Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network

Purpose The purpose of this paper is to devise a tool based on computational fluid dynamics (CFD) and machine learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A gated recurrent units neural network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking data sets. Design/methodology/approach A computational methodology is used for investigating how infectious particles that originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor airflow is obtained by means of an in-house parallel CFD solver, which uses a one equation Spalart–Allmaras turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted by human breath. The numerical results are used for the ML training. Findings In this work, it is shown that the developed ML model, based on the GRU-NN, can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results in this paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space. Originality/value This study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environment, potentially leading to the new design. A parametric study is carried out to evaluate the impact of system settings on time variation particles emitted by human breath within the space considered.

Non-Volatile Organic Compounds in Exhaled Breath Particles Correspond to Active Tuberculosis

Human breath contains trace amounts of non-volatile organic compounds (NOCs) which might inform non-invasive methods for evaluation of individual health. In previous work, we demonstrated that lipids detected in exhaled breath aerosol (EBA) could be used as markers of active tuberculosis (TB). Here, we advanced our analytical platform in characterizing small metabolites and lipids in EBA samples collected from participants enrolled in clinical trials designed to identify molecular signatures of active TB. EBA samples from 26 participants with active TB and 73 healthy participants were processed using a dual-phase extraction method, and metabolites and lipids were identified via mass spectrometry (MS) database matching. In total, 13 metabolite and 9 lipid markers were identified with optimized relative standard deviation values that were statistically different between individuals diagnosed with active TB and the healthy controls. A feature ranking algorithm reduced this number to 10 molecules, with the membrane glycerophospholipid, phosphatidylinositol 24:4, emerging as top driver of segregation between the two groups. These results support the utility of this approach to identify consistent NOC signatures from EBA samples in active TB cases and suggest the potential to apply this method to other human diseases which alter respiratory NOC release.

Flexible Ni/NiOx-Based Sensor for Human Breath Detection

We developed a simple methodology to fabricate an Ni/NiOx-based flexible breath sensor by a single-step laser digital patterning process of solution-processed NiOx thin-film deposited using NiOx nanoparticle ink. Laser-induced reductive sintering phenomenon enables for the generation of three parts of Ni electrodes and two narrow NiOx-sensing channels in between, defined on a single layer on a thin flexible polymer substrate. The Ni/NiOx-based breath sensor efficiently detects human breath at a relatively low operating temperature (50 °C) with fast response/recovery times (1.4 s/1.7 s) and excellent repeatability. The mechanism of the gas-sensing ability enhancement of the sensor was investigated by X-ray photoelectron spectroscopy analysis. Furthermore, by decoupling of the temperature effect from the breathing gas, the response of the sensor due to the temperature alone and due to the chemical components in the breathing gas could be separately evaluated. Finally, bending and cyclic bending tests (10,000 cycles) demonstrated the superior mechanical stability of the flexible breath sensor.

Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening

The objective of this study is to validate reduced graphene oxide (RGO)-based volatile organic compounds (VOC) sensors, assembled by simple and low-cost manufacturing, for the detection of disease-related VOCs in human breath using machine learning (ML) algorithms. RGO films were functionalized by four different metalloporphryins to assemble cross-sensitive chemiresistive sensors with different sensing properties. This work demonstrated how different ML algorithms affect the discrimination capabilities of RGO–based VOC sensors. In addition, an ML-based disease classifier was derived to discriminate healthy vs. unhealthy individuals based on breath sample data. The results show that our ML models could predict the presence of disease-related VOC compounds of interest with a minimum accuracy and F1-score of 91.7% and 83.3%, respectively, and discriminate chronic kidney disease breath with a high accuracy, 91.7%.

Highly Sensitive Surface Plasmon Resonance Humidity Sensor Based on a Polyvinyl-Alcohol-Coated Polymer Optical Fiber

A surface-plasmon-resonance-based fiber device is proposed for highly sensitive relative humidity (RH) sensing and human breath monitoring. The device is fabricated by using a polyvinyl alcohol (PVA) film and gold coating on the flat surface of a side-polished polymer optical fiber. The thickness and refractive index of the PVA coating are sensitive to environmental humidity, and thus the resonant wavelength of the proposed device exhibits a redshift as the RH increases. Experimental results demonstrate an average sensitivity of 4.98 nm/RH% across an ambient RH ranging from 40% to 90%. In particular, the sensor exhibits a linear response between 75% and 90% RH, with a sensitivity of 10.15 nm/RH%. The device is suitable for human breath tests and shows an average wavelength shift of up to 228.20 nm, which is 10 times larger than that of a silica-fiber-based humidity sensor. The corresponding response and recovery times are determined to be 0.44 s and 0.86 s, respectively. The proposed sensor has significant potential for a variety of practical applications, such as intensive care and human health analysis.

INTRODUCING EXHALED HUMAN BREATH WATER VAPOR AS PROPOSED MECHANISM INFLUENCING SUPERFICIAL CUTANEOUS WOUND HEALING

The concept that moist wounds heal faster than dry wounds was introduced in 1962. Most recently, in 1990 the concept was revisited with the introduction of a highly permeable wound dressing exposed to water vapors. The latter allows for water as a humidifying agent. Ideally, acceleration of superficial wound healing had been accomplished by the introduction of a highly water vapor permeable wound dressing. The breathable property allows for water vapor to interact with already present fibrin(ogen) material in blood clots. This manuscript adds a mechanism for the ultimate undisturbed success in cutaneous wound healing, being the dependency on a continuos supply of water vapor. In vitro experiments are introduced showing the cessation of exhaled human breath vapor onto a dry human blood smear as the end point of said interaction. Additionally the experiments were reproduced by exposing the blood smears to steam (water vapor) generated by machinery. In conclusion, exhaled human breath water vapor blown onto a blood clot has the same effect as water vapor emitted by machinery boiling water. Both causing a disappearance of the clot organized fibrin strands into a semisolid gelatinous state. Additionally, discontinuation of the water vapor infusion is also documented triggering a return of organized fibrin strands, albeit of greater intensity.

The Room Temperature Highly Sensitive Ammonia Gas Sensor Based on Polyaniline and Nitrogen-Doped Graphene Quantum Dot-Coated Hollow Indium Oxide Nanofiber Composite

Hollow indium trioxide (In2O3) nanofibers fabricated via an effectively combined method of electrospinning and high-temperature calcination were coated with nitrogen-doped graphene quantum dots (N-GQDs) prepared by a hydrothermal process through electrostatic interaction. The N-GQD-coated hollow In2O3 nanofibers served as a core for the synthesis of polyaniline (PANI)/N-GQD/hollow In2O3 nanofiber ternary composites using in situ chemical oxidative polymerization. The chemical structure and morphology of the fabricated ternary composites were characterized using Fourier transform infrared, field-emission scanning electron microscopy, and transmission electron microscopy. The gas-sensing performances of the ternary composites were estimated by a homemade dynamic test system which was supplied with a real-time resistance acquisition platform at room temperature. The response value of the PANI/N-GQD/hollow In2O3 nanofiber sensor with a loading of 20 wt% N-GQD-coated hollow In2O3 nanofiber and an exposure of 1 ppm NH3 was 15.2, which was approximately more than 4.4 times higher than that of the PANI sensor. This ternary composite sensor was proved to be very sensitive in the detection of NH3 at a range of concentration between 0.6 ppm and 2.0 ppm at room temperature, which is crucial in the detection of hepatic or kidney disease in human breath. The PANI/N-GQD/hollow In2O3 nanofiber sensor also revealed higher selectivity and repeatability when exposed to 1.0 and 2.0 ppm NH3 at room temperature. Because of the excellent selectivity and repeatability in the detection of 1.0 and 2.0 ppm NH3 at room temperature achieved in this study, it is considered that the PANI/N-GQD/hollow In2O3 nanofiber composite sensor will be a favored gas-sensing material applied on human breath for the detection of hepatic or kidney disease.

Experimental Investigation on Water Adsorption Using Laser Photoacoustic Spectroscopy and Numerical Simulations

In the present research we propose a model to assess the water vapors adsorption capacity of a SiO2 trap in the breathing circuit, aiming to reduce the loading of interfering compounds in human breath samples. In this study we used photoacoustic spectroscopy to analyze the SiO2 adsorption of interfering compounds from human breath and numerical simulations to study the flow of expired breath gas through porous media. As a result, the highest adsorption rate was achieved with a flow rate of 300 sccm, while the lowest rate was achieved with a flow rate of 600 sccm. In the procedure of H2O removal from the human breath air samples, we determined a quantity of 213 cm3 SiO2 pearls to be used for a 750 mL sampling bag, in order to keep the detection of ethylene free of H2O interference. The data from this study encourages the premise that the SiO2 trap is efficient in the reduction of interfering compounds (like water vapors) from the human breath.