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

Abstract

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 Oleaeuropaea 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 Oleaeuropaea 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 Oleaeuropaea 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.

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

“…Particles in suspension will repel each other and not aggregate if their zeta potential is large, either positive or negative. On the other hand, there is no force preventing particles with a low zeta potential from flocculating and aggregating 23 . The negative zeta potential value implies that GNPs are bounded by negatively charged organic molecules, which decreases the repulsion between the GNPs, prevents their aggregation, and eventually promotes their stability 62 .…”

“…Two main factors influence the construction or topology of artificial neural networks: the number of neurons or nodes in each hidden layer and the number of layers. In ANN modelling, the network design includes both learning and training processes, as well as validation and verification of the final ANN model 23 . A simple neural network topology consists of interconnected artificial neurons grouped in three distinct layers: input, hidden, and output 23 .…”

“…In ANN modelling, the network design includes both learning and training processes, as well as validation and verification of the final ANN model 23 . A simple neural network topology consists of interconnected artificial neurons grouped in three distinct layers: input, hidden, and output 23 . In this study, the input layer accepts the initial data of the four independent variables, namely CFS concentration (%), incubation period (h), HAuCl 4 concentration (μg/mL), and the initial pH level to be processed by the following layers in the system to predict the optimal conditions for GNPs biosynthesis using the cell-free supernatant of S. albogriseolus .…”

“…Additionally, algal pigments 1 , 2 , algal derived soluble polysaccharides 20 , fungi 21 and plant leaves extracts 22 – 24 were used for the biosynthesis of NPs due to their favorable impact on both the synthesis process and the final NPs characteristics. Microbial metabolites and phytochemicals can act as bio-reducers and/ or stabilizing/capping agents in the biosynthesis process 14 , 23 . Microbial biosynthesis is viewed as a great alternative method for the extracellular synthesis of GNPs 25 .…”

“…In biotechnology, artificial neural networks (ANNs) are the most used artificial learning method. ANNs can be used for many applications, including the bioprocesses optimization 23 .…”