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.
Pesticides are used worldwide to increase crop yields in agriculture. However, their toxicity and accumulation capacity can make them toxic to the environment, animals and humans. In the case of workers chronically exposed to these substances, they must be sampled continuously, so urine is an excellent option. In this sense, this study proposes to use poly(vinyl alcohol)-malic acid hydrogels, and chitosan-coated calcium alginate as new sorbent phases to be used in pesticide determination processes in urine. To better understand the behavior of these materials in the capture and desorption process, molecular dynamics simulations (MDS) were used, and desorption experiments were performed, using mechanical agitation, ultrasound, and pH variation in the desorption process, in order to optimize the parameters to obtain better recoveries. Under the optimal experimental conditions, the maximum recoveries were of the order of 11% (CFN), 3% (KCF), 53% (DMT), 18% (MTD) and 35% (MTL). Although the recoveries were not exhaustive, they are a first approximation for the use of these new sorbent phases in the determination of this type of compound in aqueous solutions and urine.
The Kirsten rat sarcoma viral oncogene (KRAS) is one
of the most
well-known proto-oncogenes, frequently mutated in pancreatic and colorectal
cancers, among others. We hypothesized that the intracellular delivery
of anti-KRAS antibodies (KRAS-Ab) with biodegradable polymeric micelles
(PM) would block the overactivation of the KRAS-associated cascades
and revert the effect of its mutation. To this end, PM-containing
KRAS-Ab (PM-KRAS) were obtained using Pluronic F127. The feasibility
of using PM for antibody encapsulation as well as the conformational
change of the polymer and its intermolecular interactions with the
antibodies was studied, for the first time, using in silico modeling. In vitro, encapsulation of KRAS-Ab allowed
their intracellular delivery in different pancreatic and colorectal
cancer cell lines. Interestingly, PM-KRAS promoted a high proliferation
impairment in regular cultures of KRAS-mutated HCT116 and MIA PaCa-2
cells, whereas the effect was neglectable in non-mutated or KRAS-independent
HCT-8 and PANC-1 cancer cells, respectively. Additionally, PM-KRAS
induced a remarkable inhibition of the colony formation ability in
low-attachment conditions in KRAS-mutated cells. In vivo, when compared
with the vehicle, the intravenous administration of PM-KRAS significantly
reduced tumor volume growth in HCT116 subcutaneous tumor-bearing mice.
Analysis of the KRAS-mediated cascade in cell cultures and tumor samples
showed that the effect of PM-KRAS was mediated by a significant reduction
of the ERK phosphorylation and a decrease in expression in the stemness-related
genes. Altogether, these results unprecedently demonstrate that the
delivery of KRAS-Ab mediated by PM can safely and effectively reduce
the tumorigenicity and the stemness properties of KRAS-dependent cells,
thus bringing up new possibilities to reach undruggable intracellular
targets.
In light of the growing bacterial resistance to antibiotics and in the absence of the development of new antimicrobial agents, numerous antimicrobial delivery systems over the past decades have been developed with the aim to provide new alternatives to the antimicrobial treatment of infections. However, there are few studies that focus on the development of a rational design that is accurate based on a set of theoretical-computational methods that permit the prediction and the understanding of hydrogels regarding their interaction with cationic antimicrobial peptides (cAMPs) as potential sustained and localized delivery nanoplatforms of cAMP. To this aim, we employed docking and Molecular Dynamics simulations (MDs) that allowed us to propose a rational selection of hydrogel candidates based on the propensity to form intermolecular interactions with two types of cAMPs (MP-L and NCP-3a). For the design of the hydrogels, specific building blocks were considered, named monomers (MN), co-monomers (CM), and cross-linkers (CL). These building blocks were ranked by considering the interaction with two peptides (MP-L and NCP-3a) as receptors. The better proposed hydrogel candidates were composed of MN3-CM7-CL1 and MN4-CM5-CL1 termed HG1 and HG2, respectively. The results obtained by MDs show that the biggest differences between the hydrogels are in the CM, where HG2 has two carboxylic acids that allow the forming of greater amounts of hydrogen bonds (HBs) and salt bridges (SBs) with both cAMPs. Therefore, using theoretical-computational methods allowed for the obtaining of the best virtual hydrogel candidates according to affinity with the specific cAMP. In conclusion, this study showed that HG2 is the better candidate for future in vitro or in vivo experiments due to its possible capacity as a depot system and its potential sustained and localized delivery system of cAMP.
Herein, we report a comparative study of ethylene activation and 1-hexene isomerization carried out with isomeric neutral and cationic methallyl nickel complexes L1Ni(η3-C3H5) and [L1Ni(η3-C3H5)][B(ArF)4] in the presence of borane...
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