Ammonia (NH3) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH3 is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical absorption spectroscopy (UV-DOAS) and spectral reconstruction fitting neural network (SRFNN) for detecting NH3 in EB. UV-DOAS is introduced to eliminate interference from slow change absorption in the EB spectrum while spectral reconstruction fitting is proposed for the first time to map the original spectra onto the sine function spectra by the principle of least absolute deviations. The sine function spectra are then fitted by the least-squares method to eliminate noise signals and the interference of exhaled nitric oxide. Finally, the neural network is built to enable the detection of NH3 in EB at parts per billion (ppb) level. The laboratory results show that the detection range is 9.50-12425.82 ppb, the mean absolute percentage error (MAPE) is 0.83%, and the detection accuracy is 0.42%. Experimental results prove that the sensor can detect breath NH3 and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH3 with high accuracy and stability in the medical field.

Highly sensitive and rapid detection of ethylene, the smallest alkene of great significance in human physiological metabolism remains a great challenge. In this study, we developed a new photoionization-induced substitution reaction chemical ionization time-of-flight mass spectrometry (PSCI-TOFMS) for trace exhaled ethylene detection. An intriguing ionization phenomenon involving a substitution reaction between the CH2Br2+ reactant ion and ethylene molecule was discovered and studied for the first time. The formation of readily identifiable [CH2Br·C2H4]+ product ion greatly enhanced the ionization efficiency of ethylene, which led to approximately 800-fold improvement of signal intensity over that in single photon ionization mode. The CH2Br2+ reactant ion intensity and ion-molecule reaction time were optimized, and a Nafion tube was employed to eliminate the influence of humidity on the ionization of ethylene. Consequently, a limit of detection (LOD) as low as 0.1 ppbv for ethylene was attained within 30 s at 100 % relative humidity. The application of PSCI-TOFMS on the rapid detection of trace amounts of exhaled ethylene from healthy smoker and non-smoker volunteers demonstrated the satisfactory performance and potential of this system for trace ethylene measurement in clinical diagnosis, atmospheric measurement, and process monitoring.

Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (n= 149) and SCC (n= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.

A novel Fe2Mo3O8/MoO2@MoS2 nanocomposite is synthesized for extremely sensitive detection of NH3 in the breath of kidney disease patients at room temperature. Compared to MoS2, α-Fe2O3/MoS2, and MoO2@MoS2, it shows the optimal gas-sensing performance by optimizing the formation of Fe2Mo3O8 at 900 °C. The annealed Fe2Mo3O8/MoO2@MoS2 nanocomposite (Fe2Mo3O8/MoO2@MoS2-900 °C) sensor demonstrates a remarkably high selectivity of NH3 with a response of 875% to 30 ppm NH3 and an ultralow detection limit of 3.7 ppb. This sensor demonstrates excellent linearity, repeatability, and long-term stability. Furthermore, it effectively differentiates between patients at varying stages of kidney disease through quantitative NH3 measurements. The sensing mechanism is elucidated through the analysis of alterations in X-ray photoelectron spectroscopy (XPS) signals, which is supported by density functional theory (DFT) calculations illustrating the NH3 adsorption and oxidation pathways and their effects on charge transfer, resulting in the conductivity change as the sensing signal. The excellent performance is mainly attributed to the heterojunction among MoS2, MoO2, and Fe2Mo3O8 and the exceptional adsorption and catalytic activity of Fe2Mo3O8/MoO2@MoS2-900 °C for NH3. This research presents a promising new material optimized for detecting NH3 in exhaled breath and a new strategy for the early diagnosis and management of kidney disease.

Herein, SnO2QDs (<10 nm) with small size instead of conventional nanoparticles was employed to modify ZnFe2O4to synthesize porous and heterogeneous SnO2/ZnFe2O4(ZFSQ) composites for gas sensing. By an immersion process combined with calcination treatment, the resultant porous ZFSQ composites with different contents of SnO2QDs were obtained, and their sensing properties were investigated. Compared with bare ZnFe2O4and SnO2QDs, porous ZFSQ composites based-sensors showed much improved sensor response to acetone. For contrast, the sensor performance of ZFSQ composites was also compared with that of ZnFe2O4sphere modified by SnO2nanoparticles with different size. The porous ZFSQ composite with 5 wt% SnO2QDs (ZFSQ-5) showed a better acetone sensing response than that of other ZFSQ composites, and it exhibited a high response value of 110-100 ppm of acetone and a low detection limit of 0.3 ppm at 240 °C. In addition to the rich heterojunctions and porous structure, the size effect of SnO2QDs was other indispensable reasons for the improved sensor performance. Finally, the ZFSQ-5 composite sensor was attempted to be applied for acetone sensing in exhaled breath, suggesting its great potential in monitoring acetone.

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

The correlation between propofol concentration in exhaled breath (CE) and plasma (CP) has been well-established, but its applicability for estimating the concentration in brain tissues (CB) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measuredCE,CP, andCBin mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team\'s developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites\' concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.

Breath hydrogen (H2) and methane (CH4) monitoring play an important role in the diagnosis of gastrointestinal disorders, such as lactose intolerance and small intestinal bacterial overgrowth (SIBO). In this paper, the photoacoustic spectroscopy method is used for H2 gas and CH4 gas detection. We present a novel approach for H2 gas concentration measurement, which is the linear relationship between the resonant frequency of breath carbon dioxide (CO2) and the H2 concentration in a resonant photoacoustic cell. Experimental results show that the minimum detectable limits of H2, CH4, and CO2 are calculated to be 8.86, 0.56, and 145.14 ppm, respectively, which can meet the requirements of breath diagnosis.

Aerosol transmission has been officially recognized by the world health authority resulting from its overwhelming experimental and epidemiological evidences. Despite substantial progress, few additional actions were taken to prevent aerosol transmission, and many key scientific questions still await urgent investigations. The grand challenge, the effective control of aerosol transmission of COVID-19, remains unsolved. A better understanding of the viral shedding into the air has been developed, but its temporal pattern is largely unknown. Sampling tools, as one of the critical elements for studying SARS-CoV-2 aerosol, are not readily available around the world. Many of them are less capable of preserving the viability of SARS-CoV-2, thus offering no clues about viral aerosol infectivity. As evidenced, the viability of SARS-CoV-2 is also directly impacted by temperature, humidity, sunlight, and air pollutants. For SARS-CoV-2 aerosol detection, liquid samplers, together with real-time polymerase chain reaction (RT-PCR), are currently used in certain enclosed or semi-enclosed environments. Sensitive and rapid COVID-19 screening technologies are in great need. Among others, the breath-borne-based method emerges with global attention due to its advantages in sample collection and early disease detection. To collectively confront these challenges, scientists from different fields around the world need to fight together for the welfare of mankind. This review summarized the current understanding of the aerosol transmission of SARS-CoV-2 and identified the key knowledge gaps with a to-do list. This review also serves as a call for efforts to develop technologies to better protect the people in a forthcoming reopening world.

Recent population and animal studies have revealed a correlation between fat content and the severity of benzene-induced hematologic toxicity. However, the precise impact of lipid deposition on benzene-induced hematotoxicity and the underlying mechanisms remain unclear. In this study, we established a mouse model with moderate lipid accumulation by subjecting the mice to an 8-week high-fat diet (45% kcal from fat, HFD), followed by 28-day inhalation of benzene at doses of 0, 1, 10, and 100 ppm. The results showed that benzene exposure caused a dose-dependent reduction of peripheral white blood cell (WBC) counts in both diet groups. Notably, this reduction was less pronounced in the HFD-fed mice, suggesting that moderate lipid accumulation mitigates benzene-related hematotoxicity. To investigate the molecular basis for this effect, we performed bioinformatics analysis of high-throughput transcriptome sequencing data, which revealed that moderate lipid deposition alters mouse metabolism and stress tolerance towards xenobiotics. Consistently, the expression of key metabolic enzymes, such as Cyp2e1 and Gsta1, were upregulated in the HFD-fed mice upon benzene exposure. Furthermore, we utilized a real-time exhaled breath detection technique to monitor exhaled benzene metabolites, and the results indicated that moderate lipid deposition enhanced metabolic activation and increased the elimination of benzene metabolites. Collectively, these findings demonstrate that moderate lipid deposition confers reduced susceptibility to benzene-induced hematotoxicity in mice, at least in part, by accelerating benzene metabolism and clearance.