Advances in Electronic-Nose Technologies Developed for Biomedical Applications

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry

Clinical Prediction Models to Predict the Risk of Multiple Binary Outcomes: a comparison of approaches

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction

Whole genome analysis of hypervirulent Klebsiella pneumoniae isolates from community and hospital acquired bloodstream infection

Abstract Background Hypervirulent K. pneumoniae (hvKp) causes severe community acquired infections, predominantly in Asia. Though initially isolated from liver abscesses, they are now prevalent among invasive infections such as bacteraemia. There have been no studies reported till date on the prevalence and characterisation of hvKp in India. The objective of this study is to characterise the hypervirulent strains isolated from bacteraemic patients for determination of various virulence genes and resistance genes and also to investigate the difference between healthcare associated and community acquired hvKp with respect to clinical profile, antibiogram, clinical outcome and molecular epidemiology. Results Seven isolates that were susceptible to all of the first and second line antimicrobials and phenotypically identified by positive string test were included in the study. They were then confirmed genotypically by presence of rmpA and rmpA2 by PCR. Among the study isolates, four were from patients with healthcare associated infections; none were fatal. All patients with community acquired infection possessed chronic liver disease with fatal outcome. Genes encoding for siderophores such as aerobactin, enterobactin, yersiniabactin, allantoin metabolism and iron uptake were identified by whole genome sequencing. Five isolates belonged to K1 capsular type including one K. quasipneumoniae. None belonged to K2 capsular type. Four isolates belonged to the international clone ST23 among which three were health-care associated and possessed increased virulence genes. Two novel sequence types were identified in the study; K. pneumoniae belonging to ST2319 and K. quasipneumoniae belonging to ST2320. Seventh isolate belonged to ST420. Conclusion This is the first report on whole genome analysis of hypervirulent K. pneumoniae from India. The novel sequence types described in this study indicate that these strains are evolving and hvKp is now spread across various clonal types. Studies to monitor the prevalence of hvKp is needed since there is a potential for the community acquired isolates to develop multidrug resistance in hospital environment and may pose a major challenge for clinical management