TyG index and insulin resistance in beta-thalassemia

Insulin resistance (IR) underlies some glucose metabolism abnormalities in thalassemia major. Recently, triglyceride glucose index (TyG) has been proposed for evaluating insulin resistance as a simple, low cost, and accessible tool. In this study, the TyG index were studied for IR monitoring in beta-thalassemia major (βTM) patients. The participants were 90 βTM patients on chronic regular transfusion therapy. The TyG index was computed based on fasting plasma glucose (FPG) and triglyceride (TG). The time gap between the first and the second TyG index survey (TyG.1 and TyG.2) was 2 years. The agreement between TyG and HOMA-IR were studied with the extension of limit of agreement (LOA). We included 90 patients 53.3 % men (n = 48). Among them, 14.4 % (14.6 % male, 14.3 % female) had impaired fasting glucose level (e.g., 100–125 mg/dl) at first test. It rose to 37.8 % (27.1 % male, 50 % female) during 2 years. Based on TyG.1, the 34.4 % of patients was detected as IR cases. After 2 years, the percent of IR based on TyG.2 was 82.2 %. The mean differences between TyG.1 and TyG.2 and their differences from the considered cutoff values were significant (P < 0.001). The prediction limits between TyG and HOMA-IR had good agreement. These data may suggest the use of TyG index for detection/monitoring of IR in βTM patients. © 2015, Research Society for Study of Diabetes in India

An overview of hydrogen as a vehicle fuel

As hydrogen fuel cell vehicles move from manifestation to commercialization, the users expec t safe, convenient and customer-friendly fuelling. Hydrogen quality affects fuel cell stack performance and life time, as well as other factors such as valve operation. In this paper, previous researcher’s development on hydrogen as a possible major fuel of the future has been studied thoroughly .Hydrogen is one of the energy carriers which can replace fossil fuel and can be used as fuel in an internal combustion engines and as a fuel cell in vehicles. To use hydrogen as a fuel of internal combustion engine, engine design should be considered for avoiding abnormal combustion. As a result it can improve engine efficiency, power output and reduce NOx emissions. The emission of fuel cell is low as compared to conventional vehicles but as penalty, fuel cell vehicles need additional space and weight to install the battery and storage tank, thus increases it production cost. The production of hydrogen can be ‘carbon-free’ only if it is generated by employing genuinely carbon-free renewable energy sources. The acceptability of hydrogen technology depends on the knowledge and awareness of the hydrogen benefits towards environment and human life. Recent study shows that people still do not have the sufficient information of hydrogen

Control Plane Compression

We develop an algorithm capable of compressing large networks into a smaller ones with similar control plane behavior: For every stable routing solution in the large, original network, there exists a corresponding solution in the compressed network, and vice versa. Our compression algorithm preserves a wide variety of network properties including reachability, loop freedom, and path length. Consequently, operators may speed up network analysis, based on simulation, emulation, or verification, by analyzing only the compressed network. Our approach is based on a new theory of control plane equivalence. We implement these ideas in a tool called Bonsai and apply it to real and synthetic networks. Bonsai can shrink real networks by over a factor of 5 and speed up analysis by several orders of magnitude.Comment: Extended version of the paper appearing in ACM SIGCOMM 201

Study of environmental enteropathy and malnutrition (SEEM) in Pakistan: protocols for biopsy based biomarker discovery and validation

Background: Environmental Enteropathy (EE), characterized by alterations in intestinal structure, function, and immune activation, is believed to be an important contributor to childhood undernutrition and its associated morbidities, including stunting. Half of all global deaths in children \u3c 5 years are attributable to under-nutrition, making the study of EE an area of critical priority. Methods: Community based intervention study, divided into two sub-studies, 1) Longitudinal analyses and 2) Biopsy studies for identification of EE features via omics analyses. Birth cohorts in Matiari, Pakistan established: moderately or severely malnourished (weight for height Z score (WHZ) \u3c − 2) children, and well-nourished (WHZ \u3e 0) children. Blood, urine, and fecal samples, for evaluation of potential biomarkers, will be collected at various time points from all participants (longitudinal analyses). Participants will receive appropriate educational and nutritional interventions; non-responders will undergo further evaluation to determine eligibility for further workup, including upper gastrointestinal endoscopy. Histopathological changes in duodenal biopsies will be compared with duodenal biopsies obtained from USA controls who have celiac disease, Crohn’s disease, or who were found to have normal histopathology. RNA-Seq will be employed to characterize mucosal gene expression across groups. Duodenal biopsies, luminal aspirates from the duodenum, and fecal samples will be analyzed to define microbial community composition (omic analyses). The relationship between histopathology, mucosal gene expression, and community configuration will be assessed using a variety of bioinformatic tools to gain better understanding of disease pathogenesis and to identify mechanism-based biomarkers. Ethical review committees at all collaborating institutions have approved this study. All results will be made available to the scientific community. Discussion: Operational and ethical constraints for safely obtaining intestinal biopsies from children in resource-poor settings have led to a paucity of human tissue-based investigations to understand and reverse EE in vulnerable populations. Furthermore, EE biomarkers have rarely been correlated with gold standard histopathological confirmation. The Study of Environmental Enteropathy and Malnutrition (SEEM) is designed to better understand the pathophysiology, predictors, biomarkers, and potential management strategies of EE to inform strategies to eradicate this debilitating pathology and accelerate progress towards the 2030 Sustainable Development Goals. Trial registration: Retrospectively registered; clinicaltrials.gov ID NCT03588013

Production and utilization aspects of waste cooking oil based biodiesel in Pakistan

Excessive fuel demand thrusts the Pakistani government to import large volumes of fuel from foreign sources, creating adverse effects on the country's economy. Therefore, exploring an alternative to fossil fuels is unavoidable. The option of environmentally friendly fuel like biodiesel produced from indigenous waste is an additional bonus for the populous developing country like Pakistan where likelihood of waste generation is huge. There exists a potential option for sustainable biodiesel production utilizing excessive waste cooking oil available in the country which otherwise is an ecological burden. The present work is focused to sturdily vindicate the appropriateness of waste cooking oil-based biodiesel generation and utilization in Pakistan through SWOT-AHP, TOWS and PESTLE analysis. The prioritization of SWOT through AHP in view of experts’ perception displayed the strengths and opportunities in highest group priority values (Strengths: 0.51, Opportunities: 0.29). Furthermore, TOWS analysis suggests promising strategies for the sustainable implementation of commercial aspect of waste oil-based biodiesel in Pakistan. Political, Economic, Social, Technological, Legal and Environmental (PESTLE) analysis favors the strengths and opportunities factors of SWOT and TOWS strategies for the application of waste cooking oil based biodiesel in country. At the end, regional recommendations have been provided for the implementation of biodiesel production scenario in country

Influence of Silica Nano-Additives on Performance and Emission Characteristics of Soybean Biodiesel Fuelled Diesel Engine

The present study examines the effect of silicon dioxide (SiO2) nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. The morphology of nano-additives was studied using scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS). The Ultrasonication process was used for the homogeneous blending of nano-additives with biodiesel, while surfactant was used for the stabilisation of nano-additives. The physicochemical properties of pure and blended fuel samples were measured as per ASTM standards. The performance and emissions characteristics of different fuel samples were measured at different loading conditions. It was found that the brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) increased by 3.48–6.39% and 5.81–9.88%, respectively, with the addition of SiO2 nano-additives. The carbon monoxide (CO), hydrocarbon (HC) and smoke emissions for nano-additive added blends were decreased by 1.9–17.5%, 20.56–27.5% and 10.16–23.54% compared to SBME25 fuel blends.</jats:p

Bio-nanotechnology application in wastewater treatment

The nanoparticles have received high interest in the field of medicine and water purification, however, the nanomaterials produced by chemical and physical methods are considered hazardous, expensive, and leave behind harmful substances to the environment. This chapter aimed to focus on green-synthesized nanoparticles and their medical applications. Moreover, the chapter highlighted the applicability of the metallic nanoparticles (MNPs) in the inactivation of microbial cells due to their high surface and small particle size. Modifying nanomaterials produced by green-methods is safe, inexpensive, and easy. Therefore, the control and modification of nanoparticles and their properties were also discussed

A Formally Verified NAT

We present a Network Address Translator (NAT) written in C and proven to be semantically correct according to RFC 3022, as well as crash-free and memory-safe. There exists a lot of recent work on network verification, but it mostly assumes models of network functions and proves properties specific to network configuration, such as reachability and absence of loops. Our proof applies directly to the C code of a network function, and it demonstrates the absence of implementation bugs. Prior work argued that this is not feasible (i.e., that verifying a real, stateful network function written in C does not scale) but we demonstrate otherwise: NAT is one of the most popular network functions and maintains per-flow state that needs to be properly updated and expired, which is a typical source of verification challenges. We tackle the scalability challenge with a new combination of symbolic execution and proof checking using separation logic; this combination matches well the typical structure of a network function. We then demonstrate that formally proven correctness in this case does not come at the cost of performance. The NAT code, proof toolchain, and proofs are available at https://vignat.github.io

Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset

Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to pre-emptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Peri-operative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre-operative variables. Secondary analyses included the use of peri-operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulin-dependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 pre-operative predictors with an optimism-corrected c-statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision-curve analysis suggested an optimal cut-off value of 20–30% predicted risk to identify high-risk individuals. Potentially modifiable risk factors included smoking status and patient-reported measures of psychological well-being. Non-modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra-operative variables (likelihood ratio χ2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain