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Nanotechnology and nanoparticles have gained massive attention in the scientific community in recent years due to their valuable properties. Among various AgNPs synthesis methods, microbial approaches offer distinct advantages in terms of cost-effectiveness, biocompatibility, and eco-friendliness. In the present research work, investigators have synthesized three different types of silver nanoparticles (AgNPs), namely AgNPs-K, AgNPs-M, and AgNPs-E, by using Klebsiella pneumoniae (MBC34), Micrococcus luteus (MBC23), and Enterobacter aerogenes (MBX6), respectively. The morphological, chemical, and elemental features of the synthesized AgNPs were analyzed by using UV-Vis spectroscopy (UV-Vis), Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscope (FESEM) and energy-dispersive spectroscopy (EDX). UV-Vis absorbance peaks were obtained at 475, 428, and 503 nm for AgNPs-K, AgNPs-M, and AgNPs-E, respectively. The XRD analysis confirmed the crystalline nature of the synthesized AgNPs, having peaks at 26.2°, 32.1°, and 47.2°. At the same time, the FTIR showed bands at 599, 963, 1,693, 2,299, 2,891, and 3,780 cm−1 for all the types of AgNPs indicating the presence of bacterial biomolecules with the developed AgNPs. The size and morphology of the AgNPs varied from 10 nm to several microns and exhibited spherical to porous sheets-like structures. The percentage of Ag varied from 37.8% (wt.%) to 61.6%, i.e., highest in AgNPs-K and lowest in AgNPs-M. Furthermore, the synthesized AgNPs exhibited potential for environmental remediation, with AgNPs-M exhibiting the highest removal efficiency (19.24% at 120 min) for methyl orange dye in simulated wastewater. Further, all three types of AgNPs were evaluated for the removal of methyl orange dye from the simulated wastewater, where the highest dye removal percentage was 19.24% at 120 min by AgNPs-M. Antibacterial potential of the synthesized AgNPs assessment against both Gram-positive (GPB) Bacillus subtilis (MBC23), B. cereus (MBC24), and Gram-negative bacteria Enterococcus faecalis (MBP13) revealed promising results, with AgNPs-M, exhibiting the largest zone of inhibition (12 mm) against GPB B. megaterium. Such investigation exhibits the potential of the bacteria for the synthesis of AgNPs with diverse morphology and potential applications in environmental remediation and antibacterial therapy-based synthesis of AgNPs. [ABSTRACT FROM AUTHOR]

Oxidative stress, resulting from dysregulation in the secretion of adrenal hormones, represents a major concern in human health. The present review comprehensively examines various categories of endocrine dysregulation within the adrenal glands, encompassing glucocorticoids, mineralocorticoids, and androgens. Additionally, a comprehensive account of adrenal hormone disorders, including adrenal insufficiency, Cushing’s syndrome, and adrenal tumors, is presented, with particular emphasis on their intricate association with oxidative stress. The review also delves into an examination of various nutritional antioxidants, namely vitamin C, vitamin E, carotenoids, selenium, zinc, polyphenols, coenzyme Q10, and probiotics, and elucidates their role in mitigating the adverse effects of oxidative stress arising from imbalances in adrenal hormone levels. In conclusion, harnessing the power of nutritional antioxidants has the potential to help with oxidative stress caused by an imbalance in adrenal hormones. This could lead to new research and therapeutic interventions. [ABSTRACT FROM AUTHOR]

Economical and nanomolar-level determination of the analgesic drug, acetaminophen (APAP), is reported in this work. A novel ternary nanocomposite based on silver-doped sugar apple-like cupric oxide (CuO)-decorated amine-functionalized multi-walled carbon nanotubes (fCNTs) was sonochemically prepared. CuO nanoparticles were synthesized based on the ascorbic acid-mediated low-temperature method, and sidewall functionalization of CNTs was carried out. Important characterizations of the synthesized materials were analyzed using SEM, TEM, HAADF-STEM, elemental mapping, EDX, lattice fringes, SAED pattern, XRD, EIS, UV-Vis, micro-Raman spectroscopy, and FTIR. It was noted the sonochemically prepared nanocomposite diligently fabricated on screen-printed carbon electrode showcased outstanding electrocatalytic performance towards APAP determination. The APAP sensor exhibited ultra-low limit of detection of 4 nM, wide linear concentration ranges of 0.02–3.77 and 3.77–90.02 μM, and high sensitivity of 30.45 μA μM−1 cm−2. Moreover, further evaluation of the sensor's performance based on electrochemical experiments showcased outstanding selectivity, stability, reproducibility, and repeatability. Further, excellent practical feasibility of the proposed APAP sensor was affirmed with excellent recovery larger than 96.86% and a maximum RSD of 3.67%. [ABSTRACT FROM AUTHOR]

An electrocatalytic platform based on a novel nanocomposite integrated with a grid search-optimized neural network (GSNN) was proposed for intelligent sensing of tryptophan. The cuprospinel-decorated chitosan-functionalized carbon nanofibers (CuFe 2 O 4 /Chit-CNFs) fabricated on a disposable electrode revealed exceptional electrocatalytic activity with a low detection limit (2 nM) and good sensitivity (79.18 μAμM -1  cm -2 ) over a broad linear range (0.05-152.55 μM). Cyclic voltammetry and differential pulse voltammetry were employed, and the sensing mechanism of tryptophan entails its electrocatalytic oxidation, where the synergistic impact of CuFe 2 O 4 and Chit-CNFs boosts electrochemical response owing to their high surface area and conductivity. GSNN-based intelligent sensing returned a root mean square error (RMSE) of 2.76 and a mean absolute error (MAE) of 1.12. Moreover, the sensor's performance was tested on samples from apple juice, tomato juice, pineapple juice, and milk for assessing practicality, demonstrating recovery between 96.93 and 101.06 % and maximum relative standard deviation of 2.63 %. The proposed sensor showcased excellent selectivity, repeatability, reproducibility, and stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

It is highly significant to develop efficient soft sensors to estimate the concentration of hazardous pollutants in a region to maintain environmental safety. In this paper, an air quality warning system based on a robust PM 2.5 soft sensor and support vector machine (SVM) classifier is reported. The soft sensor for the estimation of PM 2.5 concentration is proposed using a novel approach of Bayesian regularized neural network (BRNN) via forward feature selection (FFS). Zuoying district of Taiwan is selected as the region of study for implementation of the estimation system because of the high pollution in the region. Descriptive statistics of various pollutants in Zuoying district is computed as part of the study. Moreover, seasonal variation of particulate matter (PM) concentration is analyzed to evaluate the impact of various seasons on the increased levels of PM in the region. To investigate the linear dependence of concentration of different pollutants to the concentration of PM 2.5 , Pearson correlation coefficient, Kendall's tau coefficient, and Spearman coefficient are computed. To achieve high performance for the PM 2.5 estimation, selection of appropriate forward features from the input variables is carried out using FFS technique and Bayesian regularization is incorporated to the neural network system to avoid the overfitting problem. The comparative evaluation of performance of BRNN/FFS estimation system with various other methods shows that our proposed estimation system has the lowest mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Moreover, the coefficient of determination (R-squared) is around 0.95 for the proposed estimation method, which denotes a good fit. Evaluation of the SVM classifier showed good performance indicating that the proposed air quality warning system is efficient., (Copyright © 2019 Elsevier Inc. All rights reserved.)