Characterization and Therapeutic Applications of Biosynthesized Silver Nanoparticles Using Cassia auriculate Flower Extract.

The current study analyzes the biosynthesis of silver nanoparticles using the Cassia auriculate flower extract as the reducing and stabilizing agent. The Cassia auriculate- silver nanoparticles (Ca-AgNPs) obtained are characterized by UV-Vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) analysis. The results of the spectral characterization have revealed that the surface Plasmon resonance band observed at 448 nm confirms the formation of AgNPs. TEM analysis of the Ca-AgNPs was a predominately spherical shape with a size assortment of 30 to 80 nm and an angular size of 50 nm. The well-analyzed Ca-AgNPs were used in various biological assays, including healthcare analysis of antimicrobial, antioxidant (DPPH), and cytotoxic investigations. Ca-AgNPs showed efficient free radical scavenging activity and showed excellent antimicrobial activity against to pathogenic strains. The occurrence of Ca-AgNPs lead to reduced Live/Dead ratio of bacteria (from 36.97 ± 1.35 to 9.43 ± 0.27) but improved the accumulation of bacterial clusters. The cytotoxicity of Ca-AgNPs was carried out by MTT assay against MCF-7 breast cancer cells and a moderate cytotoxic. The approach of flower extract-mediated synthesis is a cost-efficient, eco-friendly, and easy alternative to conventional methods of silver nanoparticle synthesis.

Regulatory Small RNAs for a Sustained Eco-Agriculture.

Small RNA (sRNA) has become an alternate biotechnology tool for sustaining eco-agriculture by enhancing plant solidity and managing environmental hazards over traditional methods. Plants synthesize a variety of sRNA to silence the crucial genes of pests or plant immune inhibitory proteins and counter adverse environmental conditions. These sRNAs can be cultivated using biotechnological methods to apply directly or through bacterial systems to counter the biotic stress. On the other hand, through synthesizing sRNAs, microbial networks indicate toxic elements in the environment, which can be used effectively in environmental monitoring and management. Moreover, microbes possess sRNAs that enhance the degradation of xenobiotics and maintain bio-geo-cycles locally. Selective bacterial and plant sRNA systems can work symbiotically to establish a sustained eco-agriculture system. An sRNA-mediated approach is becoming a greener tool to replace xenobiotic pesticides, fertilizers, and other chemical remediation elements. The review focused on the applications of sRNA in both sustained agriculture and bioremediation. It also discusses limitations and recommends various approaches toward future improvements for a sustained eco-agriculture system.

Long Non-Coding RNAs (lncRNAs) in Heart Failure: A Comprehensive Review.

Heart failure (HF) is a widespread cardiovascular condition that poses significant risks to a wide spectrum of age groups and leads to terminal illness. Although our understanding of the underlying mechanisms of HF has improved, the available treatments still remain inadequate. Recently, long non-coding RNAs (lncRNAs) have emerged as crucial players in cardiac function, showing possibilities as potential targets for HF therapy. These versatile molecules interact with chromatin, proteins, RNA, and DNA, influencing gene regulation. Notable lncRNAs like Fendrr, Trpm3, and Scarb2 have demonstrated therapeutic potential in HF cases. Additionally, utilizing lncRNAs to forecast survival rates in HF patients and distinguish various cardiac remodeling conditions holds great promise, offering significant benefits in managing cardiovascular disease and addressing its far-reaching societal and economic impacts. This underscores the pivotal role of lncRNAs in the context of HF research and treatment.

A generalized framework for analytic regularization of uniform cubic B-spline displacement fields.

Image registration is an inherently ill-posed problem that lacks the constraints needed for a unique mapping between voxels of the two images being registered. As such, one must regularize the registration to achieve physically meaningful transforms. The regularization penalty is usually a function of derivatives of the displacement-vector field and can be calculated either analytically or numerically. The numerical approach, however, is computationally expensive depending on the image size, and therefore a computationally efficient analytical framework has been developed. Using cubic B-splines as the registration transform, we develop a generalized mathematical framework that supports five distinct regularizers: diffusion, curvature, linear elastic, third-order, and total displacement. We validate our approach by comparing each with its numerical counterpart in terms of accuracy. We also provide benchmarking results showing that the analytic solutions run significantly faster-up to two orders of magnitude-than finite differencing based numerical implementations.

Adverse drug reaction monitoring during antimicrobial therapy for septicemia patients at a university hospital in New Delhi

BACKGROUND/AIMS: Adverse drug reaction (ADR) is an appreciably harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product. The present study was conducted in order to monitor the frequency and severity of ADR during antimicrobial therapy of septicemia. METHODS: A prospective, observational, and noncomparative study was conducted over a period of 6 months on patients of septicemia admitted at a university hospital. Naranjo algorithm scale was used for causality assessment. Severity assessment was done by Hartwig severity scale. RESULTS: ADRs in selected hospitalized patients of septicemia was found to be in 26.5% of the study population. During the study period, 12 ADRs were confirmed occurring in 9, out of 34 admitted patients. Pediatric patients experienced maximum ADRs, 44.4%. Females experienced a significantly higher incidence of ADRs, 66.7%. According to Naranjo’s probability scale, 8.3% of ADRs were found to be definite, 58.3% as probable, and 33.3% as possible. A higher proportion of these ADRs, 66.7% were preventable in nature. Severity assessment showed that more than half of ADRs were moderate. Teicoplanin was found to be the commonest antimicrobial agent associated with ADRs, followed by gemifloxacin and ofloxacin. CONCLUSIONS: The incidence and severity of ADRs observed in the present study was substantially high indicating the need of extra vigilant during the antimicrobial therapy of septicemia.

Analytic regularization of uniform cubic B-spline deformation fields.

Image registration is inherently ill-posed, and lacks a unique solution. In the context of medical applications, it is desirable to avoid solutions that describe physically unsound deformations within the patient anatomy. Among the accepted methods of regularizing non-rigid image registration to provide solutions applicable to medical practice is the penalty of thin-plate bending energy. In this paper, we develop an exact, analytic method for computing the bending energy of a three-dimensional B-spline deformation field as a quadratic matrix operation on the spline coefficient values. Results presented on ten thoracic case studies indicate the analytic solution is between 61-1371x faster than a numerical central differencing solution.

Approximation modeling for the online performance management of distributed computing systems.

A promising method of automating management tasks in computing systems is to formulate them as control or optimization problems in terms of performance metrics. For an online optimization scheme to be of practical value in a distributed setting, however, it must successfully tackle the curses of dimensionality and modeling. This paper develops a hierarchical control framework to solve performance management problems in distributed computing systems operating in a data center. Concepts from approximation theory are used to reduce the computational burden of controlling such large-scale systems. The relevant approximations are made in the construction of the dynamical models to predict system behavior and in the solution of the associated control equations. Using a dynamic resource-provisioning problem as a case study, we show that a computing system managed by the proposed control framework with approximation models realizes profit gains that are, in the best case, within 1% of a controller using an explicit model of the system.