The structural diversity of heterocycle-fused potassium cyclopentadienides

Cyclopentadienides of d- and f-elements are highly important complexes with undoubted potential for practical applications. Annelation of a heterocyclic fragment with an η⁵-ring results in substantial improvement of the catalytic properties of these compounds, called “heterocenes”; the investigation of metal coordination with these specific ligands is a highly important problem. We prepared potassium derivatives 5–8 of heterocycle-annelated cyclopentadienes with different structures – derivatives of cyclopenta[1,2-b:4,3-b′]dithiophene (1), indeno[2,1-b]indole (2), indeno[1,2-b]indole (3), and indeno[1,2-b]indolizine (4) and studied the crystal and molecular structures of these salts by X-ray diffraction. We found that heterocycle-fused cyclopentadienides demonstrate remarkable diversity in metal–ligand coordination modes and crystal packing, with formation of two-dimensional polymeric (5), linear polymeric (6), tetrameric (7) and monomeric (8) structures. The NMR spectral data and results of DFT modeling indicate an increase in electron density in the cyclopentadienyl fragment, and this effect was found to be larger in the derivative of the new indolizine ligand precursor 4. The results of our study will be used in the design of next-generation catalysts of α-olefin polymerization.

Heterocycle-fused cyclopentadienides of potassium demonstrate remarkable diversity in metal–ligand coordination and crystal packing.     

Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer

Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.Mass spectrometry imaging (MSI) of biological tissue sections can provide topographically localized biochemical information to supplement conventional histopathological classification systems (1–3). Together with emerging metabolomics-based profiling approaches, MSI represents a highly promising approach in molecular systems oncology (4, 5) and is increasingly being used for the discovery of next-generation cancer biomarker panels (6, 7). Among the MSI techniques currently available, the three most commonly used are matrix-assisted laser desorption ionization (MALDI) (2, 6), secondary ion mass spectrometry (SIMS) (8, 9), and desorption electrospray ionization (DESI) (10, 11). With each of these described approaches, operating characteristics and experimental parameters can be modulated to suit specific analytical objectives and can be customized for the identification of particular biomolecular species. Here, we have opted to use the DESI technique as there are several practical advantages with this method for metabolome-wide imaging studies, primarily attributable to lack of requirement for matrix deposition and ambient ionization, which requires minimal sample preparation (11, 12).Currently MSI is likely to exert greatest influence at the prognostic and therapeutic stages of the disease continuum (Fig. 1), with three fundamental areas of application in cancer phenotyping. First, it offers a means of chemically mapping morphological regions of interest to develop next-generation prognostic and therapeutic biomarkers. Second, it permits compartmentalized assessment of the distribution and biochemical influence of chemotherapeutic agents and/or their downstream metabolites within different tissue regions, offering fresh insights into anti-cancer drug efficacy (13, 14). Third, MSI provides the opportunity to develop automated approaches for tissue classification based entirely on molecular ion patterns. Such automated, “machine-learned” strategies will lessen the logistical and financial burden being placed on pathology services in the modern cancer-screening era, while simultaneously ensuring quality control by minimizing interobserver variability (15).Open in a separate windowFig. 1.MS-based imaging technology in clinical settings.Until now the routine clinical application of MSI approaches has been restricted by inherent time/cost demands and associated heavy analytical workload. However, recent advances in MS technology combined with the richness of generated molecular information should ensure the widespread adoption of MSI technologies in the near- to midterm. The major impediment to this progress currently centers on the choice of chemo-informatics workflow. The standard approach applied to MSI datasets involves a series of steps designed to reduce bioanalytical complexities for improved information recovery, followed by pattern recognition analysis and molecular pattern interpretation. Conventional workflows, integrated into software packages such as BioMap (Novartis), SpectViewer (CEA), DataCubeExplorer (AMOLF), and Mirion (JLU) or within commercial packages from instrument manufacturers such as Xcalibur (Thermo Fisher Scientific) and FlexImaging (Bruker Daltonics) have capabilities limited to basic preprocessing and browsing through selected ion images. There is currently strong demand for more sophisticated chemo-informatics strategies that can streamline data processing and simultaneously maximize disease-relevant molecular information capture. In broad terms, these strategies involve (i) raw analytical signal preprocessing for improved information recovery; (ii) imaging informatics for correlation of MSI and histological information; and (iii) pattern recognition analysis for topographically localized biochemical feature extraction. These steps will influence one another and thus need to be considered within an integrated bioinformatics solution (16).Typically, data preprocessing methods involve peak detection or “binning” and filtering of solvent/matrix or noise-related peaks (17–19), followed by a normalization step. At present, the most widely applied approach involves integrating MSI spectra within a predefined “bin” size (typically ∼0.01 Da). This reduces mass detection accuracy and introduces biologically irrelevant spectral features, making unambiguous assignment of chemical species more difficult. In the case of normalization, the total ion current (TIC) scaling factor is frequently cited in the literature as an acceptable means of accounting for global intensity changes in a MSI dataset (20–22). However, we have recently demonstrated that the performance of this method can be compromised by single large molecular ion peak intensities (21, 23). An additional problem inherent to MS-based analysis of complex biological mixtures is the fact that molecules present in greater intensities within a given sample will tend to exhibit larger variations when subjected to repeated measurement (23). This disruption to variance constancy across the measurement range, known in statistical terms as heteroscedasticity, represents a significant barrier to the effective application of commonly used multivariate techniques for the downstream statistical interrogation of MSI datasets (23). To date a number of different strategies have been proposed in the literature to stabilize variance across the measurement range (24), and we have recently validated several variance-stabilizing normalization techniques in the context of MS-based profiling (23).Beyond these preprocessing steps, MSI data need to be effectively “fused” with conventional histopathological information to allow the construction of large-scale molecular databases composed of region-specific molecular biomarkers and facilitate future automated histology initiatives. Precise methods for coregistration of histological and MSI data are an essential prerequisite for these applications and represent a further challenge at present. Of the software packages currently available to the MSI analyst, only the proprietary Bruker package offers image coregistration and region-of-interest molecular ion pattern extraction, with the option to further process extracted spectra in the associated statistical toolbox ClinProTools (25). However, this approach (limited to data collected on Bruker instrumentation) requires the user to manually select features on the pre- and poststaining images to conduct coregistration and can be subject to considerable error. Other less refined platforms have sought to achieve this objective by visual selection of particular regions of interest on hematoxylin and eosin (H&E)-stained optical images, followed by selection of pixels occupying similar (but not precisely aligned) geographical coordinates on corresponding MSI heat maps (22, 26). This permits only very crude colocalization of features from the two imaging modalities and may be deemed sufficient perhaps only in instances where limited variation in cell typology is seen across the tissue section (e.g., cancerous cellular regions and healthy cellular regions only). A number of image informatics methods have been recently developed to segment and to align the objects between images (27, 28). These approaches can involve rigid or nonrigid transformation, depending on object deformation characteristics. The most commonly used methods are based on extensions of the Lucas–Canade algorithm and their relative advantages and limitations have been recently described within a unifying framework (27). However, there is no standardized image coregistration protocol in the context of histology-driven MSI, and the currently used marker-based/fiducial methods may lack the precision required for detailed definition of morphology-to-chemistry interrelationships.Histology-driven, automated tissue identification further requires efficient and robust extraction of tissue-specific molecular ion patterns (19). The multidimensional nature of MSI datasets calls for effective dimensionality reduction techniques that are capable of extracting tissue-specific multivariate molecular ion patterns. Currently, the most widely used supervised dimensionality reduction technique is partial least-squares discriminant analysis (PLS-DA) (29, 30). It has been shown that PLS-based discriminant components are derived by maximizing between-class variance (Table S1) (30). A more mathematically eloquent mode of discriminant analysis is to maximize the difference between class means while simultaneously minimizing within-class variability. This is the objective of linear discriminant analysis (LDA), which maximizes the ratio of between- vs. within-class variance (31). Unfortunately, LDA cannot be directly applied in circumstances where the number of variables exceeds the number of samples, as is the case with the dataset presented here. Principal component analysis (PCA) has been commonly applied as a preprocessing step before LDA (PCA-LDA) to mitigate this problem (32). However, a problem arises here with respect to the selection of an optimal number of components. Introducing too many components into a model will increase the likelihood of LDA model overfit, whereas retaining too few can result in the loss of discriminatory information (33). In the current study, we have proposed the use of a modified maximum margin criterion (Table S1) to improve supervised feature extraction, while simultaneously avoiding arbitrary selection of the number of principal components before discriminant analysis (34).Here, we have devised a comprehensive data analysis framework with the aim of addressing the current challenges outlined above in MSI data treatment and exploration. Specifically, innovative bioinformatics solutions proposed in this study are i) variance-stabilizing normalization for improved information recovery; ii) an automated image coregistration algorithm for intuitive, precise histology-to-chemistry feature correlation; and iii) a unique method for efficient extraction of tissue-specific multivariate ion patterns. As a validation step, the outlined workflow has been applied to the investigation of tumor-surrounding lipid signatures in colorectal cancer (Movies S1 and S2). We demonstrate that this platform provides in-depth insights into tumor biochemistry by simultaneously analyzing the spatial distribution of hundreds to thousands of lipid species across different cell types. This offers potential for the development of next-generation cancer biomarkers and also may have a translational impact beyond the field of clinical histopathology in personalized pharmacotherapy and drug discovery.     

Core–Shell Fe3O4@C Nanoparticles for the Organic Dye Adsorption and Targeted Magneto-Mechanical Destruction of Ehrlich Ascites Carcinoma Cells

The morphology, structure, and magnetic properties of Fe₃O₄ and Fe₃O₄@C nanoparticles, as well their effectiveness for organic dye adsorption and targeted destruction of carcinoma cells, were studied. The nanoparticles exhibited a high magnetic saturation value (79.4 and 63.8 emu/g, correspondingly) to facilitate magnetic separation. It has been shown that surface properties play a key role in the adsorption process. Both types of organic dyes—cationic (Rhodomine C) and anionic (Congo Red and Eosine)—were well adsorbed by the Fe₃O₄ nanoparticles’ surface, and the adsorption process was described by the polymolecular adsorption model with a maximum adsorption capacity of 58, 22, and 14 mg/g for Congo Red, Eosine, and Rhodomine C, correspondingly. In this case, the kinetic data were described well by the pseudo-first-order model. Carbon-coated particles selectively adsorbed only cationic dyes, and the adsorption process for Methylene Blue was described by the Freundlich model, with a maximum adsorption capacity of 14 mg/g. For the case of Rhodomine C, the adsorption isotherm has a polymolecular character with a maximum adsorption capacity of 34 mg/g. To realize the targeted destruction of the carcinoma cells, the Fe₃O₄@C nanoparticles were functionalized with aptamers, and an experiment on the Ehrlich ascetic carcinoma cells’ destruction was carried out successively using a low-frequency alternating magnetic field. The number of cells destroyed as a result of their interaction with Fe₃O₄@C nanoparticles in an alternating magnetic field was 27%, compared with the number of naturally dead control cells of 6%.     

The Application of Chitosan for Protection of Cultural Heritage Objects of the 15–16th Centuries in the State Tretyakov Gallery

Microorganisms are one of the main factors in the deterioration of cultural heritage, in particular art paintings. The antiseptics currently used in painting have significant limitations due to insufficient effectiveness or increased toxicity and interaction with art materials. In this regard, the actual challenge is the search for novel materials that effectively work against microorganisms in the composition with painting materials and do not change their properties. Chitosan has pronounced antimicrobial properties but was not used previously as an antiseptic for paintings. In our study we developed a number of mock layers based on sturgeon glue, supplemented which chitosan (molecular weight 25 kDa or 45 kDa), standard antiseptics for paintings (positive controls) or without additives (negative control). According to Fourier transform infrared spectroscopy and atomic force microscopy, the addition of chitosan did not significantly affect the optical and surface properties of this material. The ability of chitosan to effectively protect paintings was shown after inoculation on the created mock-up layers of 10 fungi-destructors of tempera painting, previously isolated from cultural heritage of the of the 15–16th centuries in the State Tretyakov Gallery, on the created mock layers. Our study demonstrated the principled opportunity of using chitosan in the composition of painting materials to prevent biodeterioration for the first time.     

Nanostructured Luminescent Gratings for Sensorics

Two-dimensional holographic structures based on photopolymer compositions with luminescent nanoparticles, such as quantum dots, are promising candidates for multiresponsive luminescence sensors. However, their applicability may suffer from the incompatibility of the components, and hence aggregation of the nanoparticles. We showed that the replacement of an organic shell at the CdSe/ZnS quantum dots’ surface with monomer molecules of the photopolymerizable medium achieved full compatibility with the surrounding medium. The effect was demonstrated by luminescence spectroscopy, and steady-state and time-resolved luminescent laser scanning microscopy. We observed the complete spectral independence of local photoluminescence decay, thus proving the absence of even nanoscale aggregation, either in the liquid composition or in the nodes and antinodes of the grating. Therefore, nanostructured luminescent photopolymer gratings with monomer-covered quantum dots can act as hybrid diffractive–luminescent sensor elements.     

Multi-Frequency Light Sources Based on CVD Diamond Matrices with a Mix of SiV− and GeV− Color Centers and Tungsten Complexes

Recently, nanodiamonds with negatively charged luminescent color centers based on atoms of the fourth group (SiV⁻, GeV⁻) have been proposed for use as biocompatible luminescent markers. Further improvement of the functionality of such systems by expanding the frequencies of the emission can be achieved by the additional formation of luminescent tungsten complexes in the diamond matrix. This paper reports the creation of diamond matrices by a hot filament chemical vapor deposition method, containing combinations of luminescing Si-V and Ge-V color centers and tungsten complexes. The possibility is demonstrated of creating a multicolor light source combining the luminescence of all embedded emitters. The emission properties of tungsten complexes and Si-V and Ge-V color centers in the diamond matrices were investigated, as well as differences in their luminescent properties and electron-phonon interaction at different temperatures.     

Resurgence of Influenza Circulation in the Russian Federation during the Delta and Omicron COVID-19 Era

Influenza circulation was substantially reduced after March 2020 in the European region and globally due to the wide introduction of non-pharmaceutical interventions (NPIs) against COVID-19. The virus, however, has been actively circulating in natural reservoirs. In summer 2021, NPIs were loosened in Russia, and influenza activity resumed shortly thereafter. Here, we summarize the epidemiological and virological data on the influenza epidemic in Russia in 2021–2022 obtained by the two National Influenza Centers. We demonstrate that the commonly used baseline for acute respiratory infection (ARI) is no longer sufficiently sensitive and BL for ILI incidence was more specific for early recognition of the epidemic. We also present the results of PCR detection of influenza, SARS-CoV-2 and other respiratory viruses as well as antigenic and genetic analysis of influenza viruses. Influenza A(H3N2) prevailed this season with influenza B being detected at low levels at the end of the epidemic. The majority of A(H3N2) viruses were antigenically and genetically homogenous and belonged to the clade 3C.2a1b.2a.2 of the vaccine strain A/Darwin/9/2021 for the season 2022–2023. All influenza B viruses belonged to the Victoria lineage and were similar to the influenza B/Austria/1359417/2021 virus. No influenza A(H1N1)pdm09 and influenza B/Yamagata lineage was isolated last season.     

Structure and Properties of Ti/Ti64 Graded Material Manufactured by Laser Powder Bed Fusion

Multimaterial additive manufacturing is an attractive way of producing parts with improved functional properties by combining materials with different properties within a single part. Pure Ti provides a high ductility and an improved corrosion resistance, while the Ti64 alloy has a higher strength. The combination of these alloys within a single part using additive manufacturing can be used to produce advanced multimaterial components. This work explores the multimaterial Laser Powder Bed Fusion (L-PBF) of Ti/Ti64 graded material. The microstructure and mechanical properties of Ti/Ti64-graded samples fabricated by L-PBF with different geometries of the graded zones, as well as different effects of heat treatment and hot isostatic pressing on the microstructure of the bimetallic Ti/Ti64 samples, were investigated. The transition zone microstructure has a distinct character and does not undergo significant changes during heat treatment and hot isostatic pressing. The tensile tests of Ti/Ti64 samples showed that when the Ti64 zones were located along the sample, the ratio of cross-sections has a greater influence on the mechanical properties than their shape and location. The presented results of the investigation of the graded Ti/Ti64 samples allow tailoring properties for the possible applications of multimaterial parts.     

Reduction of myocardial ischemia-reperfusion injury with pre- and postconditioning: molecular mechanisms and therapeutic targets

Reduction of infarct size as well as alleviation of other ischemia- and reperfusion-associated injuries are the goals of primary importance in cardiology. One of the remedies is considered to be myocardial preconditioning (PreCon) referred usually to as an increased myocardial tolerance to prolonged ischemia following brief ischemic or non-ischemic challenge. In this review, PreCon stimuli tested to date are considered including a number of mildly noxious factors applied either locally to the myocardium or systemically. Recently, one more mode of heart protection against reperfusion injury termed postconditioning (PostCon) has been developed. On the basis of ample evidence published, along with our findings, a detailed comparative analysis of PreCon and PostCon is presented, with special emphasis on the cellular, molecular, and pharmacological aspects of the topic as well as clinical applications, both implemented and awaiting practical approval.