No abstract

Silver nanoparticles (AgNPs) exhibit strong antimicrobial properties against many pathogens. Traditionally employed chemical methods for AgNPs synthesis are toxic for the environment. Here, we report a quicker, simpler, and environmentally benign process to synthesize AgNPs by using an aqueous ‘root extract’ of Salvadora persica (Sp) plant as a reducing agent. The synthesized Salvadora persica nano particles (SpNPs) showed significantly higher antimicrobial efficacy compared to earlier reported studies. We characterized SpNPs using UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Transmission Electron Microscopy (TEM), Field Emission Scanning Electron Microscopy (FE-SEM), Dynamic Light Scattering (DLS) and X-ray powder diffraction (P-XRD). UV–Vis spectrum showed the highest absorbance at 420 nm. FTIR analysis depicts presence of bond stretching including OH– (3300 cm−1), C=N– (2100 cm−1) and NH– (1630 cm−1) which are attributed in the involvement of phenolics, proteins or nitrogenous compounds in reduction and stabilization of AgNPs. TEM, FE-SEM and DLS analysis revealed the spherical and rod nature of SpNPs and an average size of particles as 37.5 nm. XRD analysis showed the presence of the cubic structure of Ag which confirmed the synthesis of silver nanoparticles. To demonstrate antimicrobial efficacy, we evaluated SpNPs antimicrobial activity against two bacterial pathogens (Escherichia coli (ATCC 11229) and Staphylococcus epidermidis (ATCC 12228)). SpNPs showed a significantly high inhibition for both pathogens and minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were found to be 0.39 µg/mL and 0.78 µg/mL for E. coli while 0.19 µg/mL and 0.39 µg/mL for S. epidermidis respectively. Further, Syto 16 staining of bacterial cells provided a supplemental confirmation of the antimicrobial efficacy as the bacterial cells treated with SpNPs stop to fluoresce compared to the untreated bacterial cells. Our highly potent SpNPs will likely have a great potential for many antimicrobial applications including wound healing, water purification, air filtering and other biomedical applications.

No abstract

Dysregulation of non-coding microRNAs during the course of tumor development, invasion and/or progression to the distant organs, makes them a promising candidate marker for the diagnosis of cancer and associated malignancies. This exploratory study aims at evaluating the usefulness of plasma concentration of circulating mir-146a as a non-invasive biomarker for acute lymphoblastic leukemia (ALL). Total RNA including miRNA was isolated from 110 plasma samples of patients (n = 66), healthy controls (n = 24) and follow up (n = 20) cases and reverse transcribed. Relative concentrations were assessed using real-time quantitative PCR and fold-change was calculated by 2−ΔΔCt method. Finally, relative concentrations were correlated to clinicopathological factors. Patients (n = 66) were analyzed to determine fold expression of miR-146a in plasma samples of ALL. Before chemotherapy, pediatric (n = 42) and adult (n = 24) showed overexpression of miR-146a compared with healthy controls (P < 0.0001). There was no effect of age and gender on mir-146a expression in plasma. mirR-146a expression was independent of clinical and hematological features. Moreover, miR-146a levels in plasma of paired samples (n = 20) after treatment showed significant decrease in expression (P < 0.001). Expression of plasma miR-146a may be utilized as non-invasive marker to diagnose and predict prognosis in pediatric and adult patients with ALL. Moreover predicted targets may be utilized for ALL therapy in future.

Purpose An early and accurate diagnosis of ovarian carcinoma (OC) may reduce morbidity and mortality of the patients. To improve the clinical outcome in OC patients, the present study is aimed at identifying robust biomarkers for early OC diagnosis. Experimental Design In order to look for early‐stage protein markers, a systematic protein profiling approach involving 2‐dimensional electrophoresis coupled with mass spectrometric analyses of human malignant and non‐malignant ovarian biopsy samples, is performed. Results Six 2D gel spots, corresponding to five proteins, display statistically significant differential expression in the tumor tissues compared to benign controls (FDR ≤ 0.05; PMF score ≥ 79). Ingenuity pathway analysis predicts two proteins, that is, Ca2+‐dependent membrane‐binding protein annexin A6 (AnxA6) and the metabolic enzyme l‐lactate dehydrogenase A chain, as potential predictive biomarkers. Increased expression of AnxA6 is further ascertained by Western blot and enzyme linked immunosorbent assay in the resected tissues and the plasma samples. The expression is found markedly increasing particularly in the advanced stage tumors. Conclusions and Clinical Relevance The significant upregulation of AnxA6 in OC, reported for the first time, is likely to provide insight into the mechanism of OC progression, which may lead to the design of potential diagnostic and therapeutic strategies.

This study shows expression of recombinant ovine growth hormone (roGH) and targeting to the inner membrane using signal sequence, DsbA, in Escherichia coli (E. coli) cell. Factors such as temperature, IPTG induction, and expression conditions were studied and show diverse optical density with different media compositions. The optimum expression level of roGH in terrific broth medium was at 25 °C on induction with 20 μM IPTG in early logarithmic phase. SDS-PAGE analysis of expression and subcellular fractions of recombinant constructs revealed the translocation of roGH to the inner membrane of E. coli with DsbA signal sequence at the N terminus of roGH. The protein was easily solubilized by 40 % acetonitrile with ~90 % purity and was identified by Western blot, and analysis on MALDI-TOF/TOF confirmed a size of 21,059 Da. Relatively high soluble protein yield of 65.3 mg/L of roGH was obtained. The biological function of roGH was confirmed by HeLa cell line proliferation. This is the first study describing achievement of biologically active soluble roGH targeted to the inner membrane of E. coli and rapid purification with high yield.

Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.

Better and sensitive biomarkers are needed to help understand the mechanism of disease onset, progression, prognosis and monitoring of the therapeutic response. Aim of this study was to identify the candidate circulating markers of chronic-phase chronic myeloid leukemia (CP-CML) manifestations, having potential to develop into predictive- or monitoring-biomarkers. A proteomic approach, two-dimensional gel electrophoresis in conjunction with mass spectrometry (2DE-MS), was employed for this purpose. Based on the spot intensity measurements, six proteins were found to be consistently dysregulated in CP-CML subjects compared to the healthy controls [false discovery rate (FDR) threshold ≤0.05]. These were identified as α-1-antichymotrypsin, α-1-antitrypsin, CD5 molecule-like, stress-induced phosphoprotein 1, vitamin D binding protein isoform 1 and transthyretin by MS analysis [PMF score ≥79; data accessible via ProteomeXchange with identifier PXD002757]. Quantitative ELISA, used for validation of candidate proteins both in the pre-treated and nilotinib-treated CP-CML cases, demonstrate that CD5 molecule-like, transthyretin and alpha-1-antitrypsin may serve as useful predictive markers and aid in monitoring the response of TKI-based therapy (ANOVA p < 0.0001). Two of the circulating marker proteins, identified in this study, had not previously been associated with chronic- or acute-phase myeloid leukemia. Exploration of their probable association with CP-CML, in a larger study cohort, may add to our understanding of the disease mechanism besides developing clinically useful biomarkers in future.