Investigation of Exhaled Breath Samples from Patients with Alzheimer's Disease Using Gas Chromatography-Mass Spectrometry and an Exhaled Breath Sensor System

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

Rapid detection of depression by volatile organic compounds from exhalation

Depression is a pervasive and often undetected mental health condition, which poses significant challenges for early diagnosis due to its silent and subtle nature. To evaluate exhaled volatile organic compounds (VOCs) as non-invasive biomarkers for the detection of depression using a virtual surface acoustic wave sensors array (VSAW-SA). A total of 245 participants were recruited from the Hangzhou Community Health Service Center, including 38 individuals diagnosed with depression and 207 control subjects. Breath samples were collected from all participants and subjected to analysis using VSAW-SA. Univariate and multivariate analyses were employed to assess the relationship between VOCs and depression. The findings revealed that the responses of virtual sensor ID 14, 44, 59, and 176, which corresponded respectively to ethanol, trichloroethylene or isoleucine, octanoic acid or lysine, and an unidentified compound, were sensitive to depression. Taking into account potential confounders, these sensor responses were utilized to calculate a depression detection indicator. It has a sensitivity of 81.6% and a specificity of 81.6%, with an area under the curve of 0.870 (95% CI = 0.816-0.923). Conclusions: exhaled VOCs as non-invasive biomarkers of depression could be detected by a VSAW-SA. Large-scale cohort studies should be conducted to confirm the potential ability of the VSAW-SA to diagnose depression.

A detection model for cognitive dysfunction based on volatile organic compounds from a large Chinese community cohort

INTRODUCTION

We explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals.

METHODS

The cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured.

RESULTS

Distinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver-operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877).

DISCUSSION

Detection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.

The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.

The Versatility and Diagnostic Potential of VOC Profiling for Noninfectious Diseases

A variety of volatile organic compounds (VOCs) are produced and emitted by the human body every day. The identity and concentration of these VOCs reflect an individual's metabolic condition. Information regarding the production and origin of VOCs, however, has yet to be congruent among the scientific community. This review article focuses on the recent investigations of the source and detection of biological VOCs as a potential for noninvasive discrimination between healthy and diseased individuals. Analyzing the changes in the components of VOC profiles could provide information regarding the molecular mechanisms behind disease as well as presenting new approaches for personalized screening and diagnosis. VOC research has prioritized the study of cancer, resulting in many research articles and reviews being written on the topic. This review summarizes the information gained about VOC cancer studies over the past 10 years and looks at how this knowledge correlates with and can be expanded to new and upcoming fields of VOC research, including neurodegenerative and other noninfectious diseases. Recent advances in analytical techniques have allowed for the analysis of VOCs measured in breath, urine, blood, feces, and skin. New diagnostic approaches founded on sensor-based techniques allow for cheaper and quicker results, and we compare their diagnostic dependability with gas chromatography- and mass spectrometry-based techniques. The future of VOC analysis as a clinical practice and the challenges associated with this transition are also discussed and future research priorities are summarized.

Resistive-Based Gas Sensors Using Quantum Dots: A Review

Quantum dots (QDs) are used progressively in sensing areas because of their special electrical properties due to their extremely small size. This paper discusses the gas sensing features of QD-based resistive sensors. Different types of pristine, doped, composite, and noble metal decorated QDs are discussed. In particular, the review focus primarily on the sensing mechanisms suggested for these gas sensors. QDs show a high sensing performance at generally low temperatures owing to their extremely small sizes, making them promising materials for the realization of reliable and high-output gas-sensing devices.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Real-time pharmacokinetics via online analysis of exhaled breath

The advantages that on-line breath analysis has shown in different fields have already made it stand as an interesting tool for pharmacokinetic studies. This review summarizes recent progress in the field, diving into the different analytical methods and the different advantages and hurdles encountered. We conclude that there is a wealth of limitations in the application of this technique, and key aspects like standardization are still outstanding. Nevertheless, this is an experimental field that has not yet been fully explored; and the advantages it offers for animal welfare, decrease in the amount of drug needed in experimental studies, and complementary insights to current pharmacological studies, warrant further exploration. Further studies are needed to overcome current limitations and incorporate this technique into the toolbox of pharmacological studies, both at an industrial and academic level.

Plasmonic Sensing Studies of a Gas-Phase Cystic Fibrosis Marker in Moisture Laden Air

A plasmonic sensing platform was developed as a noninvasive method to monitor gas-phase biomarkers related to cystic fibrosis (CF). The nanohole array (NHA) sensing platform is based on localized surface plasmon resonance (LSPR) and offers a rapid data acquisition capability. Among the numerous gas-phase biomarkers that can be used to assess the lung health of CF patients, acetaldehyde was selected for this investigation. Previous research with diverse types of sensing platforms, with materials ranging from metal oxides to 2-D materials, detected gas-phase acetaldehyde with the lowest detection limit at the µmol/mol (parts-per-million (ppm)) level. In contrast, this work presents a plasmonic sensing platform that can approach the nmol/mol (parts-per-billion (ppb)) level, which covers the required concentration range needed to monitor the status of lung infection and find pulmonary exacerbations. During the experimental measurements made by a spectrometer and by a smartphone, the sensing examination was initially performed in a dry air background and then with high relative humidity (RH) as an interferent, which is relevant to exhaled breath. At a room temperature of 23.1 °C, the lowest detection limit for the investigated plasmonic sensing platform under dry air and 72% RH conditions are 250 nmol/mol (ppb) and 1000 nmol/mol (ppb), respectively.

A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome

This paper comprises an updated version of the 2014 review which reported 1846 volatile organic compounds (VOCs) identified from healthy humans. In total over 900 additional VOCs have been reported since the 2014 review and the VOCs from semen have been added. The numbers of VOCs found in breath and the other bodily fluids are: blood 379, breath 1488, faeces 443, milk 290, saliva 549, semen 196, skin 623 and urine 444. Compounds were assigned CAS registry numbers and named according to a common convention where possible. The compounds have been included in a single table with the source reference(s) for each VOC, an update on our 2014 paper. VOCs have also been grouped into tables according to their chemical class or functionality to permit easy comparison. Careful use of the database is needed, as a number of the identified VOCs only have level 2-putative assignment, and only a small fraction of the reported VOCs have been validated by standards. Some clear differences are observed, for instance, a lack of esters in urine with a high number in faeces and breath. However, the lack of compounds from matrices such a semen and milk compared to breath for example could be due to the techniques used or reflect the intensity of effort e.g. there are few publications on VOCs from milk and semen compared to a large number for breath. The large number of volatiles reported from skin is partly due to the methodologies used, e.g. by collecting skin sebum (with dissolved VOCs and semi VOCs) onto glass beads or cotton pads and then heating to a high temperature to desorb VOCs. All compounds have been included as reported (unless there was a clear discrepancy between name and chemical structure), but there may be some mistaken assignations arising from the original publications, particularly for isomers. It is the authors' intention that this work will not only be a useful database of VOCs listed in the literature but will stimulate further study of VOCs from healthy individuals; for example more work is required to confirm the identification of these VOCs adhering to the principles outlined in the metabolomics standards initiative. Establishing a list of volatiles emanating from healthy individuals and increased understanding of VOC metabolic pathways is an important step for differentiating between diseases using VOCs.

Induced volatolomics of pathologies

Volatolomics allows us to elucidate cell metabolic processes in real time. In particular, a volatile organic compound (VOC) excreted from our bodies may be specific for a certain disease, such that measuring this VOC may afford a simple, fast, accessible and safe diagnostic approach. Yet, finding the optimal endogenous volatile marker specific to a pathology is non-trivial because of interlaboratory disparities in sample preparation and analysis, as well as high interindividual variability. These limit the sensitivity and specificity of volatolomics and its applications in biological and clinical fields but have motivated the development of induced volatolomics. This approach aims to overcome issues by measuring VOCs that result not from an endogenous metabolite but, rather, from the pathogen-specific or metabolic-specific enzymatic metabolism of an exogenous biological or chemical probe. In this Review, we introduce volatile-compound-based probes and discuss how they can be exploited to detect and discriminate pathogenic infections, to assess organ function and to diagnose and monitor cancers in real time. We focus on cases in which labelled probes have informed us about metabolic processes and consider the potential and drawbacks of the probes for clinical trials. Beyond diagnostics, VOC-based probes may also be effective tools to explore biological processes more generally.

MASS SPECTROMETRY-BASED PERSONALIZED DRUG THERAPY

Personalized drug therapy aims to provide tailored treatment for individual patient. Mass spectrometry (MS) is revolutionarily involved in this area because MS is a rapid, customizable, cost-effective, and easy to be used high-throughput method with high sensitivity, specificity, and accuracy. It is driving the formation of a new field, MS-based personalized drug therapy, which currently mainly includes five subfields: therapeutic drug monitoring (TDM), pharmacogenomics (PGx), pharmacomicrobiomics, pharmacoepigenomics, and immunopeptidomics. Gas chromatography-MS (GC-MS) and liquid chromatography-MS (LC-MS) are considered as the gold standard for TDM, which can be used to optimize drug dosage. Matrix-assisted laser desorption ionization-time of flight-MS (MALDI-TOF-MS) significantly improves the capability of detecting biomacromolecule, and largely promotes the application of MS in PGx. It is becoming an indispensable tool for genotyping, which is used to discover and validate genetic biomarkers. In addition, MALDI-TOF-MS also plays important roles in identity of human microbiome whose diversity can explain interindividual differences of drug response. Pharmacoepigenetics is to study the role of epigenetic factors in individualized drug treatment. MS can be used to discover and validate pharmacoepigenetic markers (DNA methylation, histone modification, and noncoding RNA). For the emerging cancer immunotherapy, personalized cancer vaccine has effective immunotherapeutic activity in the clinic. MS-based immunopeptidomics can effectively discover and screen neoantigens. This article systematically reviewed MS-based personalized drug therapy in the above mentioned five subfields. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases

We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set.

Breath-based non-invasive diagnosis of Alzheimer's disease: a pilot study

Early detection of Alzheimer's disease (AD) will help researchers to better understand the disease and develop improved treatments. Recent developments have thus focused on identifying biomarkers for mild cognitive impairment due to AD (MCI) and AD during the preclinical phase. The aim of this pilot study is to determine whether exhaled volatile organic compounds (VOCs) can be used as a non-invasive method to distinguish controls from MCI, controls from AD and to determine whether there are differences between MCI and AD. The study used gas chromatography-ion mobility spectrometry (GC-IMS) techniques. Confounding factors, such as age, smoking habits, gender and alcohol consumption are investigated to demonstrate the efficacy of results. One hundred subjects were recruited including 50 controls, 25 AD and 25 MCI patients. The subject cohort was age- and gender-matched to minimise bias. Breath samples were analysed using a commercial GC-IMS instrument (G.A.S. BreathSpec, Dortmund, Germany). Data analysis indicates that the GC-IMS signal was consistently able to separate between diagnostic groups [AUC ± 95%, sensitivity, specificity], controls versus MCI: [0.77 (0.64-0.90), 0.68, 0.80], controls versus AD: [0.83 (0.72-0.94), 0.60, 0.96], and MCI versus AD: [0.70 (0.55-0.85), 0.60, 0.84]. VOC analysis indicates that six compounds play a crucial role in distinguishing between diagnostic groups. Analysis of possible confounding factors indicate that gender, age, smoking habits and alcohol consumption have insignificant influence on breath content. This pilot study confirms the utility of exhaled breath analysis to distinguish between AD, MCI and control subjects. Thus, GC-IMS offers great potential as a non-invasive, high-throughput, diagnostic technique for diagnosing and potentially monitoring AD in a clinical setting.

Can oral health and oral-derived biospecimens predict progression of dementia?

Growing evidence indicates that oral health and brain health are interconnected. Declining cognition and dementia coincide with lack of self‐preservation, including oral hygiene. The oral microbiota plays an important role in maintaining oral health. Emerging evidence suggests a link between oral dysbiosis and cognitive decline in patients with Alzheimer's disease. This review showcases the recent advances connecting oral health and cognitive function during aging and the potential utility of oral‐derived biospecimens to inform on brain health. Collectively, experimental findings indicate that the connection between oral health and cognition cannot be underestimated; moreover, oral biospecimens are abundant and readily obtainable without invasive procedures, which may help inform on cognitive health.

An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network applied for simultaneously detection of exhaled CO and N2O

A compact high-resolution spectroscopic sensor using a thermoelectrically (TE) cooled continuous-wave (CW) room temperature (RT) quantum cascade laser (QCL) was demonstrated for simultaneous measurements of exhaled carbon monoxide (CO) and nitrous oxide (N₂O). The sampling pressure was optimized to improve the sensitivity, the optimal pressure was determined to be 150 mbar based on an optical density analysis of simulated and measured absorption spectra. An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network was developed and proposed for real-time exhaled breath analysis in order to perform fast and high precision on-line measurements. The detection limits (1σ) of 1.14 ppb and 1.12 ppb were experimentally achieved for CO and N₂O detection, respectively. Typical concentrations of exhaled CO and N₂O from smokers and non-smokers were analyzed. The experimental results indicated that the state-of-the-art CW-QCL based sensor has a great potential for non-invasive, on-line identification and quantification of biomarkers in human breath.

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.