A novel fluorescent "turn-on" chemosensor for nanomolar detection of Fe(III) from aqueous solution and its application in living cells imaging

An electronically active and spectral sensitive fluorescent “turn-on” chemosensor (BTP-1) based on the benzo-thiazolo-pyrimidine unit was designed and synthesized for the highly selective and sensitive detection of Fe³⁺ from aqueous medium. With Fe³⁺, the sensor BTP-1 showed a remarkable fluorescence enhancement at 554 nm (λex=314 nm) due to the inhibition of photo-induced electron transfer. The sensor formed a host-guest complex in 1:1 stoichiometry with the detection limit down to 0.74 nM. Further, the sensor was successfully utilized for the qualitative and quantitative intracellular detection of Fe³⁺ in two liver cell lines i.e., HepG2 cells (human hepatocellular liver carcinoma cell line) and HL-7701 cells (human normal liver cell line) by a confocal imaging technique

Anti-Inflammatory Activity of Delonix regia (Boj. Ex. Hook)

The present work was to evaluate the anti-inflammatory activity of Delonix regia leaves (Family: Caesalpiniaceae). The powder of Delonix regia leaves was subjected to extraction with ethanol in soxhlet extractor. The ethanol extract after preliminary phytochemical investigation showed the presence of sterols, triterpenoids, phenolic compounds and flavonoids. The anti-inflammatory activity was studied using carrageenan-induced rat paw edema and cotton pellet granuloma at a three different doses (100, 200, and 400 mg/kg b.w. p.o.) of ethanol extract. The ethanol extract of Delonix regia leaves was exhibited significant anti-inflammatory activity at the dose of 400 mg/kg in both models when compared with control group. Indomethacin (10 mg/kg b.w. p.o) was also shown significant anti-inflammatory activity in both models

Association of non-alcoholic fatty liver disease with anthropometric and metabolic parameters in type 2 diabetes: a retrospective analysis

Background: Type 2 diabetes (T2D) is associated with increased prevalence of non-alcoholic fatty liver disease (NAFLD) which mediates increased insulin resistance and is associated with cardiovascular disease (CVD) risk factors. Aim of the study was to understand the association of NAFLD with anthropometric and metabolic parameters in T2DM.Methods: A retrospective observation of data obtained from a private diabetes care centre in non-alcoholic T2D patients was performed. Association of presence of NAFLD with anthropometric, metabolic (glycemic, lipid) parameters, and also blood pressure were assessed. Patients were duly informed that the data collected pertaining to their illness could be used for research purposes. No changes or interventions in the management of the illness were made as part of this study.Results: In total, 300 cases were included in analysis. NAFLD was seen in 38.0% of the cases. Patients with fatty liver were much older than those without fatty liver (P<0.0001). A significant association of NAFLD was seen with all anthropometric (P<0.05 for each) and lipid (p<0.05 for each) parameters and also systolic and diastolic blood pressure measurements (p<0.0001 for both). There was no significant association with glycemic levels in patients with NAFLD. Other factors which had significant association with fatty liver include duration of diabetes, duration of hypertension and a known history of hypertension and dyslipidaemia (p<0.0001 for each).Conclusions: NAFLD has significant association with cardio-metabolic risk factors and may be an independent risk factor for CV disease. Further prospective studies with effect of diabetes treatment and progression/regression of NAFLD and its association with CV outcomes in T2D are warranted

Prediction of Cardiovascular Diseases by Integrating Electrocardiogram (ECG) and Phonocardiogram (PCG) Multi-Modal Features using Hidden Semi Morkov Model

Because the health care field generates a large amount of data, we must employ modern ways to handle this data in order to give effective outcomes and make successful decisions based on data. Heart diseases are the major cause of mortality worldwide, accounting for 1/3th of all fatalities. Cardiovascular disease detection can be accomplished by the detection of disturbance in cardiac signals, one of which is known as phonocardiography. The aim of this project is for using machine learning to categorize cardiac illness based on electrocardiogram (ECG) and phonocardiogram (PCG) readings. The investigation began with signal preprocessing, which included cutting and normalizing the signal, and was accompanied by a continuous wavelet transformation utilizing a mother wavelet analytic morlet. The results of the decomposition are shown using a scalogram, and the outcomes are predicted using the Hidden semi morkov model (HSMM). In the first phase, we submit the dataset file and choose an algorithm to run on the chosen dataset. The accuracy of each selected method is then predicted, along with a graph, and a modal is built for the one with the max frequency by training the dataset to it. In the following step, input for each cardiac parameter is provided, and the sick stage of the heart is predicted based on the modal created. We then take measures based on the patient's condition. When compared to current approaches, the suggested HSMM has 0.952 sensitivity, 0.92 specificity, 0.94 F-score, 0.91 ACC, and 0.96 AUC

Newer drugs in the management of diabetes mellitus

Modern life style with present days technological advances have made human life sedentary. This is causing increasing prevalence of obesity and physical inactivity amongst population. The number of cases of diabetes worldwide in the year 2000 among adults 20 years of age is estimated to be 171 million in recent reports   and is said to rise to more than 300 million by 2025. The raised plasma glucose levels give rise to complications in the form of microvascular and macrovascular complications diminished quality of life with reduced life expectancy. The currently available drugs used in the management of type II DM are not completely satisfactory in regard of controlling blood glucose level, many of the times they are associated with undesirable side effects. Hence there is continuous ongoing work in development of newer drugs, which are safe, efficacious and potent as well as free of undesirable effects such as sustained hypoglycaemia. Fortunately there are newer drug, few of them approved while other still knocking the door from the classes of drug such as GLP-1Mimetic, DPP-4 Inhibitors and others. Here we have tried to cover them in brief

Biochemical basis of resistance in rice against Asian rice gall midge, Orseolia oryzae (Wood-Mason) (Diptera : Cecidomyiidae)

ABSTRACT A total of 1482 genotypes at Zonal Agricultural Research Station, V. C. Farm, Mandya and 416 genotypes at Agricultural Research Station, Kankanady, Mangalore under AICRP (Rice) were evaluated both in field and greenhouse conditions during Kharif 2006 and 2007. The estimation of biochemical constituents in rice shoot epics (30 day old plants) of selected resistant and susceptible genotypes was done to establish the relationship between various biochemical contents and to compare it with resistance and susceptibility. The studies revealed that the higher level of total phenols and total free amino acids was observed in majority of the resistant genotypes compared to susceptible entries. The amount of total sugars, reducing sugar and crude proteins in all susceptible genotypes was found higher compared to resistant genotypes. However, the amount of total sugars, reducing sugars, crude proteins and amino acids were not related to resistance

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy

Shielding in whole brain irradiation in the multileaf collimator era: Dosimetric evaluation of coverage using SFOP guidelines against in-house guidelines

Aim : Compare the planning target volume (PTV) coverage in three different shielding techniques in cranial irradiation. Settings and Design : Tertiary care center, prospective study. Materials and Methods : The whole brain and meninges were contoured in ten planning CT scans, and expanded by 5 mm for the PTV. Shielding was designed using the French Society of Pediatric Oncology (SFOP) guidelines (SFOP plan), in-house recommendation (with 1 cm margin from the orbital roof and sphenoid wing) on a igitally Reconstructed Radiograph (DRR) and a third plan was generated using a 3D conformal radiation technique (3DCRT). The coverage of the PTV was noted using the isodose covering 95% of the PTV(D95), minimum dose within the PTV(D min ), and maximum dose within the PTV(D max ). The location of PTV not covered by the 95% isodose curve was noted. The median dose and maximum dose (D max ) to both eyes and maximum dose D max for the lens were noted. Statistical Analysis : General linear model method repeated the measure of analysis of variance test (ANOVA). Results : PTV coverage was significantly poorer in the SFOP and in-house plans as compared to 3DCRT plan (P=0.04). Median volume of PTV not covered by 95% isodose curve was 4.18 cc, 1.01 cc, and 0 cc in SFOP, in-house, and 3DCRT plan, respectively. Conclusions : In the absence of volumetric planning techniques, SFOP guidelines lead to inadequate coverage and the in-house method is recommended

Effect of Different Crushing Treatments on Sweet Sorghum Juice Extraction and Sugar Quality Traits in Different Seasons

Sweet sorghum (Sorghum bicolor (L.) Moench) is an important biofuel crop that produces both food (grain) and biofuel (from stalk juice). The objective of this investigation was to assess the effect of different crushing treatments on juice extraction and sugar quality traits of sweet sorghum cultivars grown in different seasons. Three sweet sorghum cultivars along with three stalk crushing treatments namely (i) stalk only crushed (leaf, sheath and panicle removed), (ii) stalk plus sheath crushed (leaf and panicle removed), and (iii) whole plant crushed (but only panicle removed) were assessed in split–split-plot design during 2009 rainy (Kharif) and 2009 post-rainy (Rabi) seasons. The percent juice extraction and juice sugar quality traits were significant (P ≤ 0.05) in different crop seasons, but were non-significant among cultivars and crushing treatments. Sweet sorghum cultivars grown during rainy season had significantly higher total soluble sugars (TSS), sucrose and purity per cent than in post-rainy season. Experimental variety SPSSV 30 showed significant superiority by 25 % in TSS and sucrose content than check namely CSH 22SS. Effect of crushing treatments on juice extraction and sugar quality traits were non-significant except juice brix. It is recommended that the complete sweet sorghum stalks after removing the panicle can be crushed without the need for removing leaf and sheath both in large research trial samples, and bulk harvested stalks at biofuel processing facility. This will reduce processing time at the sugar mill and helps avoiding rapid deterioration of stalk sugars in the ambient field condition, as removal of leaf and sheath in sweet sorghum is highly cumbersome unlike sugarcane, where it is relatively easy