heredity SVEM FSR MLR may be alternative methods to improve the variable selection and model precision. • The statistical interpretation of definitive screening design is frequently challenging. • Bootstrap PLSR was found to aid the variable selection. • The weak and strong heredity-oriented strategy was used on bootstrap PLSR results. • Backward variable selection MLR was used on the bootstrap PLSR variable subset. • The investigated method developed significantly improved models compared to several other investigated methods. [ABSTRACT FROM AUTHOR]

Rapid, sensitive, and user-friendly methods are essential for the detection of nucleic acid. The combination of isothermal amplification with CRISPR/Cas12a not only demonstrates significant advantages but also holds promising prospects for widespread application. However, the requirement for separate nucleic acid preamplification and Cas12a cleavage complicates operational procedures and increases the risk of aerosol pollution. Developing an easily implementable and widely applicable one-pot system remains a challenge. In this study, we introduced a novel approach to balance the reaction kinetics between enzymatic recombinase amplification (ERA) and Cas12a enzyme activity to establish a highly sensitive one-pot ERA/Cas12a (OPERA-Cas12a) system. We systematically identified key factors affecting reactions and utilized a definitive screening design (DSD) optimization strategy to streamline the optimization process. Using porcine-specific gene as the detection target, we successfully established the OPERA-Cas12a pork system for pork detection, which demonstrated high specificity and sensitivity. Extending this strategy to duck-specific gene, we developed the OPERA-Cas12a duck system with comparable performance. Both OPERA-Cas12a systems achieved a limit of detection (LOD) of 6 copies per reaction within 60 minutes. Moreover, the detection results can be conveniently read with an LED blue light illuminator, and images can be captured using a smartphone. This OPERA-Cas12a system offers a valuable reference for developing other CRISPR/Cas-base nucleic acids detection applications. [Display omitted] • Systematically studied and identified the factors affecting the one-pot reaction. • Proposed a simple and readily implementable OPERA-Cas12a optimization strategy. • OPERA-Cas12a detected as few as six copies of target gene within 60 minutes. • The OPERA-Cas12a system demonstrated versatility in detecting different gene sequences. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) is the most economical additive manufacturing (AM) technology available for fabricating complex part geometries. However, the involvement of numerous control process parameters and dimensional instabilities are challenges of FDM. Therefore, this study investigated the effect of 3D printing parameters on dimensional deviations, including the length, width, height, and angle of polylactic acid (PLA) printed parts. The selected printing parameters include layer height, number of perimeters, infill density, infill angle, print speed, nozzle temperature, bed temperature, and print orientation. Three-level definitive screening design (DSD) was used to plan experimental runs. The results revealed that infill density is the most consequential parameter for length and width deviation, while layer height is significant for angle and height deviation. The regression models developed for the four responses are non-linear quadratic. The optimal results are obtained considering the integrated approach of desirability and weighted aggregated sum product assessment (WASPAS). The optimal results include a layer height of 0.1 mm, a total of six perimeters, an infill density of 20%, a fill angle of 90°, a print speed of 70 mm/s, a nozzle temperature of 220 °C, a bed temperature of 70 °C, and a print orientation of 90°. The current study provides a guideline to fabricate assistive devices, such as hand and foot orthoses, that require high dimensional accuracies. [ABSTRACT FROM AUTHOR]

The preparation processes of iron-based organic framework(FeMOF) MIL-100(Fe) and MIL-101(Fe) with two different ligands were optimized and screened, and the optimized FeMOF was loaded with piperlongumine(PL) to enhance the biocompatibility and antitumor efficacy of PL. The MIL-100(Fe) and MIL-101(Fe) were prepared by solvent thermal method using the optimized reaction solvent. With particle size, polymer dispersity index(PDI), and yield as indexes, the optimal preparation processes of the two were obtained by using the definitive screening design(DSD) experiment and establishing a mathematical model, combined with the Derringer expectation function. After characterization, the best FeMOF was selected to load PL by solvent diffusion method, and the process of loading PL was optimized by a single factor combined with an orthogonal experiment. The CCK-8 method was used to preliminarily evaluate the biological safety of blank FeMOF and the antitumor effect of the drug-loaded nano preparations. The experimental results showed that the optimal preparation process of MIL-100(Fe) was as follows: temperature at 127.8 ℃, reaction time of 14.796 h, total solvent volume of 11.157 mL, and feed ratio of 1.365. The particle size of obtained MIL-100(Fe) nanoparticles was(108.84±2.79)nm; PDI was 0.100±0.023, and yield was 36.93%±0.79%. The optimal preparation process of MIL-101(Fe) was as follows: temperature at 128.1 ℃, reaction time of 6 h, total solvent volume of 10.005 mL, and feed ratio of 0.500. The particle size of obtained MIL-101(Fe) nanoparticles was(254.04±22.03)nm; PDI was 0.289±0.052, and yield was 44.95%±0.45%. The optimal loading process of MIL-100(Fe) loaded with PL was as follows: the feed ratio of MIL-100(Fe) to PL was 1∶2; the concentration of PL solution was 7 mg·mL~(-1), and the ratio of DMF to water was 1∶5. The drug loading capacity of obtained MIL-100(Fe)/PL nanoparticles was 68.86%±1.82%; MIL-100(Fe) was nontoxic to HepG2 cells at a dose of 0-120 μg·mL~(-1), and the half-inhibitory concentration(IC&#95;(50)) of free PL for 24 h treatment of HepG2 cells was 1.542 μg·mL~(-1). The IC&#95;(50) value of MIL-100(Fe)/PL was 1.092 μg·mL~(-1)(measured by PL). In this study, the optimal synthesis process of MIL-100(Fe) and MIL-101(Fe) was optimized by innovatively using the DSD to construct a mathematical model combined with the Derringer expectation function. The optimized preparation process of MIL-100(Fe) nanoparticles and the PL loading process were stable and feasible. The size and shape of MIL-100(Fe) particles were uniform, and the crystal shape was good, with a high drug loading capacity, which could significantly enhance the antitumor effect of PL. This study provides a new method for the optimization of the nano preparation process and lays a foundation for the further development and research of antitumor nano preparations of PL.

A definitive screening design was used in order to evaluate the effects of starch, glycerol and montmorillonite (MMT) concentrations, as well as the drying temperature, drying tray type and starch species, on packaging film’s functional properties. Optimization showed that in order to obtain films with the minimum possible thickness, the maximum elongation at break, the maximum tensile strength, as well as reduced water vapor permeability and low opacity, a combination of factors should be used as follows: 5.5% wt starch concentration, 30% wt glycerol concentration on a dry starch basis, 10.5% wt MMT concentration on a dry starch basis, 45 °C drying temperature, chickpea as the starch species and plexiglass as the drying tray type. Based on these results, starch films were prepared, and fresh minced meat was stored in them for 3 days. It was shown that the incorporation of MMT at 10.5% wt on a dry starch basis in the packaging films led to a decreased mesophilic and psychrotrophic bacteria growth factor compared to commercial packaging. When assessed for their biodegradability, the starch films disintegrated after 10 days of thermophilic incubation under simulated composting conditions. Finally, to prove their handling capability during industrial production, the starch films were rewound in a paper cylinder using an industrial-scale rewinding machine.

Segregation of granular materials is a critical phenomenon in various industries, such as food processing, pharmaceuticals, and mining. The Discrete Element Method (DEM) is an effective tool for gaining insight into granular segregation by providing particle-level information and the freedom to model mixtures that are often difficult or impossible to achieve through experiments. To ensure realistic material behaviour and correct representation of segregation, it is essential to calibrate the model parameters systematically. However, in the context of multi-component segregation, it is extremely challenging and computationally expensive to consider all parameters in the calibration procedure since interaction parameters between components must also be considered. This work aims to identify the dominant DEM parameters for modelling multi-component segregation during hopper discharge, chute flow and heap formation in a mixture of pellet and sinter. Utilising a representative example of a multi-component mixture with different sizes, densities and shapes used in blast furnaces, the investigation is done for various initial configurations as well as various mass ratios of the mixture. Our findings revealed that, while only particle-geometry interaction parameters dominate the segregation after the hopper and chute flow, particle-particle parameters are also significant for segregation in the heap. We also demonstrated that the downstream segregation is significantly influenced by the segregation upstream. Moreover, we found that the effect of pellet-sinter interactions is negligible. This research provides insights into the dominant DEM parameters, facilitating more efficient and robust calibration of multi-component models in future research endeavours. [Display omitted] • Sensitivity analysis on 15 DEM interaction parameters to identify dominant ones for segregation. • Segregation after hopper discharge is mainly influenced by particle-geometry interactions. • Segregation within the heap is affected by particle-particle interactions as well. • The dominant DEM parameters are strongly dependent on initial mixture configuration. • Downstream segregation is highly dependent on upstream segregation dynamics. [ABSTRACT FROM AUTHOR]

Modern experiments typically involve a very large number of variables. Screening designs allow experimenters to identify active factors in a minimum number of trials. To save costs, only low-level factorial designs are considered for screening experiments, especially two- and three-level designs. In this article, we provide a systematic method to construct screening designs that contain both two- and three-level factors based on Hadamard matrices with the fold-over structure. The proposed designs have good performance in terms of D-optimal and A-optimal criteria, and the estimates of the main effects are unbiased by the second-order effects, making them very suitable for screening experiments. Besides, some theoretical results on D- and A-optimality are obtained as a by-product. • The new method is easy to implement, utilizing Hadamard matrices and a fold-over structure. • The proposed designs have good performance in terms of D-optimal and A-optimal criteria. • The resulting designs ensure that the estimates of the main effects are unbiased for second-order effects. [ABSTRACT FROM AUTHOR]

The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the biosorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Factors such as peptone, incubation time, pH, glycerol, glucose, K2HPO4, and initial cobalt had a significant effect on the biosorption process. MgSO4 was the only insignificant factor. The DSD model was invalid and could not forecast the prediction of Co(II) removal, owing to the significant lack-of-fit (P < 0.0001). Decisively, the prediction ability of ANN was accurate with a prominent response for training (R2 = 0.9779) and validation (R2 = 0.9773) and lower errors. Applying the optimal levels of the tested variables obtained by the ANN model led to 96.32 ± 2.1% of cobalt bioremoval. During the biosorption process, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy confirmed the sorption of Co(II) ions by P. alcaliphila. FTIR indicated the appearance of a new stretching vibration band formed with Co(II) ions at wavenumbers of 562, 530, and 531 cm–1. The symmetric amino (NH2) binding was also formed due to Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the biosorption of Co(II) by P. alcaliphila via DSD or ANN paradigm. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the influence of fused deposition modeling (FDM) process parameters on the tribological behavior of PC-ABS built prototypes. Wear resistance of FDM manufactured parts in relation to the variation of FDM process parameters was analyzed and studied using definitive screening design and analysis of variance technique. The worn surfaces of some fabricated samples were also analyzed and investigated in this study by scanning electron microscope. Genetic algorithm was applied to optimize the wear resistance and to identify the exact effect of the input parameters. The results obtained from this study would have an impact on the FDM product performance in various industrial applications. This study demonstrates how the manufacturing parameters can be understood in FDM additive manufacturing technology. By implementing a similar approach, the optimum setting of process parameters, which gives the best tribological properties, can be determined whenever a new material is used for the FDM process. [ABSTRACT FROM AUTHOR]

The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the biosorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Factors such as peptone, incubation time, pH, glycerol, glucose, K2HPO4, and initial cobalt had a significant effect on the biosorption process. MgSO4 was the only insignificant factor. The DSD model was invalid and could not forecast the prediction of Co(II) removal, owing to the significant lack-of-fit (P < 0.0001). Decisively, the prediction ability of ANN was accurate with a prominent response for training (R2 = 0.9779) and validation (R2 = 0.9773) and lower errors. Applying the optimal levels of the tested variables obtained by the ANN model led to 96.32 ± 2.1% of cobalt bioremoval. During the biosorption process, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy confirmed the sorption of Co(II) ions by P. alcaliphila. FTIR indicated the appearance of a new stretching vibration band formed with Co(II) ions at wavenumbers of 562, 530, and 531 cm–1. The symmetric amino (NH2) binding was also formed due to Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the biosorption of Co(II) by P. alcaliphila via DSD or ANN paradigm.

A previous study revealed that a convex pattern surface can reduce sliding wear of a transfer chute. A convex pattern surface is a flat surface outfitted with a pattern of convexes defined by five parameters. A three-level definitive screening design (DSD) method combined with discrete element method (DEM) is used to investigate the influence of the five parameters and two operational conditions on the sliding wear. Two flow regimes are distinguished, namely continuous and discontinuous flow regimes, and both flow regimes can significantly reduce the sliding wear. The particle velocity and angular velocity profiles verify the guiding and rolling effect of the convex pattern on the motion of particles. A regression model fitted based on the DSD indicates that three main factors and one interaction have significant influence on the sliding wear. [Display omitted] • Convex pattern surface can reduce sliding wear compared with flat surface. • Continuous and discontinuous flow regimes are formed. • Definitive screening design is an effective way to sort out critical factors. • Three factors and one interaction have influence on the sliding wear. [ABSTRACT FROM AUTHOR]

Introduction: Toxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb 2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness., Methods: The lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process., Results: Five physicochemical variables showed a significant influence ( p < 0.05) on the Pb 2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO 4 .7H 2 O 0.2, resulting in a 96.12% removal rate. The Pb 2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb 2+ biosorption. The maximum Pb 2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir's isotherm and pseudo-second-order model with R 2 of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively., Discussion: The outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb 2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 El-Sharkawy, Khairy, Abbas, Zaki and El-Hadary.)

Background: Xiaochaihu capsule is composed of seven traditional Chinese medicines. The pharmacopoeia only focuses on the quantitative detection of baicalin, which cannot fully reflect the quality of the preparation. Some medium polar components were used to establish the fingerprint of Xiaochaihu capsule, but there was no report on the strong polar components. Methods: A high performance liquid chromatography-corona charged aerosol detection technology was used to establish a fingerprint analysis method for Xiaochaihu capsules following an analytical quality by design approach. Definitive screening designed experiments were used to optimize the method parameters. A stepwise regression method was used to build quantitative models. The method operable design region was calculated using the experimental error simulation method. Plackett–Burman designed experiments were carried out to test robustness. Results: The contents of four components were simultaneously determined. There were seven common peaks in the fingerprint. The common peak area accounted for 91.72%. Both fingerprint and quantitative analysis methods were validated as applicable in the methodology study. The quantitative fingerprint analysis method for sugar components can fill the gap in the detection of strong polar components in the existing methods. It provides a new technology for the comprehensive overall evaluation of Xiaochaihu capsule.

The biopharmaceutical industry prefers to culture the mammalian cells in suspension with a serum‐free media (SFM) due to improved productivity and process consistency. However, mammalian cells preferentially grow as adherent cells in a complete medium (CM) containing serum. Therefore, cells require adaptation from adherence in CM to suspension culture in SFM. This work proposes an adaptation method that includes media supplementation during the adaption of Chinese hamster ovary cells. As a result, the adaptation was accelerated compared to the traditional repetitive subculturing. Ca2+/Mg2+ supplementation significantly reduced the doubling time compared to the adaptation without supplementation during the adaptation of adherent cells from 100% CM to 75% CM (p < 0.05). Furthermore, a definitive screening design (DSD) was applied to select essential nutrients during the adaptation from 10% CM to 0% CM. The main effects of Ca2+ and Dulbecco's modified essential medium (DMEM) were found significant to both viable cell density and viability at harvest. Additionally, the interaction term between Ca2+ and DMEM was found significant, which highlights the ability of DSD to capture interaction terms. Eventually, the media supplementation method resulted in adaptation SFM in 27 days, compared to the previously reported 66 days. Additionally, the membrane surface integrin expression was found significantly decreased when adherent cells were adapted to suspension. Moreover, the Ca2+/Mg2+ supplementation correlated with faster integrin recovery after trypsinization. However, faster integrin recovery did not contribute to the accelerated cell growth when subculturing from 100% CM to 75% CM. [ABSTRACT FROM AUTHOR]

This case study describes a novel use of a definitive screening design (DSD) run in a three-step process to create a special resin. In the first step, polymerization, three factors were varied. The two most important properties of the resin result from the polymerization step. Due to a useful feature of a DSD, the measured values of these properties (responses) could be analyzed using a full quadratic model. One additional response was measured after two subsequent finish steps in which another six factors were varied. As a result, this experiment has the flavor of response surface model (RSM) design for the most important responses and screening for the remaining response. [ABSTRACT FROM AUTHOR]

After completing the experimental runs of a screening design, the responses under study are analyzed by statistical methods to detect the active effects. To increase the chances of correctly identifying these effects, a good analysis method should provide alternative interpretations of the data, reveal the aliasing present in the design, and search only meaningful sets of effects as defined by user-specified restrictions such as effect heredity. This article presents a mixed integer optimization strategy to analyze data from screening designs that possesses all these properties. We illustrate our method by analyzing data from real and synthetic experiments, and using simulations. [ABSTRACT FROM AUTHOR]

The dye molecules are scarcely biodegradable. They are stable to light and oxidizing agents, which makes their effluent treatment extremely difficult. In this work, the adsorption of methyl orange dye has been carried out using raw clay samples taken from the Jbel Lehbib region (North of Morocco) in an aqueous medium. The experiments were designed according to the definitive screening design (DSD) method. The DSD of the experiment aimed to evaluate the effect of six factors: initial solution pH, initial concentration of methyl orange dye, clay mass, stirring speed, and the temperature and contact time. Adsorbent material properties were investigated by means of X-ray diffraction and Fourier transform infrared spectroscopy. The adsorption tests were carried out in a batch system, and each experimental parameter was changed within its range. Based on the results obtained, a reduced quadratic model was determined to best fit the experiment data, and the most significant factors were found to be: pH, initial concentration, and quadratic term pH × pH. Detailed analysis of the effect of different factors as well as their interaction on the removal has been carried out as well. Maximum removal of 69% occurred at the following optimum conditions: pH = 3 and initial concentration of the dye 600 mg/L. [ABSTRACT FROM AUTHOR]

Rapidly expanding pipelines in biopharmaceutical industry and increasingly stringent requirements from regulatory agencies call for a more efficient and rational study design at early-stage cell culture process development. Recent developments in high-throughput bioreactor system and design of experiments (DoE) enable cell culture process development sector to meet such demand by integrating both technological advances. In this study, we compared varied forms of fractional factorial designs (FFDs) and definitive screening design (DSD) covering key process parameters such as temperature shift target, temperature shift timing, feed strategy, pH, dissolved oxygen, harvest time and additives in Ambr® 15 bioreactors to better understand the initial cell culture process and thus optimize it. Results showed that DSD was more effective and efficient in detecting both main and quadratic effects comparing to FFDs. Comparison of varied available model development strategies revealed that two-stage forward regression combined with corrected Akaike's information criterion (AICc) and least absolute shrinkage and selection operator (LASSO) regression combined with self-validated ensembled models (SVEM) offered superior predictive capability. Moreover, key DoE regularities was evaluated for their validity. In conclusion, DSD is a suitable experimental design for the early-stage cell culture process development if quadratic effects might exist. Statistical models derived based on DSD-based dataset could be applied for predictive optimization of antibody producing bioprocess while regularities such as effect heredity and effect hierarchy might be relaxed considering higher-order interaction effects are more likely to existed for a cell-based biological system. • Utilizing high-throughput bioreactors and DoE to streamline early-stage cell culture optimization. • DSD was more effective and efficient in detecting both main and quadratic effects comparing to FFDs. • AICc-based two-stage forward stepwise regression models and SVEM-Lasso offered superior predictive capability. • Relaxing effect heredity and hierarchy while still adhering to the principles of effect sparsity and model parsimony. [ABSTRACT FROM AUTHOR]

Abstract Background Quality by design (QbD) is an advanced drug quality control concept that has been gradually implemented in the optimization of manufacturing processes of Chinese medicines. However, the variation of Chinese medicinal material quality has rarely been considered in published works. Because manufacturing processes may lower the variation introduced through different batches of materials, a material quality control strategy should be developed considering the influences of manufacturing processes. Methods In this work, the processes of extraction, concentration, water precipitation, and chromatography for notoginseng total saponin (NTS) production were investigated while considering Panax notoginseng quality variation as a sample. Ten process parameters were studied simultaneously using a definitive screening design. After the process critical quality attributes (CQAs) were determined, critical process parameters (CPPs) and critical material attributes (CMAs) were identified simultaneously. Then, models utilizing the CMAs, CPPs, and process CQAs were developed. The design space was then calculated using a Monte Carlo simulation method with an acceptable probability of 0.90. A material quality control strategy considering the influences of manufacturing processes was proposed. Results The ginsenoside Rd purity and total saponin purity in the eluate were identified as process CQAs. The ethanol solution concentration used for extraction, the ethanol solution concentration used for elution, and elution time were identified as CPPs. The extractable dry matter content of Panax notoginseng was one of the CMAs. The extractable contents of notoginsenoside R1, ginsenoside Rg1, ginsenoside Rb1, and ginsenoside Rd were the other CMAs. The inequalities implemented to discriminate the high quality and low quality of Panax notoginseng were developed according to the NTS standard of the Xuesaitong injection. Low quality Panax notoginseng should not be released for NTS production. High quality Panax notoginseng can be treated with feasible manufacturing processing parameters. Verification experiments were carried out successfully for 2 batches of high quality Panax notoginseng. Conclusions In this work, a quality control strategy for herbal materials was developed considering the matching of process characteristics and material quality attributes. This strategy is promising for application to other Chinese medicines.

Heterogeneous Fenton catalyst Fe2(MoO4)3 was prepared and the process efficiency was evaluated for oxidation of water-based Cyan flexo dye in synthetic aqueous solution and printing wastewater. The removal process of printing dye was analyzed by UV/VIS spectrophotometry, while dye mineralization was evaluated by the determination of total organic carbon content and chemical oxygen demand. Four determinants of the heterogeneous Fenton system, including initial dye concentration, iron concentration, pH and hydrogen peroxide concentration were investigated. Statistical method, definitive screening design was applied to generate optimal operational conditions of the four variables, which maximizes the process of dye removal. The initial dye concentration of 20 mgL-1, catalyst dosage of 0.75 mgL-1, pH of 2 and H2O2 concentration of 11 mM were chosen as the best operational conditions, contributing to 82% of the process efficiency. The Fenton process efficiency of 79% was achieved within the treatment of printing wastewater under optimal conditions for a 90 minute reaction time. The maximum COD removal efficiency was 61.1%, while 67% mineralization was achieved. The obtained results confirmed synergistic effect of Fe3+ and MoO4 2− which contributed to high catalytic activity and high heterogeneous Fenton efficiency. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR 34014 and Grant no. III43005]