The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy ...prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.
Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need ...for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.
Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-based rather than mechanistic approaches have contributed. We here explore a mechanism-based disease ...definition for network pharmacology. Beginning with a primary causal target, we extend this to a second using guilt-by-association analysis. We then validate our prediction and explore synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet medical need indications in medicine, and reactive oxygen species forming NADPH oxidase type 4 (Nox4) as a primary causal therapeutic target. For network analysis, we use classical protein–protein interactions but also metabolite-dependent interactions. Based on this protein–metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic cotargets for network pharmacology. We identify the nitric oxide synthase (Nos1 to 3) gene family as the closest target to Nox4. Indeed, when combining a NOS and a NOX inhibitor at subthreshold concentrations, we observe pharmacological synergy as evidenced by reduced cell death, reduced infarct size, stabilized blood–brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraadditive manner. Thus, protein–metabolite network analysis, for example guilt by association, can predict and pair synergistic mechanistic disease targets for systems medicine-driven network pharmacology. Such approaches may in the future reduce the risk of failure in single-target and symptom-based drug discovery and therapy.
This paper presents water the footprint assessment (WFA) of carbon in pulp (CIP) gold processing. The main objectives of the study are determining grey and blue water footprints and identifying the ...hotspots of the process. Results revealed that the total blue water footprint, including the extraction and processing of the gold, was found to be 452.40 m3/kg Au, and the grey WF to be 2300.69 m3/kg Au. According to the results, the lost return flow on the direct blue WF side has the largest contribution, with a value of 260.61 m3/kg Au, and the only source of the lost return flow is the tailing pond. On the indirect side, it is seen that the oxygen consumption used for the leaching process has the highest value, with 37.38 m3/kg. Among the nine contaminants in the mine tailings, the critical component responsible for the grey water footprint is by far arsenic, with a value of 1777 m3/kg Au. The results will be used to make recommendations for reducing water consumption in mining operations, for a better design for the environment. The study is a pioneering study, being the first implementation of water footprint assessment in a gold mine in Turkey.
Current electric storage systems eagerly focus on high‐power and energy‐dense Lithium‐ion batteries to cope with increasing energy storage demands. Since cathode materials are one of the bottlenecks ...of these batteries, there is much interest in layered lithium‐rich manganese oxide‐based (LLMO) cathodes which can develop this technology. However, Initial Coulombic Efficiency (ICE) loss, poor rate performance and cycling instability issues are still persistent as problems to be solved for these materials. Recent research shows that water‐soluble binders are effective in improving the performance of LLMO materials. Herein, we describe the synthesis, characterisation, and application of a series of water‐soluble composites as a binder for LLMO cathodes. The PPy is introduced as part of the binder to improve the electronic conductivity and two different oxidants and various PPy to PSAP ratios were used to optimise the final properties. The electrochemical performance and morphology of the cathodes before and after cycling were investigated and compared with the conventional PVDF binder. The LLMO−2c electrode showed excellent charge‐discharge performance, especially at 5 C and 10 C rates, and high cycling stability at 0.2 C whilst maintaining a final capacity of 184 mAh/g after 200 cycles, which is equal to 89.3 % capacity retention.
Water‐soluble polypyrrole‐polybis(4‐oxybenzene sulfonic acid) phosphazene‐based composite binders allow the preparation of layered lithium‐rich manganese oxide‐based cathodes in an environmentally friendly and green way. They provide low charge transfer resistance due to the presence of conductive PPy in their formulation which leads to excellent capacity retention of almost 90 % even after 200 cycles and high C‐rate performance.
Genes carrying mutations associated with genetic diseases are present in all human cells; yet, clinical manifestations of genetic diseases are usually highly tissue-specific. Although some disease ...genes are expressed only in selected tissues, the expression patterns of disease genes alone cannot explain the observed tissue specificity of human diseases. Here we hypothesize that for a disease to manifest itself in a particular tissue, a whole functional subnetwork of genes (disease module) needs to be expressed in that tissue. Driven by this hypothesis, we conducted a systematic study of the expression patterns of disease genes within the human interactome. We find that genes expressed in a specific tissue tend to be localized in the same neighborhood of the interactome. By contrast, genes expressed in different tissues are segregated in distinct network neighborhoods. Most important, we show that it is the integrity and the completeness of the expression of the disease module that determines disease manifestation in selected tissues. This approach allows us to construct a disease-tissue network that confirms known and predicts unexpected disease-tissue associations.
Self-healing is the capability of materials to repair themselves after the damage has occurred, usually through the interaction between molecules or chains. Physical and chemical processes are ...applied for the preparation of self-healing systems. There are different approaches for these systems, such as heterogeneous systems, shape memory effects, hydrogen bonding or covalent-bond interaction, diffusion, and flow dynamics. Self-healing mechanisms can occur in particular through heat and light exposure or through reconnection without a direct effect. The applications of these systems display an increasing trend in both the R&D and industry sectors. Moreover, self-healing systems and their energy storage applications are currently gaining great importance. This review aims to provide general information on recent developments in self-healing materials and their battery applications given the critical importance of self-healing systems for lithium-ion batteries (LIBs). In the first part of the review, an introduction about self-healing mechanisms and design strategies for self-healing materials is given. Then, selected important healing materials in the literature for the anodes of LIBs are mentioned in the second part. The results and future perspectives are stated in the conclusion section.
The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In ...particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.
We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.
BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important ...role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the "guilt-by-association" principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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