Normal and abnormal/pathological status of physiological processes in the human organism can be characterized through Volatile Organic Compounds (VOCs) emitted in breath. Recently, a wide range of volatile analytes has risen as biomarkers. These compounds have been addressed in the scientific and medical communities as an extremely valuable metabolic window. Once collected and analysed, VOCs can represent a tool for a rapid, accurate, non-invasive, and painless diagnosis of several diseases and health conditions. These biomarkers are released by exhaled breath, urine, faeces, skin, and several other ways, at trace concentration levels, usually in the ppb_v (μg/L) range. For this reason, the analytical techniques applied for detecting and clinically exploiting the VOCs are extremely important. The present work reviews the most promising results in the field of breath biomarkers and the most common methods of detection of VOCs. A total of 16 pathologies and the respective database of compounds are addressed. An updated version of the VOCs biomarkers database can be consulted at: https://neomeditec.com/VOCdatabase/.

Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction.

Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.

The full anisotropic interaction between one Ar atom and the $\mathrm{CO}{(}^1\mathrm{\Sigma )}$ molecule treated as a rigid rotor (RR) at its equilibrium geometry is evaluated using density functional theory (DFT) to describe the short-range repulsive region (and its orientational anisotropy) as well as the well region and its angular dependence. The long-range dispersion forces are added from the results of perturbation theory and a scaling procedure is suggested for their correct matching with the DFT data. The computational results are found to agree very well with more sophisticated calculations and to improve on earlier empirical estimates. An extensive comparison with available transport property measurements is also carried out and using, among others, new experimental data for thermal diffusion [Shashkov et al., Inzh. Fiz. Zh. 71, 182 (1998)] analyzed in this work for the first time. The present modifications of DFT treatment of the interaction using the correct dispersion terms therefore appears to provide a realistic description of the Ar–CO potential and of several dynamical properties of this molecular mixture in the gas phase.

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.

Providing employees with proper work conditions should be one of the main concerns of any employer. Even so, in many cases, work shifts chronically expose the workers to a wide range of potentially harmful compounds, such as ammonia. Ammonia has been present in the composition of products commonly used in a wide range of industries, namely production in lines, and also laboratories, schools, hospitals, and others. Chronic exposure to ammonia can yield several diseases, such as irritation and pruritus, as well as inflammation of ocular, cutaneous, and respiratory tissues. In more extreme cases, exposure to ammonia is also related to dyspnea, progressive cyanosis, and pulmonary edema. As such, the use of ammonia needs to be properly regulated and monitored to ensure safer work environments. The Occupational Safety and Health Administration and the European Agency for Safety and Health at Work have already commissioned regulations on the acceptable limits of exposure to ammonia. Nevertheless, the monitoring of ammonia gas is still not normalized because appropriate sensors can be difficult to find as commercially available products. To help promote promising methods of developing ammonia sensors, this work will compile and compare the results published so far.