Innovative biosynthesis, artificial intelligence-based optimization, and characterization of chitosan nanoparticles by Streptomyces microflavus and their inhibitory potential against Pectobacterium carotovorum

Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soils contaminated with heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving an improvement of nearly 2.45 times the initial yield under specific conditions (Bennet's medium supplemented with glycerol [5 g/L] and casamino-acid [3 g/L] at 30 °C for 72 h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using energy-dispersive X-ray spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0.2 W/cm2), with temperatures increasing to 41.7 °C, after 30 min. In addition, treatment with light irradiation of E. coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5 µg/mL, thereby enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12.

Actinomycetes are a natural resource for sustainable pest control and safeguarding agriculture

Actinomycetes, a diverse group of bacteria with filamentous growth characteristics, have long captivated researchers and biochemists for their prolific production of secondary metabolites. Among the myriad roles played by actinomycete secondary metabolites, their historical significance in the field of biocontrol stands out prominently. The fascinating journey begins with the discovery of antibiotics, where renowned compounds like streptomycin, tetracycline, and erythromycin revolutionized medicine and agriculture. The history of biocontrol traces its roots back to the early twentieth century, when scientists recognized the potential of naturally occurring agents to combat pests and diseases. The emergence of synthetic pesticides in the mid-twentieth century temporarily overshadowed interest in biocontrol. However, with growing environmental concerns and the realization of the negative ecological impacts of chemical pesticides, the pendulum swung back towards exploring sustainable alternatives. Beyond their historical role as antibiotics, actinomycete-produced secondary metabolites encompass a rich repertoire with biopesticide potential. The classification of these compounds based on chemical structure and mode of action is highlighted, demonstrating their versatility against both plant pathogens and insect pests. Additionally, this review provides in-depth insights into how endophytic actinomycete strains play a pivotal role in biocontrol strategies. Case studies elucidate their effectiveness in inhibiting Spodoptera spp. and nematodes through the production of bioactive compounds. By unraveling the multifunctional roles of endophytic actinomycetes, this review contributes compelling narrative knowledge to the field of sustainable agriculture, emphasizing the potential of these microbial allies in crafting effective, environmentally friendly biocontrol strategies for combating agricultural pests.

A sustainable green-approach for biofabrication of chitosan nanoparticles, optimization, characterization, its antifungal activity against phytopathogenic Fusarium culmorum and antitumor activity

Chitosan is a natural non-toxic, biocompatible, biodegradable, and mucoadhesive polymer. It also has a broad spectrum of applications such as agriculture, medical fields, cosmetics and food industries. In this investigation, chitosan nanoparticles were produced by an aqueous extract of Cympopogon citratus leaves as a reducing agent. According to the SEM and TEM micrographs, CNPs had a spherical shape, and size ranging from 8.08 to 12.01 nm. CNPs have a positively charged surface with a Zeta potential of + 26 mV. The crystalline feature of CNPs is determined by X-ray diffraction. There are many functional groups, including C꞊C, CH2-OH, C-O, C-S, N-H, CN, CH and OH were detected by FTIR analysis. As shown by the thermogravimetric study, CNPs have a high thermal stability. For the optimization of the green synthesis of CNPs, a Face centered central composite design (FCCCD) with 30 trials was used. The maximum yield of CNPs (13.99 mg CNPs/mL) was produced with chitosan concentration 1.5%, pH 4.5 at 40 °C, and incubation period of 30 min. The antifungal activity of CNPs was evaluated against phytopathogenic fungus; Fusarium culmorum. A 100% rate of mycelial growth inhibition was gained by the application of 20 mg CNPs/mL. The antitumor activity of the green synthesized CNPs was examined using 6 different cell lines, the viability of the cells reduced when the concentration of green synthesized CNPs increased, the IC50 dose of the green synthesized CNPs on the examined cell lines HePG-2, MCF-7, HCT-116, PC-3, Hela and WI-38 was 36.25 ± 2.3, 31.21 ± 2.2, 67.45 ± 3.5, 56.30 ± 3.3, 44.62 ± 2.6 and 74.90 ± 3.8; respectively.

Biodegradation of ciprofloxacin using machine learning tools: Kinetics and modelling

Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant bacteria (XDR), antibiotic resistance bacterial variants (ARB), and genes (ARGs). The constraints, such as high costs, by-product formation, etc., associated with the physico-chemical treatment process limit their efficacy in achieving efficient wastewater remediation. Biodegradation is a cost-effective, energy-saving, sustainable alternative for removing emerging organic pollutants from environmental matrices. In view of the same, the current study aims to explore the biodegradation of ciprofloxacin using microbial consortia via metabolic pathways. The optimal parameters for biodegradation were assessed by employing machine learning tools, viz. Artificial Neural Network (ANN) and statistical optimization tool (Response Surface Methodology, RSM) using the Box-Behnken design (BBD). Under optimal culture conditions, the designed bacterial consortia degraded ciprofloxacin with 95.5% efficiency, aligning with model prediction results, i.e., 95.20% (RSM) and 94.53% (ANN), respectively. Thus, befitting amendments to the biodegradation process can augment efficiency and lead to a greener solution for antibiotic degradation from aqueous media.

Adsorption of diazinon from aqueous solution using metal organic framework and functionalized graphene: Comparison of BBD, ANN models

In the present study, metal organic frameworks (MOFs) and aminated graphitic carbonaceous structure (ACS-RGO) through chemical synthesis prepared by a simple precipitation method and used for diazinon removal. Several techniques such as XRD , FESEM and FTIR were applied for identification of MOF-5 and ACS-RGO. Also, response surface methodology (RSM) was employed in this work to look at the effectiveness of diazinon adsorption. To forecast pesticide removal, we applied artificial neural network (ANN) and Box-Behnken Design (BBD) models. For the ANN model, a sensitivity analysis was also performed. The effect of independent variables like solution pH, various concentrations of diazinon, MOFs and ACS-RGO adsorbent dose and contact time were assessed to find out the optimum conditions. Based on the model prediction, the optimal condition for adsorption ACS-RGO and MOF-5 were determined to be pH 6.6 and 6.6, adsorbent dose of 0.59 and 0.906 g/L, and mixing time of 52.15 and 36.96 min respectively. These conditions resulted in 96.69% and 80.62% diazinon removal using ACS-RGO and MOF-5, respectively. Isotherm studies proved the adsorption of ACS-RGO and MOF-5 following the Langmuir isotherm model for diazinon removal. Diazinon removal followed by the pseudo-second and Pseudo-first order kinetics model provides a better fit for analyzing the kinetic data associated with pesticide adsorption for ACS-RGO and MOF-5, respectively. Based on the obtained results, the predicted values for the efficiency of diazinon removal with the ANN and BBD were similar (R2=0.98). Therefore, two models were able to predict diazinon removal by ACS-RGO and MOF-5.

Process optimization for gold nanoparticles biosynthesis by Streptomyces albogriseolus using artificial neural network, characterization and antitumor activities

Gold nanoparticles (GNPs) are highly promising in cancer therapy, wound healing, drug delivery, biosensing, and biomedical imaging. Furthermore, GNPs have anti-inflammatory, anti-angiogenic, antioxidants, anti-proliferative and anti-diabetic effects. The present study presents an eco-friendly approach for GNPs biosynthesis using the cell-free supernatant of Streptomyces albogriseolus as a reducing and stabilizing agent. The biosynthesized GNPs have a maximum absorption peak at 540 nm. The TEM images showed that GNPs ranged in size from 5.42 to 13.34 nm and had a spherical shape. GNPs have a negatively charged surface with a Zeta potential of - 24.8 mV. FTIR analysis identified several functional groups including C-H, -OH, C-N, amines and amide groups. The crystalline structure of GNPs was verified by X-ray diffraction and the well-defined and distinct diffraction rings observed by the selected area electron diffraction analysis. To optimize the biosynthesis of GNPs using the cell-free supernatant of S. albogriseolus, 30 experimental runs were conducted using central composite design (CCD). The artificial neural network (ANN) was employed to analyze, validate, and predict GNPs biosynthesis compared to CCD. The maximum experimental yield of GNPs (778.74 μg/mL) was obtained with a cell-free supernatant concentration of 70%, a HAuCl4 concentration of 800 μg/mL, an initial pH of 7, and a 96-h incubation time. The theoretically predicted yields of GNPs by CCD and ANN were 809.89 and 777.32 μg/mL, respectively, which indicates that ANN has stronger prediction potential compared to the CCD. The anticancer activity of GNPs was compared to that of doxorubicin (Dox) in vitro against the HeP-G2 human cancer cell line. The IC50 values of Dox and GNPs-based treatments were 7.26 ± 0.4 and 22.13 ± 1.3 µg/mL, respectively. Interestingly, treatments combining Dox and GNPs together showed an IC50 value of 3.52 ± 0.1 µg/mL, indicating that they targeted cancer cells more efficiently.

Green synthesis of collagen nanoparticles by Streptomyces xinghaiensis NEAA-1, statistical optimization, characterization, and evaluation of their anticancer potential

Collagen nanoparticles (collagen-NPs) are promising biopolymeric nanoparticles due to their superior biodegradability and biocompatibility. The low immunogenicity and non-toxicity of collagen-NPs makes it preferable for a wide range of applications. A total of eight morphologically distinct actinomycetes strains were newly isolated from various soil samples in Egypt. The cell-free supernatants of these strains were tested for their ability. These strains' cell-free supernatants were tested for their ability to synthesize collagen-NPs. Five isolates had the ability to biosynthesize collagen-NPs. Among these, a potential culture, Streptomyces sp. NEAA-1, was chosen and identified as Streptomyces xinghaiensis NEAA-1 based on 16S rRNA sequence analysis as well as morphological, cultural and physiological properties. The sequence data has been deposited at the GenBank database under the accession No. OQ652077.1. Face-centered central composite design (FCCD) has been conducted to maximize collagen-NPs biosynthesis. Maximum collagen-NPs was 8.92 mg/mL under the condition of 10 mg/mL of collagen concentration, initial pH 7, incubation time of 48 h and temperature of 35 °C. The yield of collagen-NPs obtained via FCCD optimization (8.92 mg/mL) was 3.32-fold compared to the yield obtained under non-optimized conditions (2.5 mg/mL). TEM analysis of collagen-NPs showed hollow sphere nanoscale particles with mean of 32.63 ± 14.59 nm in diameter. FTIR spectra showed major peaks of amide I, amide II and amide III of collagen and also the cell-free supernatant involved in effective capping of collagen-NPs. The biosynthesized collagen-NPs exhibited anti-hemolytic, antioxidant and cytotoxic activities. The inhibitory concentrations (IC50) against MCF-7, HeP-G2 and HCT116 cell lines were 11.62 ± 0.8, 19.60 ± 1.2 and 41.67 ± 2.2 µg/mL; respectively. The in-vivo investigation showed that collagen-NPs can suppress Ehrlich ascites carcinoma (EAC) growth in mice. The collagen-NPs/DOX combination treatment showed considerable tumor growth suppression (95.58%). Collagen-NPs evaluated as nanocarrier with a chemotherapeutic agent, methotrexate (MTX). The average size of MTX loaded collagen-NPs was 42.73 ± 3.5 nm. Encapsulation efficiency percentage (EE %) was 48.91% and drug loading percentage (DL %) was 24.45%.

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

Artificial neural network approach for prediction of AuNPs biosynthesis by Streptomyces flavolimosus, characterization, antitumor potency in-vitro and in-vivo against Ehrlich ascites carcinoma

Gold nanoparticles (AuNPs) have emerged as promising and versatile nanoparticles for cancer therapy and are widely used in drug and gene delivery, biomedical imaging, diagnosis, and biosensors. The current study describes a biological-based strategy for AuNPs biosynthesis using the cell-free supernatant of Streptomyces flavolimosus. The biosynthesized AuNPs have an absorption peak at 530-535 nm. The TEM images indicate that AuNPs were spherical and ranged in size from 4 to 20 nm. The surface capping molecules of AuNPs are negatively charged, having a Zeta potential of - 10.9 mV. FTIR analysis revealed that the AuNPs surface composition contains a variety of functional groups as -OH, C-H, N-, C=O, NH3+, amine hydrochloride, amide group of proteins, C-C and C-N. The bioprocess variables affecting AuNPs biosynthesis were optimized by using the central composite design (CCD) in order to maximize the AuNPs biosynthesis. The maximum yield of AuNPs (866.29 µg AuNPs/mL) was obtained using temperature (35 °C), incubation period (4 days), HAuCl4 concentration (1000 µg/mL) and initial pH level 6. Comparison was made between the fitness of CCD versus Artificial neural network (ANN) approach based on their prediction and the corresponding experimental results. AuNPs biosynthesis values predicted by ANN exhibit a more reasonable agreement with the experimental result. The anticancer activities of AuNPs were assessed under both in vitro and in vivo conditions. The results revealed a significant inhibitory effect on the proliferation of the MCF-7 and Hela carcinoma cell lines treated with AuNPs with IC50 value of 13.4 ± 0.44 μg/mL and 13.8 ± 0.45 μg/mL for MCF-7 and Hela cells; respectively. Further, AuNPs showed potential inhibitory effect against tumor growth in tumor-bearing mice models. AuNPs significantly reduced the tumor volume, tumor weight, and decreased number of viable tumor cells in EAC bearing mice.

Green fabrication of chitosan nanoparticles using Lavendula angustifolia, optimization, characterization and in‑vitro antibiofilm activity

Chitosan nanoparticles (CNPs) are promising polymeric nanoparticles with exceptional physicochemical, antimicrobial and biological characteristics. The CNPs are preferred for a wide range of applications in the food industry, cosmetics, agriculture, medical, and pharmaceutical fields due to their biocompatibility, biodegradability, eco-friendliness, and non-toxicity. In the current study, a biologically based approach was used to biofabricate CNPs using an aqueous extract of Lavendula angustifolia leaves as a reducing agent. The TEM images show that the CNPs were spherical in shape and ranged in size from 7.24 to 9.77 nm. FTIR analysis revealed the presence of several functional groups, including C-H, C-O, CONH₂, NH₂, C-OH and C-O-C. The crystalline nature of CNPs is demonstrated by X-ray diffraction. The thermogravimetric analysis revealed that CNPs are thermally stable. The CNPs' surface is positively charged and has a Zeta potential of 10 mV. For optimising CNPs biofabrication, a face-centered central composite design (FCCCD) with 50 experiments was used. The artificial intelligence-based approach was used to analyse, validate, and predict CNPs biofabrication. The optimal conditions for maximum CNPs biofabrication were theoretically determined using the desirability function and experimentally verified. The optimal conditions that maximize CNPs biofabrication (10.11 mg/mL) were determined to be chitosan concentration 0.5%, leaves extract 75%, and initial pH 4.24. The antibiofilm activity of CNPs was evaluated in‑vitro. The results show that 1500 μg/mL of CNPs suppressed P. aeruginosa, S. aureus and C. albicans biofilm formation by 91.83 ± 1.71%, 55.47 ± 2.12% and 66.4 ± 1.76%; respectively. The promising results of the current study in biofilm inhibition by necrotizing biofilm architecture, reducing its significant constituents and inhibiting microbial cell proliferation encourage their use as natural biosafe and biocompatible anti-adherent coating in antibiofouling membranes, medical bandage/tissues and food packaging materials.

Artificial intelligence-based optimization for chitosan nanoparticles biosynthesis, characterization and in‑vitro assessment of its anti-biofilm potentiality

Chitosan nanoparticles (CNPs) are promising biopolymeric nanoparticles with excellent physicochemical, antimicrobial, and biological properties. CNPs have a wide range of applications due to their unique characteristics, including plant growth promotion and protection, drug delivery, antimicrobials, and encapsulation. The current study describes an alternative, biologically-based strategy for CNPs biosynthesis using Olea europaea leaves extract. Face centered central composite design (FCCCD), with 50 experiments was used for optimization of CNPs biosynthesis. The artificial neural network (ANN) was employed for analyzing, validating, and predicting CNPs biosynthesis using Olea europaea leaves extract. Using the desirability function, the optimum conditions for maximum CNPs biosynthesis were determined theoretically and verified experimentally. The highest experimental yield of CNPs (21.15 mg CNPs/mL) was obtained using chitosan solution of 1%, leaves extract solution of 100%, initial pH 4.47, and incubation time of 60 min at 53.83°C. The SEM and TEM images revealed that CNPs had a spherical form and varied in size between 6.91 and 11.14 nm. X-ray diffraction demonstrates the crystalline nature of CNPs. The surface of the CNPs is positively charged, having a Zeta potential of 33.1 mV. FTIR analysis revealed various functional groups including C-H, C-O, CONH2, NH2, C-OH and C-O-C. The thermogravimetric investigation indicated that CNPs are thermally stable. The CNPs were able to suppress biofilm formation by P. aeruginosa, S. aureus and C. albicans at concentrations ranging from 10 to 1500 µg/mL in a dose-dependent manner. Inhibition of biofilm formation was associated with suppression of metabolic activity, protein/exopolysaccharide moieties, and hydrophobicity of biofilm encased cells (r ˃ 0.9, P = 0.00). Due to their small size, in the range of 6.91 to 11.14 nm, CNPs produced using Olea europaea leaves extract are promising for applications in the medical and pharmaceutical industries, in addition to their potential application in controlling multidrug-resistant microorganisms, especially those associated with post COVID-19 pneumonia in immunosuppressed patients.