Parasitic infections and their tissue response: a histopathological study

Background: Many pathogenic organisms including parasites cause inflammatory lesions and microscopic findings are useful tool for the aetiological diagnosis. The causes of parasite induced tissue damage can either be due to the physical pressure exerted by the parasites or the toxic secretory products which may lead to hypersensitivity reactions. The commonly encountered tissue responses are eosinophilic infiltration, abscess formation and granulomatous inflammation. Main objective of the study is to study the parasitic infections involving various tissues and organs and to assess the tissue response elicited against these parasites.Methods: The histopathologically diagnosed parasitic infections over a period of 7 years from January 2007 to December 2013 were analysed.  The histological identification of parasite and tissue reaction was evaluated in correlation with clinical presentation. Enterobius vermicularis which is the most common parasitic infestation in appendix was excluded from the study.Results: Over a period of 7 years 9 cases of parasitic infections were found. These include 2 cases each of filariasis, cysticercosis, and hydatid cyst, and 1 case each of ascaris enteritis, amoebic colitis, and conjunctival parasite. The specific tissue reactions included eosinophilic infiltrate in filariasis and ascaris enteritis and xanthogranulomatous reaction in cysticercosis.Conclusions: Among the 9 cases only 2 hydatid cysts and one genital filariasis were clinically diagnosed. Remaining cases were incidentally found only on the histopathological examination. This emphasises that careful histopathological examination is essential for the diagnosis of these lesions to provide specific treatment for the patients.

RATIO OF TRIGLYCERIDES TO HIGH-DENSITY LIPOPROTEIN CHOLESTEROL AND MARKERS OF LIVER INJURY IN DIABETES MELLITUS

Objectives: The present study was done to evaluate and compare the triglyceride to high-density lipoprotein (TG/HDL) ratio in subjects with diabetesmellitus (DM) and diabetic prone subjects (impaired glucose tolerance [IGT]) with normal subjects without diabetes. An attempt was also made tocorrelate TG/HDL with markers of liver injury such as alanine aminotransferase, aspartate aminotransferase, (AST), and alkaline phosphatase (ALP).Methods: Lab data of 496 patients attending Pushpagiri medical health checkup were obtained. The subjects were grouped into DM: (fasting plasmaglucose >126 mg/dl), IGT: (Fasting plasma glucose: 110-126 mg/dl) and normal: (fasting plasma glucose < 110 mg/dl).Results: Statistically significant difference were observed in levels of TG, low-density lipoprotein (LDL), TG/HDL ratio, AST, ALP between diabetes, IGTand normal subjects. Statistical significance within the groups was tested using post-hoc Analysis. The level of TG and TG/HDL ratio was significantlyhigher in subjects with DM compared to normal subjects. The mean value of total cholesterol and LDL-C was found to be higher in normal subjectsthan in DM and IGT. AST, ALP values were found to be significantly higher in subjects with IGT than normal subjects.Conclusion: From this study, it can be concluded that TG and TG/HDL ratio were high in DM and IGT than subjects with normal plasma glucose. Liverinjury marker enzymes were found to be high in IGT and is correlated with TG/HDL ratio in DM.Keywords: Diabetes mellitus, Impaired glucose tolerance, Hypertriglyceridemia, Triglyceride to high-density lipoprotein ratio, Markers of liver injury

Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks

Background and Objective: Haemodynamic metrics, such as blood flow induced shear stresses at the inner vessel lumen, are associated with the development and progression of coronary artery disease. Understanding these metrics may therefore improve the assessment of an individual's coronary disease risk. However, the calculation of such luminal Wall Shear Stress (WSS) using traditional Computational Fluid Dynamics (CFD) methods is relatively slow and computationally expensive. As a result, CFD based haemodynamic computation is not suitable for integrated and large-scale use in clinical settings. Methods: In this work, deep learning techniques are proposed as an alternative method to CFD, whereby luminal WSS magnitude can be predicted in coronary bifurcations throughout the cardiac cycle based on the steady state solution (which takes <120 seconds to calculate including preprocessing), vessel geometry and additional global features. The deep learning model is trained on a dataset of 101 patient-specific and 2626 synthetic left main bifurcation models with 26 separate patient-specific cases used as the test set. Results: The model showed high fidelity predictions with <5% (normalised against mean WSS magnitude) deviation to CFD derived values as the gold-standard method, while being orders of magnitude faster with on average <2 minutes versus 3 hours computation for transient CFD. Conclusions: This method therefore offers a new approach to substantially reduce the computational cost involved in, for example, large-scale population studies of coronary haemodynamic metrics, and may therefore open the pathway for future clinical integration

Making Metadata More FAIR Using Large Language Models

With the global increase in experimental data artifacts, harnessing them in a unified fashion leads to a major stumbling block - bad metadata. To bridge this gap, this work presents a Natural Language Processing (NLP) informed application, called FAIRMetaText, that compares metadata. Specifically, FAIRMetaText analyzes the natural language descriptions of metadata and provides a mathematical similarity measure between two terms. This measure can then be utilized for analyzing varied metadata, by suggesting terms for compliance or grouping similar terms for identification of replaceable terms. The efficacy of the algorithm is presented qualitatively and quantitatively on publicly available research artifacts and demonstrates large gains across metadata related tasks through an in-depth study of a wide variety of Large Language Models (LLMs). This software can drastically reduce the human effort in sifting through various natural language metadata while employing several experimental datasets on the same topic

Essential Role of the Plasmid hik31 Operon in Regulating Central Metabolism in the Dark in Synechocystis sp. PCC 6803.

The plasmid hik31 operon (P3, slr6039-slr6041) is located on the pSYSX plasmid in Synechocystis sp. PCC 6803. A P3 mutant (ΔP3) had a growth defect in the dark and a pigment defect that was worsened by the addition of glucose. The glucose defect was from incomplete metabolism of the substrate, was pH dependent, and completely overcome by the addition of bicarbonate. Addition of organic carbon and nitrogen sources partly alleviated the defects of the mutant in the dark. Electron micrographs of the mutant revealed larger cells with division defects, glycogen limitation, lack of carboxysomes, deteriorated thylakoids and accumulation of polyhydroxybutyrate and cyanophycin. A microarray experiment over two days of growth in light-dark plus glucose revealed downregulation of several photosynthesis, amino acid biosynthesis, energy metabolism genes; and an upregulation of cell envelope and transport and binding genes in the mutant. ΔP3 had an imbalance in carbon and nitrogen levels and many sugar catabolic and cell division genes were negatively affected after the first dark period. The mutant suffered from oxidative and osmotic stress, macronutrient limitation, and an energy deficit. Therefore, the P3 operon is an important regulator of central metabolism and cell division in the dark

DETECTION OF PHISHING WEBSITES USING HYBRID MODEL

Online technologies have revolutionized the modern computing world. Thereare number of users who purchase products online and make payment through variouswebsites. There are multiple websites who ask user to provide sensitive data such asusername, password or credit card details etc. often for malicious reasons. This type ofwebsite is known as phishing website. The phishing website can be detected based on someimportant characteristics like URL (Uniform Resource Locator) and Domain identity.Several approaches have been proposed for detection of phishing websites by extracting thephishing data sets criteria to classify their legitimacy. However, there is no such approachthat can provide better results to the users from phishing attacks. This paper is an attemptto contribute in that area by presenting a hybrid model for classification to detect phishingwebsites with high accuracy and less error rate

Learning Left Main Bifurcation Shape Features with an Autoencoder

Geometric characteristics of the coronary arteries have been suggested as potential markers for disease risk. However, evaluation of such characteristics rely on judgement by human experts, and are thus variable and may lack sophistication. Here we apply recent advances in 3D deep learning to automatically obtain shape representation of the Left Main Bifurcation (LMB) of the coronary artery. We train a Variational Auto-Encoder based on the FoldingNet architecture to encode LMB shape features in a 450-dimension feature vector. The geometric features of patient-specific LMBs can then be manipulated by modifying, combining or interpolating the feature vectors before decoding. We also show that these vectors, on average, perform better than hand-crafted features in predicting measures of adverse blood flow (oscillating shear index or 'OSI', relative residence time 'RRT' and time averaged wall shear stress 'TAWSS') with a R2 goodness of fit value of 84.1% compared to 79.7%. These learned representations can also be used in other downstream predictive modelling tasks where an encoded version of a LMB is needed

The Study and Literature Review of a Feature Extraction Mechanism in Computer Vison

Detecting the Features in the image is a challenging task in computer vison and numerous image processing applications. For example to detect the corners in an image there exists numerous algorithms. Corners are formed by combining multiple edges and which sometimes may not define the boundary of an image. This paper is mainly concentrates on the study of the Harris corner detection algorithm which accurately detects the corners exists in the image. The Harris corner detector is a widely used interest point detector due to strong features such as rotation, scale, illumination and in the case of noise. It is based on the local auto-correlation function of a signal; where the local auto-correlation function measures the local changes of the signal with patches shifted by a small amount in di?erent directions. In out experiments we have shown the results for gray scale images as well as for color images which gives the results for the individual regions present in the image. This algorithm is more reliable than the conventional methods

ANTIOXIDANT POTENTIALS AND SIMULTANEOUS ESTIMATION OF QUERCETIN, RUTIN, AND GALLIC ACID IN CURCUMA SPECIES

Objective: This study was designed to provide simple and cost-effective methods to quantify the biologically active phenolic compounds such as rutin, quercetin, and gallic acid from Curcuma species and evaluation of the antioxidant potentials of different parts with different solvent extracts of Curcuma species.Methods: Ultraviolet-visible spectrophotometer was used for the analysis of quercetin, rutin, gallic acid and total flavonoid content of Curcuma species extracts. Antioxidant potentials of Curcuma species extracts were evaluated using 2-2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging activity.Results: Ethyl acetate extract of Curcuma aromatica rhizome, aerial part contain higher quantity of quercetin and rutin compared to the other extracts, and also Curcuma species such as Curcuma longa and Curcuma amada contains high antioxidant capacity. The total flavonoid content was high in ethyl acetate extract of Curcuma aromatica as 88.35±0.25 μg/g dry weight of quercetin equivalents.Conclusion: Different extracts of Curcuma species possess good free radical scavenging activity and the IC50 of Curcuma amada aerial part, Curcuma longa aerial part, and Curcuma aromatica rhizome was 61.65±1.75, 62.95±1.85, and 89.40±0.15 (μg/ml), respectively. The Curcuma species contains high total phenolic compounds and antioxidant potentials