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Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions. We represent DDIs as a complex network in which nodes refer to drugs and links refer to their potential interactions. Recently, the problem of link prediction has attracted much consideration in scientific community. We represent the process of link prediction as a binary classification task on networks of potential DDIs. We use link prediction techniques for predicting unknown interactions between drugs in five arbitrary chosen large-scale DDI databases, namely DrugBank, KEGG, NDF-RT, SemMedDB, and Twosides. We estimated the performance of link prediction using a series of experiments on DDI networks. We performed link prediction using unsupervised and supervised approach including classification tree, k-nearest neighbors, support vector machine, random forest, and gradient boosting machine classifiers based on topological and semantic similarity features. Supervised approach clearly outperforms unsupervised approach. The Twosides network gained the best prediction performance regarding the area under the precision-recall curve (0.93 for both random forests and gradient boosting machine). The applied methodology can be used as a tool to help researchers to identify potential DDIs. The supervised link prediction approach proved to be promising for potential DDIs prediction and may facilitate the identification of potential DDIs in clinical research.

Single cell RNA sequencing (scRNA-seq) represents a new large scale and high throughput technique allowing analysis of the whole transcriptome at the resolution of an individual cell. It has emerged as an imperative method in life science research, uncovering complex cellular networks and providing indices that will eventually lead to the development of more targeted and personalized therapies. The importance of scRNA-seq has been particularly highlighted through the analysis of complex biological systems, in which cellular heterogeneity is a key aspect, such as the immune system. Autoimmune inflammatory rheumatic diseases represent a group of disorders, associated with a dysregulated immune system and high patient heterogeneity in both pathophysiological and clinical aspects. This complicates the complete understanding of underlying pathological mechanisms, associated with limited therapeutic options available and their long-term inefficiency and even toxicity. There is an unmet need to investigate, in depth, the cellular and molecular mechanisms driving the pathogenesis of rheumatic diseases and drug resistance, identify novel therapeutic targets, as well as make a step forward in using stratified and informed therapeutic decisions, which could now be achieved with the use of single cell approaches. This review summarizes the current use of scRNA-seq in studying different rheumatic diseases, based on recent findings from published in vitro, in vivo , and clinical studies, as well as discusses the potential implementation of scRNA-seq in the development of precision medicine in rheumatology.

The complete mitochondrial genome of the Carniolan honeybee (Apis mellifera carnica) from Slovenia, a homeland of this subspecies, was acquired in two contigs from WGS data and annotated. The newly obtained mitochondrial genome is a circular closed loop of 16,447 bp. It comprises 37 genes (13 protein coding genes, 22 tRNA genes, and 2 rRNA genes) and an AT-rich control region. The order of the tRNA genes resembles the order characteristic of A. mellifera. The mitogenomic sequence of A. m. carnica from Slovenia contains 44 uniquely coded sites in comparison to the closely related subspecies A. m. ligustica and to A. m. carnica from Austria. Furthermore, 24 differences were recognised in comparison between A. m. carnica and A. m. ligustica subspecies. Among them, there are three SNPs that affect translation in the nd2, nd4, and cox2 genes, respectively. The phylogenetic placement of A. m. carnica from Slovenia within C lineage deviates from the expected position and changes the perspective on relationship between C and O lineages. The results of this study represent a valuable addition to the information available in the phylogenomic studies of A. mellifera—a pollinator species of worldwide importance. Such genomic information is essential for this local subspecies’ conservation and preservation as well as its breeding and selection.

Many preclinical studies have shown that birth-associated tissues, cells and their secreted factors, otherwise known as perinatal derivatives (PnD), possess various biological properties that make them suitable therapeutic candidates for the treatment of numerous pathological conditions. Nevertheless, in the field of PnD research, there is a lack of critical evaluation of the PnD standardization process: from preparation to in vitro testing, an issue that may ultimately delay clinical translation. In this paper, we present the PnD e-questionnaire developed to assess the current state of the art of methods used in the published literature for the procurement, isolation, culturing preservation and characterization of PnD in vitro . Furthermore, we also propose a consensus for the scientific community on the minimal criteria that should be reported to facilitate standardization, reproducibility and transparency of data in PnD research. Lastly, based on the data from the PnD e-questionnaire, we recommend to provide adequate information on the characterization of the PnD. The PnD e-questionnaire is now freely available to the scientific community in order to guide researchers on the minimal criteria that should be clearly reported in their manuscripts. This review is a collaborative effort from the COST SPRINT action (CA17116), which aims to guide future research to facilitate the translation of basic research findings on PnD into clinical practice.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Nanoparticulate TiO2 (TiO2 NPs) is a widely used material, whose potential toxicity towards eukaryotic cells has been addressed by multiple studies. TiO2 NPs are considered toxic due to their production of reactive oxygen species (ROS), which can, among others, lead to cellular damage, inflammatory responses, and differences in gene expression. TiO2 NPs exhibited toxicity in multiple organs in animals, generating potential health risks also in humans, such as developing tumors or progress of preexisting cancer processes. On the other hand, the capability of TiO2 NPs to induce cell death has found application in photodynamic therapy of cancers. In aquatic environments, much has been done in understanding the impact of TiO2 on bivalves, in which an effect on hemocytes, among others, is reported. Adversities are also reported from other aquatic organisms, including primary producers. These are affected also on land and though some potential benefit might exist when it comes to agricultural plants, TiO2 can also lead to cellular damage and should be considered when it comes to transfer along the food chain towards human consumers. In general, much work still needs to be done to unravel the delicate balance between beneficial and detrimental effects of TiO2 NPs on eukaryotic cells.

Background: According to the Slovenian guidelines, metformin, an oral antidiabetic drug, is a drug of choice for the treatment of type 2 diabetes. Additionally, metformin is paving its way in polycystic ovary syndrome (PCOS) treatment, although the drug has not (yet) been officially indicated to treat PCOS. Studies have shown that metformin improves clinical and biochemical features of PCOS as well as the rate of ovulation and consequently the likelihood of conception in PCOS women. At the same dosage regimen, pharmacological effects of metformin show interindividual variability in metformin response; efforts have been made to at least partly explain the variability with polymorphic variants in genes related to metformin pharmacodynamics and pharmacokinetics.Conclusions: The results of the current studies indicate that polymorphic variants in genes related to metformin pharmacodynamics and pharmacokinetics may contribute to the interindividual variability in pharmacological response to metformin treatment in diabetic as well as in PCOS patients.

The physiological and pathophysiological roles of sex hormones have been well documented and the modulation of their effects is applicable in many current treatments. On the other hand, the physiological role of endocannabinoids is not yet clearly understood and the endocannabinoid system is considered a relatively new therapeutic target. The physiological association between sex hormones and cannabinoids has been investigated in several studies; however, its involvement in the pathophysiology of common human diseases has been studied separately. Herein, we present the first systematic review of molecular pathways that are influenced by both the cannabinoids and sex hormones, including adenylate cyclase and protein kinase A, epidermal growth factor receptor, cyclic adenosine monophosphate response element-binding protein, vascular endothelial growth factor, proto-oncogene serine/threonine-protein kinase, mitogen-activated protein kinase, phosphatidylinositol-4,5-bisphosphate 3-kinase, C-Jun N-terminal kinase and extracellular-signal-regulated kinases 1/2. Most of these influence cell proliferative activity. Better insight into this association may prove to be beneficial for the development of novel pharmacological treatment strategies for many common diseases, including breast cancer, endometrial cancer, prostate cancer, osteoporosis and atherosclerosis. The associations between cannabinoids, estrogens and androgens under these conditions are also presented and the molecular interactions are highlighted.

Much biomedical research focuses on the effects of UV light on human cells. UV light sources are a prerequisite for such research. This paper presents the design and achieved performance of a UVA (Ultraviolet A: 320–400 nm) and a UVB (Ultraviolet B: 290–320 nm) LED-based lamp suitable for use in bioassays, as well as inside an incubator. Numerical simulations were used to optimise the number, layout and output power of LEDs to achieve good irradiance homogeneity while maintaining low costs. Design was optimised for the efficient transfer of generated heat away from the irradiated samples through the heatsink at the back of the lamps. The average irradiance of the target surface by the UVA lamp was 70.1 W/m2 with a maximum deviation of 4.9%, and the average irradiance by the UVB lamp was 3.1 W/m2 with a maximum deviation of 4.8%. With the UVA and UVB lamps, the temperature of samples undergoing irradiation in the incubator rises from 37 to 42 °C within 40 and 67 min, respectively. This by far exceeds the required UV irradiation time in most cases. Tests on Jurkat and HEK-293 cell cultures confirmed the suitability of our lamps for biomedical research.