Optimization of pullulan production by Aureobasidium pullulans using semi-solid-state fermentation and artificial neural networks: Characterization and antibacterial activity of pullulan impregnated with Ag-TiO2 nanocomposite.

This study presents a novel and efficient approach for pullulan production using artificial neural networks (ANNs) to optimize semi-solid-state fermentation (S-SSF) on faba bean biomass (FBB). This method achieved a record-breaking pullulan yield of 36.81 mg/g within 10.82 days, significantly exceeding previous results. Furthermore, the study goes beyond yield optimization by characterizing the purified pullulan, revealing its unique properties including thermal stability, amorphous structure, and antioxidant activity. Energy-dispersive X-ray spectroscopy and scanning electron microscopy confirmed its chemical composition and distinct morphology. This research introduces a groundbreaking combination of ANNs and comprehensive characterization, paving the way for sustainable and cost-effective pullulan production on FBB under S-SSF conditions. Additionally, the study demonstrates the successful integration of pullulan with Ag@TiO2 nanoparticles during synthesis using Fusarium oxysporum. This novel approach significantly enhances the stability and efficacy of the nanoparticles by modifying their surface properties, leading to remarkably improved antibacterial activity against various human pathogens. These findings showcase the low-cost production medium, and extensive potential of pullulan not only for its intrinsic properties but also for its ability to significantly improve the performance of nanomaterials. This breakthrough opens doors to diverse applications in various fields.

Multifaceted chemical and bioactive features of Ag@TiO2 and Ag@SeO2 core/shell nanoparticles biosynthesized using Beta vulgaris L. extract.

Due to increasing concerns about environmental impact and toxicity, developing green and sustainable methods for nanoparticle synthesis is attracting significant interest. This work reports the successful green synthesis of silver (Ag), silver-titanium dioxide (Ag@TiO2), and silver-selenium dioxide (Ag@SeO2) nanoparticles (NPs) using Beta vulgaris L. extract. Characterization by XRD, SEM, TEM, and EDX confirmed the successful formation of uniformly distributed spherical NPs with controlled size (25 ± 4.9 nm) and desired elemental composition. All synthesized NPs and the B. vulgaris extract exhibited potent free radical scavenging activity, indicating significant antioxidant potential. However, Ag@SeO2 displayed lower hemocompatibility compared to other NPs, while Ag@SeO2 and the extract demonstrated reduced inflammation in a carrageenan-induced paw edema animal model. Interestingly, Ag@TiO2 and Ag@SeO2 exhibited strong antifungal activity against Rhizoctonia solani and Sclerotia sclerotium, as evidenced by TEM and FTIR analyses. Generally, the findings suggest that B. vulgaris-derived NPs possess diverse biological activities with potential applications in various fields such as medicine and agriculture. Ag@TiO2 and Ag@SeO2, in particular, warrant further investigation for their potential as novel bioactive agents.

Turmeric extract-mediated biogenic synthesis of Ag@SeO2 magnetic nanoparticles: characterization, optimization, antibacterial and antioxidant activities.

This study bio-synthesized Ag@SeO2 bmNPs successfully, using turmeric ethanol extract, and characterized them using various techniques. The FT-IR analysis reveals the involvement of these plant-derived compounds, especially phenolics, in the reduction process by acting as electron donors and stabilizing/capping agents. Zeta potential analysis showed a slight negative surface charge for the stability of Ag@SeO2 NPs, where TEM revealed spherical nanoparticles with an average size of 20 nm. The XRD confirmed crystallinity and a core-shell structure, and EDX identified elements consistent with Ag@SeO2 and a 3 : 1 Ag/Se atomic ratio. Further, SEM supported the spherical shape and uniform size. These findings highlight the successful biosynthesis of Ag@SeO2 bmNPs with promising properties for diverse applications. Moreover, the Box-Behnken design (BBD) and artificial neural network (ANN) model were engaged to optimize Ag@SeO2 bmNP biosynthesis. BBD identified significant influences of pH, bioconversion temperature, time, and turmeric concentration on bmNP yield, with adjusted R2 and predictive R2 being 0.9075 and 0.8829, respectively. However, its limitations were revealed by a significant lack of fit. ANN modeling with a 3-5-7-1 topology showed superior predictive accuracy and identified optimal conditions for maximizing yield (pH 9.83, 51.7 °C, 1.0 h, 3.71 mg mL-1 turmeric). Validation experiments confirmed the model's reliability. Turmeric extract exhibited significantly higher amounts of phenolics, and flavonoids compared to the bmNPs, suggesting its potential for strong antioxidant activity. Both turmeric extract and bmNPs displayed antioxidant activity in ABTS and DPPH assays, with turmeric extract being the most potent due to its curcuminoid content. The potential activity of Ag@SeO2 bmNPs against S. aureus, K. pneumonia, E. coli, and B. cereus was investigated, with inhibition zones ranging from 22 to 32 mm. The MIC values of tested NPs towards pathogenic bacteria ranged from 165.625 and 331.25 µg mL-1.

A Comparative Study of Cr(VI) Sorption by Aureobasidium pullulans AKW Biomass and Its Extracellular Melanin: Complementary Modeling with Equilibrium Isotherms, Kinetic Studies, and Decision Tree Modeling.

Melanin as a natural polymer is found in all living organisms, and plays an important role in protecting the body from harmful UV rays from the sun. The efficiency of fungal biomass (Aureobasidium pullulans) and its extracellular melanin as Cr(VI) biosorbents was comparatively considered. The efficiency of Cr(VI) biosorption by the two sorbents used was augmented up to 240 min. The maximum sorption capacities were 485.747 (fungus biomass) and 595.974 (melanin) mg/g. The practical data were merely fitted to both Langmuir and Freundlich isotherms. The kinetics of the biosorption process obeyed the pseudo-first-order. Melanin was superior in Cr(VI) sorption than fungal biomass. Furthermore, four independent variables (contact time, initial concentration of Cr(VI), biosorbent dosage, and pH,) were modeled by the two decision trees (DTs). Conversely, to equilibrium isotherms and kinetic studies, DT of fungal biomass had lower errors compared to DT of melanin. Lately, the DTs improved the efficacy of the Cr(VI) removal process, thus introducing complementary and alternative solutions to equilibrium isotherms and kinetic studies. The Cr(VI) biosorption onto the biosorbents was confirmed and elucidated through FTIR, SEM, and EDX investigations. Conclusively, this is the first report study attaining the biosorption of Cr(VI) by biomass of A. pullulans and its extracellular melanin among equilibrium isotherms, kinetic study, and algorithmic decision tree modeling.

A comparative study using response surface methodology and artificial neural network towards optimized production of melanin by Aureobasidium pullulans AKW.

The effect of three independent variables (i.e., tyrosine, sucrose, and incubation time) on melanin production by Aureobasidium pullulans AKW was unraveled by two distinctive approaches: response surface methodology (i.e. Box Behnken design (BBD)) and artificial neural network (ANN) in this study for the first time ever using a simple medium. Regarding BBD, sucrose and incubation intervals did impose a significant influence on the output (melanin levels), however, tyrosine did not. The validation process exhibited a high consistency of BBD and ANN paradigms with the experimental melanin production. Concerning ANN, the predicted values of melanin were highly comparable to the experimental values, with minor errors competing with BBD. Highly comparable experimental values of melanin were achieved upon using BBD (9.295 ± 0.556 g/L) and ANN (10.192 ± 0.782 g/L). ANN accurately predicted melanin production and showed more improvement in melanin production by about 9.7% higher than BBD. The purified melanin structure was verified by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction pattern (XRD), and thermogravimetric analysis (TGA). The results verified the hierarchical architecture of the particles as small compasses by SEM analysis, inter-layer spacing in the XRD analysis, maximal atomic % for carbon, and oxygen atoms in the EDX analysis, and the great thermal stability in the TGA analysis of the purified melanin. Interestingly, the current novel endophytic strain was tyrosine-independent, and the uniquely applied ANN paradigm was more efficient in modeling the melanin production with appreciate amount on a simple medium in a relatively short time (168 h), suggesting additional optimization studies for further maximization of melanin production.

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2.

Heavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and severe health concerns. The current study is of supreme priority because there is no previous work that dealt with the modeling of the optimization of the biosorption process by the immobilized cells. The significant parameters (immobilized bacterial cells, contact time, and initial Cr6+ concentrations), affecting Cr6+ biosorption by immobilized Pseudomonas alcaliphila, was verified, using the Plackett-Burman matrix. For modeling the maximization of Cr6+ biosorption, a comparative approach was created between rotatable central composite design (RCCD) and artificial neural network (ANN) to choose the most fitted model that accurately predicts Cr6+ removal percent by immobilized cells. Experimental data of RCCD was employed to train a feed-forward multilayered perceptron ANN algorithm. The predictive competence of the ANN model was more precise than RCCD when forecasting the best appropriate wastewater treatment. After the biosorption, a new shiny large particle on the bead surface was noticed by the scanning electron microscopy, and an additional peak of Cr6+ was appeared by the energy dispersive X-ray analysis, confirming the role of the immobilized bacteria in the biosorption of Cr6+ ions.

Author Correction: Statistical modeling-approach for optimization of Cu2+ biosorption by Azotobacter nigricans NEWG-1; characterization and application of immobilized cells for metal removal.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Innovative low-cost biosorption process of Cr6+ by Pseudomonas alcaliphila NEWG-2.

Chromium is one of the heavy metal pollutants that causing risky health issues when discharged into the aquatic ecosystems. The current investigation focused on the bioremoval of Cr6+ depending on the bacterial sorption process by using Pseudomonas sp. NEWG-2 which was identified on the basis of morphological, cultural characteristics, 16S rRNA sequencing and phylogenetic analysis as Pseudomonas alcaliphila strain NEWG-2. It is clear from the FCCD experiments that the bacterium can grow normally and remove 96.60% of 200 mg/l of Cr6+ using yeast extract (5.6 g/l), glucose (4.9 g/l), pH (7) for 48 h incubation period. SEM and EDS analyses proved that the Cr6+ was biosorbed by P. alcaliphila NEWG-2. FTIR spectra indicated that the phenolic, carbonyl ester, acetyl, carboxylate, alkanes and carbonyl were the main groups involved in the chromium biosorption. Of the equilibrium isotherms models, the Langmuir model was more obedient, with a maximum uptake (qmax) of 10 mg/g (bacterial-alginate beads), than the Freundlich one. The findings reveal the efficiency of P. alcaliphila NEWG-2 in Cr6+ biosorption, with feasibility in the treatment of chromium-contaminated water as a green-technology tool. Interestingly, to the best of our knowledge, this is the first report on Cr6+ biosorption process by P. alcaliphila.

Statistical modeling-approach for optimization of Cu2+ biosorption by Azotobacter nigricans NEWG-1; characterization and application of immobilized cells for metal removal.

Heavy metals are environmental pollutants affect the integrity and distribution of living organisms in the ecosystem and also humans across the food chain. The study targeted the removal of copper (Cu2+) from aqueous solutions, depending on the biosorption process. The bacterial candidate was identified using 16S rRNA sequencing and phylogenetic analysis, in addition to morphological and cultural properties as Azotobacter nigricans NEWG-1. The Box-Behnken design was applied to optimize copper removal by Azotobacter nigricans NEWG-1 and to study possible interactive effects between incubation periods, pH and initial CuSO4 concentration. The data obtained showed that the maximum copper removal percentage of 80.56% was reached at run no. 12, under the conditions of 200 mg/L CuSO4, 4 days' incubation period, pH, 8.5. Whereas, the lowest Cu2+ removal (12.12%) was obtained at run no.1. Cells of Azotobacter nigricans NEWG-1 before and after copper biosorption were analyzed using FTIR, EDS and SEM. FTIR analysis indicates that several functional groups have participated in the biosorption of metal ions including hydroxyl, methylene, carbonyl, carboxylate groups. Moreover, the immobilized bacterial cells in sodium alginate-beads removed 82.35 ± 2.81% of copper from the aqueous solution, containing an initial concentration of 200 mg/L after 6 h. Azotobacter nigricans NEWG-1 proved to be an efficient biosorbent in the elimination of copper ions from environmental effluents, with advantages of feasibility, reliability and eco-friendly.

Lysine and Glutamic Acids as the End Products of Multi-response of Optimized Fermented Medium by Mucor mucedo KP736529.

BACKGROUND: Amino acids are important for living organisms, they acting as crucial for metabolic activities and energy generation, wherein the deficiency in these amino acids cause various physiological defects. OBJECTIVE: The aim of this study is to investigate the effect of some nutritional factors on the amino acids production by Mucor mucedo KP736529 during fermentation intervals. METHODOLOGY: Mucor mucedo KP736529 was selected according to proteolytic activity. Corn steep liquor and olive cake were used in the fermented medium during Placket-Burman and central composite design to maximize the production of lysine and glutamic acids. RESULTS: During the screening by Plackett-Burman design, olive cake and Corn Steep Liquor (CSL) had potential importance for the higher production of amino acids. The individual fractionation of total amino acids showed both lysine and glutamic as the major amino acids associated with the fermentation process. Moreover, the Central Composite Design (CCD) has been adopted to explain the interaction between olive cake and CSL on the production of lysine and glutamic acids. The model recorded significant F-value, with high values of R 2, adjusted R 2 and predicted R 2 for both lysine and glutamic, indicating the validity of the data. Solving equation for maximum production of lysine recorded theoretical levels of olive cake and CSL, being 2.58 and 1.83 g L -1, respectively, with predicting value of lysine at 1.470 µg mL -1, whereas the predicting value of glutamic acid reached 0.805 mg mL -1 at levels of 2.49 and 1.93 g L -1 from olive cake and CSL, respectively. The desirability function (D) showed the actual responses being 1.473±0.009 and 0.801±0.004 µg mL -1 for lysine and glutamic acids, respectively. CONCLUSION: The model showed adequate validity to be applied in a large-scale production of both lysine and glutamic acids.