Propeller shaped triarylamine acid: An ultra-sensitive fluorescence probe for distinguishing propanol isomers and water sensing in organic solvents

The photophysical properties of conformationally flexible (TPA-C) and partially rigidified (Cz-C) triarylamine acids were explored in solid as well as solution state and correlated with the structure. TPA-C and Cz-C exhibited moderate solid-state fluorescence (Φf = 6.2 % (TPA-C) and 5.6 % (Cz-C)) and self-reversible mechanofluorochromism. TPA-C produced fluorescent polymorphs (TPA-C-1 and TPA-C-2) with tunable fluorescence. TPA-C-1 showed unusual carboxylic acid intermolecular interactions whereas TPA-C-2 and Cz-C showed usual carboxylic acid dimer. TPA-C exhibited strong solvent polarity dependent tunable fluorescence (Φf = 0.01 to 0.11 compared to quinine sulphate standard) but Cz-C was non-emissive in the solution state. The dual emissive TPA-C showed highly sensitive fluorescence changes in organic solvents (CH3CN, THF, DMF, EtOH) when trace amount of water was added. In CH3CN, TPA-C showed weak fluorescence at 474 nm and addition of water (1 %) exhibited significant blue shift (λmax = 416 nm). The fluorescence intensity was gradually decreased with blue shifting in DMF, THF and EtOH with water addition. Importantly, TPA-C showed drastically different fluorescence in n-propanol (n-PA) and iso-propanol (IPA). TPA-C in n-PA showed fluorescence at 408 nm that was clearly red shifted to 438 nm with 0.1 % addition of IPA. The limit of detection (LOD) of water in CH3CN, DMF, THF and EtOH by TPA-C revealed 0.02, 0.7, 0.08 and 0.77 %, respectively. The LOD of IPA sensing in n-PA is 0.05 % and indicated the very efficient sensing and distinguishing propanol isomers. Thus, simple triphenylamine acid showed excellent water sensing and propanol isomers discrimination that could be attributed to the twisted intramolecular charge transfer (TICT) formation.

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

Isopropanol sensor based on sprayed In2S3 thin film using linear discriminant analysis for real-time selectivity

Metal sulfides have been studied for their high performance as new sensitive materials for gas detection. These material innovations contribute significantly to the development of more sensitive, stable and specific conductivity sensors, opening the way to new applications in detecting gases at low concentrations. Hence, this work reports on the sensing performance of In2S3 for isopropanol detection. Numerous VOCs, such as ketones, alcohols, and aldehydes, serve as cancer biomarkers. Notably, isopropanol, as a biomarker, shows a substantial increase (20-1007 ppb) in lung cancer patients, suggesting its potential as an early diagnostic criterion. To explore this, we fabricated a thin film of In2S3 onto a platinum interdigitated silicon dioxide substrate by simple and low-cost spray pyrolysis technology. Structural and morphological analyses via XRD, MEB, AFM, and TEM were conducted on the prepared samples. Isopropanol vapor response was assessed within a controlled temperature range of 250 °C-450 °C. The In2S3-based sensor demonstrated notable sensitivity (0.034 ppm-1), maintained stability over three weeks, and reliably detected isopropanol activation. In2S3 emerges as a promising candidate for detecting isopropanol, with a limit of detection (LOD) of 162 ppb. In this work, seven VOCs, including isopropanol, ethanol, methanol, butan-1-ol, formaldehyde, toluene, and acetone, were evaluated. Cross-responses among these VOCs were observed, indicating a lack of assured sensor selectivity. However, isopropanol recognition was achieved by employing linear discriminant analysis (LDA) on pertinent features derived from transient current change measurements. As a result, the sensor's sensitivity enables the deduction of the isopropanol concentration.

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

BACKGROUND

The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review.

AIM OF REVIEW

This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma, pancreatic cystic neoplasm, and patients without pancreatic lesions

BACKGROUND

Volatile organic compounds (VOCs) are a promising potential biomarker that may be able to identify the presence of cancers.

AIM

To identify exhaled breath VOCs that distinguish pancreatic ductal adenocarcinoma (PDAC) from intraductal papillary mucinous neoplasm (IPMN) and healthy volunteers.

METHODS

We collected exhaled breath from histologically proven PDAC patients, radiological diagnosis IPMN, and healthy volunteers using the ReCIVA® device between 10/2021-11/2022. VOCs were identified by thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry and compared between groups.

RESULTS

A total of 156 participants (44% male, mean age 62.6 ± 10.6) were enrolled (54 PDAC, 42 IPMN, and 60 controls). Among the nine VOCs identified, two VOCs that showed differences between groups were dimethyl sulfide [0.73 vs 0.74 vs 0.94 arbitrary units (AU), respectively; P = 0.008] and acetone dimers (3.95 vs 4.49 vs 5.19 AU, respectively; P < 0.001). After adjusting for the imbalance parameters, PDAC showed higher dimethyl sulfide levels than the control and IPMN groups, with adjusted odds ratio (aOR) of 6.98 (95%CI: 1.15-42.17) and 4.56 (1.03-20.20), respectively (P < 0.05 both). Acetone dimer levels were also higher in PDAC compared to controls and IPMN (aOR: 5.12 (1.80-14.57) and aOR: 3.35 (1.47-7.63), respectively (P < 0.05 both). Acetone dimer, but not dimethyl sulfide, performed better than CA19-9 in PDAC diagnosis (AUROC 0.910 vs 0.796). The AUROC of acetone dimer increased to 0.936 when combined with CA19-9, which was better than CA19-9 alone (P < 0.05).

CONCLUSION

Dimethyl sulfide and acetone dimer are VOCs that potentially distinguish PDAC from IPMN and healthy participants. Additional prospective studies are required to validate these findings.

Real-Time Monitoring of Air Pollution Health Impacts Using Breath-Borne Gaseous Biomarkers from Rats

Offline techniques are adopted for studying air pollution health impacts, thus failing to provide in situ observations. Here, we have demonstrated their real-time monitoring by online analyzing an array of gaseous biomarkers from rats' exhaled breath using an integrated exhaled breath array sensor (IEBAS) developed. The biomarkers include total volatile organic compounds (TVOC), CO2, CO, NO, H2S, H2O2, O2, and NH3. Specific breath-borne VOCs were also analyzed by a gas chromatography-ion mobility spectrometer (GC-IMS). After real-life ambient air pollution exposures (2 h), the pollution levels of PM2.5 and O3 were both found to significantly affect the relative levels of multiple gaseous biomarkers in rats' breath. Eleven biomarkers, especially NO, H2S, and 1-propanol, were detected as significantly correlated with PM2.5 concentration, while heptanal was shown to be significantly correlated with O3. Likewise, significant changes were also detected in multiple breath-borne biomarkers from rats under lab-controlled O3 exposures with levels of 150, 300, and 1000 μg/m3 (2 h), compared to synthetic air exposure. Importantly, heptanal was experimentally confirmed as a reliable biomarker for O3 exposure, with a notable dose-response relationship. In contrast, conventional biomarkers of inflammation and oxidative stress in rat sera exhibited insignificant differences after the 2 h exposures. The results imply that breath-borne gaseous biomarkers can serve as an early and sensitive indicator for ambient pollutant exposure. This work pioneered a new research paradigm for online monitoring of air pollution health impacts while obtaining important candidate biomarker information for PM2.5 and O3 exposures.

Breath biomarkers in Non-Carcinogenic diseases

The analysis of volatile organic compounds (VOCs) from human matrices like breath, perspiration, and urine has received increasing attention from academic and medical researchers worldwide. These biological-borne VOCs molecules have characteristics that can be directly related to physiologic and pathophysiologic metabolic processes. In this work, gathers a total of 292 analytes that have been identified as potential biomarkers for the diagnosis of various non-carcinogenic diseases. Herein we review the advances in VOCs with a focus on breath biomarkers and their potential role as minimally invasive tools to improve diagnosis prognosis and therapeutic monitoring.

Acetone Detection and Classification as Biomarker of Diabetes Mellitus Using a Quartz Crystal Microbalance Gas Sensor Array

A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing films, and deposited by the ultrasonic atomization method. The objective of this research was to propose a non-invasive technique for acetone biomarker detection, which is associated with diabetes mellitus disease. The gas sensor array was exposed to methanol, ethanol, isopropanol, and acetone biomarkers in four different concentrations, corresponding to 1, 5, 10, and 15 µL, at temperature of 22 °C and relative humidity of 20%. These samples were used because human breath contains them and they are used for disease detection. Moreover, the gas sensor responses were analyzed using principal component analysis and discriminant analysis, achieving the classification of the acetone biomarker with a 100% membership percentage when its concentration varies from 327 to 4908 ppm, and its identification from methanol, ethanol, and isopropanol.

The State of the Art on Graphene-Based Sensors for Human Health Monitoring through Breath Biomarkers

The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of "Graphene sensors" and "Biomarkers" in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed.

Smelling the Disease: Diagnostic Potential of Breath Analysis

Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.

VOCs from Exhaled Breath for the Diagnosis of Hepatocellular Carcinoma

BACKGROUND

Volatile organic compound (VOC) profiles as biomarkers for hepatocellular carcinoma (HCC) are understudied. We aimed to identify VOCs from the exhaled breath for HCC diagnosis and compare the performance of VOCs to alpha-fetoprotein (AFP). The performance of VOCs for predicting treatment response and the association between VOCs level and survival of HCC patients were also determined.

METHODS

VOCs from 124 HCC patients and 219 controls were identified using the XGBoost algorithm. ROC analysis was used to determine VOCs performance in differentiating HCC patients from controls and in discriminating treatment responders from non-responders. The association between VOCs and the survival of HCC patients was analyzed using Cox proportional hazard analysis.

RESULTS

The combination of 9 VOCs yielded 70.0% sensitivity, 88.6% specificity, and 75.0% accuracy for HCC diagnosis. When differentiating early HCC from cirrhotic patients, acetone dimer had a significantly higher AUC than AFP, i.e., 0.775 vs. 0.714, respectively, p = 0.001. Acetone dimer classified HCC patients into treatment responders and non-responders, with 95.7% sensitivity, 73.3% specificity, and 86.8% accuracy. Isopropyl alcohol was independently associated with the survival of HCC patients, with an adjusted hazard ratio of 7.23 (95%CI: 1.36-38.54), p = 0.020.

CONCLUSIONS

Analysis of VOCs is a feasible noninvasive test for diagnosing and monitoring HCC treatment response.

Fast and automated biomarker detection in breath samples with machine learning

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.

Biomarkers and Detection Platforms for Human Health and Performance Monitoring: A Review

Human health and performance monitoring (HHPM) is imperative to provide information necessary for protecting, sustaining, evaluating, and improving personnel in various occupational sectors, such as industry, academy, sports, recreation, and military. While various commercially wearable sensors are on the market with their capability of "quantitative assessments" on human health, physical, and psychological states, their sensing is mostly based on physical traits, and thus lacks precision in HHPM. Minimally or noninvasive biomarkers detectable from the human body, such as body fluid (e.g., sweat, tear, urine, and interstitial fluid), exhaled breath, and skin surface, can provide abundant additional information to the HHPM. Detecting these biomarkers with novel or existing sensor technologies is emerging as critical human monitoring research. This review provides a broad perspective on the state of the art biosensor technologies for HHPM, including the list of biomarkers and their physiochemical/physical characteristics, fundamental sensing principles, and high-performance sensing transducers. Further, this paper expands to the additional scope on the key technical challenges in applying the current HHPM system to the real field.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

Lung but not brain cancer cell malignancy inhibited by commonly used anesthetic propofol during surgery: Implication of reducing cancer recurrence risk

Introduction

Intravenous anesthesia with propofol was reported to improve cancer surgical outcomes when compared with inhalational anesthesia. However, the underlying molecular mechanisms largely remain unknown.

Objectives

The anti-tumor effects of propofol and the possible underlying mechanism including altered metabolic and signaling pathways were studied in the current study.

Methods

The cell viability, proliferation, migration, and invasion of cancer cells were analyzed with CCK-8, Ki-67 staining, wound healing, and Transwell assay, respectively. The protein changes were analyzed with Western blot and immunofluorescent staining. The metabolomics alteration was studied with 1H-NMR spectroscopy. The gene expression regulations were analyzed with PCR gene array and qRT-PCR experiments.

Results

In this study, we found that propofol reduced cell viability and inhibited cell proliferation, migration and invasion of lung cancer cells, but not neuroglioma cells. In lung cancer cells, propofol downregulated glucose transporter 1 (GLUT1), mitochondrial pyruvate carrier 1 (MPC1), p-Akt, p-Erk1/2, and hypoxia- inducible factor 1 alpha (HIF-1 α ) expressions and upregulated pigment epithelium-derived factor (PEDF) expression. Propofol increased intracellular glutamate and glycine but decreased acetate and formate whilst increased glucose, lactate, glutamine, succinate, pyruvate, arginine, valine, isoleucine, and leucine and glycerol, and decreased acetate, ethanol, isopropanol in the culture media of lung cancer cells. Furthermore, VEGFA, CTBP1, CST7, CTSK, CXCL12, and CXCR4 gene expressions were downregulated, while NR4A3, RB1, NME1, MTSS1, NME4, SYK, APC, and FAT1 were upregulated following the propofol treatment. Consistent with the phenotypical changes, these molecular and metabolic changes were not found in the neuroglioma cells.

Conclusion

Our findings indicated anti-tumor effects of propofol on the lung cancer but not brain cancer, through the regulation of tumor metastasis-related genes, multi-cellular signaling and cellular metabolism.

Exhaled breath biomarker sensing

This goal of this minireview is to introduce the reader to the area of research concerned with exhaled breath analysis for the purpose of detecting abnormal levels of physiologically-relevant chemical markers reflective of respiratory diseases. Two main two groups of sensing methods are reviewed: mass spectrometry and (bio)sensors. The discussion focuses on biosensor applications for EB and EBC analyses, which are presented in detail. The review finishes with conclusions and future perspectives, including recommendations for future near-term and long-term development of EBC biomarker sensing.

Accuracy of breath test for diabetes mellitus diagnosis: a systematic review and meta-analysis

The review aimed to investigate the accuracy of breath tests in the diagnosis of diabetes mellitus, identify exhaled volatile organic compounds with the most evidence as potential biomarkers, and summarize prospects and challenges in diabetic breath tests. Databases including Medline, PubMed, EMBASE, Cochrane Library and Science Citation Index Expanded were searched. Human studies describing diabetic breath analysis with more than 10 subjects as controls and patients were included. Population demographics, breath test conditions, biomarkers, analytical techniques and diagnostic accuracy were extracted. Quality assessment was performed with the Standards for Reporting Diagnostic Accuracy and a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). Forty-four research with 2699 patients with diabetes were included for qualitative data analysis and 14 eligible studies were used for meta-analysis. Pooled analysis of type 2 diabetes breath test exhibited sensitivity of 91.8% (95% CI 83.6% to 96.1%), specificity of 92.1% (95% CI 88.4% to 94.7%) and area under the curve (AUC) of 0.96 (95% CI 0.94 to 0.97). Isotopic carbon dioxide (CO₂) showed the best diagnostic accuracy with pooled sensitivity of 0.949 (95% CI 0.870 to 0.981), specificity of 0.946 (95% CI 0.891 to 0.975) and AUC of 0.98 (95% CI 0.97 to 0.99). As the most widely reported biomarker, acetone showed moderate diagnostic accuracy with pooled sensitivity of 0.638 (95% CI 0.511 to 0.748), specificity of 0.801 (95% CI 0.691 to 0.878) and AUC of 0.79 (95% CI 0.75 to 0.82). Our results indicate that breath test is a promising approach with acceptable diagnostic accuracy for diabetes mellitus and isotopic CO₂ is the optimal breath biomarker. Even so, further validation and standardization in subject control, breath sampling and analysis are still required.

Ultra-Sensitive Isopropanol Biochemical Gas Sensor (Bio-Sniffer) for Monitoring of Human Volatiles

Our groups have previously developed a biochemical gas sensor to measure isopropanol (IPA) in exhaled air and have applied it for breath IPA investigation in healthy subjects and diabetes patients. In this study, the original bio-sniffer was modified with a series of components that improved the limit of detection (LOD). First, the modified IPA bio-sniffer used a C8855-type photomultiplier tube (PMT) that performed well in the photon sensitivity at the peak wavelength of nicotinamide adenine dinucleotide (NADH) fluorescence. Second, the multi-core bifurcated optical fiber, which incorporated 36 fibers to replace the previous dual-core type, enhanced the fluorescence collection. Third, the optical fiber probe was reinforced for greater width, and the flow-cell was redesigned to increase the area of the enzyme-immobilized membrane in contact with the air sample. These modifications lowered the detection limit to 0.5 ppb, a significant increase over the previous 1.0 ppb. Moreover, the modified bio-sniffer successfully analyzed the IPA concentration in exhaled air from a volunteer, which confirmed its capability for real-world sample detection. The modified bio-sniffer is more applicable to breath measurement and the detection of other extremely-low-concentration samples.

Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal-Wallis and Mann-Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001-0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve-AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient's physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

Current state of bioanalytical chromatography in clinical analysis

Chromatographic methods have become popular in clinical analysis in both routine and research laboratories.

Biochemical Gas Sensors (Biosniffers) Using Forward and Reverse Reactions of Secondary Alcohol Dehydrogenase for Breath Isopropanol and Acetone as Potential Volatile Biomarkers of Diabetes Mellitus

This study describes two biosniffers to determine breath acetone and isopropanol (IPA) levels and applies them for breath measurement in healthy subjects and diabetic patients. Secondary alcohol dehydrogenase (S-ADH) can reduce acetone and oxidize nicotinamide adenine dinucleotide (NADH to NAD⁺) in a weak acid environment. NADH can be excited by 340 nm excitation lights and subsequently emit 490 nm fluorescence. Therefore, acetone can be measured by the decrease in NADH fluorescence intensity. S-ADH can also oxidize IPA and reduce NAD⁺ to NADH when it is in an alkaline environment. Thus, IPA can be detected by the increase of fluorescence. The developed biosniffers show rapid response, high sensitivity and high selectivity. The breath acetone and IPA analysis in healthy subjects shows that the mean values were 750.0 ± 434.4 ppb and 15.4 ± 11.3 ppb. Both acetone and IPA did not show a statistical difference among different genders and ages. The breath acetone analysis for diabetic patients shows a mean value of 1207.7 ± 689.5 ppb, which was higher than that of healthy subjects (p < 1 × 10^-6). In particularly, type-1 diabetic (T1D) patients exhaled a much higher concentration of acetone than type-2 diabetic (T2D) patients (p < 0.01). The breath IPA also had a higher concentration in diabetic patients (23.1 ± 20.1 ppb, p < 0.01), but only T2D patients presented a statistical difference (23.9 ± 21.3 ppb, p < 0.01). These findings are worthwhile in the study of breath biomarkers for diabetes mellitus diagnosis. Additionally, the developed biosniffers provide a new technique for volatolomics research.