Antibiotic residues in the aquatic environment have the potential to induce resistance in environmental bacteria, which ultimately might get transferred to pathogens making treatment of diseases ...difficult and poses a serious threat to public health. If antibiotic residues in the environment could be eliminated or reduced, it could contribute to minimizing antibiotic resistance. Towards this objective, water containing ciprofloxacin was treated by sunlight-assisted photocatalysis using Fe- doped ZnO nanoparticles for assessing the degradation potential of this system. Parameters like pH, temperature, catalytic dosage were assessed for the optimum performance of the system. To evaluate degradation of ciprofloxacin, both spectrophotometric as well as microbiological (loss of antibiotic activity) methods were employed. 100 mg/L Fe-doped ZnO nanoparticle catalyst and sunlight intensity of 120,000⁻135,000 lux system gave optimum performance at pH 9 at 30 °C and 40 °C. Under these conditions spectrophotometric analysis showed complete degradation of ciprofloxacin (10 mg/L) at 210 min. Microbiological studies showed loss of antibacterial activity of the photocatalytically treated ciprofloxacin-containing water against
(10⁸ CFU) in 60 min and for
(10⁸ CFU) in 75 min. The developed system, thus possess a potential for treatment of antibiotic contaminated waters for eliminating/reducing antibiotic residues from environment.
Gas foil bearings are gaining popularity for their compliance properties in various high-speed turbomachinery applications such as air cycle machine, turbocompressor, turbocharger, turboexpander etc. ...A modest attempt is made in the current research to study the feasibility of gas foil bearing for a turboexpander rotating at 1,75,000 rpm. The turboexpander rotor with 16 mm diameter and 91 mm length used for experimentation is supported by a pair of gas foil journal bearings and mounted with turbine and compressor wheels at both ends of the rotor. The feasibility study was performed based on comparison of rotodynamic analysis and experimental data for the critical speed of the rotor and unbalance response at bearing locations. The critical speeds and the unbalance response are predicted using the finite element analysis, which takes into account the gyroscopic effect, shear deformation, internal damping, inertia of the rotor and the dynamic coefficients of the gas foil bearing. The predicted and experimental variation of critical speed is found to be within a relative error of 3–6%; similarly, the variation of unbalance response was found with a relative error of 2–9%. The low relative errors suggest that the experiment and prediction methodology are credible. The author believes that the rotodynamic analysis methodology will be quite valuable for researchers working in the area of high-speed rotors supported with gas foil bearings.
Patients with heart failure (HF) are at high risk for adverse outcomes when they have COVID-19. Reports of COVID-19 vaccine-related cardiac complications may contribute to vaccine hesitancy in ...patients with HF.
To analyze the impact of COVID-19 vaccine status on clinical outcomes in patients with HF, we conducted a retrospective cohort study of the association of COVID-19 vaccination status with hospitalizations, intensive care unit admission and mortality after adjustment for covariates. Inverse probability treatment-weighted models were used to adjust for potential confounding.
Of 7094 patients with HF, 645 (9.1%) were partially vaccinated, 2200 (31.0%) were fully vaccinated, 1053 were vaccine-boosted (14.8%), and 3196 remained unvaccinated (45.1%) by January 2022. The mean age was 73.3 ± 14.5 years, and 48% were female. Lower mortality rates were observed in patients who were vaccine-boosted, followed by those who were fully vaccinated; they experienced lower mortality rates (HR 0.33; CI 0.23, 0.48) and 0.36 (CI 0.30, 0.43), respectively, compared to unvaccinated individuals (P< 0.001) over the mean follow-up time of 276.5 ± 104.9 days, whereas no difference was observed between those who were unvaccinated or only partially vaccinated.
COVID-19 vaccination was associated with significant reduction in all-cause hospitalization rates and mortality rates, lending further evidence to support the importance of vaccination implementation in the high-risk population of patients living with HF.
► Gold nanoparticles are synthesized by fungus Penicillium rugulosum and characterized by Bio-TEM, XRD, UV–vis, FTIR, and XPS spectroscopy. ► Optimization studies have been conducted with SB of the ...fungus for having more strict control over the morphology of the particles. ► Following optimization, the particles were tested for observing their binding affinity to isolated genomic DNA of E. coli and S. aureus.
Biological systems employing microorganisms have been used as an alternative to conventional chemical techniques for synthesizing gold nanoparticles. In the present study, gold nanoparticles have been synthesized from the supernatant broth (SB) and live cell filtrate (LCF) of the industrially important fungus Penicillium rugulosum. Additionally, potato dextrose broth (PDB) medium which is used for the growth of the fungus has also been able to synthesize gold nanoparticles. The size of the particles has been investigated by Bio-TEM before purification as well as after purification to find the difference in morphology pattern of the nanoparticles. Different characterization techniques like X-ray diffraction (XRD), infra-red (FTIR), X-ray photoelectron (XPS) and UV–vis spectroscopy have been used for analysis of the particles. SB of the fungus has yielded nanoparticles with better morphology and hence further optimization studies were conducted for controlling the size and shape of the above by altering pH and concentration of gold salt. A pH range of 4–6 has favored the synthesis process whereas increasing concentration of gold salt (beyond 2mM) has resulted in the formation of bigger sized and aggregated nanoparticles. The optimized nanoparticles have been used to conjugate with isolated genomic DNA of bacteria Escherichia coli and Staphylococcus aureus. Visual observation of agarose gel electrophoresis images confirmed the binding of gold nanoparticles (4μL and 6μL) with isolated DNA (2μL) fragments of both the organisms. The slight red shift of the surface plasmon (SP) band and minor aggregations noticed in Bio-TEM images for the DNA conjugated gold nanoparticles indicates that the genomic DNA could stabilize the particles against aggregation owing to negatively charged phosphate backbone.
Diabetes mellitus is a chronic endocrine disease that occurs mostly in the state of hyperglycemia (elevated blood glucose level). In the recent times, diabetes is listed under world's utmost critical ...health issues. Wound treatment procedures are complicated in diabetic individuals all over the world. Diabetic wound care not only involves high-cost, but also the primary cause of hospitalization, which can lead to amputation thereby reducing diabetic patient life expectancy. To lower the risk of amputation, wound healing requires the development of effective treatments. Traditional management systems for Diabetes are frequently chastised due to their high costs, difficulties in maintaining a sustainable supply chain and limited disposal alternatives. The worrisome rise in diabetes prevalence has sparked a surge of interest in the discovery of viable remedies to supplement existing treatments. Nanomaterials wound healing has a lot of potential for treating and preventing wound infections and it has recently gained popularity owing to its ability to transport drugs to the wound area in a regulated fashion, potentially overpowering the limits of traditional approaches. This research assessed several nanosystems, such as nanocarriers and nanotherapeutics, to explore how they can benefit in diabetic wound healing, with a focus on current obstacles and future prospects.
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•Extracts of plant Hibiscus sabdariffa was used for synthesis of gold nanoparticles.•Stability of the gold nanoparticles was studied in presence of glucose.•Cytotoxicity of ...nanoparticles was studied against U87 Glioblastoma multiforme.•Nanoparticles were investigated for cyctotoxicity under hyperglycemic condition.
In the present paper, a facile synthesis of gold nanoparticles is reported with leaf and stem extract of Hibiscus sabdariffa. Structural features of as synthesized nanoparticles are characterized by UV–vis spectroscopy, XRD, FTIR, and XPS. Morphology of the above synthesized gold nanoparticles is investigated by electron microscopy. The stability of the nanoparticles is studied in different concentrations of glucose which suggested their possible application under hyperglycemic condition. As synthesized nanoparticles has shown selective toxicity towards U87 glioblastoma multiforme cell line under normal and hyperglycemic condition, indicating their potential to be used in the development of value-added products in the biochemical industries. The possible mode of activity of the above nanoparticles has been studied by in vitro molecular techniques.
A new class of bi-luminophoric dyad has been designed, consisting of an oxygen-sensitive phosphorescent NHC-Ir
center with a remotely integrated oxygen-insensitive fluorescent terpyridine unit. The ...new terpyridine flurophore-integrated NHC-Ir
molecule was demonstrated as a potential ratiometric O
probe with built-in internal reference, exhibiting tunable dual-emissive features, as well as highly linear and reversible O
-response behavior.
One of the most essential chemical processes that is utilized in the manufacturing of a great deal of contemporary goods is called heterogeneously catalyzed reactions, and it is also one of the most ...fascinating. Metallic nanostructures are heterogeneous catalysts for range reactions due to their huge surface area, large assembly of active surface sites, and quantum confinement effects. Unprotected metal nanoparticles suffer from irreversible agglomeration, catalyst poisoning, and limited life cycle. To circumvent these technical disadvantages, catalysts are frequently spread on chemically inert materials like as mesoporous Al2O3, ZrO2, and different types of ceramic material. In this research, plentiful bauxite residue is used to create a low-cost alternative catalytic material. We have hydrogenated p-Nitrophenol to p-Aminophenol on bauxite residue (BR) supported silver nanocomposites (Ag NCs). The phase and crystal structure, bond structure and morphological analysis of the developed material will be done XRD, FTIR, and SEM-EDX respectively. The ideal conditions were 150 ppm of catalyst, 0.1 mM of p-NP, and 10 minutes overall up-to 99% conversion of p-NP to p-AP. A multi-variable predictive model created using Response Surface Methodology (RSM) and a data-based Artificial Neural Network (ANN) model were found to be the best ways to predict the maximum conversion efficiency. ANN models predicted efficiency more accurately than RSM models, and the strong agreement between model predictions and experimental data was indicated by their low relative error (RE0.10), high regression coefficient (R2>0.97), and Willmott-d index (dwill-index > 0.95) values.
Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which ...incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods.
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