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.

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.

Domiciliation of Trichoderma asperellum Suppresses Globiosporangium ultimum and Promotes Pea Growth, Ultrastructure, and Metabolic Features.

The beneficial microorganisms represent a new and hopeful solution for a sustainable environment and development. In this investigation, Trichoderma asperellum ZNW, isolated from seeds, was domiciliated within the pea plant for improving growth, disease management, and enhancement of productivity. Globisporangium ultimum NZW was isolated from deformed pea seeds, representing the first record of the pathogen caused by pea damping-off. Both fungi were molecularly identified. T. asperellum ZNW produced several lytic enzymes and bioactive metabolites as detected by GC-MC. The SEM illustrated the mycoparasitic behavior of T. asperellum ZNW on G. ultimum NZW mycelia. In the pot experiment, T. asperellum domiciliated the root and grew as an endophytic fungus, leading to root vessel lignification. Under soil infection, T. asperellum reduced damping-off, by enhancing peroxidase, polyphenol, total phenols, and photosynthetic pigments content. The vegetative growth, yield, and soil dehydrogenase activity were improved, with an enhancement in the numerical diversity of the microbial rhizosphere. This work may enable more understanding of the plant-fungal interaction, yet, working on domiciliation is recommended as a new approach to plant protection and growth promotion under various ecological setups.

Definitive Screening Design and Artificial Neural Network for Modeling a Rapid Biodegradation of Date Palm Fronds by a New Trichoderma sp. PWN6 into Citric Acid.

Generally, the bioconversion of lignocellulolytics into a new biomolecule is carried out through two or more steps. The current study used one-step bioprocessing of date palm fronds (DPF) into citric acid as a natural product, using a pioneer strain of Trichodermaharzianum (PWN6) that has been selected from six tested isolates based on the highest organic acid (OA) productivity (195.41 µmol/g), with the lowest amount of the released glucose. Trichoderma sp. PWN6 was morphologically and molecularly identified, and the GenBank accession number was MW78912.1. Both definitive screening design (DSD) and artificial neural network (ANN) were applied, for the first time, for modeling the bioconversion process of DPF. Although both models are capable of making accurate predictions, the ANN model outperforms the DSD model in terms of OA production, as ANN is characterized by a higher value of R2 (0.963) and validation R2 (0.967), and lower values of the RMSE (13.44), MDA (11.06), and SSE (9749.5). Citric acid was the only identified OA as was confirmed by GC-MS and UPLC, with a total of 1.5%. In conclusion, DPF together with T. harzianum PWN6 is considered an excellent new combination for citric acid biosynthesis, after modeling with artificial intelligence procedure.

Enhancing the Potentiality of Trichoderma harzianum against Pythium Pathogen of Beans Using Chamomile (Matricaria chamomilla, L.) Flower Extract.

Our present study was designed to investigate the role of both Trichoderma harzianum and chamomile (Matricaria chamomilla L.) flower extract in mutual reaction against growth of Pythium ultimum. In vitro, the activity of chamomile extract was found to reduce the radial growth of Pythium ultimum up to 30% compared to the control. Whereas, the radial growth reduction effect of T. harzianum against P. ultimum reached 81.6% after 120 h. Data also showed the productivity of total phenolics and total flavonoids by T. harzianum, was 12.18 and 6.33 mg QE/100 mL culture filtrate, respectively. However, these compounds were determined in chamomile flower extract at concentrations of 75.33 and 24.29 mg QE/100 mL, respectively. The fractionation of aqueous extract of chamomile flower using HPLC provided several polyphenolic compounds such as pyrogallol, myricetin, rosemarinic acid, catechol, p-coumaric acid, benzoic acid, chlorogenic acid and other minor compounds. In vivo, the potentiality of T. harzianum with chamomile flower extract against Pythium pathogen of bean was investigated. Data obtained showed a reduction in the percentage of rotted seed and infected seedling up to 28 and 8%, respectively. Whereas, the survival increased up to 64% compared to other ones. There was also a significant promotion in growth features, total chlorophyll, carotenoids, total polyphenols and flavonoids, polyphenol-oxidase and peroxidase enzymes compared to other ones. To the best of our knowledge, there are no reported studies that included the mutual association of fungus, T. harzianum with the extract taken from the chamomile flower against P. ultimum, either in vitro or in vivo. In conclusion, the application of both T. harzianum and/or M. chamomilla extracts in the control of bean Pythium pathogen showed significant results.

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.