Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics

Helicobacter pylori Breath Test via Mid-Infrared Sensor Technology

Helicobacter pylori infection has been associated with various gastrointestinal disorders, most notably with the development of gastric cancer. Therefore, it is important to develop technologies for effective, rapid, sensitive, and personalized infection detection. The present study evaluates the utility of mid-infrared (MIR) exhaled breath sensors utilizing substrate-integrated hollow waveguide (iHWG) technology for the precise determination of the isotopic ratio of 13CO2 vs 12CO2 simulating conditions relevant to the detection of the presence of Helicobacter pylori in the upper gastrointestinal tract via exhaled breath analysis. For future integration of such a sensing module, e.g., into a cell phone attachment, optimized light-gas interaction and sufficient sensitivity are essential, as the diagnosis is based on detecting the presence of 13CO2 30 min after administration of 13C-labeled urea via a gel or pill, which is metabolized by H. pylori. By optimizing the light-gas interaction volume via tailoring of the iHWG, it was demonstrated that sufficient sensitivity and accuracy are achieved for detecting small changes in the isotopic composition of exhaled CO2. While it was demonstrated that the combination of conventional Fourier-transform infrared (FTIR) spectroscopy with iHWGs indeed confirms the utility of this noninvasive breath analysis concept, further device miniaturization utilizing quantum cascade lasers is anticipated to achieve the necessary level of integration for personalized home usage.

Quartz-Enhanced Photoacoustic Spectroscopy-Based Acetone and Ammonia Measurements from Human Breath Near 8 μm Wavelength Band

Human breath gas analysis is a noninvasive disease diagnostic approach used to identify different pathological conditions in the human body. Monitoring breath acetone (C3H6O) and ammonia (NH3) as biomarkers is vital in diagnosing diabetes mellitus and liver disorders, respectively. In this article, the quartz-enhanced photoacoustic spectroscopy (QEPAS) technique is proposed and demonstrated for measuring C3H6O and NH3 in human exhaled breath samples. We present the QEPAS technique-based approach combined with a broadband tunable quantum cascade laser (QCL) operating from 7.9 to 8.4 μm to target both C3H6O and NH3 at specific absorption wavelengths. The experimental results show that minimum detection limits (MDLs) of 57 and 190 ppb were achieved for C3H6O and NH3, respectively. The proposed technique measured C3H6O and NH3 biomarkers in human breath samples collected from volunteers. The concentration measurements of exhaled breath biomarkers using the QEPAS technique in the 8 μm spectral region agreed with the blood glucose and blood ammonia measurements. The measurement limit and sensitivity met the requirement of the breath gas analysis needed to distinguish the concentrations obtained from a healthy person from those of a person with diabetes and liver disorder conditions.

Measurement of Bacterial Headspaces by FT-IR Spectroscopy Reveals Distinct Volatile Organic Compound Signatures

Ensuring prompt and precise identification of bacterial pathogens is essential for initiating appropriate antibiotic therapy and combating severe bacterial infections effectively. Traditional microbiological diagnostics, involving initial culturing and subsequent pathogen detection, are often laborious and time-consuming. Even though modern techniques such as Raman spectroscopy, MALDI-TOF, and 16S rRNA PCR have significantly expedited this process, new methods are required for the accurate and fast detection of bacterial pathogens. In this context, using bacterial metabolites for detection is promising as a future diagnostic approach. Fourier-transform infrared spectroscopy was employed in our study to analyze the biochemical composition of gas phases of bacterial isolates. We can characterize individual bacterial strains and identify specific bacteria within mixtures by utilizing volatile-metabolite-based infrared detection techniques. This approach enables rapid identification by discerning distinctive spectral features and intensities for different bacteria, offering new perspectives for bacterial pathogen diagnostics. This technique holds innovative potential to accelerate progress in the field, providing a faster and potentially more precise alternative to conventional diagnostic methods.

Tunable MEMS-based meta-absorbers for nondispersive infrared gas sensing applications

In conventional nondispersive infrared (NDIR) gas sensors, a wide-spectrum IR source or detector must be combined with a narrowband filter to eliminate the interference of nontarget gases. Therefore, the multiplexed NDIR gas sensor requires multiple pairs of narrowband filters, which is not conducive to miniaturization and integration. Although plasmonic metamaterials or multilayer thin-film structures are widely applied in spectral absorption filters, realizing high-performance, large-area, multiband, and compact filters is rather challenging. In this study, we propose and demonstrate a narrowband meta-absorber based on a planar metal-insulator-metal (MIM) cavity with a metallic ultrathin film atop. Nearly perfect absorption of different wavelengths can be obtained by controlling the thickness of the dielectric spacer. More significantly, the proposed meta-absorber exhibits angle-dependent characteristics. The absorption spectra of different gases can be matched by changing the incident angle of the light source. We also preliminarily investigate the CO2 gas sensing capability of the meta-absorber. Afterward, we propose a tunable meta-absorber integrated with a microelectromechanical system (MEMS)-based electrothermal actuator (ETA). By applying a direct current (DC) bias voltage, the inclination angle of the meta-absorber can be controlled, and the relationship between the inclination angle and the applied voltage can be deduced theoretically. The concept of a tunable MEMS-based meta-absorber offers a new way toward highly integrated, miniaturized and energy-efficient NDIR multigas sensing systems.

Integrated Optics Waveguides and Mesoporous Oxides for the Monitoring of Volatile Organic Compound Traces in the Mid-Infrared

Volatile organic compounds (VOCs) are an ever-growing hazard for health and environment due to their increased emissions and accumulation in the air. Quantum cascade laser-based infrared (QCL-IR) sensors hold significant promise for gas monitoring, thanks to their compact, rugged design, high laser intensity, and high molecule-specific detection capabilities within the mid-infrared spectrum's fingerprint region. In this work, tunable external cavity QCLs were complemented by an innovative germanium-on-silicon integrated optics waveguide sensing platform with integrated microlenses for efficient backside optical interfacing for the tunable laser spectrometer. The waveguide chip was coated with a mesoporous silica coating, thereby increasing the signal by adsorptive enhancement of VOCs while at the same time limiting water vapor interferences. Different least square fitting methods were explored to deconvolute the resulting spectra, showing subparts-per-million by volume (sub-ppmv) limits of detection and enrichment factors of up to 22 000 while keeping the footprint of the setup small (29 × 23 × 11 cm³). Finally, a use-case simulation for the continuous detection of VOCs in a process analytical technology environment confirmed the high potential of the technique for the monitoring of contaminants. By successfully demonstrating the use of photonic waveguides for the monitoring of VOCs, this work offers a promising avenue for the further development of fully integrated sensors on a chip.

Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor

Photonic integrated circuits offer miniaturized solutions for multimodal spectroscopic sensory systems by leveraging the simultaneous interaction of light with temperature, chemicals, and biomolecules, among others. The multimodal spectroscopic sensory data is complex and has huge data volume with high redundancy, thus requiring high communication bandwidth associated with high communication power consumption to transfer the sensory data. To circumvent this high communication cost, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal in-sensor computing system using an integrated silicon photonic convolutional processor. A microring resonator crossbar array is used as the photonic processor to implement convolutional operation with 5-bit accuracy, validated through image edge detection tasks. Further integrating the processor with a photonic spectroscopic sensor, the in situ processing of multimodal spectroscopic sensory data is demonstrated, achieving the classification of protein species of different types and concentrations at various temperatures. A classification accuracy of 97.58% across 45 different classes is achieved. The multimodal in-sensor computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the data processing capability of photonic devices at the edge.

Baseline correction for the infrared spectra of exhaled breath

Infrared spectroscopy appears to be a promising analytical method for the metabolic analysis of breath. However, due to the presence of trace amounts in exhaled breath, the absorption strength of the metabolites remains extremely low. In such low detection limits, the nonlinear detection sensitivity of the infrared detector and electronic noise strongly modify the baseline of the acquired infrared spectra of breath. Fitting the reference molecular spectra with the baseline-modified spectral features of breath metabolites does not provide accurate identification. Therefore, baseline correction of the acquired infrared spectra of breath is the primary requirement for the success of breath-based infrared diagnosis. A selective spectral region-based, simple baseline correction method is proposed for the infrared spectroscopy of breath.

Background-free correlation spectroscopy using an infrared mode-locked laser

The recent advances in infrared laser technology are expanding the capabilities and applications of vibrational spectroscopy. A promising approach utilizing broadband infrared mode-locked lasers is background-free (BF) absorption spectroscopy. This method captures the free-induction decay (FID) of excited molecules while suppressing the background light. It is unique in that the signal strength increases with input optical power but eventually struggles with detector noise when targeting fewer molecules. In this paper, we present a novel method of multiplexed background-free spectroscopy using a spectral mask whose transmittance has a strong correlation with the absorption spectrum of a target molecule. We successfully demonstrate an order of magnitude increase in the sensitivity due to multiplexing as well as a high molecular contrast due to the spectral correlation. The presented results indicate the promising potential of the method for sensitive and selective detection of trace molecules.

Dual-band and spectrally selective infrared absorbers based on hybrid gold-graphene metasurfaces

In this paper, we propose a dual-band and spectrally selective infrared (IR) absorber based on a hybrid structure comprising a patterned graphene monolayer and cross-shaped gold resonators within a metasurface. Rooted in full-wave numerical simulations, our study shows that the fundamental absorption mode of the gold metasurface hybridizes with the graphene pattern, leading to a second absorptive mode whose properties depend on graphene's electrical properties and physical geometry. Specifically, the central operation band of the absorber is defined by the gold resonators whereas the relative absorption level and spectral separation between the two modes can be controlled by graphene's chemical potential and its pattern, respectively. We analyze this platform using coupled-mode theory to understand the coupling mechanism between these modes and to elucidate the emergence and tuning of the dual band response. The proposed dual-band device can operate at different bands across the IR spectrum and may open new possibilities for tailored sensing applications in spectroscopy, thermal imaging, and environmental monitoring.

Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria

Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 cm-1 within the wavenumber range of 500-7500 cm-1. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia (NH3) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, NH3, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of NH3 and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique.

SARS CoV-2 infection screening via the exhaled breath fingerprint obtained by FTIR spectroscopic gas-phase analysis. A proof of concept

The COVID-19 pandemic remains a global challenge now with the long-COVID arising. Mitigation measures focused on case counting, assessment and determination of variants and their likely targets of infection and transmission, the pursuit of drug treatments, use and enhancement of masks, social distancing, vaccination, post-infection rehabilitation, and mass screening. The latter is of utmost importance given the current scenario of infections, reinfections, and long-term health effects. Research on screening platforms has been developed to provide more sensitive, specific, and reliable tests that are accessible to the entire population and can be used to assess the prognosis of the disease as well as the subsequent health follow-up of patients with sequelae of COVID-19. Therefore, the aim of the present study was the simulation of exhaled breath of COVID-19 patients by evaluation of three identified COVID-19 indicator breath biomarkers (acetone (ACE), acetaldehyde (ACH) and nitric oxide (NO)) by gas-phase infrared spectroscopy as a proof-of-concept principle for the detection of infected patients' exhaled breath fingerprint and subsequent follow-up. The specific fingerprints of each of the compounds and the overall fingerprint were obtained. The synthetic exhaled breath evaluation concept revealed a linearity of r = 0.99 for all compounds, and LODs of 6.42, 13.81, 9.22 ppm, and LOQs of 42.26, 52.57, 69.23 ppm for NO, ACE, and ACH, respectively. This study proves the fundamental feasibility of gas-phase infrared spectroscopy for fingerprinting lung damage biomarkers in exhaled breath of patients with COVID-19. This analysis would allow faster and cheaper screening and follow-up of infected individuals, which could improve mass screening in POC settings.

Diabetes noninvasive diagnostics and monitoring through volatile biomarkers analysis in the exhaled breath using optical absorption spectroscopy

The review is aimed on the analysis the abilities of noninvasive diagnostics and monitoring of diabetes mellitus (DM) and DM-associated complications through volatile molecular biomarkers detection in the exhaled breath. The specific biochemical reactions in the body of DM patients and their associations with volatile molecular biomarkers in the breath are considered. The applications of optical spectroscopy methods, including UV, IR, and terahertz spectroscopy for DM-associated volatile molecular biomarkers measurements, are described. The applications of similar technique combined with machine learning methods in DM diagnostics using the profile of DM-associated volatile molecular biomarkers in exhaled air or "pattern-recognition" approach are discussed.

A feasibility study on exhaled breath analysis using UV spectroscopy to detect COVID-19

A 23-subject feasibility study is reported to assess how UV absorbance measurements on exhaled breath samples collected from silicon microreactors can be used to detect COVID-19. The silicon microreactor technology chemoselectively preconcentrates exhaled carbonyl volatile organic compounds and subsequent methanol elution provides samples for analysis. The underlying scientific rationale that viral infection will induce an increase in exhaled carbonyls appears to be supported by the results of the feasibility study. The data indicate statistically significant differences in measured UV absorbance values between healthy and symptomatic COVID-19 positive subjects in the wavelength range from 235 nm to 305 nm. Factors such as subject age were noted as potential confounding variables.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

A novel breakthrough in wrist-worn transdermal troponin-I-sensor assessment for acute myocardial infarction

Aims

Clinical differentiation of acute myocardial infarction (MI) from unstable angina and other presentations mimicking acute coronary syndromes (ACS) is critical for implementing time-sensitive interventions and optimizing outcomes. However, the diagnostic steps are dependent on blood draws and laboratory turnaround times. We tested the clinical feasibility of a wrist-worn transdermal infrared spectrophotometric sensor (transdermal-ISS) in clinical practice and assessed the performance of a machine learning algorithm for identifying elevated high-sensitivity cardiac troponin-I (hs-cTnI) levels in patients hospitalized with ACS.

Methods and results

We enrolled 238 patients hospitalized with ACS at five sites. The final diagnosis of MI (with or without ST elevation) and unstable angina was adjudicated using electrocardiography (ECG), cardiac troponin (cTn) test, echocardiography (regional wall motion abnormality), or coronary angiography. A transdermal-ISS-derived deep learning model was trained (three sites) and externally validated with hs-cTnI (one site) and echocardiography and angiography (two sites), respectively. The transdermal-ISS model predicted elevated hs-cTnI levels with areas under the receiver operator characteristics of 0.90 [95% confidence interval (CI), 0.84-0.94; sensitivity, 0.86; and specificity, 0.82] and 0.92 (95% CI, 0.80-0.98; sensitivity, 0.94; and specificity, 0.64), for internal and external validation cohorts, respectively. In addition, the model predictions were associated with regional wall motion abnormalities [odds ratio (OR), 3.37; CI, 1.02-11.15; P = 0.046] and significant coronary stenosis (OR, 4.69; CI, 1.27-17.26; P = 0.019).

Conclusion

A wrist-worn transdermal-ISS is clinically feasible for rapid, bloodless prediction of elevated hs-cTnI levels in real-world settings. It may have a role in establishing a point-of-care biomarker diagnosis of MI and impact triaging patients with suspected ACS.

A sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor - Characterization and Multi-Component Spectra Recording in Synthetic Breath

Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8263 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO2. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbv in terms of ethanol and 250 pptv regarding acetone, our system is among the best performing presented so far.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

Methane and ethane detection from natural gas level down to trace concentrations using a compact mid-IR LITES sensor based on univariate calibration

A gas sensor based on light-induced thermo-elastic spectroscopy (LITES) capable to detect methane (C1) and ethane (C2) in a wide concentration range, from percent down to part-per-billion (ppb), is here reported. A novel approach has been implemented, exploiting a compact sensor design that accommodates both a custom 9.8 kHz quartz tuning fork (QTF) used as photodetector and the gas sample in the same housing. The resulting optical pathlength was only 2.5 cm. An interband cascade laser (ICL) with emission wavelength of 3.345 µm was used to target absorption features of C1 and C2. The effects of high concentration analytes on sensor response were firstly investigated. C1 concentration varied from 1% to 10%, while C2 concentration varied from 0.1% to 1%. These ranges were selected to retrace the typical natural gas composition in a 1:10 nitrogen dilution. The LITES sensor was calibrated for both the gas species independently and returned nonlinear but monotonic responses for the two analytes. These univariate calibrations were used to retrieve the composition of C1-C2 binary mixtures with accuracy higher than 98%, without the need for further data analysis. Minimum detection limits of ∼650 ppb and ∼90 ppb were achieved at 10 s of integration time for C1 and C2, respectively, demonstrating the capability of the developed LITES sensor to operate with concentration ranges spanning over 6 orders of magnitude.

Recent Advances in Sensing Materials Targeting Clinical Volatile Organic Compound (VOC) Biomarkers: A Review

In general, volatile organic compounds (VOCs) have a high vapor pressure at room temperature (RT). It has been reported that all humans generate unique VOC profiles in their exhaled breath which can be utilized as biomarkers to diagnose disease conditions. The VOCs available in exhaled human breath are the products of metabolic activity in the body and, therefore, any changes in its control level can be utilized to diagnose specific diseases. More than 1000 VOCs have been identified in exhaled human breath along with the respiratory droplets which provide rich information on overall health conditions. This provides great potential as a biomarker for a disease that can be sampled non-invasively from exhaled breath with breath biopsy. However, it is still a great challenge to develop a quick responsive, highly selective, and sensitive VOC-sensing system. The VOC sensors are usually coated with various sensing materials to achieve target-specific detection and real-time monitoring of the VOC molecules in the exhaled breath. These VOC-sensing materials have been the subject of huge interest and extensive research has been done in developing various sensing tools based on electrochemical, chemoresistive, and optical methods. The target-sensitive material with excellent sensing performance and capturing of the VOC molecules can be achieved by optimizing the materials, methods, and its thickness. This review paper extensively provides a detailed literature survey on various non-biological VOC-sensing materials including metal oxides, polymers, composites, and other novel materials. Furthermore, this review provides the associated limitations of each material and a summary table comparing the performance of various sensing materials to give a better insight to the readers.

A metal-organic framework loaded paper-based chemiluminescence sensor for trace acetone detection in exhaled breath

Trace acetone determination in breath can be regarded as a noninvasive method for diagnosis of diabetes. Here, a paper-based CL gas sensor combined with UiO-66 as the preconcentrator was established for sensitive detection of trace acetone in exhaled breath. UiO-66 with excellent adsorption performance and unique water stability was used for the adsorption and enrichment of acetone gas under high humidity conditions in exhaled breath. As acetone can remarkably increase the chemiluminescence (CL) of the 2,4-dinitrophenylhydrazine (DNPH)-potassium permanganate (KMnO₄) system, a sensitive CL device based on a paper substrate for trace acetone detection was established and the detection limit was 0.03 ppm. The fabricated method was used to assess the content of trace acetone in exhaled breath with satisfactory recoveries of 90-110%. It is expected to realize the noninvasive determination of acetone for diabetic patients in exhaled breath.

“Natural Laboratory Complex” for novel primate neuroscience

We propose novel strategies for primate experimentation that are ethically valuable and pragmatically useful for cognitive neuroscience and neuropsychiatric research. Specifically, we propose Natural Laboratory Complex or Natural Labs, which are a combination of indoor-outdoor structures for studying free moving and socially housed primates in natural or naturalistic environment. We contend that Natural Labs are pivotal to improve primate welfare, and at the same time to implement longitudinal and socio-ecological studies of primate brain and behavior. Currently emerging advanced technologies and social systems (including recent COVID-19 induced “remote” infrastructures) can speed-up cognitive neuroscience approaches in freely behaving animals. Experimental approaches in natural(istic) settings are not in competition with conventional approaches of laboratory investigations, and could establish several benefits at the ethical, experimental, and economic levels.

A System-on-Board Integrated Multi-analyte PoC Biosensor for Combined Analysis of Saliva and Exhaled Breath

The need for oral health monitoring Point of Care (PoC) systems is ever growing. This is effectively highlighted by the ongoing COVID-19 pandemic where the lack of rapid PoC testing has placed an unsustainable burden on centralized laboratory testing. Urgent development has furthered pathogenic nucleic acid and antibody detection in oral samples throat swabs, but without corresponding advancements in biochemical monitoring through oral biosensing. We have recently reported two novel biosensor technologies for detection of high impact hormones: cortisol in saliva by organic electrolyte gated FETs (OEGFETs), and 8-isoprostane in exhaled breath condensate (EBC) using molecularly imprinted electroimpedance spectroscopy biosensors (MIP EIS). In this work, we report a first stage integration of the two biosensors - previously bench-top proven - with a miniaturized semi-hermetically sealed soft-fluidic enclosure, onto a low-power (<300 mW) customized printed circuit board. Our findings established comparable detection thresholds for the miniaturized board-based configuration and a lab-based test setup, and their ability to characterize, calibrate, and operate these small footprint biosensors. Testing with the 8-isoprostane EBC MIP EIS biosensors showed the system-on-board had an effective frequency range of 100-100kHz, comparable to lab bench impedance analyzers. Despite internal impedance increases of 210%, the expected data features are present in the impedance graphs collected with the PCB. The system-on-board experiments using OEGFET aptasensor showed a predictable behavior and comparable sensor detection range and resolution using unadulterated supernatant and serial dilutions of cortisol over a range of 273 μM to 2.73pM. The portable, multi-analyte oral biosensor is a promising prototype for future packaging and clinical validation.

Deep Learning for Type 1 Diabetes Mellitus Diagnosis Using Infrared Quantum Cascade Laser Spectroscopy

An estimated 10.5% of the world’s population aged 20–79 years are currently living with diabetes in 2021. An urgent task is to develop a non-invasive express-diagnostics of diabetes with high accuracy. Type 1 diabetes mellitus (T1DM) diagnostic method based on infrared laser spectroscopy of human exhaled breath is described. A quantum cascade laser emitting in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm and Herriot multipass gas cell with an optical path length of 76 m were used. We propose a method for collecting and drying an exhaled human air sample and have measured 1200 infrared exhaled breath spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed T1DM (the target group). A 1-D convolutional neural network for the classification of healthy and T1DM volunteers with an accuracy of 99.7%, recall 99.6% and AUC score 99.9% was used. The demonstrated results require clarification on a larger dataset and series of clinical studies and, further, the method can be implemented in routine medical practice.

Development and preliminary validation of infrared spectroscopic device for transdermal assessment of elevated cardiac troponin

Background

The levels of circulating troponin are principally required in addition to electrocardiograms for the effective diagnosis of acute coronary syndrome. Current standard-of-care troponin assays provide a snapshot or momentary view of the levels due to the requirement of a blood draw. This modality further restricts the number of measurements given the clinical context of the patient. In this communication, we present the development and early validation of non-invasive transdermal monitoring of cardiac troponin-I to detect its elevated state.

Methods

Our device relies on infrared spectroscopic detection of troponin-I through the dermis and is tested in stepwise laboratory, benchtop, and clinical studies. Patients were recruited with suspected acute coronary syndrome.

Results

We demonstrate a significant correlation (r = 0.7774, P < 0.001, n = 52 biologically independent samples) between optically-derived data and blood-based immunoassay measurements with and an area under receiver operator characteristics of 0.895, sensitivity of 96.3%, and specificity of 60% for predicting a clinically meaningful threshold for defining elevated Troponin I.

Conclusion

This preliminary work introduces the potential of a bloodless transdermal measurement of troponin-I based on molecular spectroscopy. Further, potential pitfalls associated with infrared spectroscopic mode of inquiry are outlined including requisite steps needed for improving the precision and overall diagnostic value of the device in future studies.

The number one cause of death in the US is heart disease. With 10 million patients visiting the emergency departments in a year with chest pain, 8 million are unrelated to cardiac issues. This places a burden on hospitals leading to suboptimal patient outcomes. In patients with cardiac issues, the time clinicians take to intervene dictates reversible or irreversible heart damage. However, current markers used to test for cardiac issues require blood sampling, limiting access to and frequency of testing. This study introduces a non-invasive cardiac marker measurement device without any form of blood draw, based on measurements taken by a wearable device through the skin. Preliminary studies show high conformance to the standard of care technologies, indicating that the technology has potential to enable more rapid, frequent, accessible and non-invasive detection of cardiac issues such as heart attacks.

Titus et al. develop a technological platform for the non-invasive transdermal measurement of cardiac troponin-I, a marker of myocardial injury. Preliminary testing of their device, which works via infrared spectroscopy, indicates that troponin can be detected with reasonable performance, in the absence of a blood draw.

Detection of volatile organic compounds using mid-infrared silicon nitride waveguide sensors

Mid-infrared (mid-IR) sensors consisting of silicon nitride (SiN) waveguides were designed and tested to detect volatile organic compounds (VOCs). SiN thin films, prepared by low-pressure chemical vapor deposition (LPCVD), have a broad mid-IR transparent region and a lower refractive index (n_SiN = 2.0) than conventional materials such as Si (n_Si = 3.4), which leads to a stronger evanescent wave and therefore higher sensitivity, as confirmed by a finite-difference eigenmode (FDE) calculation. Further, in-situ monitoring of three VOCs (acetone, ethanol, and isoprene) was experimentally demonstrated through characteristic absorption measurements at wavelengths λ = 3.0–3.6 μm. The SiN waveguide showed a five-fold sensitivity improvement over the Si waveguide due to its stronger evanescent field. To our knowledge, this is the first time SiN waveguides are used to perform on-chip mid-IR spectral measurements for VOC detection. Thus, the developed waveguide sensor has the potential to be used as a compact device module capable of monitoring multiple gaseous analytes for health, agricultural and environmental applications.

Detection of SARS-CoV-2 infection by exhaled breath spectral analysis: Introducing a ready-to-use point-of-care mass screening method

BACKGROUND

The current SARS-CoV-2 pandemic created an urgent need for rapid, infection screening applied to large numbers of asymptomatic individuals. To date, nasal/throat swab polymerase chain reaction (PCR) is considered the "gold standard". However, this is inconducive to mass, point-of-care (POC) testing due to person discomfort during sampling and a prolonged result turnaround. Breath testing for disease specific organic compounds potentially offers a practical, rapid, non-invasive, POC solution. The study compares the Breath of Health, Ltd. (BOH) breath analysis system to PCR's ability to screen asymptomatic individuals for SARS-CoV-2 infection. The BOH system is mobile and combines Fourier-transform infrared (FTIR) spectroscopy with artificial intelligence (AI) to generate results within 2 min and 15 s. In contrast to prior SARS-CoV-2 breath analysis research, this study focuses on diagnosing SARS-CoV-2 via disease specific spectrometric profiles rather than through identifying the disease specific molecules.

METHODS

Asymptomatic emergency room patients with suspected SARS-CoV-2 exposure in two leading Israeli hospitals were selected between February through April 2021. All were tested via nasal/throat-swab PCR and BOH breath analysis. In total, 297 patients were sampled (mean age 57·08 ± SD 18·86, 156 males, 139 females, 2 unknowns). Of these, 96 were PCR-positive (44 males, 50 females, 2 unknowns), 201 were PCR-negative (112 males, 89 females). One hundred samples were used for AI identification of SARS-CoV-2 distinguishing spectroscopic wave-number patterns and diagnostic algorithm creation. Algorithm validation was tested in 100 proof-of-concept samples (34 PCR-positive, 66 PCR-negative) by comparing PCR with AI algorithm-based breath-test results determined by a blinded medical expert. One hundred additional samples (12 true PCR-positive, 85 true PCR-negative, 3 confounder false PCR-positive [not included in the 297 total samples]) were evaluated by two blinded medical experts for further algorithm validation and inter-expert correlation.

FINDINGS

The BOH system identified three distinguishing wave numbers for SARS-CoV-2 infection. In the first phase, the single expert identified the first 100 samples correctly, yielding a 1:1 FTIR/AI:PCR correlation. The two-expert second-phase also yielded 1:1 FTIR/AI:PCR correlation for 97 non-confounders and null correlation for the 3 confounders. Inter-expert correlation was 1:1 for all results. In total, the FTIR/AI algorithm demonstrated 100% sensitivity and specificity for SARS-CoV-2 detection when compared with PCR.

INTERPRETATION

The SARS-CoV-2 method of breath analysis via FTIR with AI-based algorithm demonstrated high PCR correlation in screening for asymptomatic individuals. This is the first practical, rapid, POC breath analysis solution with such high PCR correlation in asymptomatic individuals. Further validation is required with a larger sample size.

FUNDING

Breath of Health Ltd, Rehovot, Israel provided study funding.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies

PURPOSE

Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.

METHODS

Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.

RESULTS

Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.

CONCLUSIONS

The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.

REGISTRATION

PROSPERO CRD42020180218, registered on 05-07-2020.

Enhanced Absorption with Graphene-Coated Silicon Carbide Nanowires for Mid-Infrared Nanophotonics

The mid-infrared (MIR) is an exciting spectral range that also hosts useful molecular vibrational fingerprints. There is a growing interest in nanophotonics operating in this spectral range, and recent advances in plasmonic research are aimed at enhancing MIR infrared nanophotonics. In particular, the design of hybrid plasmonic metasurfaces has emerged as a promising route to realize novel MIR applications. Here we demonstrate a hybrid nanostructure combining graphene and silicon carbide to extend the spectral phonon response of silicon carbide and enable absorption and field enhancement of the MIR photon via the excitation and hybridization of surface plasmon polaritons and surface phonon polaritons. We combine experimental methods and finite element simulations to demonstrate enhanced absorption of MIR photons and the broadening of the spectral resonance of graphene-coated silicon carbide nanowires. We also indicate subwavelength confinement of the MIR photons within a thin oxide layer a few nanometers thick, sandwiched between the graphene and silicon carbide. This intermediate shell layer is characteristically obtained using our graphitization approach and acts as a coupling medium between the core and outer shell of the nanowires.

Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection

Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.

Preparation of defected SWCNTs decorated with en-APTAS for application in high-performance nitric oxide gas detection

We demonstrate highly sensitive and selective chemiresistive-type NO gas detection using defected single-walled carbon nanotubes (SWCNTs) decorated with N-[3-(trimethoxysilyl)propyl]ethylene diamine (en-APTAS) molecules. The defected SWCNTs were prepared via furnace annealing at 700 °C and confirmed by transmission electron microscopy. A single en-APTAS molecule has two amine groups acting as adsorption sites for NO gas, which can improve the NO response. The NO response was further enhanced when the defected SWCNTs were utilized because NO sensing reactions could occur on both the inner and outer walls of the defected SWCNTs. The en-APTAS decoration improved the NO response of the SWCNT-based gas sensing devices by 2.5 times; when the defected SWCNTs were used, the NO response was further improved by 3 times. Meanwhile, the recovery performance in a time-resolved response curve was significantly improved (45 times) via a simple rinsing process with ethanol. Specifically, the fabricated device did not respond to carbon monoxide (CO) or BTEX gas (i.e., a mixture of benzene, toluene, ethyl benzene, and xylene), indicating its high selectivity to NO gas. The results show the possibility of a high-performance SWCNT-based NO gas sensor applicable to healthcare fields requiring ppb-level detection, such as in vitro diagnostics (IVDs) of respiratory diseases.

Isotopic gas analysis by means of mid-infrared photoacoustic spectroscopy targeting human exhaled air

There is a great need for cost-efficient non-invasive medical diagnostic tools for analyzing humanly exhaled air. Compared to present day methods, photoacoustic spectroscopy (PAS) can provide a compact and portable (bedside), sensitive and inexpensive solution. We demonstrate a novel portable photoacoustic spectroscopic platform for isotopic measurements of methane (CH₄). We identify and discriminate the ¹²CH₄- and ¹³CH₄ isotopologues and determine their mixing ratio. An Allan deviation analysis shows that the noise equivalent concentration for CH₄ is 200 ppt (pmol/mol) at 100 s of integration time, corresponding to a normalized noise equivalent absorption coefficient of 5.1×10^-9Wcm^-1Hz^-1/2, potentially making the PAS sensor a truly disruptive instrument for bedside monitoring using isotope tracers by providing real-time metabolism data to clinical personnel.

Surface Functionalization Utilizing Mesoporous Silica Nanoparticles for Enhanced Evanescent-Field Mid-Infrared Waveguide Gas Sensing

This work focuses on the development of nanoparticle-based layer-by-layer (LbL) coatings for enhancing the detection sensitivity and selectivity of volatile organic compounds (VOCs) using on-chip mid-infrared (MIR) waveguides (WGs). First, we demonstrate construction of conformal coatings of polymer/mesoporous silica nanoparticles (MSNs) on the surface of Si-based WGs using the LbL technique and evaluate the coating deposition conditions, such as pH and substrate withdrawal speed, on the thickness and homogeneity of the assemblies. We then use the modified WGs to achieve enhanced sensitivity and selectivity of polar organic compounds, such as ethanol, versus non-polar ones, such as methane, in the MIR region. In addition, using density functional theory calculations, we show that such an improvement in sensing performance is achieved due to preferential adsorption of ethanol molecules within MSNs in the vicinity of the WG evanescent field.

Widely-Tunable Quantum Cascade-Based Sources for the Development of Optical Gas Sensors

Spectroscopic techniques based on Distributed FeedBack (DFB) Quantum Cascade Lasers (QCL) provide good results for gas detection in the mid-infrared region in terms of sensibility and selectivity. The main limitation is the QCL relatively low tuning range (~10 cm^-1) that prevents from monitoring complex species with broad absorption spectra in the infrared region or performing multi-gas sensing. To obtain a wider tuning range, the first solution presented in this paper consists of the use of a DFB QCL array. Tuning ranges from 1335 to 1387 cm^-1 and from 2190 to 2220 cm^-1 have been demonstrated. A more common technique that will be presented in a second part is to implement a Fabry-Perot QCL chip in an external-cavity (EC) system so that the laser could be tuned on its whole gain curve. The use of an EC system also allows to perform Intra-Cavity Laser Absorption Spectroscopy, where the gas sample is placed within the laser resonator. Moreover, a technique only using the QCL compliance voltage technique can be used to retrieve the spectrum of the gas inside the cavity, thus no detector outside the cavity is needed. Finally, a specific scheme using an EC coherent QCL array can be developed. All these widely-tunable Quantum Cascade-based sources can be used to demonstrate the development of optical gas sensors.