Construction of a Synergy Combination Model for Turmeric (Curcuma longa L.) and Black Pepper (Piper nigrum L.) Extracts: Enhanced Anticancer Activity against A549 and NCI-H292 Human Lung Cancer Cells.

Extensive research on medicinal herbs for bioactive compounds proposes that they could replace synthetic drugs, reducing side effects and economic burdens. Especially, interest in the synergistic benefits of natural products is increasing, implying that their combined use may enhance therapeutic effectiveness. This study aimed to explore the synergetic effects of turmeric (Curcuma longa L.) and black pepper (Piper nigrum L.) extract on lung normal (MRC-5) and cancer (A549 and NCI-H292) cell lines. The turmeric extract (TM) only affected the lung cancer cell lines, but it had no impact on the MRC-5 cell line. On the other hand, the black pepper extract (BP) did not cause any damage to either the lung normal or cancer cell lines, even at concentrations of up to 400 µg/mL. Response surface methodology was used to predict the ideal synergistic concentrations (EC50) of TM and BP, which were found to be 48.5 and 241.7 µg/mL, respectively. Notably, the selected condition resulted in higher cytotoxicity compared to the exposure to TM alone, indicating a potent synergetic effect. The rate of curcumin degradation under this combined treatment was significantly decreased to 49.72 ± 5.00 nmol/h/µg for A549 cells and 47.53 ± 4.78 nmol/h/µg for NCI-H292 cells, respectively, as compared to curcumin alone. Taken together, this study confirmed the potent synergistic effect of TM and BP on lung cancer cell lines. Further research is required to identify their specific synergetic mechanisms. Our findings provide crucial foundational data on the synergistic effects of TM and BP.

Response surface methodology mediated optimization of phytosulfokine and plant growth regulators for enhanced protoplast division, callus induction, and somatic embryogenesis in Angelica Gigas Nakai.

BACKGROUND: Angelica Gigas (Purple parsnip) is an important medicinal plant that is cultivated and utilized in Korea, Japan, and China. It contains bioactive substances especially coumarins with anti-inflammatory, anti-platelet aggregation, anti-cancer, anti-diabetic, antimicrobial, anti-obesity, anti-oxidant, immunomodulatory, and neuroprotective properties. This medicinal crop can be genetically improved, and the metabolites can be obtained by embryonic stem cells. In this context, we established the protoplast-to-plant regeneration methodology in Angelica gigas. RESULTS: In the present investigation, we isolated the protoplast from the embryogenic callus by applying methods that we have developed earlier and established protoplast cultures using Murashige and Skoog (MS) liquid medium and by embedding the protoplast in thin alginate layer (TAL) methods. We supplemented the culture medium with growth regulators namely 2,4-dichlorophenoxyaceticacid (2,4-D, 0, 0.75, 1.5 mg L- 1), kinetin (KN, 0, 0.5, and 1.0 mg L- 1) and phytosulfokine (PSK, 0, 50, 100 nM) to induce protoplast division, microcolony formation, and embryogenic callus regeneration. We applied central composite design (CCD) and response surface methodology (RSM) for the optimization of 2,4-D, KN, and PSK levels during protoplast division, micro-callus formation, and induction of embryogenic callus stages. The results revealed that 0.04 mg L- 1 2,4-D + 0.5 mg L- 1 KN + 2 nM PSK, 0.5 mg L- 1 2,4-D + 0.9 mg L- 1 KN and 90 nM PSK, and 1.5 mg L- 1 2,4-D and 1 mg L- 1 KN were optimum for protoplast division, micro-callus formation and induction embryogenic callus. MS basal semi-solid medium without growth regulators was good for the development of embryos and plant regeneration. CONCLUSIONS: This study demonstrated successful protoplast culture, protoplast division, micro-callus formation, induction embryogenic callus, somatic embryogenesis, and plant regeneration in A. gigas. The methodologies developed here are quite useful for the genetic improvement of this important medicinal plant.

Step-By-Step Standardization of the Bottom-Up Semi-Automated Nanocrystallization of Pharmaceuticals: A Quality By Design and Design of Experiments Joint Approach.

Nanosized drug crystals have been reported with enhanced apparent solubility, bioavailability, and therapeutic efficacy compared to microcrystal materials, which are not suitable for parenteral administration. However, nanocrystal design and development by bottom-up approaches are challenging, especially considering the non-standardized process parameters in the injection step. This work aims to present a systematic step-by-step approach through Quality-by-Design (QbD) and Design of Experiments (DoE) for synthesizing drug nanocrystals by a semi-automated nanoprecipitation method. Curcumin is used as a drug model due to its well-known poor water solubility (0.6 µg mL-1, 25 °C). Formal and informal risk assessment tools allow identifying the critical factors. A fractional factorial 24-1 screening design evaluates their impact on the average size and polydispersity of nanocrystals. The optimization of significant factors is done by a Central Composite Design. This response surface methodology supports the rational design of the nanocrystals, identifying and exploring the design space. The proposed joint approach leads to a reproducible, robust, and stable nanocrystalline preparation of 316 nm with a PdI of 0.217 in compliance with the quality profile. An orthogonal approach for particle size and polydispersity characterization allows discarding the formation of aggregates. Overall, the synergy between advanced data analysis and semi-automated standardized nanocrystallization of drugs is highlighted.

Optimization of fermentation conditions and medium components for chrysomycin a production by Streptomyces sp. 891-B6.

BACKGROUND: Chrysomycin A (CA) is a promising antibiotic for treatment of Gram-positive bacterial infections and cancers. In order to enhance CA yield, optimization of fermentation conditions and medium components was carried out on strain Streptomyces sp. 891-B6, an UV-induced mutant with improved CA titer compared with its wide-type marine strain 891. RESULTS: Using one-way experiment, the optimal fermentation conditions for CA production in 1-L shake flask were obtained as follows: 12 days of fermentation time, 5 days of seed age, 5% of inoculum volume ratio, 200 mL of loading volume and 6.5 of initial pH. By response surface methodology, the optimal medium components determined as glucose (39.283 g/L), corn starch (20.662 g/L), soybean meal (15.480 g/L) and CaCO3 (2.000 g/L). CONCLUSION: Validation tests showed that the maximum yield of CA reached 1601.9 ± 56.7 mg/L, which was a 60% increase compared to the initial yield (952.3 ± 53.2 mg/L). These results provided an important basis for scale-up production of CA by strain 891-B6.

Optimization of Epigenetic Modifier Drug Combination for Synergistic Effect against Glioblastoma Multiform Cancer Cell Lines.

Glioblastoma multiforme (GBM), is a frequent class of malignant brain tumors. Epigenetic therapy, especially with synergistic combinations is highly paid attention for aggressive solid tumors like GBM. Here, RSM optimization has been used to increase the efficient arrest of U87 and U251 cell lines due to synergistic effects. Cell lines were treated with SAHA, 5-Azacytidine, GSK-126, and PTC-209 individually and then RSM was used to find most effective combinations. Results showed that optimized combinations significantly reduce cell survival and induce cell cycle arrest and apoptosis in both cell lines. Expression of cyclin B1 and cyclin D1 were decreased while caspase3 increased expression.

Assessing the reinforcement effect by response surface methodology of holocellulose from spent coffee grounds on biopolymeric films as food packaging materials.

The pollution caused by petroleum-derived plastic materials has become a major environmental problem that has encouraged the development of new compostable and environmentally friendly materials for food packaging based on biomodified polymers with household residues. This study aims to design, synthesize, and characterize a biobased polymeric microstructure film from polyvinyl alcohol and chitosan reinforced with holocellulose from spent coffee grounds for food-sustainable packaging. Chemical isolation with a chlorite-based solution was performed to obtain the reinforced holocellulose from the spent coffee ground, and the solvent casting method was used to obtain the films to study. Physicochemical and microscopic characterizations were conducted to identify and select the best formulations using a simplex-centroid design analysis. The response surface methodology results indicate that the new packaging material obtained with equal amounts of polymers and reinforced material (1:1:1) possesses the appropriate barrier properties and microstructural character to prevent water attack and hydrophobic behavior and thus could be used as an alternative for food packaging materials.

Advances in the co-production of biosurfactant and other biomolecules: statistical approaches for process optimization.

An efficient microbial conversion for simultaneous synthesis of multiple high-value compounds, such as biosurfactants and enzymes, is one of the most promising aspects for an economical bioprocess leading to a marked reduction in production cost. Although biosurfactant and enzyme production separately have been much explored, there are limited reports on the predictions and optimization studies on simultaneous production of biosurfactants and other industrially important enzymes, including lipase, protease, and amylase. Enzymes are suited for an integrated production process with biosurfactants as multiple common industrial processes and applications are catalysed by these molecules. However, the complexity in microbial metabolism complicates the production process. This study details the work done on biosurfactant and enzyme co-production and explores the application and scope of various statistical tools and methodologies in this area of research. The use of advanced computational tools is yet to be explored for the optimization of downstream strategies in the co-production process. Given the complexity of the co-production process and with various new methodologies based on artificial intelligence (AI) being invented, the scope of AI in shaping the biosurfactant-enzyme co-production process is immense and would lead to not only efficient and rapid optimization, but economical extraction of multiple biomolecules as well.

Efficient multi-ion adsorption using chitosan-malonic acid film: Enhancement using response surface methodology.

The objective of this research is to conduct a comprehensive characterization of chitosan while also improving its attributes by crosslinking with malonic acid, with a focus on its efficacy in removing hexavalent chromium, arsenite and fluoride ions. Crosslinking chitosan in 1:0.5 mass ratio forming a film led to substantial enhancement in confiscation of these target pollutants. The characterization of the adsorbent involved several techniques, including FT-IR, TGA-DSC, SEM-EDX, XRD, and BET surface area analysis. In batch adsorption experiments, Chitosan-malonic acid (CMA) was employed to remove CrVI, AsIII and F- from aqueous solutions. These experiments were conducted while varying conditions such as pH, dosage, concentration, temperature, and time. Through the implementation of response surface methodology (RSM), parameters were optimized, resulting in over 95% removal of CrVI, AsIII and F- ions. The isotherm and kinetics data demonstrated a good fit with the Langmuir isotherm model and pseudo second-order kinetics, respectively. According to the Langmuir isotherm, the maximum adsorption capacities on CMA for CrVI, AsIII and F- were determined to be 687.05 mg g-1, 26.72 mg g-1 and 51.38 mg g-1 respectively under optimum pH of 4.0, 7.0 and 5.0 respectively under ambient temperature of 303 K. Thermodynamic analysis indicated that the adsorption process was spontaneous and driven by enthalpy. The regenerability of the adsorbent was validated through five adsorption-desorption cycles, signifying its reusability. An assessment of the adsorbent's sustainability indicated an eco-friendly synthesis, as reflected by the low E-factor value of 0.0028.

Investigation on thermochemical co-gasification of rice husk and groundnut shell in open core gasifier for the generation of producer gas: An optimization by central composite design.

Several energy-related strategies and scenarios have been suggested to address concerns about rising global temperatures. In addition to using renewable energy, the improvement in energy efficiency of conventional systems is also in focus. Policies are already in place in many countries, including India, to address the energy needs of rural and small-scale enterprises by gasifying locally available, diverse agricultural leftovers. Although rice husk and groundnut shell are two commonly used agricultural leftovers in the southern part of India, their appropriate blending must be studied to improve their conversion efficiency in co-gasification. Therefore, the primary objective of this research is to construct a statistical model utilizing response surface methodology (RSM) to analyze the thermochemical co-gasification of the aforementioned biomass materials. Since RSM can predict optimum performance with limited experimental data, this could contribute to the identification of the performance and operating parameters of an open-core gasifier. The model predicts that the mixture containing 20% rice husk and working at an ER of 0.25 and a reduction zone inlet temperature of 879.9 °C will be CO-23.53%, H2-13.97%, and CH4-3.56%. In addition, the lower heating value and gas yield can be as high as 6.17 MJ/Nm3 and 2.369 m3/kg, respectively. This outcome can contribute to the effective utilization of biomass for energy supply in rural areas. However, the economic parameters must be analyzed to implement the same in any region.

Highly efficient activation of periodate by a manganese-modified biochar to rapidly degrade methylene blue.

In this study, the manganese oxide/biochar composites (Mn@BC) were synthesized from Phytolacca acinosa Roxb. The Mn@BC was analyzed via techniques of Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction analysis (XRD). The results show that MnOx is successfully loaded on the surface of BC, and the load of MnOx can increase the number of surface functional groups of BC. X-ray photoelectron spectroscopy (XPS) shows that MnOx loaded on BC mainly exists in three valence forms: Mn(Ⅱ), Mn(Ⅲ), and Mn(Ⅳ). The ability of Mn@BC to activate periodate (PI) was studied by simulating the degradation of methylene blue (MB) dye. The degradation experiment results showed that the MB removal rate by the Mn@BC/PI system reached 97.4% within 30 min. The quenching experiment and electron paramagnetic resonance (EPR) analysis confirmed that Mn@BC can activate PI to produce iodate (IO3•), singlet oxygen (1O2), and hydroxyl radical (•OH), which can degrade MB during the reaction. Response surface methodology (RSM) based on Box-Behnken Design (BBD) was used to determine the interaction between pH, Mn@BC and PI concentration in the Mn@BC/PI system, and the optimum technological parameters were determined. When pH = 5.4, Mn@BC concentration 0.56 mg/L, PI concentration 1.1 mmol/L, MB removal rate can reach 98.05%. The cyclic experiments show that Mn@BC can be reused. After four consecutive runs, the removal rate of MB by the Mn@BC/PI system is still 82%, and the Mn@BC/PI system also shows high performance in treating MB in actual water bodies and degrading other pollutants. This study provides a practical method for degrading dyes in natural sewage.

Amputation of Remazol brilliant blue dye on crosslinked chitosan hydrogel: Statistical treatment and experimental evaluation.

The primary aim of this research is to comprehensively assess the applicability of chitosan biopolymer towards water treatment application and to enhance its adsorption capacity towards Remazol brilliant blue R-19 dye. This has been achieved through physical modification to obtain the material in hydrogel form and chemical modification by crosslinking it with barbituric acid. The characterization of the resulting Chitosan-barbituric acid hydrogel (CBH) was carried out using various analytical techniques such as SEM-EDX, FT-IR, TGA-DTA, XRD, and BET. CBH was employed as the adsorbent to eliminate R-19 dye from aqueous media. Utilizing response surface methodology (RSM), the parameters were fine-tuned, leading to the achievement of more than a 95% removal for R-19 dye. The adsorption behavior closely adhered to the Langmuir isotherm and pseudo-second-order kinetics. An interesting observation indicated that the rise in temperature leads to rise in adsorption capacity of CBH. The maximum adsorption capacities evaluated at 301.15 K, 313.15 K, 318.15 K, and 323.15 K were 566.6 mg g-1, 624.7 mg g-1, 671.3 mg g-1, and 713.5 mg g-1 respectively, in accordance with the Langmuir isotherm model. Examining the thermodynamics of the adsorption process revealed its spontaneous nature (ΔG = -21.14 to -27.09 kJ mol-1) across the entire temperature range. Furthermore, the assessment of the isosteric heat of adsorption (ΔHads) was conducted using the Clausius-Clapeyron equation, with results indicating an increase in ΔHads from 1.85 to 2.16 kJ mol-1 with temperature rise from 301.15 K to 323.15 K due to augmented surface loading. This suggested the existence of lateral interactions between the adsorbed dye molecules. The potential of adsorbent for regeneration was investigated, demonstrating the ability to reuse the material. Sustainability parameter calculated for synthesis process reflected a notably low E-factor value of 0.32 demonstrated the synthesis is environment friendly.

Ultrasonication-Assisted Synthesis of CuO-Decorated Montmorillonite K30 Nanocomposites for Photocatalytic Removal of Emerging Contaminants: A Response Surface Methodology Approach.

Environmental pollution is increasing worldwide due to population and industrialization. Among the various forms of pollution, water pollution poses a significant challenge in contemporary times. In this study, we synthesized CuO-decorated montmorillonite K30 (MK30) nanosheets via a simple ultrasonication technique. The structural, morphological, compositional, and optical properties of the synthesized nanocomposites were evaluated using advanced instrumentation techniques. The morphology of CuO was cubic and cubic CuO evenly designed on the MK30, which was proved by Field Emission Scanning Electron Microscopy (FESEM). The adsorption photocatalytic activity of the synthesized cubic CuO/MK30 composites was examined through the degradation of MB under visible light irradiation. The apparent reaction rate constant of 20% CuO/MK30 was 12.5 folds higher than that of CuO. These conditions included a catalyst dosage ranging from 5 to 15 mg, a pH level ranging from to 3-11, and a pollutant concentration ranging from 5 to 20 mg/L. The optimal conditions for MB dye removal were determined using response surface methodology (RSM). A scavenger study of the composite was conducted and verified that •O2- and •OH radicals play an important role in the degradation process. This investigation addressed the process of adsorption and potential removal pathways, with a particular emphasis on the role of functional groups. The environmentally friendly CuO/MK30 nanocomposites exhibited potential as photocatalysts for efficiently absorbing and degrading MB dye and TC drug pollutants. They represent promising candidates for the treatment of industrial wastewater, aiming to mitigate the environmental threats posed by organic pollutants.

Heavy metal (Cu2+) removal from wastewater by metal-organic framework composite adsorbent: Simulation-based- artificial neural network and response surface methodology.

Metal-organic framework (MOF)--based composites have received significant attention in a variety of applications, including pollutant adsorption processes. The current investigation was designed to model, forecast, and optimize heavy metal (Cu2+) removal from wastewater using a MOF nanocomposite. This work has been modeled by response surface methodology (RSM) and artificial neural network (ANN) algorithms. In addition, the optimization of the mentioned factors has been performed through the RSM method to find the optimal conditions. The findings show that RSM and ANN can accurately forecast the adsorption process's the Cu2+ removal efficiency (RE). The maximum values of RE are achieved at the highest value of time (150 min), the highest value of adsorbent dosage (0.008 g), and the highest value of pH (=6). The R2 values obtained were 0.9995, 0.9992, and 0.9996 for ANN modeling of adsorption capacity based on different adsorbent dosages, Cu2+ solution pHs, and different ion concentrations, respectively. The ANN demonstrated a high level of accuracy in predicting the local minima of the graph. In addition, the RSM optimization results showed that the optimum mode for RE occurred at an adsorbent dosage value of 0.007 g and a time value of 144.229 min.

Bioleaching of Cd from contaminated Helianthus annuus L. stalk and the safe utilization of its byproducts by Aspergillus niger.

Disposal and recycling of heavy metal-enriched biomass is the key to measure the success of phytoremediation. This study employed innovative approach to use Aspergillus niger (A. niger) for the treatment of Cd-contaminated Helianthus annuus L. (sunflower) stalk after phytoremediation. Single-factor results showed that the removal of Cd at an initial pH of 3 was superior to sucrose and inoculation amount. 67.67% of Cd was removed by A. niger leaching system after 11 days based on response surface methodology optimum conditions (sucrose: 76.266 g L-1; inoculation amount: 10%; initial pH: 3), while the concentrations of nitrogen, phosphorus and potassium (N, P and K) of sunflower stalk were unaffected. While physicochemical pretreatment effectively enhanced the bioleaching efficiency, it also resulted in significant loss of P and K elements, thereby reducing the value of biomass for recycling and utilization. Therefore, the direct A. niger leaching method without pretreatment is more advantageous for the safe treatment and recycling of Cd-contaminated sunflower stalks.

Optimization of syngas production from co-gasification of palm oil decanter cake and alum sludge: An RSM approach with char characterization.

The study explores co-gasification of palm oil decanter cake and alum sludge, investigating the correlation between input variables and syngas production. Operating variables, including temperature (700-900 °C), air flow rate (10-30 mL/min), and particle size (0.25-2 mm), were optimized to maximize syngas production using air as the gasification agent in a fixed bed horizontal tube furnace reactor. Response Surface Methodology with the Box-Behnken design was used employed for optimization. Fourier Transformed Infra-Red (FTIR) and Field Emission Scanning Electron Microscopic (FESEM) analyses were used to analyze the char residue. The results showed that temperature and particle size have positive effects, while air flow rate has a negative effect on the syngas yield. The optimal CO + H2 composition of 39.48 vol% was achieved at 900 °C, 10 mL/min air flow rate, and 2 mm particle size. FTIR analysis confirmed the absence of C─Cl bonds and the emergence of Si─O bonds in the optimized char residue, distinguishing it from the raw sample. FESEM analysis revealed a rich porous structure in the optimized char residue, with the presence of calcium carbonate (CaCO3) and aluminosilicates. These findings provide valuable insights for sustainable energy production from biomass wastes.

Combining pretreatments and co-fermentation as successful approach to improve biohydrogen production from dairy cow manure.

The present paper is focused on enhancing the production of biohydrogen (bioH2) from dairy cow manure (DCM) through dark fermentation (DF). Two enhancement production strategies have been tested: i) the combination of H2O2 with sonification as pretreatment and ii) the co-fermentation with cheese whey as co-substrate. Concerning the pretreatment, the best combination was investigated according to the response surface methodology (RSM) by varying H2O2 dosage between 0.0015 and 0.06 g/gTS and ultrasonic specific energy input (USEI) between 35.48 and 1419.36 J/gTS. The increase of carbohydrates concentration was used as target parameter. Results showed that the combination of 0.06 g/gTS of H2O2 with 1419.36 J/gTS of USEI maximized the concentration of carbohydrates. The optimized conditions were used to pretreat the substrate prior conducting DF tests. The use of pretreatment resulted in obtaining a cumulative bioH2 volume of 51.25 mL/L and enhanced the bioH2 production by 125% compared to the control test conducted using raw DCM. Moreover, the second strategy, i.e. co-fermentation with cheese whey (20% v/v) as co-substrate ended up to enhancing the DF performance as the bioH2 production reached a value of 334.90 mL/L with an increase of 1372% compared to the control DF test. To further improve the process, dark fermentation effluents (DFEs) were valorized via photo fermentation (PF), obtaining an additional hydrogen production aliquot.

High efficiency of treated-phengite clay by sodium hydroxide for the Congo red dye adsorption: optimization, cost estimation, and mechanism study.

Wastewater textile dye treatment is a challenge that requires the development of eco-friendly technology to avoid the alarming problems associated with water scarcity and health-environment. This study investigated the potential of phengite clay as naturally low-cost abundant clay from Tamgroute, Morocco (TMG) that was activated with a 0.1M NaOH base (TMGB) after calcination at 850°C for 3 hours (TMGC) before its application in the Congo red (CR) anionic dye from the aqueous solution. The effect of various key operational parameters: adsorbent dose, contact time, dye concentration, pH, temperature, and the effect of salts, was studied by a series of adsorption experiments in a batch system, which affected the adsorption performance of TMG, TMGC, and TMGB for CR dye removal. In addition, the properties of adsorption kinetics, isotherms, and thermodynamics were also studied. Experimental results showed that optimal adsorption occurred at an acidic pH. At a CR concentration of 100 mg L-1, equilibrium elimination rates were 68%, 38%, and 92% for TMG, TMGC, and TMGB, respectively. The adsorption process is rapid, follows pseudo-second-order kinetics, and is best described by a Temkin and Langmuir isotherm. The thermodynamic parameters indicated that the adsorption of CR onto TMGB is endothermic and spontaneous. The experimental values of CR adsorption on TMGB are consistent with the predictions of the response surface methodology. These led to a maximum removal rate of 99.97% under the following conditions: pH =2, TMGB dose of 7g L-1, and CR concentration of 50 mg L-1. The adsorbent TMGB's relatively low preparation cost of around $2.629 g-1 and its ability to regenerate in more than 6 thermal calcination cycles with a CR removal rate of around 56.98%, stimulate its use for textile effluent treatment on a pilot industrial scale.

Evaluating embodied energy, carbon impact, and predictive precision through machine learning for pavers manufactured with treated recycled construction and demolition waste aggregate.

This investigation assesses the embodied energy and carbon footprint in the manufacture of pavers using varying proportions of recycled Construction and Demolition Waste (CDW). Additionally, Thin Film Composite Polyamide fiber (TFC PA), extracted from end-of-life Reverse Osmosis (RO) membranes, is introduced as an additive to enhance the concrete's strength. Machine learning techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Response Surface Methodology (RSM), are employed to predict the mechanical properties of pavers. The study focuses on examining the energy required and embodied carbon in various mix proportions, as well as the mechanical properties-specifically compressive strength and split tensile strength of concrete with different CDW and TFC PA proportions. Findings reveal that the optimal percentage of TFC PA is 3 % for all CDW replacement proportions, resulting in low carbon content both in terms of energy and embodiment and in mechanical behavior. The implementation of ANN and SVR is conducted in MATLAB, while a Design Expert is employed to generate the experimental design for RSM. The RSM regression model demonstrates a robust correlation between variables and observed outcomes, with optimal p-values, R2 values, and f-values. The ANN model successfully captures the variability in the data. Additionally, the findings indicate a consistent superiority of the Support Vector Regression (SVR) model over both Artificial Neural Network (ANN) and Response Surface Model (RSM) models when considering diverse performance metrics such as residuals and correlation coefficients.

Optimization and chemical free fabrication of green synthesized iron nanoparticles as potential MRI contrast agent.

The current research article has investigated the synthesis and characterization of novel iron nanoparticles (INPs) from neem and betel leaves extract combination using response surface methodology-central composite design and coated with chitosan-curcumin (CCINPs) as a biocompatible and contrast agent for magnetic resonance imaging (MRI). The coating of INPs with chitosan and curcumin (CCINPs) was carried out using a simple, easy, chemical-free ultrasonication method and characteristics were confirmed by UV-visible (Vis) spectrophotometer (UV-Vis), Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscope, atomic force microscopy, and vibrating sample magnetometer. The biocompatibility of the particles was ensured by conducting hemolytic and cell viability assays. The nanoparticle was found to be nonhemolytic (<5%) up to 150 µg/mL for both INPs and CCINPs. The cell viability was stable (peripheral blood mononuclear cells-PBMCs) till 48 h at 150 µg/mL of INPs and CCINPs. Both the test results produced were found to be biocompatible and additionally, an in vitro MRI study of INPs and CCINPs demonstrated the efficiency of the nanoparticle as a negative contrast agent with enhanced contrast nature in CCINPs. Thus, overall results indicate that the green synthesized chemical-free novel CCINPs could be a potential candidate for a wide range of applications such as MRI, drug delivery, and in magnetic fluid hyperthermia.

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling.

Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one-factor-at-a-time" approach and more recent statistical and mathematical methods such as artificial neural network (ANN), genetic algorithm, etc. Every approach has advantages and disadvantages of its own, yet even when a technique has flaws, it is nevertheless used to get the best results. Here, one categorical variable and four numerical parameters, including post-induction time, inducer concentration, post-induction temperature, and pre-induction cell density, were optimized using the 232 experimental assays of the central composite design. The direct and indirect effects of factors on the yield of anti-epithelial cell adhesion molecule extracellular domain fragment antibody were examined using statistical methods. The analysis of variance results indicate that the response surface methodology (RSM) model is effective in predicting the amount of produced single-chain fragment variable (p-value = 0.0001 and R2 = 0.905). For ANN modeling, the evaluation using normalized root mean square error (NRMSE) and R2 values shows a good fit (R2 = 0.942) and accurate predictions (NRMSE = 0.145). The analysis of error parameters and R2 of a dataset, which contained 30 data points randomly selected from the complete dataset, showed that the ANN model had a higher R2 value (0.968) compared to the RSM model (0.932). Furthermore, the ANN model demonstrated stronger predictive ability with a lower NRMSE (0.048 vs. 0.064). Induction at the cell density of 0.7 and an isopropyl ß-D-1-thiogalactopyranoside concentration of 0.6 mM for 32 h at 30°C in BW25113 was the ideal culture condition leading to the protein yield of 259.51 mg/L. Under the optimum conditions, the output values predicted by the ANN model (259.83 mg/L) were more in line with the experimental data (259.51 mg/L) than the RSM (276.13 mg/L) expected value. This outcome demonstrated that the ANN model outperforms the RSM in terms of prediction accuracy.