Effect of Lactobacillus rhamnosus NCDC 298 with FOS in Combination on Viability and Toxin Production of Enterotoxigenic Escherichia coli

Probiotics and Antimicrobial Proteins - The present study was to investigate the utilization of prebiotics by Lactobacillus rhamnosus NCDC 298 and its synergistic adversary effect on both...     

Label‐free grading and staging of urothelial carcinoma through multimodal fibre‐probe spectroscopy

Urothelial carcinoma (UC) is the most common bladder tumour. Proper treatment requires tumour resection for diagnosing its grade (aggressiveness) and stage (invasiveness). White‐light cystoscopy and histopathological examination are the gold standard procedures for clinical and histopathological diagnostics, respectively. However, cystoscopy is limited in terms of specificity, histology requires long tissue processing, both procedures rely on operator's experience. Multimodal optical spectroscopy can provide a powerful tool for detecting, staging and grading bladder tumours in a fast, reliable and label‐free modality. In this study, we collected fluorescence, Raman and reflectance spectra from 50 biopsies obtained from 32 patients undergoing transurethral resection of bladder tumour using a multimodal fibre‐probe. Principal component analysis allowed distinguishing normal from pathological tissues, as well as discriminating tumour stages and grades. Each individual spectroscopic technique provided high specificity and sensitivity in classifying all tissues; however, a multimodal approach resulted in a considerable increase in diagnostic accuracy (≥95%), which is of paramount importance for tumour grading and staging. The presented method offers the potential for being applied in cystoscopy and for providing an automated diagnosis of UC at the clinical level, with an improvement with respect to current state‐of‐the‐art procedures.      

Biophysical characterization of anticoagulant hemextin AB complex from the venom of snake Hemachatus haemachatus

Hemextin AB complex from the venom of Hemachatus haemachatus is the first known natural anticoagulant that specifically inhibits the enzymatic activity of blood coagulation factor VIIa in the absence of factor Xa. It is also the only known heterotetrameric complex of two three-finger toxins. Individually only hemextin A has mild anticoagulant activity, whereas hemextin B is inactive. However, hemextin B synergistically enhances the anticoagulant activity of hemextin A and their complex exhibits potent anticoagulant activity. In this study we characterized the nature of molecular interactions leading to the complex formation. Circular dichroism studies indicate the stabilization of β-sheet in the complex. Hemextin AB complex has an increased apparent molecular diameter in both gas and liquid phase techniques. The complex formation is enthalpically favorable and entropically unfavorable with a negative change in the heat capacity. Thus, the anticoagulant complex shows less structural flexibility than individual subunits. Both electrostatic and hydrophobic interactions are important for the complexation; the former driving the process and the latter helping in the stabilization of the tetramer. The tetramer dissociates into dimers and monomers with the increase in the ionic strength of the solution and also with increase in the glycerol concentration in the buffer. The two dimers formed under each of these conditions display distinct differences in their apparent molecular diameters and anticoagulant properties. Based on these results, we have proposed a model for this unique anticoagulant complex.     

Virus and prokaryote enumeration from planktonic aquatic environments by epifluorescence microscopy with SYBR Green I

Viruses are the most abundant biological entities in aquatic environments, typically exceeding the abundance of bacteria by an order of magnitude. The reliable enumeration of virus-like particles in marine microbiological investigations is a key measurement parameter. Although the size of typical marine viruses (20-200 nm) is too small to permit the resolution of details by light microscopy, such viruses can be visualized by epifluorescence microscopy if stained brightly. This can be achieved using the sensitive DNA dye SYBR Green I (Molecular Probes-Invitrogen). The method relies on simple vacuum filtration to capture viruses on a 0.02-microm aluminum oxide filter, and subsequent staining and mounting to prepare slides. Virus-like particles are brightly stained and easily observed for enumeration, and prokaryotic cells can easily be counted on the same slides. The protocol provides an inexpensive, rapid (30 min) and reliable technique for obtaining counts of viruses and prokaryotes simultaneously.     

Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.