Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm−1, followed by peak normalization at 850 cm−1 and preprocessing by MSC.publishedVersio

Multidrug-Resistant Proteus mirabilis Strain with Cointegrate Plasmid

Proteus mirabilis is a component of the normal intestinal microflora of humans and animals, but can cause urinary tract infections and even sepsis in hospital settings. In recent years, the number of multidrug-resistant P. mirabilis isolates, including the ones producing extended-spectrum β-lactamases (ESBLs), is increasing worldwide. However, the number of investigations dedicated to this species, especially, whole-genome sequencing, is much lower in comparison to the members of the ESKAPE pathogens group. This study presents a detailed analysis of clinical multidrug-resistant ESBL-producing P. mirabilis isolate using short- and long-read whole-genome sequencing, which allowed us to reveal possible horizontal gene transfer between Klebsiella pneumoniae and P. mirabilis plasmids and to locate the CRISPR-Cas system in the genome together with its probable phage targets, as well as multiple virulence genes. We believe that the data presented will contribute to the understanding of antibiotic resistance acquisition and virulence mechanisms for this important pathogen

Genomic and Phenotypic Analysis of Multidrug-Resistant <i>Acinetobacter baumannii</i> Clinical Isolates Carrying Different Types of CRISPR/Cas Systems

Acinetobacter baumannii is an opportunistic pathogen being one of the most important causative agents of a wide range of nosocomial infections associated with multidrug resistance and high mortality rate. This study presents a multiparametric and correlation analyses of clinical multidrug-resistant A. baumannii isolates using short- and long-read whole-genome sequencing, which allowed us to reveal specific characteristics of the isolates with different CRISPR/Cas systems. We also compared antibiotic resistance and virulence gene acquisition for the groups of the isolates having functional CRISPR/Cas systems, just CRISPR arrays without cas genes, and without detectable CRISPR spacers. The data include three schemes of molecular typing, phenotypic and genotypic antibiotic resistance determination, as well as phylogenetic analysis of full-length cas gene sequences, predicted prophage sequences and CRISPR array type determination. For the first time the differences between the isolates carrying Type I-F1 and Type I-F2 CRISPR/Cas systems were investigated. A. baumannii isolates with Type I-F1 system were shown to have smaller number of reliably detected CRISPR arrays, and thus they could more easily adapt to environmental conditions through acquisition of antibiotic resistance genes, while Type I-F2 A. baumannii might have stronger “immunity” and use CRISPR/Cas system to block the dissemination of these genes. In addition, virulence factors abaI, abaR, bap and bauA were overrepresented in A. baumannii isolates lacking CRISPR/Cas system. This indicates the role of CRISPR/Cas in fighting against phage infections and preventing horizontal gene transfer. We believe that the data presented will contribute to further investigations in the field of antimicrobial resistance and CRISPR/Cas studies

Prediction of deoxynivalenol contamination in wheat via infrared attenuated total reflection spectroscopy and multivariate data analysiss

The climate crisis further exacerbates the challenges for food production. For instance, the increasingly unpredictable growth of fungal species in the field can lead to an unprecedented high prevalence of several mycotoxins, including the most important toxic secondary metabolite produced by Fusarium spp., i.e., deoxynivalenol (DON). The presence of DON in crops may cause health problems in the population and livestock. Hence, there is a demand for advanced strategies facilitating the detection of DON contamination in cereal-based products. To address this need, we introduce infrared attenuated total reflection (IR-ATR) spectroscopy combined with advanced data modeling routines and optimized sample preparation protocols. In this study, we address the limited exploration of wheat commodities to date via IR-ATR spectroscopy. The focus of this study was optimizing the extraction protocol for wheat by testing various solvents aligned with a greener and more sustainable analytical approach. The employed chemometric method, i.e., sparse partial least-squares discriminant analysis, not only facilitated establishing robust classification models capable of discriminating between high vs low DON-contaminated samples adhering to the EU regulatory limit of 1250 μg/kg but also provided valuable insights into the relevant parameters shaping these models

Molecular Typing, Characterization of Antimicrobial Resistance, Virulence Profiling and Analysis of Whole-Genome Sequence of Clinical Klebsiella pneumoniae Isolates

Klebsiella pneumoniae is one of the most important pathogens concerned with multidrug resistance in healthcare-associated infections. The treating of infections caused by this bacterium is complicated due to the emergence and rapid spreading of carbapenem-resistant strains, which are associated with high mortality rates. Recently, several hypervirulent and carbapenemase-producing isolates were reported that make the situation even more complicated. In order to better understand the resistance and virulence mechanisms, and, in turn, to develop effective treatment strategies for the infections caused by multidrug-resistant K. pneumoniae, more comprehensive genomic and phenotypic data are required. Here, we present the first detailed molecular epidemiology report based on second and third generation (long-read) sequencing for the clinical isolates of K. pneumoniae in the Russian Federation. The data include three schemes of molecular typing, phenotypic and genotypic antibiotic resistance determination, as well as the virulence and plasmid profiling for 36 K. pneumoniae isolates. We have revealed 2 new multilocus sequence typing (MLST)-based sequence types, 32 multidrug-resistant (MDR) isolates and 5 colistin-resistant isolates in our samples. Three MDR isolates belonged to a very rare ST377 type. The whole genome sequences and additional data obtained will greatly facilitate further investigations in the field of antimicrobial resistance studies

The Interaction of the Endocannabinoid Anandamide and Paracannabinoid Lysophosphatidylinositol during Cell Death Induction in Human Breast Cancer Cells

Endocannabinoid anandamide (AEA) and paracannabinoid lysophosphatidylinositol (LPI) play a significant role in cancer cell proliferation regulation. While anandamide inhibits the proliferation of cancer cells, LPI is known as a cancer stimulant. Despite the known endocannabinoid receptor crosstalk and simultaneous presence in the cancer microenvironment of both molecules, their combined activity has never been studied. We evaluated the effect of LPI on the AEA activity in six human breast cancer cell lines of different carcinogenicity (MCF-10A, MCF-7, BT-474, BT-20, SK-BR-3, MDA-MB-231) using resazurin and LDH tests after a 72 h incubation. AEA exerted both anti-proliferative and cytotoxic activity with EC50 in the range from 31 to 80 µM. LPI did not significantly affect the cell viability. Depending on the cell line, the response to the LPI–AEA combination varied from a decrease in AEA cytotoxicity to an increase in it. Based on the inhibitor analysis of the endocannabinoid receptor panel, we showed that for the former effect, an active GPR18 receptor was required and for the latter, an active CB2 receptor. The data obtained for the first time are important for the understanding the manner by which endocannabinoid receptor ligands acting simultaneously can modulate cancer growth at different stages

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied

Preclassification of broadband and sparse infrared data by multiplicative signal correction approach

Abstract Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied