A new tidal breathing measurement device detects bronchial obstruction during methacholine challenge test.

PURPOSE: Bronchial hyperresponsiveness (BHR), a hallmark of bronchial asthma, is typically diagnosed through a methacholine inhalation test followed by spirometry, known as the methacholine challenge test (MCT). While spirometry relies on proper patients' cooperation and precise execution of forced breathing maneuvers, we conducted a comparative analysis with the portable nanomaterial-based sensing device, SenseGuard™, to non-intrusively assess tidal breathing parameters. MATERIALS AND METHODS: In this prospective study, 37 adult participants with suspected asthma underwent sequential spirometry and SenseGuard™ measurements after inhaling increasing methacholine doses. RESULTS: Among the 37 participants, 18 were MCT responders, 17 were non-responders and 2 were excluded due to uninterpretable data. The MCT responders exhibited a significant lung function difference when comparing the change from baseline to maximum response. This was evident through a notable decrease in forced expiratory volume in 1 â€‹s (FEV1) levels in spirometry, as well as in prominent changes in tidal breathing parameters as assessed by SenseGuard™, including the expiratory pause time (Trest) to total breath time (Ttot) ratio, and the expiratory time (Tex) to Ttot ratio. Notably, the ratios Trest/Ttot (∗p â€‹= â€‹0.02), Tex/Ttot (∗p â€‹= â€‹0.002), and inspiratory time (Tin) to Tex (∗p â€‹= â€‹0.04) identified MCT responders distinctly, corresponding to spirometry (∗p â€‹< â€‹0.0001). CONCLUSIONS: This study demonstrates that tidal breathing assessment using SenseGuard™ device reliably detects clinically relevant changes of respiratory parameter during the MCT. It effectively distinguishes between responders and non-responders, with strong agreement to conventional spirometry-measured FEV1. This technology holds promise for monitoring clinical respiratory changes in bronchial asthma patients pending further studies.

Changes in tidal breathing biomarkers as indicators of treatment response in AECOPD patients in an acute care setting.

PURPOSE: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a complication of COPD that typically necessitates intensified treatment and hospitalization. It is linked to higher morbidity, mortality and healthcare spending. Assessment of therapy response for AECOPD is difficult due to the variability of symptoms and limitations in current measures. Hence, there is a need for new biomarkers to aid in the management of AECOPD in acute care settings. MATERIALS AND METHODS: Fifteen hospitalized AECOPD patients (GOLD 3-4) were enrolled in this study. Treatment response was assessed daily through clinical evaluations and by monitoring tidal breathing biomarkers (respiratory rate [RR], expiratory time [Tex], inspiratory time [Tin], expiratory pause [Trst], total breath time [Ttot]), using a novel, wearable nanosensor-based device (SenseGuard™). RESULTS: Patients who showed significant clinical improvement had substantial changes in ΔTex/Ttot (+14%), ΔTrst/Ttot (-18%), and ΔTin/Tex (+0.09), whereas patients who showed mild or no clinical improvement had smaller changes (+5%, +3%, and -0.03, respectively). Linear regression between change in physician's assessment score and the median change in tidal breathing parameters was significant for Tin/Tex (R2 â€‹= â€‹0.449, ∗p â€‹= â€‹0.017), Tex/Ttot (R2 â€‹= â€‹0.556, ∗p â€‹= â€‹0.005) and Trst/Ttot (R2 â€‹= â€‹0.446, ∗p â€‹= â€‹0.018), while no significant regression was observed for RR, Tin/(Trst â€‹+ â€‹Tex) and Tin/Ttot. CONCLUSIONS: Our study demonstrates the potential of the SenseGuard™ to monitor treatment response in AECOPD patients by measuring changes in tidal breathing biomarkers, which were shown to be associated with significant changes in the patients' respiratory condition as evaluated by physicians. However, further large-scale clinical studies are needed to confirm these findings.

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

BACKGROUND: Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening. METHODS: A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant analysis (LDA) was used as a classifying model of the sensing signals obatined from the examined groups. For the testing step, an additional device was added. The study group included 274 patients: 94 with gastric cancer, 67 who were in the high-risk group, and 113 controls. RESULTS: The results of the test set showed a clear discrimination between patients with gastric cancer and controls using the 2-device LDA model (area under the curve, 93.8%; sensitivity, 100%; specificity, 87.5%; overall accuracy, 91.1%), and acceptable results were also achieved for patients with high-risk lesions (the corresponding values for dysplasia were 84.9%, 45.2%, 87.5%, and 65.9%, respectively). The test-phase analysis showed lower accuracies, though still clinically useful. CONCLUSION: Our results demonstrate that a portable breath sensor device could be useful in point-of-care settings. It shows a promise for detection of gastric cancer as well as for other types of disease. LAY SUMMARY: A portable sensor-based breath analyzer for detection of gastric cancer can be used in point-of-care settings. The results are transferrable between devices via advanced IoT technology. Both the hardware and software of the reported breath analyzer could be easily modified to enable detection and monitirng of other disease states.

Non-contact breath sampling for sensor-based breath analysis.

Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a cohort of 23 volunteers using proton transfer reaction mass spectrometry. The breathing maneuvers embraced distinct depths of respiration, exhalation manners, size of the mouth opening and different sampling distances. Two inhalation modes (normal, relaxed breathing and deep breathing) and two exhalation manners (via smaller and wider lips opening) forming four sampling scenarios were selected. A sampling distance of approximately 2 cm was found to be a reasonable trade-off between sample dilution and requirement of no physical contact of the subject with the analyzer. All four scenarios exhibited comparable measurement reproducibility spread of around 10%. For normal, relaxed inspiration both dead-space and end-tidal phases of exhalation lasted approximately 1.5 s for both expiration protocols. Deep inhalation prolongs the end-tidal phase to about 3 s in the case of blowing via a small lips opening, and by 50% when the air is exhaled via a wide one. In conclusion, non-contact breath sampling can be considered as a promising alternative to the existing breath sampling methods, being relatively close to natural spontaneous breathing.

Tunable touch sensor and combined sensing platform: toward nanoparticle-based electronic skin.

In this paper, we present touch (or pressure) flexible sensors based on monolayer-capped nanoparticles (MCNPs) that are potentially inexpensive, could allow low-voltage operation, and could provide a platform for multifunctional applications. We show that modifying the mechanical and geometrical properties of the flexible substrates, on which the MCNP films are deposited, allows measuring a large span of loads ranging between tens of mg to tens of grams. All flexible sensors exhibited repeatable responses even after a large number of bending cycles. In addition, we show that modified platforms of those touch (or pressure) sensors allow precise detection and monitoring of environmental temperature and humidity. Relying on their superior characteristics, we were able to build an MCNP-based prototype allowing simultaneous detection and monitoring of multiple environmental parameters of touch (or pressure), humidity, and temperature. The excellent temperature (resolution higher than 1 °C and average error of ~5%) and relative humidity (resolution higher than 1% RH and average error of ~9%) sensitivities and the possibility to integrate those sensing abilities makes the suggested platform interesting for potentially inexpensive and low-voltage multifunctional electronic-skin applications.

Classification of breast cancer precursors through exhaled breath.

Certain benign breast diseases are considered to be precursors of invasive breast cancer. Currently available techniques for diagnosing benign breast conditions lack accuracy. The purpose of this study was to deliver a proof-of-concept for a novel method that is based on breath testing to identify breast cancer precursors. Within this context, the authors explored the possibility of using exhaled alveolar breath to identify and distinguish between benign breast conditions, malignant lesions, and healthy states, using a small-scale, case-controlled, cross-sectional clinical trial. Breath samples were collected from 36 volunteers and were analyzed using a tailor-made nanoscale artificial NOSE (NA-NOSE). The NA-NOSE signals were analyzed using two independent methods: (i) principal component analysis, ANOVA and Student's t-test and (ii) support vector machine analysis to detect statistically significant differences between the sub-populations. The NA-NOSE could distinguish between all studied test populations. Breath testing with a NA-NOSE holds future potential as a cost-effective, fast, and reliable diagnostic test for breast cancer risk factors and precursors, with possible future potential as screening method.