Detection of SARS-CoV-2 using RT-PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT-PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples. The outbreak of coronavirus disease 2019 (COVID-19) is a crisis that affects rich and poor countries alike 1. Detection of SARS-CoV-2 in patient samples is a critical tool for monitoring spread of the disease, guiding therapeutic decisions and devising social distancing protocols 2. Detection assays based on RT-PCR are the most effective and sensitive method for diagnosis of SARS-CoV-2 infection and are used in laboratories around the world 3. However, some countries lack the laboratory resources and access to PCR kits to conduct testing at the required levels. Therefore, other reliable diagnostic techniques are needed. Most clinical diagnostic laboratories have MALDI-MS equipment, which is used to identify bacterial and fungal infections. We propose to leverage the ease-of-use and robustness of MALDI-MS pathogen identification for large-scale SARS-CoV-2 testing in developing countries. MALDI-MS-based assays rely on reference spectra of strains and bioinformatics for high-sensitivity and high-specificity species identification through proteomic profiling. This approach is well established and accepted in many countries for routine diagnostics of yeast and bacterial infections. However, no spectral libraries for SARS-CoV-2 identification using MALDI-MS are publicly available to our knowledge. We first acquired MALDI mass spectra of nasal swab samples that had been tested for SARS-CoV-2 by RT-PCR and analyzed them using machine learning (ML). In this experiment (Fig. 1a), a total of 362 samples (211 SARS-CoV-2-positive and 151 negative, unequivocally confirmed by PCR), which came from three different countries, Argentina (Lab 1), Chile (Lab 2) and Peru (Lab 3), were placed on the MALDI plate without prior sample purification.

Electrospray ionization mass spectrometry (ESI-MS) is found to gently and efficiently transfer small to large as well as singly to multiply charged [X+]n[A-]m supramolecules of imidazolium ion (X+) ionic liquids to the gas phase, and to reveal "magic numbers" for their most favored assemblies. Tandem mass spectrometric experiments (ESI-MS/MS) were then used to dissociate, via low-energy collision activation, mixed and loosely bonded [A- - - -X- - - -A']- and [X- - - -A- - - -X']+ gaseous supramolecules, as well as their higher homologues, and to estimate and order via Cooks' kinetic method (CKM) and B3LYP/6-311G(d,p) calculations the intrinsic solvent-free magnitude of hydrogen bonds. For the five anions studied, the relative order of intrinsic hydrogen-bond strengths to the 1-n-butyl-3-methylimidazolium ion [X1]+ is: CF3CO2- (zero) > BF4- (-3.1) > PF6- (-10.0) > InCl4- (-16.4) and BPh4- (-17.6 kcal mol(-1)). The relative hydrogen-bond strength for InCl4- was measured via CKM whereas those for the other anions were calculated and used as CKM references. A good correlation coefficient (R=0.998) between fragment ion ratios and calculated hydrogen-bond strengths and an effective temperature (Teff) of 430 K demonstrate the CKM reliability for measuring hydrogen-bond strengths in gaseous ionic liquid supramolecules. Using CKM and Teff of 430 K, the intrinsic hydrogen-bond strengths of BF4- for the three cations investigated is: 1-n-butyl-3-methyl-imidazolium ion (0) > 1,3-di-[(R)-3-methyl-2-butyl]-imidazolium ion (-2.4) > 1,3-di-[(R)-alpha-methylbenzyl]-imidazolium ion (-3.0 kcal mol(-1)). As evidenced by "magic" numbers, greater stabilities are found for the [(X1)2(BF4)3]- and [(X1)5A4]+ supramolecules (A not equal InCl4-).

The search for efficient methodologies to form CÀC bonds continues to challenge chemists who face the construction of organic molecules of increasing complexity and functions. Multifaceted functional-group transformations and the creation and control of new asymmetric centers are key steps in the total synthesis of organic molecules, with special emphasis on catalytic activation.[1] Atom economy and high chemo-, regio-, and stereoselectivity are also essential requisites of new, efficient synthetic reactions.

Direct evidence: An ESI mass spectrometer coupled online to a microreactor was used to intercept the catalytically active cationic intermediates of the Ziegler–Natta polymerization of ethene with the homogeneous catalyst system [Cp2ZrCl2]/MAO (see picture; MAO=methylaluminoxane, Cp=cyclopentadienyl, X=CH3, Cl). For the first time these intermediates were studied directly from the solution and their catalytic activity proved.

High performance liquid chromatography (HPLC), ultraviolet spectroscopy (UV), and total organic carbon (TOC) analyses show that caffeine is quickly and completely degraded underthe oxidative conditions of the UV/H2O2,TiO2/ UV, and Fenton systems but that the organic carbon content of the solution decreases much more slowly. Continuous on-line and real-time monitoring by electrospray ionization mass (ESI-MS) and tandem mass spectrometric experiments (ESI-MS/MS) as well as high accuracy MS measurements and gas chromatography-mass spectrometry analysis show that caffeine is first oxidized to N-dimethylparabanic acid likely via initial OH insertion to the C4=C8 caffeine double bond. A second degradation intermediate, di(N-hidroxymethyl)parabanic acid, has been identified by ESI-MS and characterized by ESI-MS/MS and high accuracy mass measurements. This polar and likely relatively unstable compound, which is not detected by off-line GC-MS analysis, is likely formed via further oxidation of N-dimethylparabanic acid at both of its N-methyl groups and constitutes an unprecedented intermediate in the degradation of caffeine.