Green synthesis of nanomaterials by using plant extracts as reducing and capping agents

An alternative method to conventional synthesis is examined in this review by the use of plant extracts as reducing and capping agents. The use of plant extracts represents an economically viable and environmentally friendly alternative to conventional synthesis. In contrast to previous reviews, this review focuses on the synthesis of nano-compounds utilizing plant extracts, which lack comprehensive reports. In order to synthesize diverse nanostructures, researchers have discovered a sustainable and cost-effective method of harnessing functional groups in plant extracts. Each plant extract is discussed in detail, along with its potential applications, demonstrating the remarkable morphological diversity achieved by using these green synthesis approaches. A reduction and capping agent made from plant extracts is aligned with the principles of green chemistry and offers economic advantages as well as paving the way for industrial applications. In this review, it is discussed the significance of using plant extracts to synthesize nano-compounds, emphasizing their potential to shape the future of nanomaterials in a sustainable and ecologically friendly manner.

Effects of CO2 concentration and time on algal biomass film, NO3-N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II

The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3-N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3-N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3-N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3-N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3-N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3-N content.

Fluorescence Resonance Energy Transfer (FRET)-Based Sensor for Detection of Foodborne Pathogenic Bacteria: A Review

Sensitive and rapid determination of foodborne pathogenic bacteria is of practical importance for the control and prevention of foodborne illnesses. Nowadays, with the prosperous development of fluorescence assays, fluorescence resonance energy transfer (FRET)-derived diagnostic strategies are extensively employed in quantitative analysis of different pathogenic bacteria in food-related matrices, which displays a rapid, simple, stable, reliable, cost-effective, selective, sensitive, and real-time way. Considering the extensive efforts that have been made in this field so far, we here discuss the up-to-date developments of FRET-based diagnostic approaches for the determination of key foodborne pathogens like Staphylococcus aureus, Escherichia coli, Vibrio parahaemolyticus, Salmonella spp., Campylobacter spp., and Bacillus cereus in complex food-related matrices. Moreover, the principle of this technology, the choosing standards of acceptor-donor pairs, and the fluorescence properties are also profiled. Finally, the current prospects and challenges in this field are also put forward.

Frequency of TLR4 (1063A/G and 1363C/T) polymorphisms in healthy and HIV-infected Omani individuals and their relationship to viral load and T cell count

Toll-like receptors (TLRs) are essential elements of the innate immune response to different infections including the infection with human immunodeficiency virus (HIV). Single nucleotide polymorphisms (SNPs) in TLRs such as TLR4 1063A/G and 1363C/T have been found to be associated with changes in CD4 count, viral load (VL), and disease progression during HIV infection. However, the association of these SNPs with the pathogenesis during HIV infection is controversial. We investigated the frequency of TLR4 1063A/G and 1363C/T SNPs in 168 Omani donors [68 HIV-infected patients (>3% of Omani HIV-infected patients) and 100 healthy controls] and the association of these SNPs with the VL, CD8 and CD4 counts, and the immune recovery after cART as observed by CD4 T cell increase. SNPs were analyzed after the amplification of the regions that contain them by polymerase chain reaction (PCR) and sequencing of the PCR products. The TLR4 1063GG genotype was detected in the HIV-infected group only. No association was found between the studied SNPs and the average VL during 1 year of infection, the average CD4 and CD8 count during 1 year of viremia, the nadir CD4 count, the CD4 count when the patient reached VL < 50 copies/mL due to cART, and the ratio of the CD4 count 3 and 6 months after reaching VL < 50 copies/mL after cART to the last CD4 count before reaching VL < 50 copies/mL. Our study suggests that TLR4 (1063A/G and 1363C/T) SNPs have no association with the VL or the CD4 and CD8 counts during HIV infection.

Association of single-nucleotide polymorphisms in TLR7 (Gln11Leu) and TLR9 (1635A/G) with a higher CD4T cell count during HIV infection

BACKGROUND

Toll-like receptors (TLRs) are essential elements of the innate immune response to different infections including HIV-1 infection. The single-nucleotide polymorphisms (SNPs) in TLRs have been associated with CD4T cell count and HIV disease progression. The TLR7 (Gln11Leu) SNP was shown to be associated with a rapid decline of CD4T cell count. A relation between TLR9 (1635A/G) SNP and CD4T cells count in HIV-infected patients is suggested, although the outcome associated with this SNP is still controversial.

OBJECTIVES

To determine the relation of the TLR7 (Gln11Leu) and TLR9 (1635A/G) SNPs with the damage to the immune system during HIV infection as reflected by the average CD4T cell count.

METHODS

A total of 63 HIV-infected patients and 100 healthy individuals (controls) were enrolled in this study. The above named SNPs were analyzed after amplification of the regions that potentially contain the SNPs by polymerase chain reaction (PCR) and sequencing of the PCR products. The frequency of these SNPs and their relation with the CD4T cell count were investigated.

RESULTS

The TLR7 (AA) genotype 'Gln' had a trend toward being associated with a CD4T cell count >400cells/μl after controlling viremia via HAART. Additionally, the TLR9 1635 (GG) genotype was associated with a low average CD4T cell count and the TLR9 1635 (AG) genotype was significantly related to a higher average CD4T cell count during the viremic period in HIV-infected patients.

CONCLUSION

The results of this longitudinal study supports the presence of an association between the TLR9 (1635A/G) genotype and the CD4T cell count, which helps clarifying the controversial results regarding this association. It also suggests that the CD4T cell count during the viremic period might be linked to the combination of both TLR7 (Gln11Leu) and TLR9 (1635A/G) genotypes. These results may help predicting the damage to the immune system, and thus impacting the planning for novel anti-HIV strategies.