Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring

Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.

Continuous Monitoring of Psychosocial Stress by Non-Invasive Volatilomics

Stress is becoming increasingly commonplace in modern times, making it important to have accurate and effective detection methods. Currently, detection methods such as self-evaluation and clinical questionnaires are subjective and unsuitable for long-term monitoring. There have been significant studies into biomarkers such as HRV, cortisol, electrocardiography, and blood biomarkers, but the use of multiple electrodes for electrocardiography or blood tests is impractical for real-time stress monitoring. To this end, there is a need for non-invasive sensors to monitor stress in real time. This study looks at the possibility of using breath and skin VOC fingerprinting as stress biomarkers. The Trier social stress test (TSST) was used to induce acute stress and HRV, cortisol, and anxiety levels were measured before, during, and after the test. GC-MS and sensor array were used to collect and measure VOCs. A prediction model found eight different stress-related VOCs with an accuracy of up to 78%, and a molecularly capped gold nanoparticle-based sensor revealed a significant difference in breath VOC fingerprints between the two groups. These stress-related VOCs either changed or returned to baseline after the stress induction, suggesting different metabolic pathways at different times. A correlation analysis revealed an association between VOCs and cortisol levels and a weak correlation with either HRV or anxiety levels, suggesting that VOCs may include complementary information in stress detection. This study shows the potential of VOCs as stress biomarkers, paving the way into developing a real-time, objective, non-invasive stress detection tool for well-being and early detection of stress-related diseases.

Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management

As of today, there is a lack of a perfect non-invasive test for the surveillance of patients for potential relapse following curative treatment. Breath volatile organic compounds (VOCs) have been demonstrated to be an accurate diagnostic tool for gastric cancer (GC) detection; here, we aimed to prove the yield of the markers in surveillance, i.e., following curative surgical management. Patients were sampled in regular intervals before and within 3 years following curative surgery for GC; gas chromatography-mass spectrometry (GC-MS) and nanosensor technologies were used for the VOC assessment. GC-MS measurements revealed a single VOC (14b-Pregnane) that significantly decreased at 12 months, and three VOCs (Isochiapin B, Dotriacontane, Threitol, 2-O-octyl-) that decreased at 18 months following surgery. The nanomaterial-based sensors S9 and S14 revealed changes in the breath VOC content 9 months after surgery. Our study results confirm the cancer origin of the particular VOCs, as well as suggest the value of breath VOC testing for cancer patient surveillance, either during the treatment phase or thereafter, for potential relapse.

Noninvasive Detection of Stress by Biochemical Profiles from the Skin

Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist (e.g., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats (n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group (n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health.

Liquid Biopsy-Based Volatile Organic Compounds from Blood and Urine and Their Combined Data Sets for Highly Accurate Detection of Cancer

Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.

Noninvasive Pregestational Genetic Testing of Embryos Using Smart Sensors Array

Pregestational genetic testing of embryos is the conventional tool in detecting genetic disorders (fetal aneuploidy and monogenic disorders) for in vitro fertilization (IVF) procedures. The accepted clinical practice for genetic testing still depends on biopsy, which has the potential to harm the embryo. Noninvasive genetic prenatal testing has not yet been achieved. In this study, embryos with common genetic disorders created through IVF were tested with an artificially intelligent nanosensor array. Volatile organic compounds emitted by the culture fluid of embryos were analyzed with chemical gas sensors. The obtained results showed significant discrimination between the embryos with different genetic diseases and their wild-types. Embryos were obtained from the same clinical center for avoiding differences based on clinical and demographical characteristics. The achieved discrimination accuracy was 81% for PKD disease, 90% for FRAX disease, 85% for HOCM disease, 90% for BRCA disease, and 100% for HSCR disease. These proof-of-concept findings might launch the development of a noninvasive approach for early assessment of embryos by examining the culture fluid of the embryos, potentially enabling noninvasive diagnosis and screening of genetic diseases for IVF.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Detecting Coronary Artery Disease using Exhaled Breath Analysis

INTRODUCTION

Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide, and there is an unmet need for a simple, inexpensive, non-invasive tool aimed at CAD detection. The aim of this pilot study was to evaluate the possible use of breath analysis in detecting the presence of CAD.

MATERIALS AND METHODS

In a prospective study, breath from patients with no history of CAD who presented with acute chest pain to the emergency room was sampled using a designated portable electronic nose (eNose) system. First, breath samples from 60 patients were analyzed and categorized as obstructive, non-obstructive and no-CAD according to the actual presence and extent of CAD as was demonstrated on cardiac imaging (either computerized tomography angiography or coronary angiography). Classification models were built according to the results, and their diagnostic performance was then examined in a blinded manner on a new set of 25 patients. The data were compared with the actual results of coronary arteries evaluation. Sensitivity, specificity and accuracy were calculated for each model.

RESULTS

Obstructive CAD was correctly distinguished from non-obstructive and no-CAD with 89% sensitivity, 31% specificity, 83% negative predictive value (NPV), 42% positive predictive value (PPV) and 52% accuracy. In another model, any extent of CAD was successfully distinguished from no-CAD with 69% sensitivity, 67% specificity, 54% NPV, 79% PPV and 68% accuracy.

CONCLUSION

This proof-of-concept study shows that breath analysis has the potential to be used as a novel rapid, non-invasive diagnostic tool to help identify presence of CAD in patients with acute chest pain.

The Utility of Breath Analysis in the Diagnosis and Staging of Parkinson's Disease

BACKGROUND

The analysis of volatile organic compounds (VOCs) collected in breath samples has the potential to be a rapid, non-invasive test to aid in the clinical diagnosis and tracking of chronic conditions such as Parkinson's disease (PD).

OBJECTIVE

To assess the feasibility and utility of breath sample analysis done, both at point of collection in clinic and when sent away to be analyzed remotely, to diagnose, stratify and monitor disease course in a moderately large cohort of patients with PD.

METHODS

Breath samples were collected from 177 people with PD and 37 healthy matched control individuals followed over time. Standard clinical data (MDS-UPDRS & cognitive assessments) from the PD patients were collected at the same time as the breath sample was taken, these measures were then correlated with the breath test analysis of exhaled VOCs.

RESULTS

The breath test was able to distinguish patients with PD from healthy control participants and correlated with disease stage. The off-line system (remote analysis) gave good results with overall classification accuracies across a range of clinical measures of between 73.6% to 95.6%. The on-line (in clinic) system showed comparable results but with lower levels of correlation, varying between 33.5% to 82.4%. Chemical analysis identified 29 potential molecules that were different and which may relate to pathogenic pathways in PD.

CONCLUSION

Breath analysis shows potential for PD diagnostics and monitoring. Both off-line and on-line sensor systems were easy to do and provided comparable results which will enable this technique to be easily adopted in clinic if larger studies confirm our findings.

A Wearable Microneedle-Based Extended Gate Transistor for Real-Time Detection of Sodium in Interstitial Fluids

Sodium is a prominent prognostic biomarker for assessing health status, such as dysnatremia. As of now, detection and monitoring of sodium levels in the human body is carried out by means of laborious and bulky laboratory equipmentand/or by offline analysis of various body fluids. Herein, an innovative stretchable, skin-conformal and fast-response microneedle extended-gate FET biosensor is reported for real-time detection of sodium in interstitial fluids for minimally invasive health monitoring along with high sensitivity, low limit of detection, excellent biocompatibility, and on-body mechanical stability. The integration of the reported device with a wireless-data transmitter and the Internet-of-Things cloud for real-time monitoring and long-term analysis is reported and discussed. This platform would eventually help bringing unlimited possibilities for effecient medical care and accurate clinical decision-making.

Biodiagnostics in an era of global pandemics-From biosensing materials to data management

The novel corona virus SARS‐CoV‐2 (COVID‐19) has exposed the world to challenges never before seen in fast diagnostics, monitoring, and prevention of the outbreak. As a result, different approaches for fast diagnostic and screening are made and yet to find the ideal way. The current mini‐review provides and examines evidence‐based innovative and rapid chemical sensing and related biodiagnostic solutions to deal with infectious disease and related pandemic emergencies, which could offer the best possible care for the general population and improve the approachability of the pandemic information, insights, and surrounding contexts. The review discusses how integration of sensing devices with big data analysis, artificial Intelligence or machine learning, and clinical decision support system, could improve the accuracy of the recorded patterns of the disease conditions within an ocean of information. At the end, the mini‐review provides a prospective on the requirements to improve our coping of the pandemic‐related biodiagnostics as well as future opportunities.

Fabricating and printing chemiresistors based on monolayer-capped metal nanoparticles

Chemiresistors that are based on monolayer-capped metal nanoparticles (MCNPs) have been used in a wide variety of innovative sensing applications, including detection and monitoring of diagnostic markers in body fluids, explosive materials, environmental contaminations and food quality control. The sensing mechanism is based on reversible swelling or aggregation and/or changes in dielectric constant of the MCNPs. In this protocol, we describe a procedure for producing MCNP-based chemiresistive sensors that is reproducible from device to device and from batch to batch. The approach relies on three main steps: (i) controlled synthesis of gold MCNPs, (ii) fabrication of electrodes that are surrounded with a microbarrier ring to confine the deposited MCNP solution and (iii) a tailor-made drying process to enable evaporation of solvent residues from the MCNP sensing layer to prevent a coffee-ring effect. Application of this approach has been shown to produce devices with ±1.5% variance-a value consistent with the criterion for commercial sensors-as well as long shelf life and stability. Fabrication of chemical sensors based on dodecanethiol- or 2-ethylhexanethiol-capped MCNPs with this approach provides high sensitivity and accuracy in the detection of volatile organic compounds (e.g., octane and decane), toxic gaseous species (e.g., HCl and NH₃) in air and simulated mixtures of lung and gastric cancer from exhaled breath.

Detection of Single Cancer Cells in Blood with Artificially Intelligent Nanoarray

Detection and monitoring of single cancer cells (SCCs), such as circulating tumor cells (CTCs), would be of aid in an efficient early detection of cancer, a tailored (personalized) therapy, and in a fast bedside assessment of treatment efficacy. Nevertheless, currently available techniques, which mostly rely on the isolation of SCCs based on their physical or biological properties, suffer from low sensitivity, complicated technical procedures, low cost-effectiveness, and being unsuitable for continuous monitoring. We report here on the design and use of an artificially intelligent nanoarray based on a heterogeneous set of chemisensitive nanostructured films for the detection of SCCs using volatile organic compounds emanating in the air trapped above blood samples containing SCCs. For demonstration purposes, we have focused on samples containing A549 lung cancer cells (hereafter, SCC^A549). The nanoarray developed to detect SCC^A549 has >90% accuracy, >85% sensitivity, and >95% specificity. Detection works irrespective of the medium and/or the environment. These results were validated by complementary mass spectrometry. The ability to continuously record, store, and preprocess the signals increases the chances that this nanotechnology might also be useful in the early detection of cancer cells in the blood and continuous monitoring of their possible progression.

Sensing gastric cancer via point-of-care sensor breath analyzer

BACKGROUND

Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening.

METHODS

A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant analysis (LDA) was used as a classifying model of the sensing signals obatined from the examined groups. For the testing step, an additional device was added. The study group included 274 patients: 94 with gastric cancer, 67 who were in the high-risk group, and 113 controls.

RESULTS

The results of the test set showed a clear discrimination between patients with gastric cancer and controls using the 2-device LDA model (area under the curve, 93.8%; sensitivity, 100%; specificity, 87.5%; overall accuracy, 91.1%), and acceptable results were also achieved for patients with high-risk lesions (the corresponding values for dysplasia were 84.9%, 45.2%, 87.5%, and 65.9%, respectively). The test-phase analysis showed lower accuracies, though still clinically useful.

CONCLUSION

Our results demonstrate that a portable breath sensor device could be useful in point-of-care settings. It shows a promise for detection of gastric cancer as well as for other types of disease.

LAY SUMMARY

A portable sensor-based breath analyzer for detection of gastric cancer can be used in point-of-care settings. The results are transferrable between devices via advanced IoT technology. Both the hardware and software of the reported breath analyzer could be easily modified to enable detection and monitirng of other disease states.

Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath

This article reports on a noninvasive approach in detecting and following-up individuals who are at-risk or have an existing COVID-19 infection, with a potential ability to serve as an epidemic control tool. The proposed method uses a developed breath device composed of a nanomaterial-based hybrid sensor array with multiplexed detection capabilities that can detect disease-specific biomarkers from exhaled breath, thus enabling rapid and accurate diagnosis. An exploratory clinical study with this approach was examined in Wuhan, China, during March 2020. The study cohort included 49 confirmed COVID-19 patients, 58 healthy controls, and 33 non-COVID lung infection controls. When applicable, positive COVID-19 patients were sampled twice: during the active disease and after recovery. Discriminant analysis of the obtained signals from the nanomaterial-based sensors achieved very good test discriminations between the different groups. The training and test set data exhibited respectively 94% and 76% accuracy in differentiating patients from controls as well as 90% and 95% accuracy in differentiating between patients with COVID-19 and patients with other lung infections. While further validation studies are needed, the results may serve as a base for technology that would lead to a reduction in the number of unneeded confirmatory tests and lower the burden on hospitals, while allowing individuals a screening solution that can be performed in PoC facilities. The proposed method can be considered as a platform that could be applied for any other disease infection with proper modifications to the artificial intelligence and would therefore be available to serve as a diagnostic tool in case of a new disease outbreak.

Volatile Compounds Are Involved in Cellular Crosstalk and Upregulation

Cell-cell cross talk is of great importance in cancer research due to its major role in proliferation, differentiation, migration, and influence on the apoptotic pathway. Different cell-cell communication mechanisms have come mainly from proteomic and genomic approaches. In this paper, a new route is reported for cross talk between cancer cells that occurs, even when they are far away from each other. Single-cell and culture analysis shows that upregulation of cancer cells emits hundreds of volatile organic compounds (VOCs) into their headspace. Part of the VOCs remains without any change, disregarding the biological environment around it. The other part of the VOCs is exchanged between monocultures of the cells as well as between co-cultures of the cells with no physical contact between them, leading to different changes in growth than when left on their own. The chemical nature and composition of these VOCs have been determined and are discussed herein. Cell-to-cell cross talk has the advantage of being suitable for transfer/diffusion over relatively long distances. It would thus be expected to serve as a shuttling pad toward the development of advanced approaches that could enable very early detection of cancer and/or monitoring of metastasis and related cancer therapy.

Screening for gastric cancer using exhaled breath samples

BACKGROUND

The aim was to derive a breath-based classifier for gastric cancer using a nanomaterial-based sensor array, and to validate it in a large screening population.

METHODS

A new training algorithm for the diagnosis of gastric cancer was derived from previous breath samples from patients with gastric cancer and healthy controls in a clinical setting, and validated in a blinded manner in a screening population.

RESULTS

The training algorithm was derived using breath samples from 99 patients with gastric cancer and 342 healthy controls, and validated in a population of 726 people. The calculated training set algorithm had 82 per cent sensitivity, 78 per cent specificity and 79 per cent accuracy. The algorithm correctly classified all three patients with gastric cancer and 570 of the 723 cancer-free controls in the screening population, yielding 100 per cent sensitivity, 79 per cent specificity and 79 per cent accuracy. Further analyses of lifestyle and confounding factors were not associated with the classifier.

CONCLUSION

This first validation of a nanomaterial sensor array-based algorithm for gastric cancer detection from breath samples in a large screening population supports the potential of this technology for the early detection of gastric cancer.

Profiling Single Cancer Cells with Volatolomics Approach

Single-cell analysis is a rapidly evolving to characterize molecular information at the individual cell level. Here, we present a new approach with the potential to overcome several key challenges facing the currently available techniques. The approach is based on the identification of volatile organic compounds (VOCs), viz. organic compounds having relatively high vapor pressure, emitted to the cell's headspace. This concept is demonstrated using lung cancer cells with various p53 genetic status and normal lung cells. The VOCs were analyzed by gas chromatography combined with mass spectrometry. Among hundreds of detected compounds, 18 VOCs showed significant changes in their concentration levels in tumor cells versus control. The composition of these VOCs was found to depend, also, on the sub-molecular structure of the p53 genetic status. Analyzing the VOCs offers a complementary way of querying the molecular mechanisms of cancer as well as of developing new generation(s) of biomedical approaches for personalized screening and diagnosis.

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Measurement of VOCs was achieved at the single-cell level

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Genetic changes influence the emitted volatiles of single and bulk cancer cells

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Single-cell VOC analysis measures population heterogeneity in initial stage of tumors

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Volatolomics research can promote non-invasive, simple, and cost-effective diagnostics

Sensor Array for Detection of Early Stage Parkinson's Disease before Medication

Early diagnosis of Parkinson's disease (PD) is important because it affects the choice of therapy and is subject to a relatively high degree of error. In addition, early detection of PD can potentially enable the start of neuroprotective therapy before extensive loss of dopaminergic neurons of the substantia nigra occurs. However, until now, studies for early detection of PD using volatile biomarkers sampled only treated and medicated patients. Therefore, there is a great need to evaluate untreated patients for establishing a real world screening and diagnostic technology. Here we describe for the first time a clinical trial to distinguish between de novo PD and control subjects using an electronic system for detection of volatile molecules in exhaled breath (sensor array). We further determine for the first time the association to other common tests for PD diagnostics as smell, ultrasound, and nonmotor symptoms. The test group consisted of 29 PD patients after initial diagnosis by an experienced neurologist, compared with 19 control subjects of similar age. The sensitivity, specificity, and accuracy values of the sensor array to detect PD from controls were 79%, 84%, and 81% respectively, in comparison with midbrain ultrasonography (93%, 90%, 92%) and smell detection (62%, 89%, 73%). The results confirm previous data showing the potential of sensor arrays to detect PD.

Breath volatolomics for diagnosing chronic rhinosinusitis

Purpose

Chronic rhinosinusitis (CRS) is one of the most common chronic diseases treated by primary care physicians. It is increasingly recognized that CRS and nasal polyposis (NP) comprise several disease processes with diverse causes. Hence, subgroups of sinusitis need to be differentiated so that patients can be screened appropriately and personalized medical treatment provided.

Patients and methods

To address this need, we use a cross-reactive nanoarray based on either molecularly modified gold nanoparticles or molecularly modified single-walled carbon nanotubes, combined with pattern recognition for analyzing breath samples. Breath samples were collected from three groups of volunteers (total 71) at the Hillel Yaffe Medical Center: CRS, NP, and control.

Results

Nanoarray results discriminated between patients with sinusitis and the control group with 87% sensitivity, 83% specificity, and 85% accuracy. The system also discriminated well between the subpopulations: 1) CRS vs control (76% sensitivity, 90% specificity); 2) CRS vs NP (82% sensitivity, 71% specificity); and 3) NP vs control (71% sensitivity, 90% specificity).

Conclusion

This preliminary study shows that a nanoarray-based breath test for screening population for sinusitis-related conditions is feasible.

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.

Exhaled Breath Markers for Nonimaging and Noninvasive Measures for Detection of Multiple Sclerosis

Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids (CSF) or by magnetic resonance imaging (MRI) of the brain or spinal cord or both. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease state. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on noninvasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 participants, 146 MS and 58 healthy control individuals. Analysis was performed by gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensor array. Predictive models were derived from the sensors, using artificial neural networks (ANNs). GC-MS analysis revealed significant differences in VOC abundance between MS patients and controls. Sensor data analysis on training sets was able to discriminate in binary comparisons between MS patients and controls with accuracies up to 90%. Blinded sets showed 95% positive predictive value (PPV) between MS-remission and control, 100% sensitivity with 100% negative predictive value (NPV) between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics.

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Hybrid volatolomics and disease detection

This Review presents a concise, but not exhaustive, didactic overview of some of the main concepts and approaches related to "volatolomics"-an emerging frontier for fast, risk-free, and potentially inexpensive diagnostics. It attempts to review the source and characteristics of volatolomics through the so-called volatile organic compounds (VOCs) emanating from cells and their microenvironment. It also reviews the existence of VOCs in several bodily fluids, including the cellular environment, blood, breath, skin, feces, urine, and saliva. Finally, the usefulness of volatolomics for diagnosis from a single bodily fluid, as well as ways to improve these diagnostic aspects by "hybrid" approaches that combine VOC profiles collected from two or more bodily fluids, will be discussed. The perspectives of this approach in developing the field of diagnostics to a new level are highlighted.

Monolayer-capped gold nanoparticles for disease detection from breath

The recognition of volatile organic compounds in breath samples is a promising approach for noninvasive safe diagnosis of disease. Spectrometry and spectroscopy methods used for breath analysis suffer from suboptimal accuracy, are expensive and are unsuitable for diagnostics. This article presents a concise review on arrays of monolayer-capped gold nanoparticle (GNP) sensors in conjugation with pattern recognition methods for cost-effective, fast and high-throughput point-of-care diagnostic results from exhaled breath samples. The article starts with a general introduction to the rationale and advantages of breath analysis as well as with a presentation of the utility of monolayer-capped GNP sensors in this field. The article continues with a presentation of the main fabrication and operation principles of these GNP sensors and concludes with selected examples regarding their utility in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology.

Combined volatolomics for monitoring of human body chemistry

Analysis of volatile organic compounds (VOCs) is a promising approach for non-invasive, fast and potentially inexpensive diagnostics. Here, we present a new methodology for profiling the body chemistry by using the volatile fraction of molecules in various body fluids. Using mass spectrometry and cross-reactive nanomaterial-based sensors array, we demonstrate that simultaneous VOC detection from breath and skin would provide complementary, non-correlated information of the body's volatile metabolites profile. Eventually with further wide population validation studies, such a methodology could provide more accurate monitoring of pathological changes compared to the information provided by a single body fluid. The qualitative and quantitative methods presented here offers a variety of options for novel mapping of the metabolic properties of complex organisms, including humans.

Assessment of the exhalation kinetics of volatile cancer biomarkers based on their physicochemical properties

The current review provides an assessment of the exhalation kinetics of volatile organic compounds (VOCs) that have been linked with cancer. Towards this end, we evaluate various physicochemical properties, such as 'breath:air' and 'blood:fat' partition coefficients, of 112 VOCs that have been suggested over the past decade as potential markers of cancer. With these data, we show that the cancer VOC concentrations in the blood and in the fat span over 12 and 8 orders of magnitude, respectively, in order to provide a specific counterpart concentration in the exhaled breath (e.g., 1 ppb). This finding suggests that these 112 different compounds have different storage compartments in the body and that their exhalation kinetics depends on one or a combination of the following factors: (i) the VOC concentrations in different parts of the body; (ii) the VOC synthesis and metabolism rates; (iii) the partition coefficients between tissue(s), blood and air; and (iv) the VOCs' diffusion constants. Based on this analysis, we discuss how this knowledge allows modeling and simulating the behavior of a specific VOC under different sampling protocols (with and without exertion of effort). We end this review by a brief discussion on the potential role of these scenarios in screening and therapeutic monitoring of cancer.

Assessment, origin, and implementation of breath volatile cancer markers

A new non-invasive and potentially inexpensive frontier in the diagnosis of cancer relies on the detection of volatile organic compounds (VOCs) in exhaled breath samples. Breath can be sampled and analyzed in real-time, leading to fascinating and cost-effective clinical diagnostic procedures. Nevertheless, breath analysis is a very young field of research and faces challenges, mainly because the biochemical mechanisms behind the cancer-related VOCs are largely unknown. In this review, we present a list of 115 validated cancer-related VOCs published in the literature during the past decade, and classify them with respect to their "fat-to-blood" and "blood-to-air" partition coefficients. These partition coefficients provide an estimation of the relative concentrations of VOCs in alveolar breath, in blood and in the fat compartments of the human body. Additionally, we try to clarify controversial issues concerning possible experimental malpractice in the field, and propose ways to translate the basic science results as well as the mechanistic understanding to tools (sensors) that could serve as point-of-care diagnostics of cancer. We end this review with a conclusion and a future perspective.

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.

Geographical variation in the exhaled volatile organic compounds

Breath-gas analysis has demonstrated that concentration profiles of volatile organic compounds (VOCs) could be used for detecting a variety of diseases, among them gastric cancer (GC) and peptic ulcer disease (PUD). Here, we explore how geographical variation affects the disease-specific changes in the chemical composition of breath samples, as compared to control states (less severe gastric conditions). Alveolar exhaled breath samples from 260 patients were collected at two remotely different geographic locations (China and Latvia), following similar breath-collection protocols. Each cohort included 130 patients that were matched in terms of diagnosis (37 GC/32 PUD/61 controls), average age, gender ratio and smoking habits. Helicobacter Pylori infection, which is a major cause for GC and PUD, was found in part of the patients, as well as in part of the controls, at both locations. The breath samples were analyzed by gas chromatography/mass spectrometry, using the same equipment and protocol-of-experiment. We observed similar characteristic differences in the chemical composition of the breath samples between the study groups at the two locations, even though the exact composition of the breath samples differed. Both in China and Latvia, the GC patients and controls could be distinguished by differences in the average levels of 6-methyl-5-hepten-2-one; PUD patients were distinguished from controls by the levels of aromatic compounds and alcohols; GC and PUD patients could not be distinguished at either site. This pilot study indicates the limitations of chemical breath-gas analysis alone for identifying gastric diseases based on the concentration profiles of separate VOCs in international patient cohorts. We assume that these limitations would apply to other diseases as well. The presented data could potentially be useful for developing an alternative, universally applicable diagnostic method that relies on the detection of changes in the collective patterns of the disease-specific classes of exhaled VOCs.

A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions

Background:

Upper digestive endoscopy with biopsy and histopathological evaluation of the biopsy material is the standard method for diagnosing gastric cancer (GC). However, this procedure may not be widely available for screening in the developing world, whereas in developed countries endoscopy is frequently used without major clinical gain. There is a high demand for a simple and non-invasive test for selecting the individuals at increased risk that should undergo the endoscopic examination. Here, we studied the feasibility of a nanomaterial-based breath test for identifying GC among patients with gastric complaints.

Methods:

Alveolar exhaled breath samples from 130 patients with gastric complaints (37 GC/32 ulcers / 61 less severe conditions) that underwent endoscopy/biopsy were analyzed using nanomaterial-based sensors. Predictive models were built employing discriminant factor analysis (DFA) pattern recognition, and their stability against possible confounding factors (alcohol/tobacco consumption; Helicobacter pylori) was tested. Classification success was determined (i) using leave-one-out cross-validation and (ii) by randomly blinding 25% of the samples as a validation set. Complementary chemical analysis of the breath samples was performed using gas chromatography coupled with mass spectrometry.

Results:

Three DFA models were developed that achieved excellent discrimination between the subpopulations: (i) GC vs benign gastric conditions, among all the patients (89% sensitivity; 90% specificity); (ii) early stage GC (I and II) vs late stage (III and IV), among GC patients (89% sensitivity; 94% specificity); and (iii) ulcer vs less severe, among benign conditions (84% sensitivity; 87% specificity). The models were insensitive against the tested confounding factors. Chemical analysis found that five volatile organic compounds (2-propenenitrile, 2-butoxy-ethanol, furfural, 6-methyl-5-hepten-2-one and isoprene) were significantly elevated in patients with GC and/or peptic ulcer, as compared with less severe gastric conditions. The concentrations both in the room air and in the breath samples were in the single p.p.b._v range, except in the case of isoprene.

Conclusion:

The preliminary results of this pilot study could open a new and promising avenue to diagnose GC and distinguish it from other gastric diseases. It should be noted that the applied methods are complementary and the potential marker compounds identified by gas-chromatography/mass spectrometry are not necessarily responsible for the differences in the sensor responses. Although this pilot study does not allow drawing far-reaching conclusions, the encouraging preliminary results presented here have initiated a large multicentre clinical trial to confirm the observed patterns for GC and benign gastric conditions.

Volatile fingerprints of cancer specific genetic mutations

We report on a new concept for profiling genetic mutations of (lung) cancer cells, based on the detection of patterns of volatile organic compounds (VOCs) emitted from cell membranes, using an array of nanomaterial-based sensors. In this in-vitro pilot study we have derived a volatile fingerprint assay for representative genetic mutations in cancer cells that are known to be associated with targeted cancer therapy. Five VOCs were associated with the studied oncogenes, using complementary chemical analysis, and were discussed in terms of possible metabolic pathways. The reported approach could lead to the development of novel methods for guiding treatments, so that patients could benefit from safer, more timely and effective interventions that improve survival and quality of life while avoiding unnecessary invasive procedures. Studying clinical samples (tissue/blood/breath) will be required as next step in order to determine whether this cell-line study can be translated into a clinically useful tool.

FROM THE CLINICAL EDITOR

In this novel study, a new concept for profiling genetic mutations of (lung) cancer cells is described, based on the detection of patterns of volatile organic compounds emitted from cell membranes, using an array of nano-gold based sensors.

A nanomaterial-based breath test for short-term follow-up after lung tumor resection

In this case study, we demonstrate the feasibility of nanomaterial-based sensors for identifying the breath-print of early-stage lung cancer (LC) and for short-term follow-up after LC-resection. Breath samples were collected from a small patient cohort prior to and after lung resection. Gas-chromatography/mass-spectrometry showed that five volatile organic compounds were significantly reduced after LC surgery. A nanomaterial-based sensor-array distinguished between pre-surgery and post-surgery LC states, as well as between pre-surgery LC and benign states. In contrast, the same sensor-array could neither distinguish between pre-surgery and post-surgery benign states, nor between LC and benign states after surgery. This indicates that the observed pattern is associated with the presence of malignant lung tumors. The proof-of-concept presented here has initiated a large-scale clinical study for post-surgery follow-up of LC patients.

FROM THE CLINICAL EDITOR

Monitoring for tumor recurrence remains very challenging due to post-surgical and radiation therapy induced changes in target organs, which often renders standard radiological identification of recurrent malignancies inaccurate. In this paper a novel nanotechnology-based sensor array is used for identification of volatile organic compounds in exhaled air that enable identification of benign vs. malignant states.

Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors

Background:

Tumour growth is accompanied by gene and/or protein changes that may lead to peroxidation of the cell membrane species and, hence, to the emission of volatile organic compounds (VOCs). In this study, we investigated the ability of a nanosensor array to discriminate between breath VOCs that characterise healthy states and the most widespread cancer states in the developed world: lung, breast, colorectal, and prostate cancers.

Methods:

Exhaled alveolar breath was collected from 177 volunteers aged 20–75 years (patients with lung, colon, breast, and prostate cancers and healthy controls). Breath from cancerous subjects was collected before any treatment. The healthy population was healthy according to subjective patient's data. The breath of volunteers was examined by a tailor-made array of cross-reactive nanosensors based on organically functionalised gold nanoparticles and gas chromatography linked to the mass spectrometry technique (GC-MS).

Results:

The results showed that the nanosensor array could differentiate between ‘healthy’ and ‘cancerous’ breath, and, furthermore, between the breath of patients having different cancer types. Moreover, the nanosensor array could distinguish between the breath patterns of different cancers in the same statistical analysis, irrespective of age, gender, lifestyle, and other confounding factors. The GC-MS results showed that each cancer could have a unique pattern of VOCs, when compared with healthy states, but not when compared with other cancer types.

Conclusions:

The reported results could lead to the development of an inexpensive, easy-to-use, portable, non-invasive tool that overcomes many of the deficiencies associated with the currently available diagnostic methods for cancer.

Environmental monitoring of Vibrio cholerae using chironomids in India

Environmental Vibrio cholerae strains belonging to the non-O1/non-O139 serogroups are natural inhabitants of freshwater including estuarine environments. Recent findings indicated that chironomids (Diptera: Chironomidae), the most widely distributed insects in freshwater, serve as a natural reservoir of these bacteria. Here we study the role of chironomids, particularly exuviae as carriers and as a monitoring tool for the distribution of V. cholerae in the environment. During a survey conducted in India (June 2006), 326 V. cholerae non-O1/non-O139 isolates were isolated from chironomid egg masses, larvae and exuviae. In addition, a heat-stable enterotoxin (nag-st) positive strain was isolated from exuviae during the local cholera outbreak. We identified 62 different strains in a subset of 102 isolates by analysis of variable number of tandem repeats (VNTR), demonstrating a high variation of V. cholerae on hosting chironomids. Our results show that chironomids can both maintain and distribute this overwhelming diversity of environmental V. cholerae strains, including toxigenic ones. Exuviae proved to be an efficient tool for the monitoring of environmental V. cholerae, offering simple, direct and practical access for on-shore collection. Finally, finding toxigenic V. cholerae on chironomids in endemic areas, together with molecular typing, may potentially improve monitoring of cholera in the future.

Epidemiologic study of Vibrio vulnificus infections by using variable number tandem repeats

A 3-year environmental and clinical Vibrio vulnificus survey using simple-sequence repeats typing shows that V. vulnificus biotype 3 constitutes ≈21% of the bacterium population in tested aquaculture ponds as opposed to ≈86% of clinical cases. Simple-sequence repeats proved to be a useful epidemiologic tool, providing information on the environmental source of the pathogen.

Diagnosing lung cancer in exhaled breath using gold nanoparticles

Conventional diagnostic methods for lung cancer are unsuitable for widespread screening because they are expensive and occasionally miss tumours. Gas chromatography/mass spectrometry studies have shown that several volatile organic compounds, which normally appear at levels of 1-20 ppb in healthy human breath, are elevated to levels between 10 and 100 ppb in lung cancer patients. Here we show that an array of sensors based on gold nanoparticles can rapidly distinguish the breath of lung cancer patients from the breath of healthy individuals in an atmosphere of high humidity. In combination with solid-phase microextraction, gas chromatography/mass spectrometry was used to identify 42 volatile organic compounds that represent lung cancer biomarkers. Four of these were used to train and optimize the sensors, demonstrating good agreement between patient and simulated breath samples. Our results show that sensors based on gold nanoparticles could form the basis of an inexpensive and non-invasive diagnostic tool for lung cancer.

Vibrio vulnificus typing based on simple sequence repeats: insights into the biotype 3 group

Vibrio vulnificus is an opportunistic, highly invasive human pathogen with worldwide distribution. V. vulnificus strains are commonly divided into three biochemical groups (biotypes), most members of which are pathogenic. Simple sequence repeats (SSR) provide a source of high-level genomic polymorphism used in bacterial typing. Here, we describe the use of variations in mutable SSR loci for accurate and rapid genotyping of V. vulnificus. An in silico screen of the genomes of two V. vulnificus strains revealed thousands of SSR tracts. Twelve SSR with core motifs longer than 5 bp in a panel of 32 characterized and 56 other V. vulnificus isolates, including both clinical and environmental isolates from all three biotypes, were tested for polymorphism. All tested SSR were polymorphic, and diversity indices ranged from 0.17 to 0.90, allowing a high degree of discrimination among isolates (27 of 32 characterized isolates). Genetic analysis of the SSR data resulted in the clear distinction of isolates that belong to the highly virulent biotype 3 group. Despite the clonal nature of this new group, SSR analysis demonstrated high-level discriminatory power within the biotype 3 group, as opposed to other molecular methods that failed to differentiate these isolates. Thus, SSR are suitable for rapid typing and classification of V. vulnificus strains by high-throughput capillary electrophoresis methods. SSR (>/=5 bp) by their nature enable the identification of variations occurring on a small scale and, therefore, may provide new insights into the newly emerged biotype 3 group of V. vulnificus and may be used as an efficient tool in epidemiological studies.

Vibrio cholerae strain typing and phylogeny study based on simple sequence repeats

Vibrio cholerae is the etiological agent of cholera. Its natural reservoir is the aquatic environment. To date, practical typing of V. cholerae is mainly serological and requires about 200 antisera. Simple sequence repeats (SSR), also termed VNTR (for variable number of tandem repeats), provide a source of high genomic polymorphism used in bacterial typing. Here we describe an SSR-based typing method that combines the variation in highly mutable SSR loci, with that of shorter, relatively more stable mononucleotide repeat (MNR) loci, for accurate and rapid typing of V. cholerae. In silico screening of the V. cholerae genome revealed thousands of perfect SSR tracts with an average frequency of one SSR every 152 bp. A panel of 32 V. cholerae strains, representing both clinical and environmental isolates, was tested for polymorphism in SSR loci. Two strategies were applied to identify SSR variation: polymorphism of SSR tracts longer than 12 bp (L-SSR) assessed by capillary fragment-size analysis and MNR polymorphism assessed by sequencing. The nine L-SSR loci tested were all polymorphic, displaying 2 to 13 alleles per locus. Sequence analysis of eight MNR-containing loci (MNR-multilocus sequence typing [MLST]) provided information on both variations in the MNR tract itself, and single nucleotide polymorphism (SNP) in their flanking sequences. Phylogenetic analysis of the combined SSR data showed a clear discrimination between the clinical strains belonging to O1 and O139 serogroups, and the environmental isolates. Furthermore, discrimination between 27 strains of the 32 strains was achieved. SSR-based typing methods combining L-SSR and MNR-MLST were found to be efficient for V. cholerae typing.