Exhaled biomarkers in lung cancer

Exhaled volatolomics profiling facilitates personalized screening for gastric cancer

Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87-0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89-0.94, 95%CI) in discovery cohort and 0.88 (0.83-0.91, 95%CI) accuracy with an AUC of 0.91 (0.87-0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.

Prediction of response to neoadjuvant chemo-immunotherapy in patients with esophageal squamous cell carcinoma by a rapid breath test

BACKGROUND

Neoadjuvant chemo-immunotherapy combination has shown remarkable advances in the management of esophageal squamous cell carcinoma (ESCC). However, the identification of a reliable biomarker for predicting the response to this chemo-immunotherapy regimen remains elusive. While computed tomography (CT) is widely utilized for response evaluation, its inherent limitations in terms of accuracy are well recognized. Therefore, in this study, we present a novel technique to predict the response of ESCC patients before receiving chemo-immunotherapy by testing volatile organic compounds (VOCs) in exhaled breath.

METHODS

This study employed a prospective-specimen-collection, retrospective-blinded-evaluation design. Patients' baseline breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). Subsequently, patients were categorized as responders or non-responders based on the evaluation of therapeutic response using pathology (for patients who underwent surgery) or CT images (for patients who did not receive surgery).

RESULTS

A total of 133 patients were included in this study, with 91 responders who achieved either a complete response (CR) or a partial response (PR), and 42 non-responders who had stable disease (SD) or progressive disease (PD). Among 83 participants who underwent both evaluations with CT and pathology, the paired t-test revealed significant differences between the two methods (p < 0.05). For the breath test prediction model using breath test data from all participants, the validation set demonstrated mean area under the curve (AUC) of 0.86 ± 0.06. For 83 patients with pathological reports, the breath test achieved mean AUC of 0.845 ± 0.123.

CONCLUSIONS

Since CT has inherent weakness in hollow organ assessment and no other ideal biomarker has been found, our study provided a noninvasive, feasible, and inexpensive tool that could precisely predict ESCC patients' response to neoadjuvant chemo-immunotherapy combination using breath test based on HPPI-TOFMS.

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

Towards detection of cancer biomarkers in human exhaled air by transfer-learning-powered analysis of odor-evoked calcium activity in rat olfactory bulb

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

Toward the development of sensors for lung cancer: The adsorption of 1-propanol on hydrophobic zeolites

A healthy breath is mainly composed of water, carbon dioxide, molecular nitrogen, and oxygen and it contains many species, in small quantities, which are related to the ambient atmosphere and the metabolism. The breath of a person affected by lung cancer presents a concentration of 1-propanol higher than usual. In this context, the development of specific sensors to detect 1-propanol from breath is of high interest. The amount of propanol usually detected on the breath is of few ppb; this small quantity is a handicap for a reliable diagnostic. This limitation can be overcome if the sensor is equipped with a pre-concentrator. Our studies aim to provide an efficient material playing this role. This will contribute to the development of reliable and easy to use lung cancer detectors. For this, we investigate the properties of a few hydrophobic porous materials (chabazite, silicalite-1, and dealuminated faujasite). Hydrophobic structures are used to avoid saturation of materials by the water present in the exhaled breath. Our experimental and simulation results suggest that silicalite -1 (MFI) is the most suitable structure to be used as a pre-concentrator.

Detection technologies of volatile organic compounds in the breath for cancer diagnoses

Although there are new approaches in both cancer treatment and diagnosis, overall mortality is a major concern. New technologies have attempted to look at breath volatile organic compounds (VOCs) detection to diagnose cancer. Gas Chromatography and Mass Spectrometry (GC - MS) have remained the gold standard of VOC analysis for decades, but it has limitations in differentiating VOCs between cancer subtypes. To increase efficacy and accuracy, new methods to analyze these breath VOCs have been introduced, such as Solid Phase Microextraction/Gas Chromatography-Mass Spectrometry (SPME/GC-MS), Selected Ion Flow Tube - Mass Spectrometry (SIFT-MS), Proton Transfer Reaction - Mass Spectrometry (PRT-MS), Ion Mobility Spectrometry (IMS), and Colorimetric Sensors. This article highlights new technologies that have been studied and applied in the detection and quantification of breath VOCs for possible cancer diagnoses.

Smart Sensors and Microtechnologies in the Precision Medicine Approach against Lung Cancer

BACKGROUND AND RATIONALE

The therapeutic interventions against lung cancer are currently based on a fully personalized approach to the disease with considerable improvement of patients' outcome. Alongside continuous scientific progresses and research investments, massive technologic efforts, innovative challenges, and consolidated achievements together with research investments are at the bases of the engineering and manufacturing revolution that allows a significant gain in clinical setting.

AIM AND METHODS

The scope of this review is thus to focus, rather than on the biologic traits, on the analysis of the precision sensors and novel generation materials, as semiconductors, which are below the clinical development of personalized diagnosis and treatment. In this perspective, a careful revision and analysis of the state of the art of the literature and experimental knowledge is presented.

RESULTS

Novel materials are being used in the development of personalized diagnosis and treatment for lung cancer. Among them, semiconductors are used to analyze volatile cancer compounds and allow early disease diagnosis. Moreover, they can be used to generate MEMS which have found an application in advanced imaging techniques as well as in drug delivery devices.

CONCLUSIONS

Overall, these issues represent critical issues only partially known and generally underestimated by the clinical community. These novel micro-technology-based biosensing devices, based on the use of molecules at atomic concentrations, are crucial for clinical innovation since they have allowed the recent significant advances in cancer biology deciphering as well as in disease detection and therapy. There is an urgent need to create a stronger dialogue between technologists, basic researchers, and clinicians to address all scientific and manufacturing efforts towards a real improvement in patients' outcome. Here, great attention is focused on their application against lung cancer, from their exploitations in translational research to their application in diagnosis and treatment development, to ensure early diagnosis and better clinical outcomes.

Breath Testing in the Surgical Setting: Applications, Challenges, and Future Perspectives

BACKGROUND

The potential for exhaled breath to be a valuable diagnostic tool is often overlooked as it can be difficult to imagine how a barely visible sample of breath could hold such a rich source of information about the state of our health. However, technological advances over the last 50 years have enabled us to detect volatile organic compounds (VOCs) present in exhaled breath, and this provides the key to understanding the wealth of information contained within these readily available samples.

SUMMARY

VOCs are produced as a by-product of metabolism; hence, changes in the underlying physiological processes will be reflected in the exact composition of VOCs in exhaled breath. It has been shown that characteristic changes occur in the breath VOC profile associated with certain diseases including cancer, which may enable the non-invasive detection of cancer at primary care level for patients with vague symptoms. The use of breath testing as a diagnostic tool has many advantages. It is non-invasive and quick, and the test is widely accepted by patients and clinicians. However, breath samples provide a snapshot of the VOCs present in a particular patient at a given point in time, so this can be heavily influenced by external factors such as diet, smoking, and the environment. These must all be accounted for when attempting to draw conclusions about disease status. This review focuses on the current applications for breath testing in the field of surgery, as well as discussing the challenges encountered with developing a breath test in a clinical environment. The future of breath testing in the surgical setting is also discussed, including the translation of breath research into clinical practice.

KEY MESSAGES

Analysis of VOCs in exhaled breath can identify the presence of underlying disease including cancer as well as other infectious or inflammatory conditions. Despite the patient factors, environmental factors, storage, and transport considerations that must be accounted for, breath testing demonstrates ideal characteristics for a triage test, being non-invasive, simple, and universally acceptable to patients and clinicians. Many novel biomarkers and diagnostic tests fail to translate into clinical practice because their potential clinical application does not align with the requirements and unmet needs of the healthcare sector. Non-invasive breath testing, however, has the great potential to revolutionise the early detection of diseases, such as cancer, in the surgical setting for patients with vague symptoms.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

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.

GC-MS analysis of exhaled gas for fine detection of inflammatory diseases

Exhaled gas analysis is a non-invasive test ideal for continuous monitoring of biological metabolic information. We analyzed the exhaled gas of patients with inflammatory diseases for trace gas components that could serve as biomarkers that enable early detection of inflammatory diseases and assessment of treatment efficacy. Furthermore, we examined the clinical potential of this method. We enrolled 34 patients with inflammatory disease and 69 healthy participants. Volatile components from exhaled gas were collected and analyzed by a gas chromatography-mass spectrometry system, and the data were examined for gender, age, inflammatory markers, and changes in markers before and after treatment. The data were tested for statistical significance through discriminant analysis by Volcano plot, Analysis of variance test, principal component analysis, and cluster analysis comparing healthy and patient groups. There were no significant differences in the trace components of exhaled gas by gender or age. However, we found differences in some components of the exhaled gas between healthy and untreated patients. In addition, after treatment, gas patterns including the patient-specific components changed to a state closer to the inflammation-free status. We identified trace components in the exhaled gas of patients with inflammatory diseases and found that some of these regressed after treatment.

Sniffer Dogs Diagnose Lung Cancer by Recognition of Exhaled Gases: Using Breathing Target Samples to Train Dogs Has a Higher Diagnostic Rate Than Using Lung Cancer Tissue Samples or Urine Samples

INTRODUCTION

Sniffer dogs can diagnose lung cancer. However, the diagnostic yields of different samples and training methods for lung cancer remain undetermined.

OBJECTIVE

Six dogs were trained in three stages with the aim of improving the diagnostic yield of lung cancer by comparing training methods and specimens.

METHODS

The pathological tissues of 53 lung cancer patients and 6 non-lung cancer patients in the Department of Thoracic Surgery of Kaohsiung Chang Gung Hospital were collected, and the exhaled breath samples and urine samples were collected. Urine and exhaled breath samples were also collected from 20 healthy individuals. The specimens were sent to the Veterinary Department of Pingtung University of Science and Technology.

RESULTS

The dogs had a very low response rate to urine target samples in the first and second stages of training. The experimental results at the second stage of training found that after lung cancer tissue training, dogs were less likely to recognize lung cancer and healthy controls than through breath target training: the response rate to exhaled breathing target samples was about 8-55%; for urine target samples, it was only about 5-30%. When using exhaled air samples for training, the diagnosis rate of these dogs in lung cancer patients was 71.3% to 97.6% (mean 83.9%), while the false positive rate of lung cancer in the healthy group was 0.5% to 27.6% (mean 7.6%). Compared with using breathing target samples for training, the diagnosis rate of dogs trained with lung cancer tissue lung cancer was significantly lower (p < 0.05). The sensitivity and specificity of lung cancer tissue training (50.4% and 50.1%) were lower than the exhaled breath target training (91.7% and 85.1%). There is no difference in lung cancer diagnostic rate by sniff dogs among lung cancer histological types, location, and staging.

CONCLUSION

Training dogs using breathing target samples to train dogs then to recognize exhaled samples had a higher diagnostic rate than training using lung cancer tissue samples or urine samples. Dogs had a very low response rate to urine samples in our study. Six canines were trained on lung cancer tissues and breathing target samples of lung cancer patients, then the diagnostic rate of the recognition of exhaled breath of lung cancer and non-lung cancer patients were compared. When using exhaled air samples for training, the diagnosis rate of these dogs in lung cancer patients was 71.3% to 97.6% (mean 83.9%), while the false positive rate of lung cancer in the healthy group was 0.5% to 27.6% (mean 7.6%). There was a significant difference in the average diagnosis rate of individual dog and overall dogs between the lung cancer group and the healthy group (p < 0.05). When using lung cancer tissue samples for training, lung cancer diagnosis rate of these dogs among lung cancer patients was only 15.5% to 40.9% (mean 27.7%). Compared with using breathing target samples for training, the diagnosis rate of dogs trained with lung cancer tissue lung cancer was significantly lower (p < 0.05). The sensitivity and specificity of lung cancer tissue training (50.4% and 50.1%) were lower than the exhaled breath target training (91.7% and 85.1%). The diagnostic rate of lung cancer by sniffer dogs has nothing to do with the current stage of lung cancer, pathologic type, and the location of tumor mass. Even in stage IA lung cancer, well-trained dogs can have a diagnostic rate of 100%. Using sniffer dogs to screen early lung cancer may have good clinical and economic benefits.

LC-MS/MS Based Volatile Organic Compound Biomarkers Analysis for Early Detection of Lung Cancer

(1) Background: lung cancer is the world's deadliest cancer, but early diagnosis helps to improve the cure rate and thus reduce the mortality rate. Annual low-dose computed tomography (LD-CT) screening is an efficient lung cancer-screening program for a high-risk population. However, LD-CT has often been characterized by a higher degree of false-positive results. To meet these challenges, a volatolomic approach, in particular, the breath volatile organic compounds (VOCs) fingerprint analysis, has recently received increased attention for its application in early lung cancer screening thanks to its convenience, non-invasiveness, and being well tolerated by patients. (2) Methods: a LC-MS/MS-based volatolomics analysis was carried out according to P/N 5046800 standard based breath analysis of VOC as novel cancer biomarkers for distinguishing early-stage lung cancer from the healthy control group. The discriminatory accuracy of identified VOCs was assessed using subject work characterization and a random forest risk prediction model. (3) Results: the proposed technique has good performance compared with existing approaches, the differences between the exhaled VOCs of the early lung cancer patients before operation, three to seven days after the operation, as well as four to six weeks after operation under fasting and 1 h after the meal were compared with the healthy controls. The results showed that only 1 h after a meal, the concentration of seven VOCs, including 3-hydroxy-2-butanone (TG-4), glycolaldehyde (TG-7), 2-pentanone (TG-8), acrolein (TG-11), nonaldehyde (TG-19), decanal (TG-20), and crotonaldehyde (TG-22), differ significantly between lung cancer patients and control, with the invasive adenocarcinoma of the lung (IAC) having the most significant difference. (4) Conclusions: this novel, non-invasive approach can improve the detection rate of early lung cancer, and LC-MS/MS-based breath analysis could be a promising method for clinical application.

Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics

Cancers, chronic diseases and respiratory infections are major causes of mortality and present diagnostic and therapeutic challenges for health care. There is an unmet medical need for non-invasive, easy-to-use biomarkers for the early diagnosis, phenotyping, predicting and monitoring of the therapeutic responses of these disorders. Exhaled breath sampling is an attractive choice that has gained attention in recent years. Exhaled nitric oxide measurement used as a predictive biomarker of the response to anti-eosinophil therapy in severe asthma has paved the way for other exhaled breath biomarkers. Advances in laser and nanosensor technologies and spectrometry together with widespread use of algorithms and artificial intelligence have facilitated research on volatile organic compounds and artificial olfaction systems to develop new exhaled biomarkers. We aim to provide an overview of the recent advances in and challenges of exhaled biomarker measurements with an emphasis on the applicability of their measurement as a non-invasive, point-of-care diagnostic and monitoring tool.

Potential Role of Oxidative Stress in the Production of Volatile Organic Compounds in Obesity

Obesity is associated with numerous health issues such as sleep disorders, asthma, hepatic dysfunction, cancer, renal dysfunction, diabetes, cardiovascular complications, and infertility. Previous research has shown that the distribution of excess body fat, rather than excess body weight, determines obesity-related risk factors. It is widely accepted that abdominal fat is a serious risk factor for illnesses associated with obesity and the accumulation of visceral fat promotes the release of pro-oxidants, pro-inflammatory, and reactive oxygen species (ROS). The metabolic process in the human body produces several volatile organic compounds (VOCs) via urine, saliva, breath, blood, skin secretions, milk, and feces. Several studies have shown that VOCs are released by the interaction of ROS with underlying cellular components leading to increased protein oxidation, lipid peroxidation, or DNA damage. These VOCs released via oxidative stress in obese individuals may serves as a biomarker for obesity-related metabolic alterations and disease. In this review, we focus on the relationship between oxidative stress and VOCs in obesity.

Mapping the landscape of lung cancer breath analysis: A scoping review (ELCABA)

Lung cancer is the leading cause of cancer death worldwide due to its late-stage detection. Lung cancer screening, including low-dose computed tomography (low-dose CT), provides an initial clinical solution. Nevertheless, further innovations and refinements would help to alleviate remaining limitations. The non-invasive, gentle, and fast nature of breath analysis (BA) makes this technology highly attractive to supplement low-dose CT for an improved screening algorithm. However, BA has not taken hold in everyday clinical practice. One reason might be the heterogeneity and variety of BA methods. This scoping review is a comprehensive summary of study designs, breath analytical methods, and suggested biomarkers in lung cancer. Furthermore, this synthesis provides a framework with core outcomes for future studies in lung cancer BA. This work supports future research for evidence synthesis, meta-analysis, and translation into clinical routine workflows.

Helicene-Hydrazide Encapsulated Ethyl Cellulose as a Potential Fluorescence Sensor for Highly Specific Detection of Nonanal in Aqueous Solutions and a Proof-of-Concept Clinical Study in Lung Fluid

Over the past years, lung cancer has been one of the vital cancer-related mortalities worldwide and has inevitably exhibited the highest death rate with the subsequent need for facile and convenient diagnosis approaches to identify the severity of cancer. Previous research has reported long-chain aldehyde compounds such as hexanal, heptanal, octanal, and nonanal as potential biomarkers of lung cancer. Herein, the helicene dye-encapsulated ethyl cellulose (EC@dye-NH) nanosensors have been applied for the potentially sensitive and specific detection of long-chain aldehydes in aqueous media. The sensors contain the intrinsic hydrazide group of dye-NH, which is capable of reacting an aldehyde group via imine formation and the EC backbone. This offers the synergistic forces of hydrophobic interactions with alkyl long-chain aldehydes, which could induce self-assembly encapsulation of EC@dye-NH nanosensors and strong fluorescence responses. The addition of long-chain aldehyde would induce the complete micellar-like nanoparticle formation within 15 min in acetate buffer pH 5.0. The limit of detection (LOD) values of EC@dye-NH nanosensors toward heptanal, octanal, and nonanal were 40, 100, and 10 μM, respectively, without interference from the lung fluid matrices and short-chain aldehydes. For practical applicability, this sensing platform was developed for quantification of the long-chain aldehydes in lung fluid samples with 98-101% recoveries. This EC@dye-NH nanosensor was applied to quantify nonanal contents in lung fluid samples. The results of this method based on EC@dye-NH nanosensors were then validated using standard gas chromatography-mass spectrometry (GC-MS), which gave results consistent with the proposed method. With intracellular imaging application, the EC@dye-NH nanosensors demonstrated excellent intracellular uptake and strong green fluorescence emission upon introducing the nonanal into the lung cancer cells (A549). Thus, the developed nanosensing approach served as the potential fluorescent probes in medical and biological fields, especially for lung cancer disease diagnosis based on highly selective and sensitive detection of long-chain aldehydes.

Study on the Discrimination of Possible Error Sources That Might Affect the Quality of Volatile Organic Compounds Signature in Dairy Cattle Using an Electronic Nose

Simple Summary

In recent decades, remarkable progress in the development of electronic nose (EN) technologies, particularly for disease detection, has been accomplished through the disclosure of novel methods and associated devices, mainly for the detection of volatile organic compounds (VOCs). Herein, we assessed the ability of a novel EN technology (MENT-EGAS prototype) to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. Principal Component Analyses (PCA) evidenced the presence in the analyzed samples of sufficient information to consent the discrimination of different environmental backgrounds, feed headspaces and exhalated breath between two groups of cows fed with two different types of feed. Moreover, discrimination was also observed within the same group between exhalated breaths sampled before and after feed intake. Based on these findings, we provided evidence that the MENT-EGAS prototype can identify error sources with accuracy. Livestock precision farming technologies are powerful tools for monitoring animal health and welfare parameters in a continuous and automated way.

Abstract

Electronic nose devices (EN) have been developed for detecting volatile organic compounds (VOCs). This study aimed to assess the ability of the MENT-EGAS prototype-based EN to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. This study was performed on a dairy farm using 11 (n = 11) multiparous Holstein-Friesian cows. The cows were divided into two groups housed in two different barns: group I included six lactating cows fed with a lactating diet (LD), and group II included 5 non-lactating late pregnant cows fed with a far-off diet (FD). Each group was offered 250 g of their respective diet; 10 min later, exhalated breath was collected for VOC determination. After this sampling, 4 cows from each group were offered 250 g of pellet concentrates. Ten minutes later, the exhalated breath was collected once more. VOCs were also measured directly from the feed’s headspace, as well as from the environmental backgrounds of each. Principal component analyses (PCA) were performed and revealed clear discrimination between the two different environmental backgrounds, the two different feed headspaces, the exhalated breath of groups I and II cows, and the exhalated breath within the same group of cows before and after the feed intake. Based on these findings, we concluded that the MENT-EGAS prototype can recognize several error sources with accuracy, providing a novel EN technology that could be used in the future in precision livestock farming.

Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study

BACKGROUND

Breathomics testing has been considered a promising method for detection and screening for lung cancer. This study aimed to identify breath biomarkers of lung cancer through perioperative dynamic breathomics testing.

METHODS

The discovery study was prospectively conducted between Sept 1, 2020 and Dec 31, 2020 in Peking University People's Hospital in China. High-pressure photon ionisation time-of-flight mass spectrometry was used for breathomics testing before surgery and 4 weeks after surgery. 28 volatile organic compounds (VOCs) were selected as candidates based on a literature review. VOCs that changed significantly postoperatively in patients with lung cancer were selected as potential breath biomarkers. An external validation was conducted to evaluate the performance of these VOCs for lung cancer diagnosis. Multivariable logistic regression was used to establish diagnostic models based on selected VOCs.

FINDINGS

In the discovery study of 84 patients with lung cancer, perioperative breathomics demonstrated 16 VOCs as lung cancer breath biomarkers. They were classified as aldehydes, hydrocarbons, ketones, carboxylic acids, and furan. In the external validation study including 157 patients with lung cancer and 368 healthy individuals, patients with lung cancer showed elevated spectrum peak intensity of the 16 VOCs after adjusting for age, sex, smoking, and comorbidities. The diagnostic model including 16 VOCs achieved an area under the curve (AUC) of 0.952, sensitivity of 89.2%, specificity of 89.1%, and accuracy of 89.1% in lung cancer diagnosis. The diagnostic model including the top eight VOCs achieved an AUC of 0.931, sensitivity of 86.0%, specificity of 87.2%, and accuracy of 86.9%.

INTERPRETATION

Perioperative dynamic breathomics is an effective approach for identifying lung cancer breath biomarkers. 16 lung cancer-related breath VOCs (aldehydes, hydrocarbons, ketones, carboxylic acids, and furan) were identified and validated. Further studies are warranted to investigate the underlying mechanisms of identified VOCs.

FUNDING

National Natural Science Foundation of China (82173386) and Peking University People's Hospital Scientific Research Development Founds (RDH2021-07).

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath-Based Biomarker Diagnosis

A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.

MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

The VICTORY (Investigation of Inflammacheck to Measure Exhaled Breath Condensate Hydrogen Peroxide in Respiratory Conditions) Study: Protocol for a Cross-sectional Observational Study

Background

More than 7% of the world’s population is living with a chronic respiratory condition. In the United Kingdom, lung disease affects approximately 1 in 5 people, resulting in over 700,000 hospital admissions each year. People with respiratory conditions have several symptoms and can require multiple health care visits and investigations before a diagnosis is made. The tests available can be difficult to perform, especially if a person is symptomatic, leading to poor quality results. A new, easy-to-perform, point-of-care test that can be performed in any health care setting and that can differentiate between various respiratory conditions would have a significant, beneficial impact on the ability to diagnose respiratory diseases.

Objective

The objective of this study is to use a new handheld device (Inflammacheck) in different respiratory conditions to measure the exhaled breath condensate hydrogen peroxide (EBC H₂O₂) and compare these results with those of healthy controls and with each other. This study also aims to determine whether the device can measure other parameters, including breath humidity, breath temperature, breath flow dynamics, and end tidal carbon dioxide.

Methods

We will perform a single-visit, cross-sectional observational study of EBC H₂O₂ levels, as measured by Inflammacheck, in people with respiratory disease and volunteers with no known lung disease. Participants with a confirmed diagnosis of asthma, chronic obstructive pulmonary disease, lung cancer, bronchiectasis, pneumonia, breathing pattern disorder, and interstitial lung disease as well as volunteers with no history of lung disease will be asked to breathe into the Inflammacheck device to record their breath sample.

Results

The results from this study will be available in 2022, in anticipation of COVID-19–related delays.

Conclusions

This study will investigate the EBC H₂O₂, as well as other exhaled breath parameters, for use as a future diagnostic tool.

Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challenging to detect using current techniques due to the diseases’ location (inaccessibility to biopsy) and size (below normal CT/MRI resolution). In this review, we present a new method for lung disease detection and treatment in small airways based on exhaled aerosols, whose patterns are uniquely related to the health of the lungs. Proof-of-concept studies are first presented in idealized lung geometries. We subsequently describe the recent developments in feature extraction and classification of the exhaled aerosol images to establish the relationship between the images and the underlying airway remodeling. Different feature extraction algorithms (aerosol density, fractal dimension, principal mode analysis, and dynamic mode decomposition) and machine learning approaches (support vector machine, random forest, and convolutional neural network) are elaborated upon. Finally, future studies and frequent questions related to clinical applications of the proposed aerosol breath testing are discussed from the authors’ perspective. The proposed breath testing has clinical advantages over conventional approaches, such as easy-to-perform, non-invasive, providing real-time feedback, and is promising in detecting symptomless lung diseases at early stages.

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

Application of chemoresistive gas sensors and chemometric analysis to differentiate the fingerprints of global volatile organic compounds from diseases. Preliminary results of COPD, lung cancer and breast cancer

BACKGROUND

Analysis of volatile organic compounds (VOCs) in exhaled breath has been proposed as a screening method that discriminates between disease and healthy subjects, few studies evaluate whether these chemical fingerprints are specific when compared between diseases. We evaluated global VOCs and their discrimination capacity in chronic obstructive pulmonary disease (COPD), lung cancer, breast cancer and healthy subjects by chemoresistive sensors and chemometric analysis.

METHODS

A cross-sectional study was conducted with the participation of 30 patients with lung cancer, 50 with breast cancer, 50 with COPD and 50 control subjects. Each participant's exhaled breath was analyzed with the electronic nose. A multivariate analysis was carried: principal component analysis (PCA) and, canonical analysis of principal coordinates (CAP). Twenty single-blind samples from the 4 study groups were evaluated by CAP.

RESULTS

A separation between the groups of patients to the controls was achieved through PCA with explanations of >90% of the data and with a correct classification of 100%. In the CAP of the 4 study groups, discrimination between the diseases was obtained with 2 canonical axes with a correct general classification of 91.35%. This model was used for the prediction of the single-blind samples resulting in correct classification of 100%.

CONCLUSIONS

The application of chemoresistive gas sensors and chemometric analysis can be used as a useful tool for a screening test for lung cancer, breast cancer and COPD since this equipment detects the set of VOCs present in the exhaled breath to generate a characteristic chemical fingerprint of each disease.

Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer

IMPORTANCE

Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer.

OBJECTIVE

To investigate whether the breath test is able to detect lung cancer using the highly sensitive high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS).

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted with a prospective-specimen collection, retrospective-blinded evaluation design. Exhaled breath samples were collected before surgery and detected by HPPI-TOFMS. The detection model was constructed by support vector machine (SVM) algorithm. Patients with pathologically confirmed lung cancer were recruited from Peking University People's Hospital, and healthy adults without pulmonary noncalcified nodules were recruited from Aerospace 731 Hospital. Data analysis was performed from August to October 2020.

EXPOSURES

Breath testing and SVM algorithm.

MAIN OUTCOMES AND MEASURES

The detection performance of the breath test was measured by sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC).

RESULTS

Exhaled breath samples were from 139 patients with lung cancer and 289 healthy adults, and all breath samples were collected and tested. Of all participants, 228 (53.27%) were women and the mean (SD) age was 57.0 (11.4) years. After clinical outcomes were ascertained, all participants were randomly assigned into the discovery data set (381 participants) and the blinded validation data set (47 participants). The discovery data set was further broken into a training set (286 participants) and a test set (95 participants) to construct and test the detection model. The detection model reached a mean (SD) of 92.97% (4.64%) for sensitivity, 96.68% (2.21%) for specificity, and 95.51% (1.93%) for accuracy in the test set after 500 iterations. In the blinded validation data set (47 participants), the model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586.

CONCLUSIONS AND RELEVANCE

This diagnostic study's results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings.

Early diagnosis of breast cancer from exhaled breath by gas chromatography-mass spectrometry (GC/MS) analysis: A prospective cohort study

BACKGROUND

It has proved that there is an association between cancer and volatile organic compounds (VOCs) of exhaled breath. This study targets on verifying the existence of specific VOCs in breathing in breast cancer patients, especially those with ductal carcinoma in situ (DCIS).

METHODS

There were a total of 203 participants included in the final analysis, which included 71 (35.0%) patients with histologically confirmed breast cancer (including 13 with DCIS, 31 with lymph node metastasis-negative status, and 27 with lymph node metastasis-positive status), 78 (38.4%) healthy volunteers, and 54 (26.6%) patients with histologically confirmed gastric cancer. Gas chromatography-mass spectrometry and solid-phase microextraction were used to analyze the breath samples for the presence of VOCs.

RESULTS

There were significant differences in the volatile organic metabolites between the DCIS, lymph node metastasis-negative breast cancer, and lymph node metastasis-positive breast cancer groups compared with the healthy controls as well as between the breast cancer and gastric cancer patients. An overlapping set of seven VOCs, including (S)-1,2-propanediol, cyclopentanone, ethylene carbonate, 3-methoxy-1,2-propanediol, 3-methylpyridine, phenol, and tetramethylsilane, was significantly different between the breast cancer patients and healthy individuals as well as between the breast cancer and gastric cancer patients. The combination of these seven compounds was considered as a biomarker for breast cancer. The sensitivity for predicting DCIS by this set of seven compounds was determined to be 80.77%, and the specificity was determined to be 100%.

CONCLUSIONS

This set of seven breast cancer-specific VOCs can be regarded as one particular expiratory marker for DCIS and will help to establish new screening methods for early breast cancer.

Volatile organic compounds in human matrices as lung cancer biomarkers: a systematic review

Volatile organic compounds (VOCs) have shown potential as non-invasive breath biomarkers for lung cancer, but their unclear biological origin currently limits clinical applications. This systematic review explores headspace analysis of VOCs in patient-derived body fluids and lung cancer cell lines to pinpoint lung cancer-specific VOCs and uncover their biological origin. A search was performed in the databases MEDLINE and Web of Science. Twenty-two articles were included in this systematic review. Since there is no standardised approach to analyse VOCs, a plethora of techniques and matrices/cell lines were explored, which is reflected in the various VOCs identified. However, comparing VOCs in the headspace of urine, blood and pleural effusions from patients and lung cancer cell lines showed some overlapping VOCs, indicating their potential use in lung cancer diagnosis. This review demonstrates that VOCs are promising biomarkers for lung cancer. However, due to lack of inter-matrix consensus, standardised prospective trials will have to be conducted to validate clinically relevant biomarkers.

Applications and strategies in nanodiagnosis and nanotherapy in lung cancer

Lung cancer is the second most common cancer and the leading cause of death in both men and women in the world. Lung cancer is heterogeneous in nature and diagnosis is often at an advanced stage as it develops silently in the lung and is frequently associated with high mortality rates. Despite the advances made in understanding the biology of lung cancer, progress in early diagnosis, cancer therapy modalities and considering the mechanisms of drug resistance, the prognosis and outcome still remains low for many patients. Nanotechnology is one of the fastest growing areas of research that can solve many biological problems such as cancer. A growing number of therapies based on using nanoparticles (NPs) have successfully entered the clinic to treat pain, cancer, and infectious diseases. Recent progress in nanotechnology has been encouraging and directed to developing novel nanoparticles that can be one step ahead of the cancer reducing the possibility of multi-drug resistance. Nanomedicine using NPs is continuingly impacting cancer diagnosis and treatment. Chemotherapy is often associated with limited targeting to the tumor, side effects and low solubility that leads to insufficient drug reaching the tumor. Overcoming these drawbacks of chemotherapy by equipping NPs with theranostic capability which is leading to the development of novel strategies. This review provides a synopsis of current progress in theranostic applications for lung cancer diagnosis and therapy using NPs including liposome, polymeric NPs, quantum dots, gold NPs, dendrimers, carbon nanotubes and magnetic NPs.

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.

Non-invasive Biodiversified Sensors: A Modernized Screening Technology for Cancer

Background:

Biological sensors revolutionize the method of diagnoses of diseases from early to final stages using the biomarkers present in the body. Biosensors are advantageous due to the involvement of minimal sample collection with improved specificity and sensitivity for the detection of biomarkers.

Methods:

Conventional biopsies restrict problems like patient non-compliance, cross-infection and high cost and to overcome these issues biological samples like saliva, sweat, urine, tears and sputum progress into clinical and diagnostic research for the development of non-invasive biosensors. This article covers various non-invasive measurements of biological samples, optical-based, mass-based, wearable and smartphone-based biosensors for the detection of cancer.

Results:

The demand for non-invasive, rapid and economic analysis techniques escalated due to the modernization of the introduction of self-diagnostics and miniature forms of devices. Biosensors have high sensitivity and specificity for whole cells, microorganisms, enzymes, antibodies, and genetic materials.

Conclusion:

Biosensors provide a reliable early diagnosis of cancer, which results in faster therapeutic outcomes with in-depth fundamental understanding of the disease progression.

Accuracy of Canine Scent Detection of Non-Small Cell Lung Cancer in Blood Serum

CONTEXT

Early detection provides the best opportunity for lung cancer survival; however, lung cancer is difficult to detect early because symptoms do not often appear until later stages. Current screening methods such as x-ray and computed tomographic imaging lack the sensitivity and specificity needed for effective early diagnosis. Dogs have highly developed olfactory systems and may be able to detect cancer in its primary stages. Their scent detection could be used to identify biomarkers associated with various types of lung cancer.

OBJECTIVE

To determine the accuracy of trained beagles' ability to use their olfactory system to differentiate the odor of the blood serum of patients with lung cancer from the blood serum of healthy controls.

METHODS

Over the course of 8 weeks, operant conditioning via clicker training was used to train dogs to use their olfactory system to distinguish blood serum from patients with malignant lung cancer from blood serum from healthy controls in a double-blind study. After training, non-small cell lung cancer and healthy control blood serum samples were presented to the dogs, and the sensitivity and specificity of each dog were analyzed.

RESULTS

Four dogs were trained for the study, but 1 was unmotivated by training and removed from the study. Three dogs were able to correctly identify the cancer samples with a sensitivity of 96.7%, specificity of 97.5%, positive predictive value of 90.6%, and negative predictive value of 99.2%.

CONCLUSION

Trained dogs were able to identify non-small cell lung cancer samples from healthy controls. The findings of this study provide a starting point for a larger-scale research project designed to explore the use of canine scent detection as a tool for cancer biomarkers.

Breath Analysis: A Systematic Review of Volatile Organic Compounds (VOCs) in Diagnostic and Therapeutic Management of Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a rare neoplasm related to asbestos exposure and with high mortality rate. The management of patients with MPM is complex and controversial, particularly with regard to early diagnosis. In the last few years, breath analysis has been greatly implemented with this aim. In this review the strengths of breath analysis and preliminary results in searching breath biomarkers of MPM are highlighted and discussed, respectively. Through a systematic electronic literature search, collecting papers published from 2000 until December 2018, fifteen relevant scientific papers were selected. All papers considered were prospective, comparative, observational case-control studies although every single one pilot and based on a relatively small number of samples. The identification of diagnostic VOCs pattern, through breath sample characterization and the statistical data treatment, allows to obtain a strategic information for clinical diagnostics. To date the collected data provide just preliminary information and, despite the promising results and diagnostic accuracy, conclusions cannot be generalized due to the limited number of individuals included in each cohort study. Furthermore none of studies was externally validated, although validation process is a necessary step towards clinical implementation. Breathomics-based biomarker approach should be further explored to confirm and validate preliminary findings and to evaluate its potential role in monitoring the therapeutic response.

Electronic Noses in Medical Diagnostics

BACKGROUND

Electronic nose technology is being developed in order to analyse complex mixtures of volatiles in a way parallel to biologic olfaction. When applied in the field of medicine, the use of such devices should enable the identification and discrimination between different diseases. In this review, a comprehensive summary of research in medical diagnostics using electronic noses is presented. A special attention has been paid to the application of these devices and sensor technologies, in response to current trends in medicine.

METHODS

Peer-reviewed research literature pertaining to the subject matter was identified based on a search of bibliographic databases. The quality and relevance of retrieved papers was assessed using standard tools. Their content was critically reviewed and certain information contained therein was compiled in tabularized form.

RESULTS

The majority of reviewed studies show promising results, often surpassing the accuracy and sensitivity of established diagnostic methods. However, only a relatively small number of devices have been field tested. The methods used for sample collection and data processing in various studies were listed in a table, together with electronic nose models used in these investigations.

CONCLUSION

Despite the fact that devices equipped with arrays of chemical sensors are not routinely used in everyday medical practice, their prospective use would solve some established issues in medical diagnostics, as well as lead to developments in prophylactics by facilitating a widespread use of non-invasive screening tests.

Sensors for Lung Cancer Diagnosis

The positive outcome of lung cancer treatment is strongly related to the earliness of the diagnosis. Thus, there is a strong requirement for technologies that could provide an early detection of cancer. The concept of early diagnosis is immediately extended to large population screening, and then, it is strongly related to non-invasiveness and low cost. Sensor technology takes advantage of the microelectronics revolution, and then, it promises to develop devices sufficiently sensitive to detect lung cancer biomarkers. A number of biosensors for the detection of cancer-related proteins have been demonstrated in recent years. At the same time, the interest is growing towards the analysis of volatile metabolites that could be measured directly from the breath. In this paper, a review of the state-of-the-art of biosensors and volatile compound sensors is presented.

Non-Invasive Detection of Anastomotic Leakage Following Esophageal and Pancreatic Surgery by Urinary Analysis

BACKGROUND

Esophagectomy or pancreaticoduodenectomy is the standard surgical approach for patients with tumors of the esophagus or pancreatic head. Postoperative mortality is strongly correlated with the occurrence of anastomotic leakage (AL). Delay in diagnosis leads to delay in treatment, which ratifies the need for development of novel and accurate non-invasive diagnostic tests for detection of AL. Urinary volatile organic compounds (VOCs) reflect the metabolic status of an individual, which is associated with a systemic immunological response. The aim of this study was to determine the diagnostic accuracy of urinary VOCs to detect AL after esophagectomy or pancreaticoduodenectomy.

METHODS

In the present study, urinary VOCs of 63 patients after esophagectomy (n = 31) or pancreaticoduodenectomy (n = 32) were analyzed by means of field asymmetric ion mobility spectrometry. AL was defined according to international study groups.

RESULTS

AL was observed in 15 patients (24%). Urinary VOCs of patients with AL after pancreaticoduodenectomy could be distinguished from uncomplicated controls, area under the curve 0.85 (95% CI 0.76-0.93), sensitivity 76%, and specificity 77%. However, this was not observed following esophagectomy, area under the curve 0.51 (95% CI 0.37-0.65).

CONCLUSION

In our study population AL following pancreaticoduodenectomy could be discriminated from uncomplicated controls by means of urinary VOC analysis, NTC03203434.

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC × GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6 h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data.

Confounding effect of benign pulmonary diseases in selecting volatile organic compounds as markers of lung cancer

Lung cancer (LC) is a leading cause of cancer-related morbidity and mortality globally, and exhaled breath testing has been considered as a fast, convenient and non-invasive way to diagnose LC in its early stages. Volatile organic compounds (VOCs), as markers of LC in exhaled breath, have been widely investigated for cancer diagnosis. However, few studies have reported on the interference of benign pulmonary diseases (BPD) in the selection of VOC markers for LC. During this study, 207 samples were analyzed using thermal desorption instrumentation/gas chromatography/mass spectrometry (TD-GCMS) to detect C₆-C₃₀ VOCs, and all samples were divided into four groups: LC group, BPD group, lung disease (LD) group (including LC group and BPD group) and healthy group. To make up for the deficiency of detecting low carbon hydrocarbons (<C₆), 277 samples were analyzed using solid-phase micro-extraction/gas chromatography/mass spectrometry (SPME-GCMS), divided among the four groups. VOC markers were selected by reference to the receiver operating characteristics curve. With the comparisons among the LC group, BPD group and healthy group from TD-GCMS and SPME-GCMS results, we found that exhaled VOCs are capable of discriminating LC group versus healthy group and BPD group versus healthy group with a consistency of 70%-80%. However, no VOCs can be selected with good discrimination capability between the LC group and BPD group, indicating that BPD interferes significantly in VOC marker selection for LC. To discriminate breath samples from the LD group and healthy group, 11 VOCs, including ten selected from TD-GCMS and one from SPME-GCMS, were chosen as markers for LD diagnosis. The sensitivity, specificity and overall accuracy of the diagnostic model established using ten VOCs were 80.8%, 84% and 82.7%, and those of the model established by using one VOC were 75.6%, 78.9% and 76.7%. These results validate that LD patients can be effectively discriminated and diagnosed using exhaled VOC analysis. (Clinical trial registration number: ChiCTR-DCD-15007106.).

Positive matrix factorization: A data preprocessing strategy for direct mass spectrometry-based breath analysis

Interest in exhaled breath has grown considerably in recent years, as breath biosampling has shown promise for non-invasive disease diagnosis, therapeutic drug monitoring, and environmental exposure. Real time breath analysis can be accomplished via direct online mass spectrometry (MS)-based methods, which can provide more accurate and detailed data and an enhanced understanding of the temporal evolution of exhaled VOCs in the breath; however, the complicated chemical composition and large raw datasets involved in breath analysis have hindered the discovery of sources contributing to the exhaled VOCs. The positive matrix factorization (PMF) receptor model has been widely used for source apportionment in atmospheric studies. Since the exhaled VOCs contain compounds from various sources, such as alveolar air, mouth air and respiratory dead-space air, PMF may be also helpful for source apportionment of exhaled VOCs in the breath. Thus, this study explores the application of PMF in the pretreatment of direct breath measurement data. The results indicate that (i) endogenous compounds and background contaminants sources can be readily distinguished by PMF in data obtained from replicate measurements of human exhaled breath at single time points (~30 s/measurement), which may benefit both exhalome investigations and the identification of exposure biomarkers; (ii) sources resolved from online measurement data collected over longer periods (1.5 h) can be used to isolate the evolution of exhaled VOCs and investigate processes such as the pharmacokinetics of ketamine and its major metabolites. Therefore, PMF has shown promise for both data processing and subsequent data mining for the ambient MS-based breath analysis.

Automated in Vivo Nanosensing of Breath-Borne Protein Biomarkers

Toxicology and bedside medical condition monitoring is often desired to be both ultrasensitive and noninvasive. However, current biomarker analyses for these purposes are mostly offline and fail to detect low marker quantities. Here, we report a system called dLABer (detection of living animal's exhaled breath biomarker) that integrates living rats, breath sampling, microfluidics, and biosensors for the automated tracking of breath-borne biomarkers. Our data show that dLABer could selectively detect (online) and report differences (of up to 10³-fold) in the levels of inflammation agent interleukin-6 (IL-6) exhaled by rats injected with different ambient particulate matter (PM). The dLABer system was further shown to have an up to 10⁴ higher signal-to-noise ratio than that of the enzyme-linked immunosorbent assay (ELISA) when analyzing the same breath samples. In addition, both blood-borne IL-6 levels analyzed via ELISA in rats injected with different PM extracts and PM toxicity determined by a dithiothreitol (DTT) assay agreed well with those determined by the dLABer system. Video recordings further verified that rats exposed to PM with higher toxicity (according to a DTT assay and as revealed by dLABer) appeared to be less physically active. All the data presented here suggest that the dLABer system is capable of real-time, noninvasive monitoring of breath-borne biomarkers with ultrasensitivity. The dLABer system is expected to revolutionize pollutant health effect studies and bedside disease diagnosis as well as physiological condition monitoring at the single-protein level.