Anurag Gupta

Elevated plasminogen activator inhibitor type-1 (PAI-1) as contributing factor in pathogenesis of hypercoagulable state in antiphospholipid syndrome

The aim of this study is to explore the role of plasminogen activator inhibitor type 1 (PAI-1) in primary and secondary antiphospholipid syndrome (APS). Thirty patients of APS (24 primary and 6 secondary) were recruited in the study who fulfilled the revised Sapporo criteria. Control groups comprised of age- and sex-matched 10 healthy volunteers and 10 patients each of systemic lupus erythematosus and rheumatoid arthritis without any antecedent thrombotic event and/or APS-related pregnancy morbidity. Serum samples were tested for PAI-1 antigen levels measured by quantitative ELISA. Positivity rate of PAI-1 in patients of primary, secondary as well as total APS patients was significantly higher in relation to age- and sex-matched healthy volunteers (pxa0=xa00.010, pxa0=xa00.003 and pxa0<xa00.001, respectively). Meanxa0±xa0SEM levels of PAI-1 in primary and secondary as well as total APS patients were significantly higher (pxa0=xa00.006, pxa0<xa00.001 and pxa0<xa00.001) in relation to healthy controls. Correlation of PAI-1 levels (meanxa0±xa0SEM) with clinical characteristics, that is, thrombosis and pregnancy morbidity, revealed significantly higher levels of PAI-1 (pxa0<xa00.001) in patients having thrombosis and APS-related pregnancy morbidity. Elevated PAI-1 level leading to impaired fibrinolysis plays a significant role in producing hypercoagulable state in primary and secondary APS.

Role of anti-annexin A5 in pathogenesis of hypercoagulable state in patients with antiphospholipid syndrome

Despite several attempts made during the last decade, the exact pathogenesis of exceedingly high thrombotic events and bad obstetric outcome in antiphospholipid syndrome (APS) remains elusive. Anti‐annexin A5 (aANX IgG) is thought to have a role in pathophysiology of APS. We studied role of aANX IgG in the pathogenesis of hypercoagulable state in APS patients.

Oxidative stress detection by MEMS cantilever sensor array based electronic nose

This paper is concerned with analyzing the role of polymer swelling induced surface stress in MEMS chemical sensors. The objective is to determine the impact of surface stress on the chemical discrimination ability of MEMS resonator sensors. We considered a case study of hypoxia detection by MEMS sensor array and performed several types of simulation experiments for detection of oxidative stress volatile organic markers in human breath. Both types of sensor response models that account for the surface stress effect and that did not were considered for the analyses in comparison. It is found that the surface stress (hence the polymer swelling) provides better chemical discrimination ability to polymer coated MEMS sensors.This paper is concerned with analyzing the role of polymer swelling induced surface stress in MEMS chemical sensors. The objective is to determine the impact of surface stress on the chemical discrimination ability of MEMS resonator sensors. We considered a case study of hypoxia detection by MEMS sensor array and performed several types of simulation experiments for detection of oxidative stress volatile organic markers in human breath. Both types of sensor response models that account for the surface stress effect and that did not were considered for the analyses in comparison. It is found that the surface stress (hence the polymer swelling) provides better chemical discrimination ability to polymer coated MEMS sensors.

Application of Fuzzy Clustering for Selection of Coating Materials for MEMS Sensor Array

In this paper we apply fuzzy subtractive and fuzzy c-means clustering (FSC and FCM) methods for polymer data mining for making MEMS (microelectromechanical system) sensor array-based electronic nose (E-nose). A MEMS cantilever coated with a selective polymer responds to the target chemical by its preferential sorption and ensuing modification of the cantilever parameters (mass, stiffness, and surface stress). An E-nose system operates by using an array of broadly selective sensors (polymer coatings) and application of pattern recognition. The selection of polymer coatings is crucial for desired performance features of an E-nose. A minimal set of polymers from a large list of prospective polymers were selected by the FSC and FCM methods. The detection and monitoring of fish spoilage was taken to be the case study. Both the static and the dynamic mode MEMS sensing were employed for data generation and principal component analysis. A comparative analysis of polymer selection method vis-a-vis sensing mode is carried out.

MEMS sensor array based electronic nose for breath analysis ─ a simulation study

The paper presents a simulation study of breath analysis based on theoretical models of microelectromechanical structure (MEMS) cantilever sensor array. The purpose of this study is to suggest a methodology for the development of MEMS electronic nose (e-nose) for monitoring disease-specific volatiles in exhaled breath. Oxidative stress and diabetes are taken as case studies for the assessment of e-nose designs. The detection of ethane for general oxidative stress, isoprene for hypoxia, and acetone for diabetes are considered for targeted detection. A number of volatiles concurrently present in the exhaled breath are taken as interferents. The MEMS cantilevers are coated with volatile-selective polymers and are analyzed in both the static and dynamic modes. The sensor array is defined by polymer selections based on three data mining methods: principal component analysis (PCA), fuzzy c-means clustering (FCM), and fuzzy subtractive clustering (FSC). This utilizes vapor/polymer partition coefficients as a database. Analyses are carried out to find optimal combinations of the polymer selection method and cantilever sensing mode. Virtual breath analysis experiments are analyzed by PCA for target discrimination. It is found that no single combination works best in all conditions. The acetone (diabetes) detection is best in both sensing modes with the polymers selected by FSC; the isoprene (hypoxia) is detectable only in static sensing mode with polymers selected by FCM clustering; and the ethane (oxidative stress) detection is possible by all sensing modes and polymer selections, provided the breath samples are preconcentrated. This study suggests that it is difficult to realize a single general-purpose MEMS breath analyzer. The dedicated analyzers for specific disease indications can however be made with an optimal combination of sensing mode and polymer coatings.