Caffeic Acid/Eu(III) Complexes: Solution Equilibrium Studies, Structure Characterization and Biological Activity

Caffeic acid (CFA) is one of the various natural antioxidants and chemoprotective agents occurring in the human diet. In addition, its metal complexes play fundamental roles in biological systems. Nevertheless, research on the properties of CFA with lanthanide metals is very scarce, and little to no chemical or biological information is known about these particular systems. Most of their properties, including their biological activity and environmental impact, strictly depend on their structure, stability, and solution behaviour. In this work, a multi-analytical-technique approach was used to study these relationships for the Eu(III)/CFA complex. The synthesized metal complex was studied by FT-IR, FT-Raman, elemental, and thermal (TGA) analysis. In order to examine the chemical speciation of the Eu(III)/CFA system in an aqueous solution, several independent potentiometric and spectrophotometric UV-Vis titrations were performed at different M:L (metal:ligand) and pH ratios. The general molecular formula of the synthesized metal complex in the solid state was [Eu(CFA)3(H2O)3]∙2H2O (M:L ratio 1:3), while in aqueous solution the 1:1 species were observed at the optimum pH of 6 ≤ pH ≤ 10, ([Eu(CFA)] and [Eu(CFA)(OH)]−). These results were confirmed by 1H-NMR experiments and electrospray-ionization mass spectrometry (ESI-MS). To evaluate the interaction of Eu(III)/CFA and CFA alone with cell membranes, electrophoretic mobility assays were used. Various antioxidant tests have shown that Eu(III)/CFA exhibits lower antioxidant activity than the free CFA ligand. In addition, the antimicrobial properties of Eu(III)/CFA and CFA against Escherichia coli, Bacillus subtilis and Candida albicans were investigated by evaluation of the minimum inhibitory concentration (MIC). Eu(III)/CFA shows higher antibacterial activity against bacteria compared to CFA, which can be explained by the highly probable increased lipophilicity of the Eu(III) complex.

Comparison of oral metabolome profiles of stimulated saliva, unstimulated saliva, and mouth-rinsed water

Saliva includes a substantial amount of biological information, which has enabled us to understand the relationship between oral metabolites and various oral and systemic disorders. However, collecting saliva using a controlled protocol is time-consuming, making saliva an unsuitable analyte in large cohort studies. Mouth-rinsed water (MW), the water used to rinse the mouth, can be collected easily in less time with less difference between subjects than saliva and could be used as an alternative in oral metabolome analyses. In this study, we investigated the potential of MW collection as an efficient alternative to saliva sample collection for oral metabolome profiling. MW, stimulated saliva, and unstimulated saliva were collected from 10 systemically healthy participants. The samples were subjected to metabolome analysis using capillary electrophoresis time-of-flight mass spectrometry, and the types and amounts of metabolites in the samples were compared. Qualitatively, MW contained the same metabolites as unstimulated and stimulated saliva. While the quantity of the metabolites did not drastically change between the sampling methods, all three reflected individual differences, and the features of MW were the same as those of the unstimulated saliva. Overall, these results suggest that MW may be an appropriate alternative to saliva in oral metabolome profile analysis.

The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

Emerging Role of Cancer-Associated Fibroblasts-Derived Exosomes in Tumorigenesis

Cancer-associated fibroblasts (CAFs) are the most important component of the stromal cell population in the tumor microenvironment and play an irreplaceable role in oncogenesis and cancer progression. Exosomes, a class of small extracellular vesicles, can transfer biological information (e.g., proteins, nucleic acids, and metabolites as messengers) from secreting cells to target recipient cells, thereby affecting the progression of human diseases, including cancers. Recent studies revealed that CAF-derived exosomes play a crucial part in tumorigenesis, tumor cell proliferation, metastasis, drug resistance, and the immune response. Moreover, aberrant expression of CAF-derived exosomal noncoding RNAs and proteins strongly correlates with clinical pathological characterizations of cancer patients. Gaining deeper insight into the participation of CAF-derived exosomes in tumorigenesis may lead to novel diagnostic biomarkers and therapeutic targets in human cancers.

Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data

Background Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data. Results In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets. Conclusions Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

The methodology of food design. Part 2. Digital nutritiology in personal food

Atherosclerosis (the main cause of a wide range of cardiovascular diseases) and other multifactorial diseases depend on several nutrition factors, defined in general by lifestyle that directly and constantly affects the human body. The modern level of science and technology development is able to form a diet, taking into account all personal characteristics in such a way that makes nutrition an effective preventive measure against diseases in order to keep a person healthy. The purpose of this article is to define and study all the limitations (the scope of its coverage in the scientific literature) that arose in the process of research aimed to formation of an integrated personal approach to designing of nutrition to prevent atherosclerosis. The object of the study was scientific literature, which is available in open source and free access databases: PubMed, ScienceDirect, eLIBRARY.RU, www.fips.ru, Patentscope. The language of search is Russian and English, search depth is 12 years. In the course of the research two food design concepts were found that affect process of digitalization in the food sector: the concept “FoodTech” (food technology) and digital nutritiology. It was established that in Russia only one company — LLC “City Supermarket” (Moscow), that works with the brand “Azbuka Vkusa” — acts in the sphere of “FoodTech” on the Russian market. This company selects personalized food, taking into account the results of personal nutrigenetic tests, in cooperation with LLC “Genotech” (Moscow). There is a need to use biological information, statistical information processing (nutrigenetic studies, nutrigenomic research) and machine-aided data processing (machine learning) for further generation of automatic algorithm that compiles personal recommendations. The relevance of generation of a national domestic database on chemical composition of food products (presented in the market) to simplify the preparation of individual personal diets is observed. We underline the necessity to use the test-organisms, i. e. dorio fish / zebrafish (Danio rerio) and nematodes (Caenorhabditis elegans)), which were used to determine the activity of candidate substances — the biologically active substances that feature antiatherosclerotic properties. In the future the authors plan to conduct a nutrigenomic and nutrigenetic study, using digital achievements. To collect information about consumers, it is necessary to apply digital devices, and use biological informatics to process the results; after that it is necessary to generate the algorithm for automatic selection of personalized dietary recommendations.

Biological Trace Information Extracted from Bioaerosols Using NGS Analysis

Bioaerosols are atmospheric particles with a biological trace, such as viruses, bacteria, fungi, and plant material such as pollen and plant debris. In this study, we analyzed the biological information in bioaerosols using next generation sequencing of the trace DNA. The samples were collected using an Andersen air sampler and separated into two groups according to particulate matter (PM) size: small (PM2.5) and large (PM10). Amplification and sequencing of the bacterial 16S rDNA gene, prokaryotic internal transcribed spacer 1 (ITS1) region and DNA sequence of a plant chloroplast gene (rbcL) were carried out using several sets of specific primers targeting animal and plant sequences. Lots of bacterial information was detected from the bioaerosols. The most abundant bacteria in several samples were of the Actinobacteria (class), Alphaproteobacteria, Bacilli, and Clostridia. For the animal detection using internal transcribed spacer 1, only uncultured fungi were detected in more than half of the hits, with a high number of Cladosporium sp. in the samples. For the plant identification, the ITS1 information only matched fungal species. However, targeting of the rbcL region revealed diverse plant information, such as Medicago papillosa. In conclusion, traces of bacteria, fungi, and plants could be detected in the bioaerosols, but not of animals using our primers.

How the mind comes to life: The physics and metaphysics of dissipative structures

This monograph tries to illuminate the process of live and mind by focusing on a thermodynamic perspective. In doing so a fundamental link is offered between psychology and neuroscience on the one hand and physics on the other hand. Non-equilibrium thermodynamics applied on human structures leads to explanations for highly complex processes like self-healing, the forecast of the structuring of stable neuronal connections or the importance of feedback loops for the formation of metastable neuronal structures. Besides energy there is evidence for the necessity of a second concept: information. To integrate this into a resilient ontology a new definition for „biological information” is required and will be provided. Transferring in analogy the thermodynamic framework of energy to biological information leads to what I shall call Dual Dissipation Theory (DDT). Its essentials will be presented and a short introduction, why dualism can be supported if introduced correctly. Finally, DDT suggests the following insight:The common concept for life is „energy flow“ and „information flow“, grounded in dissipative structures. It could offer a plausible heuristic for the explanation of mind and consciousness closing the gap from physics, i.e. non-ecquilibrium thermodynamics, to neuroscience and psychology.

Systems Biology–Derived Genetic Signatures of Mastitis in Dairy Cattle: A New Avenue for Drug Repurposing

Mastitis, a disease with high incidence worldwide, is the most prevalent and costly disease in the dairy industry. Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the leading agents causing acute severe infection with clinical signs. E. Coli, environmental mastitis pathogens, are the primary etiological agents of bovine mastitis in well-managed dairy farms. Response to E. Coli infection has a complex pattern affected by genetic and environmental parameters. On the other hand, the efficacy of antibiotics and/or anti-inflammatory treatment in E. coli mastitis is still a topic of scientific debate, and studies on the treatment of clinical cases show conflicting results. Unraveling the bio-signature of mastitis in dairy cattle can open new avenues for drug repurposing. In the current research, a novel, semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration, was used to potentially identify novel therapeutic avenues for the treatment of E. coli mastitis. Online data repositories relevant to known diseases, drugs, and gene targets, along with other specialized biological information for E. coli mastitis, including critical genes with robust bio-signatures, drugs, and related disorders, were used as input data for analysis with the Heter-LP algorithm. Our research identified novel drugs such as Glibenclamide, Ipratropium, Salbutamol, and Carbidopa as possible therapeutics that could be used against E. coli mastitis. Predicted relationships can be used by pharmaceutical scientists or veterinarians to find commercially efficacious medicines or a combination of two or more active compounds to treat this infectious disease.

Knowledge, Attitudes and Perceptions Regarding Endemic Vivax Malaria in Inhabitants and Patients in Two Cities of Northern Gyeonggi-do, Korea, 2020

An understanding of the knowledges, attitudes and perceptions of different populations is key for public health policy makers. Here, a survey was performed on knowledge, attitudes, and perceptions about malaria diagnosis, prevention, control, and treatment. The 407 survey participants included both uninfected inhabitants and patients from 2 cities (Gimpo- and Paju-si) of Northern Gyeonggi-do, known as high-risk areas for vivax malaria. We used community-based study design and non-probability sampling method using the primary data. Association between variables were tested using χ2-tests. In general, the information on malaria reported by the participants in this study was unsystematic and included inaccurate details. The knowledge of malaria symptoms, identified as headache, chills and fever, was high, but the surveyed community lacks knowledge of the specific medications used for malaria treatment, with a large number of respondents having no knowledge of any form of medication. Survey questions with high correct answer rates included questions about easy treatment of malaria in Korea, the high daytime activity of malaria-borne mosquitoes, and the infection risk posed by outdoor activities. However, a large portion of the respondents was unable to provide simple medical and biological information about the disease. This study aimed to comprehensively evaluate the knowledge, attitude, and practical behavior of the surveyed community with respect to malaria and the implications reported here could be applicable to other malaria endemic areas in Korea.