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The study aimed was to determine the effects of drought stress (0, -4, -8, -12 bar) and osmopriming (-15 bar PEG 6000 for 15 at 24 h) on seed germination, seedling growth and enzyme activity at different temperatures were assessed in the laboratory for wheat. Results showed that the highest germination percentage (GP) (94.33%), normal seedling percentage (NSP) (92%), germination index (GI) (44.85) and seedling length (11.03 cm) were attained from osmo-priming in control conditions. Therefore, seed priming with PEG 6000 significantly (p≤ 0.01) increased germination characteristics as compared to the unprimed seeds under drought stress. Also, osmopriming increased catalase (CAT) and ascorbate peroxidase (APX) as compared to the unprimed.

Seed moisture content (MC) and storage temperature are the most important factors affecting seed longevity and vigor. Exposure to warm, moist air is principally responsible for this. Proper storage and optimum seed moisture content can affect the grain quality significantly. The purpose of this study was to evaluate the different storage treatments on seed quality of sorghum. The seed materials were fresh without any storage period. For storage treatments, 3 seed moisture contents (6, 10, 14 %) were stored for 8 month in 0.5 L capacity sealed aluminum foil packet in 0.3 bar inside incubators set at 4 temperatures (5, 15, 25, 35 °C). After storage time, the higher the storage temperature, the lower was the grain quality of sorghum. The highest germination percentage, germination index, normal seedling percentage were achieved in control conditions (0 day of storage). Our results showed that increasing storage duration resulted higher reduction in germination characteristics. Also our results showed that, germination percentage, means time to germination, germination index, normal seedling percentage decrease significantly by storage. Enzyme activity decrease significantly by increased in storage.

Seed priming techniques have been used to increase germination, improve germination uniformity and seedling establishment under stressed conditions. Seed priming was used in Rye Mountain (Secale montanum) to increase seed germination and tolerance on stress exposure. Rye seeds were treated with various priming agents for different time and temperatures. The effect of priming was assessed on germination characteristics on subsequent exposure to drought (PEG -14 bar) stress for 7 days. Seed priming treatments significantly (p≤ 0.01) affected, Germination percentage (GP), normality seedling percentage (NSP), germination Index (GI), germination uniformity (GU), means time to germination (MTG), coefficient of velocity of germination (CVG), seedling vigour index (SVI) and coefficient of allometry (AC). Seed priming with gibberelic acid (GA) and salicylic acid (SA) (p≤ 0.01) increased germination characteristics as the compared to the unprimed. Seed priming with concentrations 25, 50 ppm of GA for 15 h at 10 °C and 25 ppm of SA for 12 h at 10 °C, may be considered as optimal treatment for priming of Secale montanum seeds in drought stress conditions. Also priming increased catalase (CAT) and ascorbate peroxidase (APX) as compared to the unprimed.

After intense scientific exploration and more than a decade of failed trials, Alzheimer’s disease (AD) remains a fatal global epidemic. A traditional research and drug development paradigm continues to target heterogeneous late-stage clinically phenotyped patients with single 'magic bullet' drugs. Here, we propose that it is time for a paradigm shift towards the implementation of precision medicine (PM) for enhanced risk screening, detection, treatment, and prevention of AD. The overarching structure of how PM for AD can be achieved will be provided through the convergence of breakthrough technological advances, including big data science, systems biology, genomic sequencing, blood-based biomarkers, integrated disease modeling and P4 medicine. It is hypothesized that deconstructing AD into multiple genetic and biological subsets existing within this heterogeneous target population will provide an effective PM strategy for treating individual patients with the specific agent(s) that are likely to work best based on the specific individual biological make-up. The Alzheimer’s Precision Medicine Initiative (APMI) is an international collaboration of leading interdisciplinary clinicians and scientists devoted towards the implementation of PM in Neurology, Psychiatry and Neuroscience. It is hypothesized that successful realization of PM in AD and other neurodegenerative diseases will result in breakthrough therapies, such as in oncology, with optimized safety profiles, better responder rates and treatment responses, particularly through biomarker-guided early preclinical disease-stage clinical trials. [ABSTRACT FROM PUBLISHER]

Background: Bisphenol A (BPA), an endocrine-disrupting agent, is widely used as polycarbonate plastics for producing food containers. BPA exposure at environmentally relevant concentrations can cause reproductive disorders., Objective: The effect of Naringenin (NG) on BPA-induced Sertoli cell toxicity and its mechanism was examined in the present study., Materials and Methods: In this experimental-laboratory study, the mouse TM4 cells were treated to BPA (0.8 μM) or NG for 24 hr at concentrations of 10, 20, and 50 μg/ml. Cell viability, reactive oxygen species (ROS) production, malondialdehyde (MDA) content, antioxidant level, and mitochondrial membrane potential (MMP) were examined. The expression of mitophagy-related genes, including Parkin and PTEN-induced putative kinase 1 (Pink1), was also evaluated., Results: BPA significantly lowered the viability of the Sertoli cells (p= 0.004). Pink1 and Parkin levels of the BPA group were significantly increased (p < 0.001), while the MMP was considerably decreased (p < 0.001). BPA raised MDA and ROS levels (p < 0.001) and reduced antioxidant biomarkers (p= 0.003). NG at the 20 and 50 μg/ml concentrations could significantly improve the viability and MMP of TM4 cells (p= 0.034). NG depending on concentration, could decrease Pink1 and Parkin at mRNA and protein levels compared to the BPA group (p = 0.024). NG enhanced antioxidant factors, while ROS and MDA levels were decreased in the BPA-exposed cells., Conclusion: The beneficial impacts of NG on BPA-exposed Sertoli cells are related to the suppression of mitophagy and the reduction of oxidative stress., Competing Interests: The authors declare that there is no conflict of interest., (Copyright © 2024 Khorsandi et al.)

Background: This Phase 1/2 study is based on the hypothesis that immune checkpoint inhibitors are more effective when given earlier in the course of the disease for advanced soft tissue sarcoma., Methods: Phase I endpoints-maximum tolerated dose in previously treated patients; Phase II endpoints-best response, progression free survival and overall survival and incidence of adverse events in previously untreated patients; Phase I treatments-escalating doses of trabectedin (1.0, 1.2, 1.5 mg/m 2 ) as continuous intravenous infusion over 24 h every 3 weeks, 1 mg/kg of ipilimumab given intravenously every 12 weeks, and 3 mg/kg of nivolumab given intravenously every 2 weeks; Phase II treatments-maximum tolerated dose of trabectedin and defined doses of ipilimumab and nivolumab., Results: Phase I ( n = 9)-the maximum tolerated dose of trabectedin was 1.2 mg/m 2 ; Phase II ( n = 79)-6 complete responses, 14 partial responses, 49 stable disease, 25.3% best response rate, 87.3% disease control rate; median progression-free survival, 6.7 months (CI 95%: 4.4-7.9), median overall survival, 24.6 months (CI 95%: 17.0-.); Grade 3/4 therapy-related adverse events ( n = 92)-increased ALT (25%), fatigue (8.7%), increased AST (8.7%), decreased neutrophil count (5.4%) and anemia (4.6%)., Conclusion: SAINT is a safe and effective first-line treatment for advanced soft tissue sarcoma., Competing Interests: The authors E.M.G., S.P.C., W.A.T., E.Y., S.T., V.S.C.-A., H.C., C.V., D.A.B., A.M., S.G.W., D.Q., A.S., N.F., W.T. have no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Introduction: Ontology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke., Methods: The stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions., Results: The stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users' views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence., Conclusions: The stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.

Aim To describe experiences of patients with COVID-19 infection. Methods This qualitative research was conducted using a phenomenological approach, and participants were selected via purposive sampling. In total, 14 patients with COVID-19 were selected (nine women and five men) aged 20-60 years. Data were collected via in-depth interviews with open questions and through observation. Data analysis was performed using Colizzi's phenomenological approach. Results Three themes and nine categories were extracted; the main themes were mental strains, physical manifestations, and coping mechanisms. Mental strains entailed concerns, fears and isolation, on the other hand, physical manifestations comprised nervous, respiratory and gastrointestinal system, and systemic disorders. Coping mechanisms included religiosity and home remedies categories. Conclusion Mental strains were the most important issues in the patients with COVID-19, which were interwoven with concerning physical manifestations. Most of the participants used self-medication and spiritual resources to cope with the disease. Therefore, it is recommended to carry out proper planning by healthcare personnel to psychologically and spiritually support these patients, while alleviating physical manifestations of the disease., (Copyright© by the Medical Assotiation of Zenica-Doboj Canton.)

Objective: Many studies have suggested the anti-nociceptive role for magnesium either as an adjunct for postoperative pain. Although several studies have been carried out to evaluate the anti-nociceptive effect of magnesium, there is still considerable uncertainty., Patients and Methods: Eighty patients who underwent posterior spinal fusion were randomly divided into two groups (magnesium and saline). Changes in cell count, magnesium concentration and coagulation status were assessed one hour after operation at both group and compared to baseline. At recovery room, their pain score was assessed according to 10 points visual analogue scale (VAS). Morphine consumption was evaluated at regular times after the surgery by patient controlled analgesia (PCA) device., Results: VAS scores were significantly lower in the magnesium group. Cumulative PCA morphine consumption after the surgery was significantly lower in the magnesium group. Pre and postoperative values for haemoglobin, platelet count, Prothrombin Time (PT), fibrinogen were not significantly different. There was a significant increase in activated Partial Thromboplastin Time (aPTT), International Normalized Ratio (INR), and bleeding time (BT), one hour after the operation in the magnesium group but intraoperative blood loss was similar in both groups., Conclusions: Perioperative magnesium sulfate infusion improves the postoperative analgesia, decreases the amount of morphine consumption after the operation and does not change the intraoperative bleeding in patients undergoing posterior spinal fusion surgery., (Copyright © 2020. Published by Elsevier B.V.)

Background: Stroke is a leading cause of death and disability worldwide. According to the Iranian Ministry of Medical Health and Education, out of 100,000 stroke incidents in the country, 25,000 lead to death. Thus, identifying risk factors of stroke can help healthcare providers to establish prevention strategies. This study was conducted to investigate the prevalence of stroke risk factors and their distribution based on stroke subtypes in Sayad Shirazi Hospital, Gorgan, Northeastern Iran., Material and Methods: A retrospective hospital-based study was conducted at Sayad Shirazi Hospital in Gorgan, the only referral university hospital for stroke patients in Gorgan city. All medical records with a diagnosis of stroke were identified based on the International Classification of Diseases, Revision 10, from August 23, 2015, to August 22, 2016. A valid and reliable data gathering form was used to capture data about demographics, diagnostics, lifestyle, risk factors, and medical history., Results: Out of 375 cases, two-thirds were marked with ischemic stroke with mean ages (standard deviation) of 66.4 (14.2) for men and 64.6 (14.2) for women. The relationship between stroke subtypes and age groups (P=0.008) and hospital outcome (P=0.0001) was significant. Multiple regression analysis showed that hypertension (Exp. (B) =1.755, P=0.037), diabetes mellitus (Exp. (B) =0.532, P=0.021), and dyslipidemia (Exp. (B) =2.325, P=0.004) significantly increased the risk of ischemic stroke., Conclusion: Overall, hypertension, diabetes mellitus, and dyslipidemia were the major risk factors of stroke in Gorgan. Establishment of stroke registry (population- or hospital-based) for the province is recommended.

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Neurodegenerative diseases including Alzheimer's disease are complex to tackle because of the complexity of the brain, both in structure and function. Such complexity is reflected by the involvement of various brain regions and multiple pathways in the etiology of neurodegenerative diseases that render single drug target approaches ineffective. Particularly in the area of neurodegeneration, attention has been drawn to repurposing existing drugs with proven efficacy and safety profiles. However, there is a lack of systematic analysis of the brain chemical space to predict the feasibility of repurposing strategies. Using a mechanism-based, drug-target interaction modeling approach, we have identified promising drug candidates for repositioning. Mechanistic cause-and-effect models consolidate relevant prior knowledge on drugs, targets, and pathways from the scientific literature and integrate insights derived from experimental data. We demonstrate the power of this approach by predicting two repositioning candidates for Alzheimer's disease and one for amyotrophic lateral sclerosis.

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

Perturbance in inflammatory pathways have been identified as one of the major factors which leads to neurodegenerative diseases (NDD). Owing to the limited access of human brain tissues and the immense complexity of the brain, animal models, specifically mouse models, play a key role in advancing the NDD field. However, many of these mouse models fail to reproduce the clinical manifestations and end points of the disease. NDD drugs, which passed the efficacy test in mice, were repeatedly not successful in clinical trials. There are numerous studies which are supporting and opposing the applicability of mouse models in neuroinflammation and NDD. In this paper, we assessed to what extend a mouse can mimic the cellular and molecular interactions in humans at a mechanism level. Based on our mechanistic modeling approach, we investigate the failure of a neuroinflammation targeted drug in the late phases of clinical trials based on the comparative analyses between the two species.

Molecular signaling pathways have been long used to demonstrate interactions among upstream causal molecules and downstream biological effects. They show the signal flow between cell compartments, the majority of which are represented as cartoons. These are often drawn manually by scanning through the literature, which is time-consuming, static, and non-interoperable. Moreover, these pathways are often devoid of context (condition and tissue) and biased toward certain disease conditions. Mining the scientific literature creates new possibilities to retrieve pathway information at higher contextual resolution and specificity. To address this challenge, we have created a pathway terminology system by combining signaling pathways and biological events to ensure a broad coverage of the entire pathway knowledge domain. This terminology was applied to mining biomedical papers and patents about neurodegenerative diseases with focus on Alzheimer's disease. We demonstrate the power of our approach by mapping literature-derived signaling pathways onto their corresponding anatomical regions in the human brain under healthy and Alzheimer's disease states. We demonstrate how this knowledge resource can be used to identify a putative mechanism explaining the mode-of-action of the approved drug Rasagiline, and show how this resource can be used for fingerprinting patents to support the discovery of pathway knowledge for Alzheimer's disease. Finally, we propose that based on next-generation cause-and-effect pathway models, a dedicated inventory of computer-processable pathway models specific to neurodegenerative diseases can be established, which hopefully accelerates context-specific enrichment analysis of experimental data with higher resolution and richer annotations.

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

Introduction: The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. Here, we propose a model-driven approach supporting the systematic identification of putative disease mechanisms., Methods: We have created a model for AD and a corresponding model for the normal physiology of neurons using biological expression language to systematically model causal and correlative relationships between biomolecules, pathways, and clinical readouts. Through model-model comparison we identify "chains of causal relationships" that lead to new insights into putative disease mechanisms., Results: Using differential analysis of our models we identified a new mechanism explaining the effect of amyloid-beta on apoptosis via both the neurotrophic tyrosine kinase receptor, type 2 and nerve growth factor receptor branches of the neurotrophin signaling pathway. We also provide the example of a model-guided interpretation of genetic variation data for a comorbidity analysis between AD and type 2 diabetes mellitus., Discussion: The two computable, literature-based models introduced here provide a powerful framework for the generation and validation of rational, testable hypotheses across disease areas., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism., Methods: The Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology., Results: The Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson's disease., Conclusions: Parkinson's disease ontology delivers the knowledge domain of Parkinson's disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson's-related computable models. A practical version of the Parkinson's disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON .

Protein interaction networks are widely used in computational biology as a graphical means of representing higher-level systemic functions in a computable form. Although, many algorithms exist that seamlessly collect and measure protein interaction information in network models, they often do not provide novel mechanistic insights using quantitative criteria. Measuring information content and knowledge representation in network models about disease mechanisms becomes crucial particularly when exploring new target candidates in a well-defined functional context of a potential disease mechanism. To this end, we have developed a knowledge-based scoring approach that uses literature-derived protein interaction features to quantify protein interaction confidence. Thereby, we introduce the novel concept of knowledge cliffs, regions of the interaction network where a significant gap between high scoring and low scoring interactions is observed, representing a divide between established and emerging knowledge on disease mechanism. To show the application of this approach, we constructed and assessed reliability of a protein-protein interaction model specific to Alzheimer's disease, which led to screening, and prioritization of four novel protein candidates. Evaluation of the identified candidates showed that two of them are already followed in clinical trials for testing potential AD drugs.

Access to autologous Schwann cells is limited due to lack of donor site and its difficult isolation and culture. Therefore, one of the possible ways to obtain to Schwann cells is to differentiate mesenchymal stem cells into glial pathway using various materials and protocols. The aim of this study was to compare the effects of fetal bovine serum and human serum on Schwann cell differentiation of adipose-derived stem cells to choose the best serum for use in future research. For this purpose, after isolation of human adipose-derived stem cells, it was characterized and differentiated into Schwann cell lineage using two protocols which one of them contained fetal bovine serum and the other human serum. At the end, morphological evaluation declared an increased detachment of cells in response to human serum. On the other side, immunocytochemistry showed that there was a significant increase in the number of cells expressing glial fibrillary acidic proteins and S100 in fetal bovine serum-treated group when compared to human serum-treated one (P<0.05). It was concluded that fetal bovine serum was more effective than allogeneic human serum in Schwann cell differentiation of adipose-derived stem cells.

Background: A number of compelling candidate Alzheimer's biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully., Methods: The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer's disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level., Results: Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these 'emerging' potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail., Conclusions: Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed.

Background: In the past years, significant progress has been made to develop and use experimental settings for extensive data collection on tobacco smoke exposure and tobacco smoke exposure-associated diseases. Due to the growing number of such data, there is a need for domain-specific standard ontologies to facilitate the integration of tobacco exposure data., Results: The CSEO (version 1.0) is composed of 20091 concepts. The ontology in its current form is able to capture a wide range of cigarette smoke exposure concepts within the knowledge domain of exposure science with a reasonable sensitivity and specificity. Moreover, it showed a promising performance when used to answer domain expert questions. The CSEO complies with standard upper-level ontologies and is freely accessible to the scientific community through a dedicated wiki at https://publicwiki-01.fraunhofer.de/CSEO-Wiki/index.php/Main&#95;Page., Conclusions: The CSEO has potential to become a widely used standard within the academic and industrial community. Mainly because of the emerging need of systems toxicology to controlled vocabularies and also the lack of suitable ontologies for this domain, the CSEO prepares the ground for integrative systems-based research in the exposure science.

Background: Biomedical ontologies offer the capability to structure and represent domain-specific knowledge semantically. Disease-specific ontologies can facilitate knowledge exchange across multiple disciplines, and ontology-driven mining approaches can generate great value for modeling disease mechanisms. However, in the case of neurodegenerative diseases such as Alzheimer's disease, there is a lack of formal representation of the relevant knowledge domain., Methods: Alzheimer's disease ontology (ADO) is constructed in accordance to the ontology building life cycle. The Protégé OWL editor was used as a tool for building ADO in Ontology Web Language format., Results: ADO was developed with the purpose of containing information relevant to four main biological views-preclinical, clinical, etiological, and molecular/cellular mechanisms-and was enriched by adding synonyms and references. Validation of the lexicalized ontology by means of named entity recognition-based methods showed a satisfactory performance (F score = 72%). In addition to structural and functional evaluation, a clinical expert in the field performed a manual evaluation and curation of ADO. Through integration of ADO into an information retrieval environment, we show that the ontology supports semantic search in scientific text. The usefulness of ADO is authenticated by dedicated use case scenarios., Conclusions: Development of ADO as an open ADO is a first attempt to organize information related to Alzheimer's disease in a formalized, structured manner. We demonstrate that ADO is able to capture both established and scattered knowledge existing in scientific text., (Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

The advent of state-of-the-art brain imaging technologies in recent years and the ability of such technologies to provide high-resolution information at both structural and functional levels has spawned large efforts to introduce novel non-invasive imaging biomarkers for early prediction and diagnosis of brain disorders; however, their utility in both clinic and drug development at their best resolution remains limited to visualizing and monitoring disease progression. Given the fact that efficient translation of valuable information embedded in brain scans into clinical application is of paramount scientific and public health importance, a strategy is needed to bridge the current gap between imaging and molecular biology, particularly in neurodegenerative diseases. As an attempt to address this issue, we present a novel computational method to link readouts of imaging biomarkers to their underlying molecular pathways with the aim of guiding clinical diagnosis, prognosis and even target identification in drug discovery for Alzheimer's disease.

Background: Large biomedical simulation initiatives, such as the Virtual Physiological Human (VPH), are substantially dependent on controlled vocabularies to facilitate the exchange of information, of data and of models. Hindering these initiatives is a lack of a comprehensive ontology that covers the essential concepts of the simulation domain., Results: We propose a first version of a newly constructed ontology, HuPSON, as a basis for shared semantics and interoperability of simulations, of models, of algorithms and of other resources in this domain. The ontology is based on the Basic Formal Ontology, and adheres to the MIREOT principles; the constructed ontology has been evaluated via structural features, competency questions and use case scenarios.The ontology is freely available at: http://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads.html (owl files) and http://bishop.scai.fraunhofer.de/scaiview/ (browser)., Conclusions: HuPSON provides a framework for a) annotating simulation experiments, b) retrieving relevant information that are required for modelling, c) enabling interoperability of algorithmic approaches used in biomedical simulation, d) comparing simulation results and e) linking knowledge-based approaches to simulation-based approaches. It is meant to foster a more rapid uptake of semantic technologies in the modelling and simulation domain, with particular focus on the VPH domain.

With the significant advancement of high-throughput technologies and diagnostic techniques throughout the past decades, molecular underpinnings of many disorders have been identified. However, translation of patient-specific molecular mechanisms into tailored clinical applications remains a challenging task, which requires integration of multi-dimensional molecular and clinical data into patient-centric models. This task becomes even more challenging when dealing with complex diseases such as neurodegenerative disorders. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. We argue that integrative disease modeling will be an indispensable part of any P4 medicine research and development in the near future and that it supports the shift from descriptive to causal mechanistic diagnosis and treatment of complex diseases. For each 'P' in predictive, preventive, personalized and participatory (P4) medicine, we demonstrate how integrative disease modeling can contribute to addressing the real-world issues in development of new predictive, preventive, personalized and participatory measures. With the increasing recognition that application of integrative systems modeling is the key to all activities in P4 medicine, we envision that translational bioinformatics in general and integrative modeling in particular will continue to open up new avenues of scientific research for current challenges in P4 medicine.

Background: While the majority of studies have focused on the association between sex hormones and dementia, emerging evidence supports the role of other hormone signals in increasing dementia risk. However, due to the lack of an integrated view on mechanistic interactions of hormone signaling pathways associated with dementia, molecular mechanisms through which hormones contribute to the increased risk of dementia has remained unclear and capacity of translating hormone signals to potential therapeutic and diagnostic applications in relation to dementia has been undervalued., Methods: Using an integrative knowledge- and data-driven approach, a global hormone interaction network in the context of dementia was constructed, which was further filtered down to a model of convergent hormone signaling pathways. This model was evaluated for its biological and clinical relevance through pathway recovery test, evidence-based analysis, and biomarker-guided analysis. Translational validation of the model was performed using the proposed novel mechanism discovery approach based on 'serendipitous off-target effects'., Results: Our results reveal the existence of a well-connected hormone interaction network underlying dementia. Seven hormone signaling pathways converge at the core of the hormone interaction network, which are shown to be mechanistically linked to the risk of dementia. Amongst these pathways, estrogen signaling pathway takes the major part in the model and insulin signaling pathway is analyzed for its association to learning and memory functions. Validation of the model through serendipitous off-target effects suggests that hormone signaling pathways substantially contribute to the pathogenesis of dementia., Conclusions: The integrated network model of hormone interactions underlying dementia may serve as an initial translational platform for identifying potential therapeutic targets and candidate biomarkers for dementia-spectrum disorders such as Alzheimer's disease.

Recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for drug discovery companies to invest in novel strategies for biomarker discovery. In parallel with the rapid advancement and clinical adoption of high-throughput technologies, a number of knowledge management and systems biology approaches have been developed to analyze an ever increasing collection of OMICs data. This review discusses current biomarker discovery technologies highlighting challenges and opportunities of knowledge capturing and presenting a perspective of the future integrative modeling approaches as an emerging trend in biomarker prediction., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors., (Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)