Does prophylactic sotalol and magnesium decrease the incidence of atrial fibrillation following coronary artery bypass surgery: a propensity-matched analysis

BACKGROUND: Atrial fibrillation can occur in up to 40% of patients undergoing coronary surgery. METHODS: We retrospectively analysed 103 consecutive coronary surgery patients under the care of one surgeon between April 2003 and September 2003. These patients received 40 mg of sotalol orally twice daily from the first post-operative day for 6 weeks and 2 g of magnesium intravenously immediately post surgery and on the first post-operative day. We developed a propensity score for the probability of receiving sotalol and magnesium after coronary surgery. 89 patients from the sotalol and magnesium group were successfully matched with 89 unique coronary surgery patients who did not receive either sotalol or magnesium with an identical propensity score. RESULTS: Preoperative characteristics were well matched between groups. There was no significant difference with respect to in-hospital mortality between groups (sotalol and magnesium 1.1% versus control 4.5%; p = 0.17). The incidence of atrial fibrillation in the sotalol and magnesium group was 13.5% compared to 27.0% in the controls (p = 0.025). CONCLUSION: The combination of sotalol and magnesium can significantly reduce the incidence of post-operative atrial fibrillation following coronary surgery

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identification. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID’19 patients based on the current context

An efficient ensemble VTOPES multi-criteria decision-making model for sustainable sugarcane farms

© 2019 by the authors. The role of Information Technology based decision models for sustainable agriculture has gained immense prominence in recent years. Ranking of agriculture farms based on their yield plays a vital role in sustainable agriculture. In this work, an ensemble decision-making model, namely VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), entropy, and standard deviation (VTOPES), is proposed for ranking the sustainable sugarcane farms. VTOPES system model comprises of four significant steps: (i) determination of significance scores of the sub-parameters, (ii) transformation of sub-parameter sequences into main parameter values, (iii) computation of significant scores of main parameters, and (iv) generation of assessment values and deploying it for ranking the sugarcane farms. The ranking results of the proposed VTOPES model are compared with the ranking patterns obtained from five years average yield data acquired from the selected sugarcane farms. Moreover, the outcomes of the VTOPES model are also compared with other prevalent methods. Subsequently, Spearman's rank correlation method is applied for evaluating the impact of correlation of VTOPES ranks in comparison with the average yield ranks. Thus, it can be noticed that the empirical results of the VTOPES model provide reliable and sustainable results. Therefore, it suffices to be a sustainable decision model for any problem where multiple parameters are involved

A contemporary review on drought modeling using machine learning approaches

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include singular vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics

Role of H- and D- MATE-Type Transporters from Multidrug Resistant Clinical Isolates of Vibrio fluvialis in Conferring Fluoroquinolone Resistance

Background: The study seeks to understand the role of efflux pumps in multidrug resistance displayed by the clinical isolates of Vibrio fluvialis, a pathogen known to cause cholera-like diarrhoea. Methodology: Two putative MATE family efflux pumps (H- and D-type) were PCR amplified from clinical isolates of V. fluvialis obtained from Kolkata, India, in 2006 and sequenced. Bioinformatic analysis of these proteins was done to predict protein structures. Subsequently, the genes were cloned and expressed in a drug hypersusceptible Escherichia coli strain KAM32 using the vector pBR322. The recombinant clones were tested for the functionality of the efflux pump proteins by MIC determination and drug transport assays using fluorimeter. Results: The sequences of the genes were found to be around 99 % identical to their counterparts in V. cholerae. Protein structure predicting servers TMHMM and I-TASSER depicted ten-twelve membrane helical structures for both type of pumps. Real time PCR showed that these genes were expressed in the native V. fluvialis isolates. In the drug transport assays, the V. fluvialis clinical isolates as well as recombinant E. coli harbouring the efflux pump genes showed the energydependent and sodium ion-dependent drug transport activity. KAM32 cells harbouring the recombinant plasmids showed elevated MIC to the fluoroquinolones, norfloxacin and ciprofloxacin but H-type pumps VCH and VFH from V. cholerae and V. fluvialis respectively, showed decreased MIC to aminoglycosides like gentamicin, kanamycin and streptomycin. Decrease i

The Natural Variation of a Neural Code

The way information is represented by sequences of action potentials of spiking neurons is determined by the input each neuron receives, but also by its biophysics, and the specifics of the circuit in which it is embedded. Even the “code” of identified neurons can vary considerably from individual to individual. Here we compared the neural codes of the identified H1 neuron in the visual systems of two families of flies, blow flies and flesh flies, and explored the effect of the sensory environment that the flies were exposed to during development on the H1 code. We found that the two families differed considerably in the temporal structure of the code, its content and energetic efficiency, as well as the temporal delay of neural response. The differences in the environmental conditions during the flies' development had no significant effect. Our results may thus reflect an instance of a family-specific design of the neural code. They may also suggest that individual variability in information processing by this specific neuron, in terms of both form and content, is regulated genetically

GLAST: Understanding the High Energy Gamma-Ray Sky

We discuss the ability of the GLAST Large Area Telescope (LAT) to identify, resolve, and study the high energy gamma-ray sky. Compared to previous instruments the telescope will have greatly improved sensitivity and ability to localize gamma-ray point sources. The ability to resolve the location and identity of EGRET unidentified sources is described. We summarize the current knowledge of the high energy gamma-ray sky and discuss the astrophysics of known and some prospective classes of gamma-ray emitters. In addition, we also describe the potential of GLAST to resolve old puzzles and to discover new classes of sources.Comment: To appear in Cosmic Gamma Ray Sources, Kluwer ASSL Series, Edited by K.S. Cheng and G.E. Romer

Studies on an alkali-thermostable xylanase from Aspergillus fumigatus MA28

An alkalitolerant fungus, Aspergillus fumigatus strain MA28 produced significant amounts of cellulase-free xylanase when grown on a variety of agro-wastes. Wheat bran as the sole carbon source supported higher xylanase production (8,450 U/L) than xylan (7,500 U/L). Soybean meal was observed to be the best nitrogen source for xylanase production (9,000 U/L). Optimum medium pH for xylanase production was 8 (9,800 U/L), though, significant quantities of the enzyme was also produced at pH 7 (8,500 U/L), 9 (8,200 U/L) and 10 (4,600 U/L). The xylanase was purified by ammonium sulphate precipitation and carboxymethyl cellulose chromatography, and was found to have a molecular weight of 14.4 kDa with a Vmax of 980 μmol/min/mg of protein and a Km of approximately 4.9 mg/mL. The optimum temperature and pH for enzyme activity was 50 °C and pH 8, respectively. However, the enzyme also showed substantial residual activity at 60–70 °C (53–75%) and at alkaline pH 8–9 (56–88%)