COVID-19 Detection from Mass Spectra of Exhaled Breath

According to the World Health Organization, the SARS-CoV-2 virus generated a global emergency between 2020 and 2023 resulting in about 7 million deaths out of more than 750 million individuals diagnosed with COVID-19. During these years, polymerase-chain-reaction and antigen testing played a prominent role in disease control. In this study, we propose a fast and non-invasive detection system exploiting a proprietary mass spectrometer to measure ions in exhaled breath. We demonstrated that infected individuals, even if asymptomatic, exhibit characteristics in the air expelled from the lungs that can be detected by a nanotech-based technology and then recognized by soft-computing algorithms. A clinical trial was ran on about 300 patients: the mass spectra in the 10-351 mass-to-charge range were measured, suitably pre-processed, and analyzed by different classification models; eventually, the system shown an accuracy of 95% and a recall of 94% in identifying cases of COVID-19. With performances comparable to traditional methodologies, the proposed system could play a significant role in both routine examination for common diseases and emergency response for new epidemics.Comment: 15 page

Identification of miRNAs regulating MAPT expression and their analysis in plasma of patients with dementia

Background: Dementia is one of the most common diseases in elderly people and hundreds of thousand new cases per year of Alzheimer’s disease (AD) are estimated. While the recent decade has seen significant advances in the development of novel biomarkers to identify dementias at their early stage, a great effort has been recently made to identify biomarkers able to improve differential diagnosis. However, only few potential candidates, mainly detectable in cerebrospinal fluid (CSF), have been described so far. Methods: We searched for miRNAs regulating MAPT translation. We employed a capture technology able to find the miRNAs directly bound to the MAPT transcript in cell lines. Afterwards, we evaluated the levels of these miRNAs in plasma samples from FTD (n = 42) and AD patients (n = 33) and relative healthy controls (HCs) (n = 42) by using qRT-PCR. Results: Firstly, we found all miRNAs that interact with the MAPT transcript. Ten miRNAs have been selected to verify their effect on Tau levels increasing or reducing miRNA levels by using cell transfections with plasmids expressing the miRNAs genes or LNA antagomiRs. Following the results obtained, miR-92a-3p, miR-320a and miR-320b were selected to analyse their levels in plasma samples of patients with FTD and AD respect to HCs. The analysis showed that the miR-92a-1-3p was under-expressed in both AD and FTD compared to HCs. Moreover, miR-320a was upregulated in FTD vs. AD patients, particularly in men when we stratified by sex. Respect to HC, the only difference is showed in men with AD who have reduced levels of this miRNA. Instead, miR-320b is up-regulated in both dementias, but only patients with FTD maintain this trend in both genders. Conclusions: Our results seem to identify miR-92a-3p and miR-320a as possible good biomarkers to discriminate AD from HC, while miR-320b to discriminate FTD from HC, particularly in males. Combining three miRNAs improves the accuracy only in females, particularly for differential diagnosis (FTD vs. AD) and to distinguish FTD from H

COVID-19 detection from exhaled breath

The SARS-CoV-2 coronavirus emerged in 2019 causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next 4 years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast, and non-invasive COVID-19 detection system, which exploits only exhaled breath. Specifically, provided an air sample, the mass spectra in the 10-351 mass-to-charge range are measured using an original micro and nano-sampling device coupled with a high-precision spectrometer; then, the raw spectra are processed by custom software algorithms; the clean and augmented data are eventually classified using state-of-the-art machine-learning algorithms. An uncontrolled clinical trial was conducted between 2021 and 2022 on 302 subjects who were concerned about being infected, either due to exhibiting symptoms or having recently recovered from illness. Despite the simplicity of use, our system showed a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19 (that is, 95% accuracy, 94% recall, 96% specificity, and 92% [Formula: see text]-score). In light of these outcomes, we think that the proposed system holds the potential for substantial contributions to routine screenings and expedited responses during future epidemics, as it yields results comparable to state-of-the-art methods, providing them in a more rapid and less invasive manner