Data reduction in headspace analysis of blood and urine samples for robust bacterial identification

Noninvasive Detection of Bacterial Infection in Children Using Piezoelectric E-Nose

Currently, antibiotics are often prescribed to children without reason due to the inability to quickly establish the presence of a bacterial etiology of the disease. One way to obtain additional diagnostic information quickly is to study the volatile metabolome of biosamples using arrays of sensors. The goal of this work was to assess the possibility of using an array of chemical sensors with various sensitive coatings to determine the presence of a bacterial infection in children by analyzing the equilibrium gas phase (EGP) of urine samples. The EGP of 90 urine samples from children with and without a bacterial infection (urinary tract infection, soft tissue infection) was studied on the "MAG-8" device with seven piezoelectric sensors in a hospital. General urine analysis with sediment microscopy was performed using a Uriscan Pro analyzer and using an Olympus CX31 microscope. After surgical removal of the source of inflammation, the microbiological studies of the biomaterial were performed to determine the presence and type of the pathogen. The most informative output data of an array of sensors have been established for diagnosing bacterial pathology. Regression models were built to predict the presence of a bacterial infection in children with an error of no more than 15%. An indicator of infection is proposed to predict the presence of a bacterial infection in children with a high sensitivity of 96%.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

Synergy between nanomaterials and volatile organic compounds for non-invasive medical evaluation

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

Innovative Diagnostic Methods for Early Prostate Cancer Detection through Urine Analysis: A Review

Prostate cancer is the second most common cause of cancer death among men. It is an asymptomatic and slow growing tumour, which starts occurring in young men, but can be detected only around the age of 40–50. Although its long latency period and potential curability make prostate cancer a perfect candidate for screening programs, the current procedure lacks in specificity. Researchers are rising to the challenge of developing innovative tools able of detecting the disease during its early stage that is the most curable. In recent years, the interest in characterisation of biological fluids aimed at the identification of tumour-specific compounds has increased significantly, since cell neoplastic transformation causes metabolic alterations leading to volatile organic compounds release. In the scientific literature, different approaches have been proposed. Many studies focus on the identification of a cancer-characteristic “odour fingerprint” emanated from biological samples through the application of sensorial or senso-instrumental analyses, others suggest a chemical characterisation of biological fluids with the aim of identifying prostate cancer (PCa)-specific biomarkers. This paper focuses on the review of literary studies in the field of prostate cancer diagnosis, in order to provide an overview of innovative methods based on the analysis of urine, thereby comparing them with the traditional diagnostic procedures.

Application and Uses of Electronic Noses for Clinical Diagnosis on Urine Samples: A Review

The electronic nose is able to provide useful information through the analysis of the volatile organic compounds in body fluids, such as exhaled breath, urine and blood. This paper focuses on the review of electronic nose studies and applications in the specific field of medical diagnostics based on the analysis of the gaseous headspace of human urine, in order to provide a broad overview of the state of the art and thus enhance future developments in this field. The research in this field is rather recent and still in progress, and there are several aspects that need to be investigated more into depth, not only to develop and improve specific electronic noses for different diseases, but also with the aim to discover and analyse the connections between specific diseases and the body fluids odour. Further research is needed to improve the results obtained up to now; the development of new sensors and data processing methods should lead to greater diagnostic accuracy thus making the electronic nose an effective tool for early detection of different kinds of diseases, ranging from infections to tumours or exposure to toxic agents.

Analysis of Airborne Biomarkers for Point-of-Care Diagnostics

Treatable diseases continue to exact a heavy burden worldwide despite powerful advances in treatment. Diagnostics play crucial roles in the detection, management, and ultimate prevention of these diseases by guiding the allocation of precious medical resources. Motivated by globalization and evolving disease, and enabled by advances in molecular pathology, the scientific community has produced an explosion of research on miniaturized integrated biosensor platforms for disease detection. Low-cost, automated tests promise accessibility in low-resource settings by loosening constraints around infrastructure and usability. To address the challenges raised by invasive and intrusive sample collection, researchers are exploring alternative biomarkers in various specimens. Specifically, patient-generated airborne biomarkers suit minimally invasive collection and automated analysis. Disease biomarkers are known to exist in aerosols and volatile compounds in breath, odor, and headspace, media that can be exploited for field-ready diagnostics. This article reviews global disease priorities and the characteristics of low-resource settings. It surveys existing technologies for the analysis of bioaerosols and volatile organic compounds, and emerging technologies that could enable their translation to the point of care. Engineering advances promise to enable appropriate diagnostics by detecting chemical and microbial markers. Nonetheless, further innovation and cost reduction are needed for these technologies to broadly affect global health.

Nanomaterial-based sensors for detection of disease by volatile organic compounds

The importance of developing new diagnostic and detection technologies for the growing number of clinical challenges is rising each year. Here, we present a concise, yet didactic review on a new diagnostics frontier based on the detection of disease-related volatile organic compounds (VOCs) by means of nanomaterial-based sensors. Nanomaterials are ideal for such sensor arrays because they are easily fabricated, chemically versatile and can be integrated into currently available sensing platforms. Following a general introduction, we provide a brief description of the VOC-related diseases concept. Then, we focus on detection of VOC-related diseases by selective and crossreactive sensing approaches, through chemical, optical and mechanical transducers incorporating the most important classes of nanomaterials. Selected examples of the integration of nanomaterials into selective sensors and crossreactive sensor arrays are given. We conclude with a brief discussion on the integration possibilities of different types of nanomaterials into sensor arrays, and the expected outcomes and limitations.

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.

Predicting the receptive range of olfactory receptors

Although the family of genes encoding for olfactory receptors was identified more than 15 years ago, the difficulty of functionally expressing these receptors in an heterologous system has, with only some exceptions, rendered the receptive range of given olfactory receptors largely unknown. Furthermore, even when successfully expressed, the task of probing such a receptor with thousands of odors/ligands remains daunting. Here we provide proof of concept for a solution to this problem. Using computational methods, we tune an electronic nose to the receptive range of an olfactory receptor. We then use this electronic nose to predict the receptors' response to other odorants. Our method can be used to identify the receptive range of olfactory receptors, and can also be applied to other questions involving receptor–ligand interactions in non-olfactory settings.

A key goal in biology is to identify specific ligands for specific receptors. One example is where the ligand is a drug. In turn, in the olfactory system the ligand is the odorant that binds to olfactory receptors. There are many olfactory receptor types, and which odorants will activate which receptors remains largely unknown. One way to answer this is to systematically vary the molecular features of ligands and to measure the olfactory receptor response. However, the vast number of molecular features and their combinations renders such an effort potentially unsolvable. Here, rather than looking at the trees (each molecular feature), we looked at the forest (the smell they generate). We used a device called an electronic nose that generates a patterned response to odorants. We then obtained the response to a set of odorants that are known to activate a particular olfactory receptor, and we used this pattern to predict the response of that receptor to other odorants. We found that, on average in three out of four we could predict the response of olfactory receptors. This result provides a new method for probing the olfactory system, and also suggests a novel method for identifying potential drugs.

Assessment of bioinspired models for pattern recognition in biomimetic systems

The increasing complexity of the artificial implementations of biological systems, such as the so-called electronic noses (e-noses) and tongues (e-tongues), poses issues in sensory feature extraction and fusion, drift compensation and pattern recognition, especially when high reliability is required. In particular, in order to achieve effective results, the pattern recognition system must be carefully designed. In order to investigate a novel biomimetic approach for the pattern recognition module of such systems, the classification capabilities of an artificial model inspired by the mammalian cortex, a cortical-based artificial neural network (CANN), are compared with several artificial neural networks present in the e-nose and e-tongue literature, a multilayer perceptron (MLP), a Kohonen self-organizing map (KSOM) and a fuzzy Kohonen self-organizing map (FKSOM). Each network was tested with large datasets coming from a conducting polymer-sensor-based e-nose and a composite array-based e-tongue. The comparison of results showed that the CANN model is able to strongly enhance the performances of both systems.