Search Results (22)

Green synthesis of metallic nanoparticles is an auspicious method of preparing nanoparticles using plant extracts that have lesser toxicity to animal cells and the host. In the present work, we analyzed the antibacterial activity of Citrullus colocynthis and Psidium guajava-mediated silver nanoparticles (Cc-AgNPs and Pg-AgNPs, respectively) against Aeromonas hydrophila (A. hydrophila) in an in vivo assay employing Labeo rohita (L. rohita). L. rohita were divided into six groups for both Cc-AgNPs and Pg-AgNPs treatments separately: Control, A. hydrophila infected, A. hydrophila + Ampicillin, A. hydrophila + Cc/Pg-AgNPs (25 µg/L), A. hydrophila + Cc/Pg-AgNPs (50 µg/L), and A. hydrophila + Cc/Pg-AgNPs (75 µg/L). Changes in different bio-indicators such as hematological, histological, oxidative stress, and cytokine analysis were observed. Interestingly, the infected fish treated with both types of AgNPs (Cc-AgNPs and Pg-AgNPs) exhibited a higher survival rate than the untreated infected fish and demonstrated signs of recovery from the infection, providing a compelling indication of the positive impact of phytosynthesized AgNPs. Disruptions in hematological and histological parameters were found in the infected fish. Both Cc-AgNPs and Pg-AgNPs showed recovery on the hematological and histological parameters. Analysis of oxidative stress and cytokine markers also revealed provoking evidence of the positive impact of Cc-AgNPs and Pg-AgNPs treatment against disease progression in the infected fish. The major finding of the study was that the higher concentrations of the nanoparticles (50 µg/L in the case of Cc-AgNPs and 75 µg/L in the case of Pg-AgNPs) were more effective in fighting against disease. In conclusion, our work presents novel insights for the use of green-synthesized AgNPs as economic and innocuous antibacterial candidates in aquaculture. Full article

The present study reports the green synthesis of silver nanoparticles from leaves’ extract of Mangifera indica (M. indica) and their antibacterial efficacy against Aeromonas hydrophila (A. hydrophila) in Cirrhinus mrigala (C. mrigala). The prepared M. indica mediated silver nanoparticles (Mi-AgNPs) were found to be polycrystalline in nature, spherical in shapes with average size of 62 ± 13 nm. C. mrigala (n = ±15/group) were divided into six groups i.e., G1: control, G2: A. hydrophila challenged, G3: A. hydrophila challenged + Mi-AgNPs (0.01 mg/L), G4: A. hydrophila challenged + Mi-AgNPs (0.05 mg/L), G5: A. hydrophila challenged + Mi-AgNPs (0.1 mg/L) and G6: A. hydrophila challenged + M. indica extract (0.1 mg/L). Serum biochemical, hematological, histological and oxidative biomarkers were evaluated after 15 days of treatment. The liver enzyme activities, serum proteins, hematological parameters and oxidative stress markers were found to be altered in the challenged fish but showed retrieval effects with Mi-AgNPs treatment. The histological analysis of liver, gills and kidney of the challenged fish also showed regaining effects following Mi-AgNPs treatment. A CFU assay from muscle tissue provided quantitative data that Mi-AgNPs can hinder the bacterial proliferation in challenged fish. The findings of this work suggest that M. indica based silver nanoparticles can be promising candidates for the control and treatment of microbial infections in aquaculture. Full article

In nanotechnology, the synthesis of carbon quantum dots (CQDs) by mixed doping with metals and non-metals has emerged as an appealing path of investigation. This review offers comprehensive insights into the synthesis, properties, and emerging applications of mixed-doped CQDs, underlining their potential for revolutionary advancements in chemical sensing, biosensing, bioimaging, and, thereby, contributing to advancements in diagnostics, therapeutics, and the under standing of complex biological processes. This synergistic combination enhances their sensitivity and selectivity towards specific chemical analytes. The resulting CQDs exhibit remarkable fluorescence properties that can be involved in precise chemical sensing applications. These metal-modified CQDs show their ability in the selective and sensitive detection from Hg to Fe and Mn ions. By influencing their exceptional fluorescence properties, they enable precise detection and monitoring of biomolecules, such as uric acid, cholesterol, and many antibiotics. Moreover, when it comes to bioimaging, these doped CQDs show unique behavior towards detecting cell lines. Their ability to emit light across a wide spectrum enables high-resolution imaging with minimal background noise. We uncover their potential in visualizing different cancer cell lines, offering valuable insights into cancer research and diagnostics. In conclusion, the synthesis of mixed-doped CQDs opens the way for revolutionary advancements in chemical sensing, biosensing, and bioimaging. As we investigate deeper into this field, we unlock new possibilities for diagnostics, therapeutics, and understanding complex biological processes. Full article

The work here reflects synthesis, DFT studies, Hirshfeld charge analysis and crystal data exploration of pharmacologically important (R)-2-(2-(1,3-dioxoisoindolin-2-yl)propanamido)benzoic acid methyl ester (5) to understand its properties for further chemical transformations. The methyl anthranilate (2) was produced by the esterification of anthranilic acid in an acidic medium. The phthaloyl-protected alanine (4) was rendered by the fusion of alanine with phthalic anhydride at 150 °C, followed by coupling with (2) furnished isoindole (5). The characterization of products was performed using IR, UV-Vis, NMR and MS. Single-crystal XRD also verified the structure of (5) in which N-H⋯O bonding stabilizes the molecular configuration of (5), resulting in the formation of S(6) hydrogen-bonded loop. The molecules of isoindole (5) are connected in the form of dimers, and the π⋯π stacking interaction between aromatic rings further stabilizes the crystal packing. DFT studies suggest that HOMO is over the substituted aromatic ring, the LUMO is present mainly over the indole side, and nucleophilic and electrophilic corners point out the reactivity of the product (5). In vitro and in silico analysis of (5) shows its potential as an antibacterial agent targeting DNA gyrase and Dihydroorotase from E. coli and tyrosyl-tRNA synthetase and DNA gyrase from Staphylococcus aureus. Full article

Increased demand for fast edge computation and privacy concerns have shifted researchers’ focus towards a type of distributed learning known as federated learning (FL). Recently, much research has been carried out on FL; however, a major challenge is the need to tackle the high diversity in different clients. Our research shows that using highly diverse data sets in FL can lead to low accuracy of some local models, which can be categorised as anomalous behaviour. In this paper, we present FedBranched, a clustering-based framework that uses probabilistic methods to create branches of clients and assigns their respective global models. Branching is performed using hidden Markov model clustering (HMM), and a round of branching depends on the diversity of the data. Clustering is performed on Euclidean distances of mean absolute percentage errors (MAPE) obtained from each client at the end of pre-defined communication rounds. The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an artificial neural network (ANN). FedBranched took two clustering rounds and resulted in two different branches having individual global models. The results show a substantial increase in the average MAPE of all clients; the biggest improvement of 11.36% was observed in one client. Full article

Citrus production is harmed worldwide by yellow dragon disease, also known as Huanglongbing (HLB), or citrus greening. As a result, it has negative effects and a significant impact on the agro-industrial sector. There is still no viable biocompatible treatment for Huanglongbing, despite enormous efforts to combat this disease and decrease its detrimental effects on citrus production. Nowadays, green-synthesized nanoparticles are gaining attention for their use in controlling various crop diseases. This research is the first scientific approach to examine the potential of phylogenic silver nanoparticles (AgNPs) to restore the health of Huanglongbing-diseased ‘Kinnow’ mandarin plants in a biocompatible manner. AgNPs were synthesized using Moringa oleifera as a reducing, capping, and stabilizing agent and characterized using different characterization techniques, i.e., UV–visible spectroscopy with a maximum average peak at 418 nm, scanning electron microscopy (SEM) with a size of 74 nm, and energy-dispersive spectroscopy (EDX), which confirmed the presence of silver ions along with different elements, and Fourier transform infrared spectroscopy served to confirm different functional groups of elements. Exogenously, AgNPs at various concentrations, i.e., 25, 50, 75, and 100 mgL⁻¹, were applied against Huanglongbing-diseased plants to evaluate the physiological, biochemical, and fruit parameters. The findings of the current study revealed that 75 mgL⁻¹ AgNPs were most effective in boosting the plants’ physiological profiles, i.e., chl a, chl b, total chl, carotenoid content, MSI, and RWC up to 92.87%, 93.36%, 66.72%, 80.95%, 59.61%, and 79.55%, respectively; biochemical parameters, i.e., 75 mgL⁻¹ concentration decreased the proline content by up to 40.98%, and increased the SSC, SOD, POD, CAT, TPC, and TFC content by 74.75%, 72.86%, 93.76%, 76.41%, 73.98%, and 92.85%, respectively; and fruit parameters, i.e., 75 mgL⁻¹ concentration increased the average fruit weight, peel diameter, peel weight, juice weight, rag weight, juice pH, total soluble solids, and total sugarby up to 90.78%, 8.65%, 68.06%, 84.74%, 74.66%, 52.58%, 72.94%, and 69.69%, respectively. These findings enable us to develop the AgNP formulation as a potential citrus Huanglongbing disease management method. Full article

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively. Full article

Axons in the peripheral nervous system have the ability to repair themselves after damage, whereas axons in the central nervous system are unable to do so. A common and important characteristic of damage to the spinal cord, brain, and peripheral nerves is the disruption of axonal regrowth. Interestingly, intrinsic growth factors play a significant role in the axonal regeneration of injured nerves. Various factors such as proteomic profile, microtubule stability, ribosomal location, and signalling pathways mark a line between the central and peripheral axons’ capacity for self-renewal. Unfortunately, glial scar development, myelin-associated inhibitor molecules, lack of neurotrophic factors, and inflammatory reactions are among the factors that restrict axonal regeneration. Molecular pathways such as cAMP, MAPK, JAK/STAT, ATF3/CREB, BMP/SMAD, AKT/mTORC1/p70S6K, PI3K/AKT, GSK-3β/CLASP, BDNF/Trk, Ras/ERK, integrin/FAK, RhoA/ROCK/LIMK, and POSTN/integrin are activated after nerve injury and are considered significant players in axonal regeneration. In addition to the aforementioned pathways, growth factors, microRNAs, and astrocytes are also commendable participants in regeneration. In this review, we discuss the detailed mechanism of each pathway along with key players that can be potentially valuable targets to help achieve quick axonal healing. We also identify the prospective targets that could help close knowledge gaps in the molecular pathways underlying regeneration and shed light on the creation of more powerful strategies to encourage axonal regeneration after nervous system injury. Full article

The present study investigated the biomedical potential of eco-friendly Citrullus colocynthis-mediated silver nanoparticles (Cc-AgNPs). The antibacterial efficacy of Cc-AgNPs was evaluated against two multidrug-resistant pathogenic bacterial strains, Escherichia coli and Pseudomonas aeruginosa. The antiproliferative and antilipidemic performance of the prepared particles was determined against the MCF7 cell line, a breast cancer cell line. The in vitro antibacterial assay revealed that Cc-AgNPs induced dose-dependent bactericidal activity, as a considerable increase in the zone of inhibition (ZOI) was noted at higher concentrations. Reduced proliferation, migration, spheroid size, and colony formation exhibited the substantial antiproliferative potential of Cc-AgNPs against MCF7 cells. Significant alterations in the expression of cell surface markers, apoptosis, and cell proliferation genes further confirmed the antiproliferative impact of Cc-AgNPs. Moreover, Cc-AgNPs exhibited antilipidemic activity by reducing cellular cholesterol and triglyceride levels and regulating key genes involved in lipogenesis. In conclusion, these results propose that Cc-AgNPs can be employed as a potent tool for future antibacterial and anticancer applications Full article

Background and Objective: Helicobacter pylori is a human-stomach-dwelling organism that causes many gastric illnesses, including gastritis, ulcer, and gastric cancer. The purpose of the study was to perform differential proteomic analysis on H. pylori isolates from gastritis, ulcer, and gastric cancer patients. Materials and Methods: H. pylori was isolated from antrum and fundus biopsies obtained from patients who visited the Department of Gastroenterology. Using nano-LC-QTOF MS/MS analysis, differentially regulated proteins were identified through proteome profiling of pooled samples of H. pylori isolated from gastritis, ulcer, and gastric cancer patients. Antigenic scores and cellular localization of proteins were determined using additional prediction tools. Results: A total of 14 significantly regulated proteins were identified in H. pylori isolated from patients with either gastritis, ulcer, or gastric cancer. Comparative analysis of groups revealed that in the case of cancer vs. gastritis, six proteins were overexpressed, out of which two proteins, including hydrogenase maturation factor (hypA) and nucleoside diphosphate kinase (ndk) involved in bacterial colonization, were only upregulated in isolates from cancer patients. Similarly, in cancer vs. ulcer, a total of nine proteins were expressed. Sec-independent protein translocase protein (tatB), involved in protein translocation, and pseudaminic acid synthase I (pseI), involved in the synthesis of functional flagella, were upregulated in cancer, while hypA and ndk were downregulated. In ulcer vs. gastritis, eight proteins were expressed. In this group, tatB was overexpressed. A reduction in thioredoxin peroxidase (bacterioferritin co-migratory protein (bcp)) was observed in ulcer vs. gastritis and cancer vs. ulcer. Conclusion: Our study suggested three discrete protein signatures, hypA, tatB, and bcp, with differential expression in gastritis, ulcer, and cancer. Protein expression profiles of H. pylori isolated from patients with these gastric diseases will help to understand the virulence and pathogenesis of H. pylori. Full article

Access to clean drinking water is essentially required for human existence. It is a formalized fact that contaminated drinking water poses a serious threat to human life as the endowment of unpolluted drinking water to Pakistan’s inhabitants is the local government’s foremost duty. Thus, to conquer this purpose, the local government, with the coordination of the community development department (CD), fixed drinking water filtration plants at several places in Lahore. This study aimed to discover the health threats and health-associated costs endured by households in the target study areas and the effects of drinking water on infants’ and children’s health in areas having and lacking water filtration plants by employing the health lifestyle model. Moreover, this study compared waterborne disease incidence in households in targeted areas with and without local government filtration plants. For this purpose, a multistage random sampling technique was employed to collect the data from 300 households residing at different locations in Pakistan. This study revealed that people who make life choices to drink filtration plants’ water installed by the local government are less likely to contract waterborne diseases. Besides, class circumstances such as the size of the family, education of the family head, and plant water usage are highly correlated to the quality and use of drinking water and, ultimately, to the health-associated outcomes by improving a healthy lifestyle. In contrast, the age of the household head was found to be insignificant in making choices regarding drinking water choices and reducing waterborne illness. The more the education of the family head, the fewer family members found to indulge in practices for using plant water. Thus, infants/children and other people are less likely to contract waterborne incidents in areas equipped with these filtration plants. Moreover, the probability of contracting waterborne illness is higher in males than in females in areas lacking filtration plants. Full article

Green synthesis differs in the way that the plant produces chemicals that act as reducing and stabilizing agents, and by adopting this green synthesis, we have synthesized silver nanoparticles (AgNPs) from the leaf and fruit extracts of Annona squamosa (also known as Sharifa), where these extracts have played an important role as reducing and capping agents. The nanoparticles were synthesized as the consequence of a reduction that happened between plant extracts and the precursor solution. The prepared AgNPs were then characterized using scanning electron microscopy, UV-Visible spectroscopy, and X-ray diffraction to study their morphology, optical response, and crystallinity. A single distinctive absorption peak of colloidal AgNPs samples was observed at 430 nm and 410 nm for leaf and fruit extract samples, having an optical bandgap of 2.97 eV and 2.88 eV, respectively, with a spherical shape having a diameter in the range of 35–90 nm and 15–50 nm, respectively, whilst XRD studies supported the FCC cubic structure of the mediated AgNPs. These green synthesized AgNPs have a wide variety of uses, particularly in the biomedical domain, where they have the potential to treat numerous diseases and are reported to be efficient against antibacterial, anti-cancer, and anti-diabetic activities. Full article

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic. Full article

Climate change scenarios predict that an extended period of drought is a real threat to food security, emphasizing the need for new crops that tolerate these conditions. Quinoa is the best option because it has the potential to grow under water deficit conditions. There is considerable variation in drought tolerance in quinoa genotypes, and the selection of drought-tolerant quinoa germplasms is of great interest. The main goal of this work is to evaluate the crop yield and characterize the physiology of 20 quinoa genotypes grown under water deficit in a wirehouse. The experiment was a complete randomized design (CRD) factorial with three replications. Seedling growth, i.e., fresh weight (FW), dry weight (DW), root length (RL), shoot length (SL), relative growth rate of root length (RGR-RL), shoot length (RGR-SL), and physiological performance, i.e., chlorophyll content (a and b), carotenoid, leaf phenolic content, leaf proline content, membrane stability index (MSI), and leaf K⁺ accumulation were evaluated in a hydroponic culture under different water-deficit levels developed by PEG 6000 doses (w/v) of 0% (control), 0.3%, and 0.6%. Yield attributes were evaluated in a pot at three different soil moisture levels, as determined by soil gravimetric water holding capacity (WHC) of 100 (control), 50% WHC (50 % drought stress) and 25% WHC (75% stress). In both experiments, under the water stress condition, the growth (hydroponic study) and yield traits (pot study) were significantly reduced compared to control treatments. On the drought tolerance index (DTI) based on seed yield, genotype 16 followed by 10, 1, 4, 5, 7, and 12 could be considered drought-tolerant genotypes that produced maximum grain yield and improved physiological characteristics under severe water stress conditions in hydroponic culture. In both studies, genotypes 3, 8, 13, and 20 performed poorly and were considered drought-sensitive genotypes with the lowest DTI values under water-stressed conditions. All the studied agronomic traits (grain yield, root and shoot length, shoot fresh and dry weights) and physiological traits (leaf phenolic, proline content, carotenoid, K⁺ accumulation, membrane stability index, and relative water content) were firmly inter-correlated and strongly correlated with DTI. They can be regarded as screening criteria, employing a large set of quinoa genotypes in a breeding program. Full article

Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO₂ emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities. Full article