Biodiesel production from agricultural biomass wastes: Duroc breed fat oil, Citrillus lanatus rind, and Sorghum Bagasse.

This research study synthesized a base catalyst from the waste Citrullus lanatus rind (WCLR) for the synthesis of biodiesel from the waste pig fat oil. The high-acid-value oil (high free fatty acid: FFA) was converted to low-acid-value oil through adsorption in sorghum bagasse ash with high particle sizes. The developed base catalyst was obtained from the WCLR and was characterized via thermogravimetric analysis (TGA), Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX), Fourier Transform Infrared Spectroscopy (FTIR), X-ray diffraction (XRD-FT), and Brunauer-Emmett-Teller (BET) adsorption analysis. The properties of biodiesel were compared with the recommended standard. Results reflected that the duroc breed pig fat is rich in oil, and the oil is unsaturated. Sorghum bagasse proved to be a good bio-adsorbent for the unsaturated fat FFA reduction. The catalyst produced from WCLR was found to be rich in potassium-calcium-magnesium (K-Ca-Mg) base salts. The predicted yield of 98.69 % (wt./wt.) at 69.96 min, 79.93 °C, 3.15 % (wt.), and 8.57 (vol.) at desirability of 100 % was validated as 98.52 % (wt./wt.). Catalytic strength can be recycled in five cycles. The cost implications indicated that the cost of producing 25 L of biodiesel is $2.61. This study proved to be the most economical way of producing biodiesel that is environmentally friendly, cost-effective, and easy to produce for future energy needs.•Oil was obtained via rendering from duroc breed waste fat oil.•Sorghum bagasse was used as adsorbent for acid reduction of high FFA pig fat oil.•Base catalyst used was obtained from calcined waste Citrullus lanatus rind.

Optimisation of quality features of new wheat beers fermented through sequential inoculation of non-Saccharomyces and Saccharomyces yeasts.

The choice of the starchy ingredients as well as that of the yeasts strongly can represent a useful tool to differentiate the final beers. Our research investigated twelve white beers obtained applying a 2-factor mixed 3-level/4-level experimental design. The first factor was the cereal mixture, with 3 combinations of barley malt (65 %) and unmalted wheat (35 % of common, durum, or emmer). The second factor was the yeast used to carry out the fermentation trials, i.e.: a S. cerevisiae starter strain (WB06); an oenological S. cerevisiae strain (9502); two mixed starters made of an oenological Schizosaccharomyces pombe strain (6956) and, alternatively, one of the two S. cerevisiae strains. Most beer attributes were significantly (p < 0.05) influenced by the two considered factors with the following exceptions: the wheat species did not affect maltotriose, maltose, pH, total and volatile acidity, floral flavour, and sweetness; the yeast did not exert significant effects on foam colour, turbidity, overall olfactory intensity, yeast flavour, and body. The flavour of fruits and aromatic herbs were not influenced by the factors studied. Alcohol content was maximised using the unmalted durum wheat (∼7 %) and S. cerevisiae WB06 (∼6.8 %). The beer antioxidant content was increased by the use of emmer (566 mg/L) and by the application of the mixed inoculum (478-487 mg/L). The beers made with unmalted common wheat and fermented by the S. cerevisiae strains alone obtained the best overall sensory score (3.7). As shown by the Principal Component Analysis, the beers were better classified by the type of unmalted wheat than by the fermenting yeast. A multiple regression analysis was performed by fitting the analytical parameters that highlighted significant differences among the beers to a second-order polynomial model. Data concerning colour, glycerol concentration, FC-TPC, and antioxidant activity were satisfactorily predicted (R2 > 0.8) by the fitted models.

Influence of Defects and Microstructure on the Thermal Expansion Behavior and the Mechanical Properties of Additively Manufactured Fe-36Ni.

Laser-based powder bed fusion of metals (PBF-LB/M) is a widely used additive manufacturing process characterized by a high degree of design freedom. As a result, near fully dense complex components can be produced in near-net shape by PBF-LB/M. Recently, the PBF-LB/M process was found to be a promising candidate to overcome challenges related to conventional machining of the Fe64Ni36 Invar alloy being well known for a low coefficient of thermal expansion (CTE). In this context, a correlation between process-induced porosity and the CTE was presumed in several studies. Therefore, the present study investigates whether the unique thermal properties of the PBF-LB/M-processed Fe64Ni36 Invar alloy can be tailored by the selective integration of defects. For this purpose, a full-factorial experimental design, representing by far the largest processing window in the literature, was considered, correlating the thermal expansion properties with porosity and hardness. Furthermore, the microstructure and mechanical properties were investigated by scanning electron microscopy and quasi-static tensile tests. Results by means of statistical analysis reveal that a systematic correlation between porosity and CTE properties could not be determined. However, by using specific process parameter combinations, the microstructure changed from a fine-grained fan-like structure to a coarse columnar structure.

Quality by design (QbD) based approach for development of itraconazole-loaded transferosomes for skin cancer: in vitro, ex vivo and cell line studies.

OBJECTIVE: Itraconazole (ITZ), a widely used systemic antifungal drug, has been ingeniously repurposed for its antitumor effects. In the present work, we have prepared and optimized the ITZ-loaded transferosomes by Quality by Design (QbD) approach and repurposed them for skin cancer. METHODS: The transferosomal formulation was optimized by employing a QbD approach with the design of experiment. A combination of screening and optimization design was used for formulation optimization. The optimized formulation was characterized by particle size, PDI, zeta potential, FTIR, XRD, and surface morphology using TEM. In vitro and ex vivo studies were performed using Franz diffusion cells. An in vitro cell line study was performed on the human melanoma A375 cell line. RESULTS: The optimized formulation has a particle size of 192.37 ± 13.19 nm, PDI of 0.41 ± 0.03, zeta potential -47.80 ± 3.66, and an entrapment efficiency of 64.11 ± 3.75%. In vitro release studies showed that ITZ encapsulated transferosomes offer higher and sustained release than pure drugs. Ex vivo drug penetration and retention studies show that the penetration and retention of transferosomes are more visible in the skin than in the drug. The cell viability study confirms that ITZ encapsulated transferosomes have almost 2-fold more potency against the A375 cell line than pure drug. CONCLUSION: ITZ encapsulated transferosomes were successfully prepared and optimized using a combination of screening and optimization designs. Based on ex vivo and cell line studies, we conclude that ITZ-loaded transferosomes could aid melanoma management alongside standard therapies.

Handshake of deep eutectic solvent and ionic liquid: Two liquid-liquid microextraction procedures for plant analysis.

Plants are subjects of interest due to the secondary metabolites in their extracts which are promising as new pharmaceuticals. Phytochemistry do not have united system of sample preparation or analysis still due to different structure of plant cells, wide broad range of chemical properties and concentrations of bioactive compounds. Such challenges can be addressed in a green chemistry manner using new approaches through smart materials in routine monitoring and researches. Liquid smart materials, such as ionic liquids (ILs) and deep eutectic solvents (DESs) are attractive due to flexible properties, lots of extraction approaches, recycle potential, and direct compatibility with powerful analytical methods. In this study DES-based microextraction procedure with pH-switching was developed. Four choline chloride DESs were suggested as selective extraction phases for polar compounds from acetonitrile extracts. Method was successfully tested on four plants (Iris sibirica L., Hypericum perforatum L., Scutellaria baicalensis G, Citrus reticulata B.). Developed procedure was optimized and validated for the choline chloride - urea (1:2 mol/mol) DES that demonstrated better results in extraction. LOD for rutin was found as 0.05 mg ml-1. For low-polar compound, imidazolium ionic liquid-based dispersive liquid-liquid microextraction procedure was developed. 1-hexyl-3-methylimidazolium salts have demonstrated desired selectivity. The main factors influencing the extraction efficiency have been identified and optimized by design of experiment on two model plants (Iris sibirica L. and Scutellaria baicalensis G.). Validation procedures were done for thymol. LOD for thymol was found as 0.021 mg ml-1. The methods were compared with each other and traditional methanol extraction. The selectivity of the smart materials supports each other, usage of such extraction phases provides same or better results as obtained with methanol.

Optimization of Glyburide-Loaded Nanosuspensions via Ball Milling and Homogenization Techniques: A Central Composite Design Approach for Enhanced Solubility.

INTRODUCTION: Glyburide is a drug for the treatment of diabetes mellitus and has a potential effect on Alzheimer's disease. It is also a BCS Class 2 drug with low solubility and low permeability. Developing a nanosuspension formulation and increasing the solubility and dissolution rate of glyburide is required to overcome this challenge. METHODS: Thus, the goal of this work was to create glyburide nanosuspensions by ball milling and homogenizing glyburide to increase its solubility and rate of dissolution. To achieve this, the nanosuspension formulation was optimized using a central composite design. Zeta potential, particle size distribution and solubility were selected by way of dependent variables, and ball milling time, homogenization cycles, and Pluronic F-127/glyburide ratio were chosen as independent variables. Glyburide nanosuspensions were obtained with a particle size of 244.6 ± 2.685 nm. In vitro release and solubility studies were conducted following optimization. RESULTS: The saturation solubility of glyburide was nearly doubled as a result of the nanocrystal formation. Xray diffraction (XRD), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and Fourier-transform infrared spectroscopy (FT-IR) were used to assess the nanosuspension. SEM images confirmed that the nanocrystal formation process was successful. Glyburide and the excipients have no incompatibilities, their physical states have not changed, and the preparation method has not affected the stability of glyburide, according to DCS, XRD, and FT-IR analyses. CONCLUSION: These studies indicated that a combination of ball milling and homogenization techniques significantly enhanced the solubility of glyburide and its release from the formulation. Consequently, this approach can be applied to formulations characterized by low absorption and limited bioavailability.

Optimizing the fermentation parameters in the Lactic Acid Fermentation of Legume-based Beverages- a statistically based fermentation.

BACKGROUND: The market for beverages is highly changing within the last years. Increasing consumer awareness towards healthier drinks led to the revival of traditional and the creation of innovative beverages. Various protein-rich legumes were used for milk analogues, which might be also valuable raw materials for refreshing, protein-rich beverages. However, no such applications have been marketed so far, which might be due to unpleasant organoleptic impressions like the legume-typical "beany" aroma. Lactic acid fermentation has already been proven to be a remedy to overcome this hindrance in consumer acceptance. RESULTS: In this study, a statistically based approach was used to elucidate the impact of the fermentation parameters temperature, inoculum cell concentration, and methionine addition on the fermentation of lupine- and faba bean-based substrates. A total of 39 models were found and verified. The majority of these models indicate a strong impact of the temperature on the reduction of aldehydes connected to the "beany" impression (e.g., hexanal) and on the production of pleasantly perceived aroma compounds (e.g., ß-damascenone). Positively, the addition of methionine had only minor impacts on the negatively associated sulfuric compounds methional, dimethyl sulfide, dimethyl disulfide, and dimethyl trisulfide. Moreover, in further fermentations, the time was added as an additional parameter. It was shown that the strains grew well, strongly acidified the both substrates (pH ≤ 4.0) within 6.5 h, and reached cell counts of > 9 log10 CFU/mL after 24 h. Notably, most of the aldehydes (like hexanal) were reduced within the first 6-7 h, whereas pleasant compounds like ß-damascenone reached high concentrations especially in the later fermentation (approx. 24-48 h). CONCLUSIONS: Out of the fermentation parameters temperature, inoculum cell concentration, and methionine addition, the temperature had the highest influence on the observed aroma and taste active compounds. As the addition of methionine to compensate for the legume-typical deficit did not lead to an adverse effect, fortifying legume-based substrates with methionine should be considered to improve the bioavailability of the legume protein. Aldehydes, which are associated with the "beany" aroma impression, can be removed efficiently in fermentation. However, terminating the process prematurely would lead to an incomplete production of pleasant aroma compounds.

Anion exchange-HPLC method for evaluating the encapsulation efficiency of mRNA-loaded lipid nanoparticles using analytical quality by design.

Lipid nanoparticles (LNPs) are emerging nucleic acid delivery systems in the development of mRNA therapeutics such as the severe acute respiratory syndrome coronavirus 2 vaccines. However, a suitable analytical method for evaluating the encapsulation efficiency (EE) of the LNPs is required to ensure drug efficacy, as current analytical methods exhibit throughput issues and require long analysis times. Hence, we developed and validated an anion-exchange HPLC method using Analytical Quality by Design. Three critical method parameters (CMPs) were identified using risk assessment and Design of Experiments: column temperature, flow rate, and sodium perchlorate concentration. The CMPs were optimized using Face-Centered Central Composite Design. The discriminating power of the optimized HPLC method and RiboGreen assay was comparable. The main advantage of this method is that LNPs can be directly injected into the HPLC system without bursting the LNPs loaded with encapsulated poly(A). The optimized HPLC method was validated as robust, high-throughput, and sufficiently sensitive according to the ICH Q2 guidelines. We believe our findings could promote efficient LNPs-based drug development.

[Design of nonlinear locking mechanism for shape memory alloy archwire of miniature orthodontic device].

The locking mechanism between bracket and shape memory alloy (SMA) archwire in the newly developed domestic orthodontic device is the key to controlling the precise alignment of the teeth. To meet the demand of locking force in clinical treatment, the tightening torque angle of the locking bolt and the required torque magnitude need to be precisely designed. For this purpose, a design study of the locking mechanism is carried out to analyze the correspondence between the tightening torque angle and the locking force and to determine the effective torque value, which involves complex coupling of contact, material and geometric nonlinear characteristics. Firstly, a simulation analysis based on parametric orthogonal experimental design is carried out to determine the SMA hyperelastic material parameters for the experimental data of SMA archwire with three-point bending. Secondly, a two-stage fine finite-element simulation model for bolt tightening and archwire pulling is established, and the nonlinear analysis is converged through the optimization of key contact parameters. Finally, multiple sets of calibration experiments are carried out for three tightening torsion angles. The comparison results between the design analysis and the calibration experiments show that the deviation between the design analysis and the calibration mean value of the locking force in each case is within 10%, and the design analysis method is valid and reliable. The final tightening torque angle for clinical application is determined to be 10° and the rated torque is 2.8 N∙mm. The key data obtained can be used in the design of clinical protocols and subsequent mechanical optimization of novel orthodontic devices, and the research methodology can provide a valuable reference for force analysis of medical devices containing SMA materials.

Quality by design steered approach for co-encapsulation of timolol maleate and dorzolamide hydrochloride in injectable liposomes.

Glaucoma is caused by high intraocular pressure, which can causes blindness. Combinations of timolol and dorzolamide are used for its treatment with a requirement of multiple dosing with dosing being twice or four times a day. Conventional eye drops have poor pre-corneal retention and is thus less available for action. This study utilizes principles of Quality by Design for formulation of injectable liposomes coloaded with timolol maleate and dorzolamide HCl, which overcomes limitations of conventional eye drops. For implementation of Quality by Design principles a systematic approach involving defining Quality Target Product Profile, identification of Critical Quality Attributes, mapping Critical Quality Attributes to Critical Process Parameters and Critical Material Attributes, Failure Mode and Effect Analysis based risk assessment, Taguchi screening, and 32 full factorial Design of Experiments design were utilized. A robust model for formulation of coloaded liposomes was successfully developed. Design of Experiments approach allowed to obtain optimized batch having particle size of 116.1 nm, encapsulation efficiency of dorzolamide HCl of 72.12 % and encapsulation efficiency of timolol maleate of 71.94 %. In-vitro drug release showed a sustained release for 4 days. The prepared formulation was in the desired osmolarity range. Biosafety was proved using histopathological characterization. In-vivo studies for assessing the Intra Ocular Pressure reduction showed that there was no significant difference in Intra Ocular Pressure reduction between prepared liposomes and marketed formulation but were superior than marketed formulation because of less fluctuations in Intra Ocular Pressure. Prepared coloaded injectable liposomes lays the foundation for further research in the area and can be translated from to bench side for commercial clinical use.

Design of experiment (DoE) of mucoadhesive valproic acid-loaded nanostructured lipid carriers (NLC) for potential nose-to-brain application.

Epilepsy is a highly prevalent neurological disease and valproic acid (VPA) is used as a first-line chronic treatment. However, this drug has poor oral bioavailability, which requires the administration of high doses, resulting in adverse effects. Alternative routes of VPA administration have therefore been investigated, such as the nose-to-brain route, which allows the drug to be transported directly from the nasal cavity to the brain. Here, the use of nanostructured lipid carriers (NLC) to encapsulate drugs administered in the nasal cavity has proved advantageous. The aim of this work was to optimise a mucoadhesive formulation of VPA-loaded NLC for intranasal administration to improve the treatment of epilepsy. The Design of Experiment (DoE) was used to optimise the formulation, starting with component optimisation using Mixture Design (MD), followed by optimisation of the manufacturing process parameters using Central Composite Design (CCD). The optimised VPA-loaded NLC had a particle size of 76.1 ± 2.8 nm, a polydispersity index of 0.190 ± 0.027, a zeta potential of 28.1 ± 2.0 mV and an encapsulation efficiency of 85.4 ± 0.8%. The in vitro release study showed VPA release from the NLC of 50 % after 6 h and 100 % after 24 h. The in vitro biocompatibility experiments in various cell lines have shown that the optimised VPA-loaded NLC formulation is safe up to 75 µg/mL, in neuronal (SH-SY5Y), nasal (RPMI 2650) and hepatic (HepG2) cells. Finally, the interaction of the optimised VPA-loaded NLC formulation with nasal mucus was investigated and mucoadhesive properties were observed. The results of this study suggest that the use of intranasal VPA-loaded NLC may be a promising alternative to promote VPA targeting to the brain, thereby improving bioavailability and minimising adverse effects.

Optimization of headspace high-capacity tool coupled to two-dimensional gas chromatography-mass spectrometry for mapping the volatile organic compounds of raw pistachios. A proof-of-concept on the classification ability by geographic origin.

An optimized procedure for extracting and analyzing raw pistachio volatiles was developed through headspace sampling with high-capacity tools and subsequent analysis using comprehensive two-dimensional gas chromatography coupled with mass spectrometry. The examination of 18 pistachio samples belonging to different geographic areas led to the identification of a set of 99 volatile organic compounds (VOCs). Molecules were putatively identified using linear retention index, mass spectra similarity, and two-dimensional plot location. The impact of preprocessing and processing techniques on the aligned data matrix from a set of samples of different geographical origins, after removing contaminants, was evaluated. The combination of scaling with log-transformation, normalization with z-score, and data reduction with random forest machine learning algorithm generated a panel of 16 discriminatory VOC molecules. As a proof of concept, raw pistachios' VOC profile was employed for the first time to tentatively classify them based on their geographical origin.

Removal of Rare Earth Elements from complex mixtures by using manganese ferrite nanoparticles: Optimization through surface response methodology.

The crucial role of Rare Earth Elements (REEs) in the development of hi-tech in addition to their limited availability have urged countries to develop sustainable alternatives to their conventional primary sources (ore mining). Sorption technologies using magnetic materials such as spinel ferrite nanoparticles provide efficient removal of REEs from contaminated solutions and ease of separation through application of an external magnetic field. However, there is still limited knowledge available regarding the optimal operational conditions in which to use these materials, especially in complex aqueous mixtures with different REEs. In this study, we have used Surface Response Methodology (SRM) applied to MnFe2O4 nanosorbents to identify their ideal sorption conditions of pH (4-8), REEs concentration (1-5 µM) and sorbent mass (20-180 mg L-1) in a mixture of nine REEs in water samples of distinct salinity (NaCl: 0-30 g L-1). Our results indicated that high pH favored REEs sorption because of the material's surface charge, which promoted interactions with REEs ions at pH 6-8. Yttrium was the least removed element, but total removal was achieved for lowest REEs concentration using 151 mg L-1 of sorbent. High removals were also obtained for the concentration of 5 µM (100 % removal, except for Y and La). Salinity did not impair sorption significantly (<10 %), which was owed to the high sorbent mass used in those assays. An increase in sorbent mass and initial REEs concentration also promoted faster kinetics. The spinel type MnFe2O4 nanoparticles showed great promise in a realistic application, which is the next proposed step in this line of research.

Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery.

This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and design of experiments (ANN-DoE) and to compare it with other machine learning (ML) models. The central composite design (CCD) and ML algorithms were used to screen the effects of concentrations of both the polymers (polyethyleneimine and fucoidan) on the outcome responses, i.e., particle size and entrapment efficiency with defined constraints. The prediction from different ML models (bootstrap forest, K-nearest neighbors, artificial neural network, generalized regression-lasso and support vector machines) were compared with ANN-DoE model. The ANN-DoE model showed better accuracy and predictability and outperformed all the other models. This depicted that the concept of using ANN and DoE combination approach provided the best, uncomplicated and cost-effective way to optimized the nanoformulations. The optimized formulation generated from the ANN-DoE combined model was further evaluated for characterization and anticancer activity. The optimized SFB-SANPs were prepared using the polyelectrolyte complexation method with Polyethyleneimine (PEI) as a cationic polymer and fucoidan (FCD) as an anionic. The SFB-SANPs were nanometric in size (280.4 ± 0.089 nm) and slightly anionic in nature (zeta potential = -6.03 ± 0.92 mV) with an encapsulation efficiency of 95.56 ± 0.30 %. The drug release from SFB-SANPs was controlled and sustained in the cancer microenvironment (pH 5.0). The SFB-SANPs were compatible with red blood cells (RBCs), and the % hemolysis was found to be <5.0 %. The anticancer activity of the SFB-SANPs exhibited an IC50 at 2.017 ± 0.516 µM against MDMB-231 cells, showing a significantly high inhibitory effect on breast cancer cell lines. Therefore, the nanocarriers developed using various ML tools inherit a huge promise in anticancer drug delivery.

Application of quality by design in optimization of nanoformulations: Principle, perspectives and practices.

Nanoparticulate drug delivery systems (NDDS) based nanoformulations have emerged as promising drug delivery systems. Various NDDS-based formulations have been reported such as polymeric nanoparticles (NPs), nanoliposomes, solid lipid NPs, nanocapsules, liposomes, self-nano emulsifying drug delivery systems, pro liposomes, nanospheres, microemulsion, nanoemulsion, gold NPs, silver NPs and nanostructured lipid carrier. They have shown numerous advantages such as enhanced bioavailability, aqueous solubility, permeability, controlled release profile, and blood-brain barrier (BBB) permeability. This advantage of NDDS can help to deliver pure drugs to the target site. However, the formulation of nanoparticles is a complex process that requires optimization to ensure product quality and efficacy. Quality by Design (QbD) is a systemic approach that has been implemented in the pharmaceutical industry to improve the quality and reliability of drug products. QbD involves the optimization of different parameters like zeta potential (ZP), particle size (PS), entrapment efficiency (EE), polydispersity index (PDI), and drug release using statistical experimental design. The present article discussed the detailed role of QbD in optimizing nanoformulations and their advantages, advancement, and applications from the industrial perspective. Various case studies of QbD in the optimization of nanoformulations are also discussed.

Predictive modelling of laser powder bed fusion of Fe-based nanocrystalline alloys based on experimental data using multiple linear regression analysis.

This study harnessed bivariate correlational analysis, multiple linear regression analysis and tree-based regression analysis to examine the relationship between laser process parameters and the final material properties (bulk density, saturation magnetization (M s ), and coercivity (H c )) of Fe-based nano-crystalline alloys fabricated via laser powder bed fusion (LPBF). A dataset comprising of 162 experimental data points served as the foundation for the investigation. Each data point encompassed five independent variables: laser power (P), laser scan speed (v), hatch spacing (h), layer thickness (t), and energy density (E), along with three dependent variables: bulk density, M s , and H c . The bivariate correlational analysis unveiled that bulk density exhibited a significant correlation with P, v, h, and E, whereas M s and H c displayed significant correlations exclusively with v and P, respectively. This divergence may stem from the strong influence of microstructure on magnetic properties, which can be impacted not only by the laser process parameters explored in this study but also by other factors such as oxygen levels within the build chamber. Furthermore, our statistical analysis revealed that bulk density increased with rising P, h, and E, while decreased with higher v. Regarding the magnetic properties, a high M s was achievable through low v, while low H c resulted from high P. It was concluded that P and v were considered as the primary laser process parameters, influencing h and t due to their control over the melt-pool size. The application of multiple linear regression analysis allowed the prediction of the bulk density by using both laser process parameters and energy density. This approach offered a valuable alternative to time-consuming and costly trial-and-error experiments, yielding a low error of less than 1 % between the mean predicted and experimental values. Although a slightly higher error of approximately 6 % was observed for M s , a clear association was established between M s and v, with lower v values corresponding to higher M s values. Additionally, a further comparison was conducted between multiple linear regression and three tree-based regression models to explore the effectiveness of these approaches.

Optimal development of apoptotic cells-mimicking liposomes targeting macrophages.

Macrophages are multifunctional innate immune cells that play indispensable roles in homeostasis, tissue repair, and immune regulation. However, dysregulated activation of macrophages is implicated in the pathogenesis of various human disorders, making them a potential target for treatment. Through the expression of pattern recognition and scavenger receptors, macrophages exhibit selective uptake of pathogens and apoptotic cells. Consequently, the utilization of drug carriers that mimic pathogenic or apoptotic signals shows potential for targeted delivery to macrophages. In this study, a series of mannosylated or/and phosphatidylserine (PS) -presenting liposomes were developed to target macrophages via the design of experiment (DoE) strategy and the trial-and-error (TaE) approach. The optimal molar ratio for the liposome formulation was DOPC: DSPS: Chol: PEG-PE = 20:60:20:2 based on the results of cellular uptake and cytotoxicity evaluation on RAW 264.7 and THP-1 in vitro. Results from in vivo distribution showed that, in the DSS-induced colitis model and collagen II-induced rheumatoid arthritis model, PS-presenting liposomes (PS-Lipo) showed the highest accumulation in intestine and paws respectively, which holds promising potential for macrophage target therapy since macrophages are abundant at inflammatory sites and contribute to the progression of corresponding diseases. Organs such as the heart, liver, spleen, lung, and kidney did not exhibit histological alterations such as inflammation or necrosis when exposed to PC-presenting liposomes (PC-Lipo) or PS-Lipo. In addition, liposomes demonstrated hemobiocompatibility and no toxicity to liver or kidney for circulation and did not induce metabolic injury in the animals. Thus, the well-designed PS-Lipo demonstrated the most potential for macrophage target therapy.

Cultivation optimization promotes ginsenoside and universal triterpenoid production by engineered yeast.

Ginseng, a cornerstone of traditional herbal medicine in Asia, garnered significant attention for its therapeutic potential. Central to its pharmacological effects are ginsenosides, the primary active metabolites, many of which fall within the dammarane-type and share protopanaxadiol as a common precursor. Challenges in extracting protopanaxadiol and ginsenosides from ginseng arise due to their low concentrations in the roots. Emerging solutions involve leveraging microbial cell factories employing genetically engineered yeasts. Here, we optimized the fermentation conditions via the Design of Experiment, realizing 1.2 g/L protopanaxadiol in simple shake flask cultivations. Extrapolating the optimized setup to complex ginsenosides, like compound K, achieved 7.3-fold (0.22 g/L) titer improvements. Our adaptable fermentation conditions enable the production of high-value products, such as sustainable triterpenoids synthesis. Through synthetic biology, microbial engineering, and formulation studies, we pave the way for a scalable and sustainable production of bioactive compounds from ginseng.

Identifying Optimal Processing Variables and Investigating Mechanisms of Grain Alignment in Hot-Deformed NdFeB Magnets through Design of Experiments.

This study introduces a novel approach for investigating hot-deformed NdFeB magnets by combining the minimal stress deformation process (MSDP) with the design of experiment (DoE) methodology. This study focused on enhancing the crystallographic alignment, particularly the c-axis alignment of the Nd2Fe14B grains, to optimize the magnetic properties. By utilizing the Box-Behnken design matrix and response surface regression, critical processes and variables were identified, determining that a hot-pressing temperature of 700 °C is crucial for achieving optimal grain alignment. Changing the strain rate to 0.019 mm/s under a stress of 110 MPa led to significant enhancements in the alignment, yielding magnets with a remanence of approximately 13.4 kG and a coercivity of 21 kOe. These findings highlight the effectiveness of combining the MSDP and DoE for predicting and achieving improved magnetic properties. Despite the challenges associated with understanding the complexity of crystal alignment mechanisms, this integrated approach successfully improved magnetic characteristics. The methodology represents a significant advancement in the fabrication of high-performance hot-deformed NdFeB magnets, marking a notable contribution to the field.

TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.

BACKGROUND: Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data. RESULTS: We apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions. CONCLUSION: These results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN models.