Synthesis, characterization, and applications of iron oxide nanoparticles.

Objective: The green synthesis method for nanoparticles is getting more attention globally, due to its lesser cost, non-hazardous, and eco-friendly nature. The novelty of the present work is to investigate the anti-bacterial and degradation activity of the green synthesized Iron Oxide NPs. Methods: In this study, the Iron Oxide NPs were synthesized through a green synthesis route from leaves of Ficus Palmata. UV-Vis confirmed Iron Oxide NP's peaks between (230-290 nm), while Fourier transforms infrared spectroscopy analysis showed that several groups were involved in reduction and stabilization. Results: Results indicated that the highest photo thermal activity was shown in light and it was almost 4 folds greater than the control. Similarly, Iron Oxide NPs showed excellent antimicrobial potential against bacterial species "Salmonella typhi" "Xanthomonas Oryzae" and "Lactobacillus" at low concentrations (150 µg/mL). Hemolytic assay results showed that the toxicity was lesser than 5% at both dark and light conditions. Moreover, we also evaluated the photo-catalytic potential of Iron Oxide NPs against methylene orange. Results indicated that almost complete degradation was noted after 90 min in the presence of continuous light. All tests were performed in triplicates. All the data was subjected to P-test (P < 0.5) using Excel and graph pad (V.5.0). Conclusion: Iron Oxide NPs holds a promising future and could be used in treating diseases, and microbial pathogenesis and also could be used as a vector in drug delivery. Moreover, they can also eradicate persistent dyes and could be used as an alternative to remediate pollutants from the environment.

Study of Association of Metabolic Syndrome and Risk Factors of Nephrolithiasis.

BACKGROUND AND AIM: The increasing incidence of nephrolithiasis in recent decades is coinciding with rising epidemic of obesity, metabolic syndrome, and type 2 diabetes. This temporal concordance suggests that a link might exist between these metabolic abnormalities and urinary stone disease. Therefore, the present study was aimed to investigate the association between presence of risk factors of nephrolithiasis and metabolic syndrome. METHODS: In a hospital-based, case control study, hundred patients of metabolic syndrome diagnosed according to IDF criteria and hundred age and matched controls were studied for presence of risk factors of nephrolithiasis. RESULTS: Patients with metabolic syndrome had significantly higher uricosuri a,hypercalciuria,oxaluria and hypocitraturia. The prevalence of risk factors of nephrolithiasis was also higher in patients with metabolic syndrome. The most prevalent was low urinary pH in 40% patients with mean pH of 5.8±1.6. Amongst other factors, 33% had hyperuricemia, 29% had hypercalciuria, 15% had oxaluria 13% had hypocitraturia and 10% had hyperuricosuria. Significant correlation was observed between risk factors of nephrolithiasis and components of metabolic syndrome. CONCLUSION: The present study provides an evidence of association between risk factors of nephrolithiasis and metabolic syndrome and suggests that nephrolithiasis may be a systemic disorder representing the interaction of multiple metabolic derangements. Determining common modifiable risk factors for the development of kidney stones might uncover new preventive strategies.

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning.

Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development-a process referred to as plant phenotyping-is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyze the data in microscopy images depicting plant growth, either locating or counting regions of cellular features in images. In this paper, we present to the plant community an introduction to and exploration of two machine learning approaches to address the problem of marker localization in confocal microscopy. First, a comparative study is conducted on the classification accuracy of common conventional machine learning algorithms, as a means to highlight challenges with these methods. Second, a 3D (volumetric) deep learning approach is developed and presented, including consideration of appropriate loss functions and training data. A qualitative and quantitative analysis of all the results produced is performed. Evaluation of all approaches is performed on an unseen time-series sequence comprising several individual 3D volumes, capturing plant growth. The comparative analysis shows that the deep learning approach produces more accurate and robust results than traditional machine learning. To accompany the paper, we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (FIJI) software. Network models and example datasets will also be available online.

Assessment of noninvasive risk markers of subclinical atherosclerosis in premenopausal women with previous history of gestational diabetes mellitus.

INTRODUCTION: Gestational diabetes mellitus (GDM) is state of carbohydrate intolerance detected first time during pregnancy. GDM represents a significant risk factor for the development of CVD in women. The degree to which women with histories of gestational diabetes are at risk for cardiovascular disease, beyond their predisposition to future diabetes, is still unclear. The aim of our study was to assess the presence of surrogate markers of subclinical atherosclerosis which can be present in them even without developing type 2 diabetes. SUBJECTS AND METHODS: In this descriptive cross-sectional hospital based study, 50 patients 20-45 yrs of age, premenopausal, at least 1 yr past her most recent pregnancy, and not more than 5 yr past her index pregnancy with GDM. These patients and controls who did not have GDM were assessed for carotid intima media thickness,endothelial dysfunction, epicardial fat thickness and other cardiovascular risk factors. RESULTS: Women with pGDM were found to have unfavourable cardiovascular risk parameters. They also demonstrated more frequent occurrence of metabolic syndrome(64% vs 10%) than control subjects. Individual components of MS increased with increasing BMI in both the groups. As far as markers of subclinical atherosclerosis were concerned women with pGDM had significantly higher CIMT, FMD and epicardial fat thickness than control group. CONCLUSION: Women with pGDM, even before development of diabetes have significant differences in CVD risk factors when compared to those who do not have such history. Postpartum screening for glucose intolerance and efforts to minimize modifiable cardiovascular risk factors, including hypertension, viscerall adiposity, and dyslipidemia should be the most effective measures for lowering of cardiovascular risk.