GREEN TECHNOLOGY MEDIATED SYNTHESIS OF SILVER NANOPARTICLES FROM MOMORDICA CHARANTIA FRUIT EXTRACT AND ITS BACTERICIDAL ACTIVITY

ABSTRACTObjective: The synthesis of nanoparticles from biological processes is evolving a new era of research interests in nanotechnology. Silver nanoparticlesare usually synthesized by chemicals and physical method, which are quite toxic and flammable in nature. This study deals with an environmentfriendly biosynthesis process of antibacterial silver nanoparticles using Momordica charantia fruit.Methods: AgNO3 (5 mM) was allowed to react with fruit extract of M. charantia. Biosynthesis of AgNPs was optimized by changing temperature,pH, and solvent. The silver nanoparticles so formed were characterized using ultraviolet-visible (UV-VIS) spectroscopy, Fourier transform infraredspectroscopy (FTIR), dynamic light scattering (DLS), atomic force microscope (AFM), and scanning electron microscopy (SEM).Results: UV-VIS spectra show absorption peak between 420 and 430 nm. The FTIR analysis showed the alcoholic, lactam, and nitro group presentin the plant extract, which were responsible for the reduction in AgNPs. The SEM images showed the size distribution of the nanoparticles and theaverage size was found to be 50-100 nm. By DLS analysis and AFM analysis, average sizes of the silver nanoparticles were of 150 nm. The results ofthese analyses confirmed the formation of silver nanoparticles. Silver nanoparticles were tested against Bacillus cereus and Staphylococcus epidermidisstrains using disc diffusion method and were found to be effective.Conclusion: Silver nanoparticles so synthesized in this study using fruit extract of M. charantia are simple, easy, and effective technique of nanoparticlesproduction.Keywords: Silver nanoparticles, Momordica charantia, Optimization, Antibacterial, Atomic force microscope, Scanning electron microscopy

Effects of sex chromosome dosage on corpus callosum morphology in supernumerary sex chromosome aneuploidies.

BackgroundSupernumerary sex chromosome aneuploidies (sSCA) are characterized by the presence of one or more additional sex chromosomes in an individual's karyotype; they affect around 1 in 400 individuals. Although there is high variability, each sSCA subtype has a characteristic set of cognitive and physical phenotypes. Here, we investigated the differences in the morphometry of the human corpus callosum (CC) between sex-matched controls 46,XY (N =99), 46,XX (N =93), and six unique sSCA karyotypes: 47,XYY (N =29), 47,XXY (N =58), 48,XXYY (N =20), 47,XXX (N =30), 48,XXXY (N =5), and 49,XXXXY (N =6).MethodsWe investigated CC morphometry using local and global area, local curvature of the CC boundary, and between-landmark distance analysis (BLDA). We hypothesized that CC morphometry would vary differentially along a proposed spectrum of Y:X chromosome ratio with supernumerary Y karyotypes having the largest CC areas and supernumerary X karyotypes having significantly smaller CC areas. To investigate this, we defined an sSCA spectrum based on a descending Y:X karyotype ratio: 47,XYY, 46,XY, 48,XXYY, 47,XXY, 48,XXXY, 49,XXXXY, 46,XX, 47,XXX. We similarly explored the effects of both X and Y chromosome numbers within sex. Results of shape-based metrics were analyzed using permutation tests consisting of 5,000 iterations.ResultsSeveral subregional areas, local curvature, and BLDs differed between groups. Moderate associations were found between area and curvature in relation to the spectrum and X and Y chromosome counts. BLD was strongly associated with X chromosome count in both male and female groups.ConclusionsOur results suggest that X- and Y-linked genes have differential effects on CC morphometry. To our knowledge, this is the first study to compare CC morphometry across these extremely rare groups

Impact of vegetation on the simulation of seasonal monsoon rainfall over the Indian subcontinent using a regional model

The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation, surface radiation, etc., that in turn induces changes in the model simulated outputs. The present study deals with the effects of vegetation in climate modeling over the Indian region using the MM5 mesoscale model. The main objective of the present study is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization) versus that of USGS (United States Geological Survey) vegetation dataset on the simulation of the Indian summer monsoon. The present study has been conducted for five monsoon seasons (1998-2002), giving emphasis over the two contrasting southwest monsoon seasons of 1998 (normal) and 2002 (deficient). The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the monsoon. Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India and along the western coast. The MM5- USGS has greater tendency of overestimation of rainfall. It has higher standard deviation indicating that it induces a dispersive effect on the rainfall simulation. Among the five years of study, it is seen that the RMSE of July and JJAS (June-July-August-September) for All India Rainfall is mostly lower for MM5-ISRO. Also, the bias of July and JJAS rainfall is mostly closer to unity for MM5-ISRO. The wind fields at 850 hPa and 200 hPa are also better simulated by MM5 using ISRO vegetation. The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the verification analysis by ISRO vegetation. The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India, showing greater spatial variability over the region. However, the JJAS total rainfall over north India and Deccan coast is better simulated using the USGS vegetation. Sensible heat flux over north-west India is also better simulated by MM5-USGS

Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study.

Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches

Successful Surgical Resection of Advanced Gastrointestinal Stromal Tumor Post Neoadjuvent Therapy

We report a case of a 48-year-old Indian male who presented with swelling and firmness in his left upper part of the abdomen of one month duration with anorexia and weight loss. Initial examination revealed an intra abdominal mass of around 16.8x11.0x24.5cm with minimal left sided pleural effusion. A biopsy from the mass confirmed the diagnosis of gastrointestinal stromal tumour (GISTs) as supported by immmunohistochemistry results which showed strong positivity for c-kit while stains for smooth muscle actin, desmin, myoglobin, S100 Protein and cytokerstin remained negative. The patient was not suitable for surgical intervention in view of advanced tumor, and Imatinib Mesylate 400mg daily was started with the aim of making the tumor operable. Such therapy lasted for twenty months and was tolerated well by the patient. It then resulted in gradual tumor regression, following which the patient underwent successful tumor resection. Post surgical resection patient had no radiological evidence of intra abdominal tumor but mild left sided pleural effusion with left lower lobe atelectasis. The patient had uneventful post operative recovery and he is currently on Imatinib mesylate and tolerating treatment well with mild skin rash. The experience with preoperative imatinib on surgical resection rates and post operative outcomes is limited especially with primary locally advanced GISTs. In our case successful surgical resection was possible for a huge locally advanced GIST with unusually prolonged treatment of twenty months with imatinib preoperatively

Integrated positron emission tomography/computed tomography fusion imaging: An emerging gold standard in lung cancer

Positron emission tomography (PET) has emerged as an important diagnostic tool in the management of lung cancers. Although PET is sensitive in detection of lung cancer, but FDG (2-deoxy-2- 18 fluro-D-glucose) is not tumor specific and may accumulate in a variety of nonmalignant conditions occasionally giving false positive result. Addition of CT to PET improves specificity foremost, but also sensitivity in tumor imaging. Thus, PET/CT fusion images are a more accurate test than either of its individual components and are probably also better than side-by-side viewing of images from both modalities. PET/CT fusion images are useful in differentiating between malignant and benign disease, fibrosis and recurrence, staging and in changing patient management to more appropriate therapy. With analysis and discussion it appears that PET/ CT fusion images have the potential to dramatically improve our ability to manage the patients with lung cancer and is contributing to our understanding of cancer cell biology and in development of new therapies

Self-Supervised Discovery of Anatomical Shape Landmarks

Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.Comment: Early accept at MICCAI 202

Approximation Strategies for Incomplete MaxSAT

Incomplete MaxSAT solving aims to quickly find a solution that attempts to minimize the sum of the weights of the unsati sfied soft clauses without providing any optimality guarantees. In th is paper, we propose two approximation strategies for improving incomp lete MaxSAT solving. In one of the strategies, we cluster the weights and approximate them with a representative weight. In another strategy, we b reak up the problem of minimizing the sum of weights of unsatisfiable clauses into multiple minimization subproblems. Experimental res ults show that approximation strategies can be used to find better solution s than the best incomplete solvers in the MaxSAT Evaluation 2017

Therapeutic Radionuclides: Making the Right Choice

Recently, there has been a resurgence of interest in nuclear medicine therapeutic procedures. Using unsealed sources for therapy is not a new concept; it has been around since the beginnings of nuclear medicine. Treatment of thyroid disorders with radioiodine is a classic example. The availability of radionuclides with suitable therapeutic properties for specific applications, as well as methods for their selective targeting to diseased tissue have, however, remained the main obstacles for therapy to assume a more widespread role in nuclear medicine. Nonetheless, a number of new techniques that have recently emerged, (e.g., tumor therapy with radiolabeled monoclonal antibodies, treatment of metastatic bone pain, etc.) appear to have provided a substantial impetus to research on production of new therapeutic radionuclides. Although there are a number of new therapeutic approaches requiring specific radionuclides, only selected broad areas will be used as examples in this article