Harvesting Health: Phytochemicals in Cognitive Impairment Therapy.

Mild Cognitive Impairment (MCI) is swiftly emerging as a prevalent clinical concern within the elderly demographic. Willoughby spearheaded the pioneering investigation into the evolution of memory decline spanning from the age of 20 to 70. Employing a computerized substitution examination, he pinpointed a zenith in memory prowess at the age of 22, signifying the shift from infancy, succeeded by a gradual decline in later years in 1929. Cognitive impairment impacts various facets, encompassing cognition, memory, perceptual acuity, and linguistic proficiency. Compelling evidence indicates that genetic, dietary, and metabolic factors influence the trajectory of cognitive decline in this patient cohort. In addition to the widely recognized influence of the Mediterranean diet on cognitive function, numerous studies have delved into the potential impact of diverse phytochemicals on cognitive deterioration. Many of these compounds are renowned for their inflammation reducer or free-radical scavenger properties, coupled with their commendable acceptability and defense profiles. Phytochemicals sourced from medicinal plants play an essential role in upholding the intricate chemical equilibrium of the brain by modulating receptors linked to crucial inhibitory neurotransmitters. Across the annals of historical medicinal traditions, a multitude of plants have been cataloged for their efficacy in mitigating cognitive disorders. This study presents a concise examination of distinct medicinal herbs, highlighting their neuroprotective phytochemical components such as fatty acids, phenols, alkaloids, flavonoids, saponins, terpenes, and beyond. The principal objective of this inquiry is to meticulously inspect and provide discernment into the extant evidence concerning phytochemicals exhibiting clinically demonstrable effects on cognitive decline.

Synthesis, In vivo, and In silico Evaluation of New Pyrazoline-Benzothiazole Conjugates as Antiepileptic Agents.

New 2-(4-benzothiazol-2-yl-phenoxy)-1-(3,5-diphenyl-4,5-dihydro-pyrazol-1-yl)-ethanones (9a-o) have been designed and synthesized. All the synthesized compounds were characterized by thin layer chromatography and spectral analysis. The antiepileptic potential of the synthesized compounds has been tested by following standard animal screening models, including maximal electroshock (MES) and subcutaneous pentylenetetrazole (scPTZ) models. The neurotoxic and antidepression effects of the synthesized compounds were checked by utilizing rotarod apparatus, and motor impairment test (by actophotometer) respectively. The study concluded that compounds 9c, 9d, 9f, 9i, 9n, and 9o possessed good antiepileptic potential compared to standard drugs like carbamazepine and phenytoin. The results of the rotarod performance test also established them without any neurotoxicity. The motor impairment test revealed that the synthesized compounds are also good antidepressants. In-silico studies have been performed for calculation of pharmacophore pattern, prediction of pharmacokinetic properties which determine the eligibility of synthesized compounds as orally administered molecules and interactions with the target proteins. The result of in-silico studies reinforced results obtained by in vivo study of the synthesized compounds and their possible mechanism of antiepileptic action i. e. via inhibiting voltage-gated sodium channels (VGSCs) and gamma-aminobutyric acid-A receptor.

Biosynthesis and assessment of antibacterial and antioxidant activities of silver nanoparticles utilizing Cassia occidentalis L. seed.

This research explores the eco-friendly synthesis of silver nanoparticles (AgNPs) using Cassia occidentalis L. seed extract. Various analytical techniques, including UV-visible spectroscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive X-ray spectroscopy (EDX), were employed for comprehensive characterization. The UV-visible spectra revealed a distinct peak at 425 nm, while the seed extract exhibited peaks at 220 and 248 nm, indicating the presence of polyphenols and phytochemicals. High-resolution TEM unveiled spherical and oval-shaped AgNPs with diameters ranging from 6.44 to 28.50 nm. The SEM exhibiting a spherical shape and a polydisperse nature, thus providing insights into the morphology of the AgNPs. EDX analysis confirmed the presence of silver atoms at 10.01% in the sample. XRD results unequivocally confirm the crystalline nature of the AgNPs suspension, thereby providing valuable insights into their structural characteristics and purity. The antioxidant properties of AgNPs, C. occidentalis seed extract, and butylated hydroxytoluene (BHT) were assessed, revealing IC50 values of 345, 500, and 434 µg/mL, respectively. Antibacterial evaluation against Bacillus subtilis, Staphylococcus aureus, and Escherichia coli demonstrated heightened sensitivity of bacteria to AgNPs compared to AgNO3. Standard antibiotics, tetracycline, and ciprofloxacin, acting as positive controls, exhibited substantial antibacterial efficacy. The green-synthesized AgNPs displayed potent antibacterial activity, suggesting their potential as a viable alternative to conventional antibiotics for combating pathogenic bacterial infections. Furthermore, potential biomedical applications of AgNPs were thoroughly discussed.

Development and characterization of biogenic copper oxide nanoparticles, with an exploration of their antibacterial and antioxidant potential.

This study outlines the synthesis of biogenic copper oxide nanoparticles (CuONPs) using an extract derived from Cassia fistula Linn (Cf) leaves through a green synthesis approach. Characterization of the synthesized CfBio-CuONPs was carried out using UV- VIS, FTIR, DLS, XRD, and TEM studies. The CfBio-CuONPs exhibited a prominent peak at 272 nm in UV-VIS spectroscopy, and XRD measurements confirmed their crystalline nature. The FTIR spectrum of CfBio-CuONPs revealed the presence of functional groups such as O-H and aromatic groups. TEM analysis confirmed that the CfBio-CuONPs were predominantly spherical with diameters ranging from 15 to 25 nm. Subsequently, the antibacterial potential of CfBio-CuONPs was evaluated against four pathogenic bacteria, including Escherichia coli, Pseudomonas aeruginosa, Staphylococcus epidermidis, and Bacillus subtilis. Among these, B. subtilis exhibited the highest zone of inhibition (26.93 ± 2.01 mm), followed by E. coli (24.25 ± 1.04 mm), P. aeruginosa (23.98 ± 0.97 mm), and S. epidermidis (22.97 ± 1.20 mm). CfBio-CuONPs demonstrated maximum antioxidant activity (78 ± 1.54%) at a dose-dependent concentration of 2000 µg/ml. Furthermore, in vitro toxicity assessment using the toxtrak test indicated that CfBio-CuONPs exhibited a significantly stronger toxic effect value/PI against E. coli (93.52%) compared to P. aeruginosa (92.65%), B. subtilis (91.25%), and S. epidermidis (82.89%). These results underscore the notable toxicity of CfBio-CuONPs against E. coli, surpassing that against other bacteria and conventional antibiotics. This study highlights the potential utility of CfBio-CuONPs for eradicating pathogenic microorganisms and suggests potential implications for ecotoxicology. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03869-5.

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests.

Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.

Recent development of multi-targeted inhibitors of human topoisomerase II enzyme as potent cancer therapeutics.

Multi-target therapies have been considered one of the viable options to overcome the challenges to eradicate intrinsic and acquired drug-resistant cancer cells. While to increase the efficacy of therapeutics, the use of a single drug against multiple structurally similar sites, which noncommittedly modulate several vital cellular pathways proposed as a potential alternative to a 'single drug single target'. Besides, it reduces the usage of a number of drugs and their side effects. Topoisomerase II enzyme plays a very significant role in DNA replication and thus served as an important target for numerous anti-cancer agents. However, in spite of promising clinical results, in several cases, it was found that cancer cells have developed resistance against the anti-cancer agents targeting this enzyme. Therefore, multi-target therapies have been proposed as an alternative to overcome different drug resistance mechanisms while topoisomerases II are a primary target site. In this review, we have tried to discuss the characteristics of the binding cavity available for interactions of drugs, and potent inhibitors concurrently modulate the functions of topoisomerases II as well as other structurally related target sites. Additionally, the mechanism of drug resistance by considering molecular and cellular insights by including various types of cancers.

Botanicals and Oral Stem Cell Mediated Regeneration: A Paradigm Shift from Artificial to Biological Replacement.

Stem cells are a well-known autologous pluripotent cell source, having excellent potential to develop into specialized cells, such as brain, skin, and bone marrow cells. The oral cavity is reported to be a rich source of multiple types of oral stem cells, including the dental pulp, mucosal soft tissues, periodontal ligament, and apical papilla. Oral stem cells were useful for both the regeneration of soft tissue components in the dental pulp and mineralized structure regeneration, such as bone or dentin, and can be a viable substitute for traditionally used bone marrow stem cells. In recent years, several studies have reported that plant extracts or compounds promoted the proliferation, differentiation, and survival of different oral stem cells. This review is carried out by following the PRISMA guidelines and focusing mainly on the effects of bioactive compounds on oral stem cell-mediated dental, bone, and neural regeneration. It is observed that in recent years studies were mainly focused on the utilization of oral stem cell-mediated regeneration of bone or dental mesenchymal cells, however, the utility of bioactive compounds on oral stem cell-mediated regeneration requires additional assessment beyond in vitro and in vivo studies, and requires more randomized clinical trials and case studies.

DeepOlf: Deep Neural Network Based Architecture for Predicting Odorants and Their Interacting Olfactory Receptors.

Olfaction transduction mechanism is triggered by the binding of odorants to the specific olfactory receptors (OR's) present in the nasal cavity. Different odorants stimulate different OR's due to the difference in shape, physical and chemical properties. In this paper, a deep neural network architecture DeepOlf, based on molecular features and fingerprints of odorants and ORs, to predict whether a chemical compound is a potential odorant or not along with its interacting OR is proposed. Odorant identification and Odorant-OR interaction were modeled as a binary classification through multiple classifiers. The evaluation of these classifier's performance showed that the deep-neural network framework not only fits data with better accuracy in comparison to other classical methods (SVM, RF, k-NN) but also able to predict odorant-OR interactions more accurately. To our knowledge, this study is the first realization of deep learning ideas for the problem of odorant and interacting OR prediction. The accuracy of DeepOlf was found to be 94.83 and 99.92 percent for the prediction of odorants and Odorant- OR interactions respectively. Comparison of DeepOlf prediction with the existing SVM based prediction server, ODORactor, showed that better performance can be achieved with the proposed deep learning approach. The DeepOlf tool can be accessed at https://bioserver.iiita.ac.in/deepolf/.

Antimicrobial and dye degradation application of fungi-assisted silver nanoparticles and utilization of fungal retentate biomass for dye removal.

The present study utilized Aspergillus spp. for the synthesis of silver nanoparticles (AgNPs); the developed AgNPs were categorized using analytical techniques, that is, ultraviolet-visible (UV-vis) spectrophotometer, Zeta-potential, dynamic light scattering (DLS), and transmission electron microscopy (TEM). A sharp peak of 463 nm highlighted the synthesis of AgNPs; further Zeta-potential of -16 mV indicates stability of synthesized AgNPs. The TEM micrograph showed spherical and hexagonal shapes of synthesized AgNPs of 6-25 nm. The photocatalytic activity of fungal-mediated AgNPs was evaluated for degradation of reactive yellow dye in the concentration range of 20-100 mg L-1 . The results showed efficient degradation of dye using AgNPs in short span of time. For antibacterial activity, synthesized AgNPs, antibiotic, and AgNPs + antibiotic were tested. As per results, the zone of inhibition (ZOI) of AgNPs showed the values of 13 and 10 mm for Escherichia coli and Staphylococcus aureus, respectively. Further, the ZOI of penicillin highlighted the values of 18 and 17 mm for E. coli and S. aureus, respectively. When AgNPs and penicillin were used in combination, a clear synergistic effect was observed; the ZOI showed 0.49- and 0.36-fold increase in area against E. coli and S. aureus, respectively, in comparison with penicillin or AgNPs alone. Further, the leftover biomass (retentate biomass) was used to decolorize the reactive yellow dye at different initial concentration ranging from 20 to 100 mg L-1 . It was observed that 1 g L-1 retentate biomass (BR ) can effectively remove 82%-100% dye at 20 and 100 mg L-1 initial dye concentration. Results also indicated that with increase in initial reactive dye concentration from 20 to 100 mg L-1 , the decolorization capacity of retentate biomass (BR ) (at 0.2 g L-1 ) decreased from 79.2% to 32.3%. However, the use of AgNPs synthesized leftover fungal biomass can be a good option for up taking the additional dyes/contaminants, and also as leftover biomass can be utilized effectively, it can prove to be an excellent approach for environment safety. As the literature studies did not mentioned the further use of retentate biomass, the present study provides an excellent approach for further research on this aspect. PRACTITIONER POINTS: Synthesis of AgNPs from Aspergillus spp. and characterized with the help of a U.V-vis spectrophotometer, a zeta potential, DLS and TEM. The developed AgNPs were used for antibacterial and dye degradation activity. The left over (retentate) fungal biomass was used further for additional dye degradation activity.

Potential of Pueraria tuberosa (Willd.) DC. to rescue cognitive decline associated with BACE1 protein of Alzheimer's disease on Drosophila model: An integrated molecular modeling and in vivo approach.

The indispensable role of Beta-site amyloid precursor protein cleaving enzyme-1 (BACE1) in Amyloid beta (Aß) plaques generation and Aß-mediated synaptic dysfunctions makes it a crucial target for therapeutic intervention in Alzheimer's disease (AD). In order to find out the potential inhibitors of BACE1, the present study focused on five phytochemicals from Pueraria tuberosa, namely, daidzin, genistin, mangiferin, puerarin, and tuberosin. A molecular docking study showed that all five phytochemicals presented the strongest BACE1 inhibition. Integrated molecular dynamics simulations and free energy calculations demonstrated that all five natural compounds have stable and favorable energies causing strong binding with the pocket site of BACE1 on 50 ns. All these molecules also passed Lipinski's rule of five. To validate the molecular modeling based findings, we primarily targeted the cognitive decline associated with BACE1 expression in AD flies with P. tuberosa. Significant improvement in cognitive decline was observed in AD flies in different behavioral assays such as Larval crawling assay (16.38%), Larval light preference assay (26.39%), Climbing assay (32.97%), Cold sensitivity assay (43.6%), and Thermal sensitivity assay (44.42%). The present findings suggest that P. tuberosa may be considered as a promising dietary supplement that can significantly ameliorate cognitive decline caused by BACE1 in Alzheimer's disease (AD).

Identification of phytochemical inhibitors against main protease of COVID-19 using molecular modeling approaches.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel corona virus that causes corona virus disease 2019 (COVID-19). The COVID-19 rapidly spread across the nations with high mortality rate even as very little is known to contain the virus at present. In the current study, we report novel natural metabolites namely, ursolic acid, carvacrol and oleanolic acid as the potential inhibitors against main protease (Mpro) of COVID-19 by using integrated molecular modeling approaches. From a combination of molecular docking and molecular dynamic (MD) simulations, we found three ligands bound to protease during 50 ns of MD simulations. Furthermore, the molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations showed that these chemical molecules have stable and favourable energies causing strong binding with binding site of Mpro protein. All these three molecules, namely, ursolic acid, carvacrol and oleanolic acid, have passed the ADME (Absorption, Distribution, Metabolism, and Excretion) property as well as Lipinski's rule of five. The study provides a basic foundation and suggests that the three phytochemicals, viz. ursolic acid, carvacrol and oleanolic acid could serve as potential inhibitors in regulating the Mpro protein's function and controlling viral replication. Communicated by Ramaswamy H. Sarma.

DeEPn: a deep neural network based tool for enzyme functional annotation.

With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly in the recent past. All of these raw sequences are required to be precisely mapped to their respective functional attributes, which helps in deciphering their biological role. In the recent past, various prediction models have been proposed to predict the enzyme functional class; however, all of these models were able to quantify at most six functional enzyme classes (EC1 to EC6) out of existing seven functional classes, making these approaches inappropriate for handling enzymes corresponding to the seventh functional class (EC7). In this study, a Deep Neural Network-based approach, DeEPn, has been proposed, which can quantify enzymes corresponding to all seven functional classes with high precision and accuracy. The proposed model was compared with two recently developed tools, ECPred and SVM-Prot. The result demonstrated that DeEPn outperformed ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/.Communicated by Ramaswamy H. Sarma.

Correction: Synthesis of non-toxic, biocompatible, and colloidal stable silver nanoparticle using egg-white protein as capping and reducing agents for sustainable antibacterial application.

[This corrects the article DOI: 10.1039/C8RA03649G.].

Genome-wide identification, characterization, and expression profiling of SPX gene family in wheat.

The SPX gene family, ubiquitous in all vascular plants, plays a critical role in plant development and growth as well as in response to phosphorus stress. Based on genomic census, 46 TaSPX genes were identified in the wheat genome. All of them are evenly distributed on 13 of the 21 wheat chromosomes and chromosome 7A contains the largest members. As many as 57 gene specific SSRs were discovered among genomic sequences of identified TaSPXs. MicroRNA target analysis revealed that TaSPX genes were targeted by 9 different miRNAs including tae-miR1120a, tae-miR1120b-3p, tae-miR1120c-5p, tae-miR1122b-3p, tae-miR1122c-3p, tae-miR1130a, tae-miR1130b-3p, tae-miR1137a, and tae-miR1137b-5p. Expression profiles derived from transcriptome data and real-time quantitative PCR revealed that TaSPX genes were significantly induced by Pi starvation. The modeled 3D structure of wheat SPX proteins shared high level of homology with template structures, providing information to understand their functions at proteomic level. We have also refined the modeled 3D structures on 10 ns using molecular dynamics simulations for conformational stability. The discovered members of SPX gene family and their targeting miRNAs may provide resource for genetic improvement and promote P use efficiency in cereals.

Sense of Smell: Structural, Functional, Mechanistic Advancements and Challenges in Human Olfactory Research.

Olfaction, the sense of smell detects and discriminate odors as well as social cues which influence our innate responses. The olfactory system in human beings is found to be weak as compared to other animals; however, it seems to be very precise. It can detect and discriminate millions of chemical moieties (odorants) even in minuscule quantities. The process initiates with the binding of odorants to specialized olfactory receptors, encoded by a large family of Olfactory Receptor (OR) genes belonging to the G-protein-coupled receptor superfamily. Stimulation of ORs converts the chemical information encoded in the odorants, into respective neuronal action-potentials which causes depolarization of olfactory sensory neurons. The olfactory bulb relays this signal to different parts of the brain for processing. Odors are encrypted using a combinatorial approach to detect a variety of chemicals and encode their unique identity. The discovery of functional OR genes and proteins provided an important information to decipher the genomic, structural and functional basis of olfaction. ORs constitute 17 gene families, out of which 4 families were reported to contain more than hundred members each. The olfactory machinery is not limited to GPCRs; a number of non- GPCRs is also employed to detect chemosensory stimuli. The article provides detailed information about such olfaction machinery, structures, transduction mechanism, theories of odor perception, and challenges in the olfaction research. It covers the structural, functional and computational studies carried out in the olfaction research in the recent past.

Functional and structural insights into candidate genes associated with nitrogen and phosphorus nutrition in wheat (Triticum aestivum L.).

An extensive bioinformatics based study has been performed to gain insight into the structural and functional aspects of candidate genes involved in Nitrogen and Phosphorus nutrition in wheat. Based on our study, 37 N and P nutrition candidate genes were identified (24 NUE and 13 inorganic phosphate transporters) in wheat genome. 23 gene specific novel microsatellites were discovered using genomic sequences of identified N and P nutrition genes. We also identified the microRNAs that target ten candidate genes including TaAS1-3A, TaAS1-3D, TaASN2-1A, TaASN2-1B, TaANR1-6A, TaANR1-6B, TaNRT2.4-6A, TaNRT2.6-6A, TaNRT2.6-6B and TaPHT1.5-5B. Expression profiling of identified genes showed altered expression under N and P starvation. The proposed 3D structure of wheat N and P nutrition proteins shared high level homology with known experimental structures providing information to understand their functions at the biochemical level. Molecular dynamics simulations of refined modeled structures of wheat N and P nutrition proteins show conformational stability. The identified N and P nutrition candidate genes and their targeting miRNAs may provide resources for the genetic improvement and promote N and P use efficiency. Our study provides first-hand structural prospective of N and P nutrition candidate genes towards development of wheat varieties resilient to N and P stress.

Synthesis of non-toxic, biocompatible, and colloidal stable silver nanoparticle using egg-white protein as capping and reducing agents for sustainable antibacterial application.

For nearly a decade, silver nanoparticles (AgNPs) have been the most prevalent commercial nanomaterials products widely used in different biomedical applications due to their broad-spectrum antimicrobial activity. However, their poor long-term stability in different environments, namely, pH, ionic strength, and temperature, and cytotoxicity toward mammalian cells has restricted their more extensive applications. Hence, there is urgent need to develop highly biocompatible, non-toxic, and stable silver nanoparticles for wide-ranging environments and applications. In the present study, a simple, sustainable, cost-effective and green method has been developed to prepare highly stable aqueous colloidal silver nanoparticles (AgNPs-EW) using the ovalbumin, ovotransferrin, and ovomucoid of egg-white as reducing and capping agents accomplished under the irradiation of direct sunlight. Then, we evaluated the effects of freezing-drying (lyophilization) and freeze-thaw cycles on the stability of AgNPs-EW in aqueous solution under visual inspection, transmission electron microscopy, and absorbance spectroscopy. In addition, we studied the antibacterial activity against Salmonella typhimurium and Escherichia coli, carried out biocompatibility studies on chicken blood, and tested acute, chronic toxicity in Drosophila melanogaster. The results suggest that AgNPs-EW did not aggregate upon freeze-thawing and lyophilization, thus exhibiting remarkable stability. The antibacterial activity results showed that the AgNPs-EW had the highest antibacterial activity, and the minimum inhibitory concentration (MIC) of AgNPs-EW for E. Coli and S. typhimurium were 4 and 6 µg ml-1, respectively. The biocompatibility study revealed that the AgNPs-EW did not induce any hemolytic effect or structural damage to the cell membranes of chicken erythrocytes up to a concentration of 12 µg ml-1. Similarly, no acute and chronic toxicity was observed on melanization, fecundity, hatchability, viability, and the duration of development in the 1st generation of Drosophila melanogaster at the concentration range of 10 mg L-1 to 100 mg L-1 of AgNPs-EW, and all the flies completed their full developmental cycle. Therefore, the present study successfully demonstrated the green and sustainable preparation of non-toxic AgNPs-EW having good biocompatibility, enhanced colloidal stability, and antibacterial activity. Hence, the synthesized AgNPs-EW could be used for the development of an antimicrobial formulation for controlling microbial infection.