Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence.

The high infectivity of SARS-CoV-2 makes it essential to develop a rapid and accurate diagnostic test so that carriers can be isolated at an early stage. Viral RNA in nasopharyngeal samples by RT-PCR is currently considered the reference method although it is not recognized as a strong gold standard due to certain drawbacks. Here we develop a methodology combining the analysis of from human nasopharyngeal (NP) samples by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with the use of machine learning (ML). A total of 236 NP samples collected in two different viral transport media were analyzed with minimal sample preparation and the subsequent mass spectra data was used to build different ML models with two different techniques. The best model showed high performance in terms of accuracy, sensitivity and specificity, in all cases reaching values higher than 90%. Our results suggest that the analysis of NP samples by MALDI-TOF MS and ML is a simple, safe, fast and economic diagnostic test for COVID-19.

Laser ablation synthesis of arsenic-phosphide Asm Pn clusters from As-P mixtures. Laser desorption ionisation with quadrupole ion trap time-of-flight mass spectrometry: The mass spectrometer as a synthesizer.

RATIONALE: Only a few arsenic phosphides are known. A high potential for the generation of new compounds is offered by Laser Ablation Synthesis (LAS) and when Laser Desorption Ionization (LDI) is coupled with simultaneous Time-Of-Flight Mass Spectrometry (TOFMS), immediate identification of the clusters can be achieved. METHODS: LAS was used for the generation of arsenic phosphides via laser ablation of phosphorus-arsenic mixtures while quadrupole ion trap time-of-flight mass spectrometry (QIT-TOFMS) was used to acquire the mass spectra. RESULTS: Many new Asm Pn± clusters (479 binary and 369 mono-elemental) not yet described in the literature were generated in the gas phase and their stoichiometry determined. The likely structures for some of the observed clusters arbitrary selected (20) were computed by density functional theory (DFT) optimization. CONCLUSIONS: LAS is an advantageous approach for the generation of new Asm Pn clusters, while mass spectrometry was found to be an efficient technique for the determination of cluster stoichiometry. The results achieved might inspire the synthesis of new materials.

Laser Ablation Synthesis of Gold Selenides by using a Mass Spectrometer as a Synthesizer: Laser Desorption Ionization Time-of-Flight Mass Spectrometry.

Methods for the rapid construction of new chemical motifs have the potential to accelerate the development of nanoscience. The synthesis of new chemical entities by laser ablation has been systematically demonstrated by using mixtures of gold and selenium. The compounds generated are detected by time-of-flight mass spectrometry and, for selected compounds, the structure is investigated by using density functional theory optimization. In total, 67 new gold selenide clusters have been synthesized, demonstrating an unsuspected richness in gold chemistry. Chemical species generated in the gas phase might inspire new routes for the synthesis of novel compounds in the solid state.

Laser ablation synthesis of new gold arsenides using nano-gold and arsenic as precursors. Laser desorption ionisation time-of-flight mass spectrometry and spectrophotometry.

RATIONALE: Currently, a limited number of gold arsenides have been described, some of which have important industrial applications, Laser ablation synthesis (LAS) has been employed in an attempt to generate some novel gold arsenide compounds. METHODS: LAS of gold arsenides was performed using nano-gold (NG) and arsenic as precursors. The clusters formed during laser desorption ionisation (LDI) were analysed by mass spectrometry using a quadrupole ion trap and reflectron time-of-flight analyser to determine the stoichiometry. UV/VIS spectrophotometry was used to follow possible hydrothermal synthesis of gold arsenides. RESULTS: LAS of NG yielded singly charged gold clusters Aum (+(-)) (m = 1-35). LAS of bulk arsenic and nano-arsenic produced Asn (+(-)) clusters with n = 2-10 and n = 2-20, respectively. Laser ablation of Au-As nano-composites or NG-As mixtures generated Aum (+(-)) (m = 1-12), Asn (+(-)) (n = 3-4), and several series of Aum Asn (+(-)) (m = 1-60, n = 1-18) clusters. Over 450 species of gold arsenide clusters and 212 mixed chlorinated Aum Asn Clx clusters were detected and their stoichiometry determined. CONCLUSIONS: Many new gold arsenides were synthesised via LAS for the first time with Au-As composites and NG-As mixtures of different Au:As ratios using mass spectrometry to determine cluster stoichiometry. The resolved stoichiometry of Aum Asn clusters determined in this study could accelerate the development of advanced Au-As nano-materials.

Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the Canary Islands according to the island of origin.

Eleven metals (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li, and Rb) were determined in 83 red wines from the Canary Islands. The wines presented high concentrations of Na, and the concentrations of Cu and Zn were much lower than the maximum concentrations established by the International Office of Vine and Wine (OIV). Applying principal component analysis, the dimension space was reduced to five principal components that explain 76.4% of the total variance, and the wines tend to separate on the basis of the island of production. Linear discriminant analysis (LDA) allowed a reasonable classification of wines according to the island of production. When artificial neural networks (Kohonen self-organizing maps and back-propagation feed-forward as unsupervised and supervised techniques, respectively) were applied on the matrix of data constituted by the analyzed metals, the results improved in relation to those obtained by other multivariate methods observing a differentiation of wines according to island of production.