A New Mass Spectroscopy-Based Method for Assessing the Periodontal-Endodontic Interface after Intracanal Placement of Biomaterials In Vitro.

Optimizing the interface between biomaterials and dental hard tissues can prevent leakage of bacteria or inflammatory mediators into periapical tissues and thus avoid alveolar bone inflammation. In this study, an analysis system for testing the periodontal-endodontic interface using gas leakage and subsequent mass spectrometry was developed and validated using the roots of 15 single-rooted teeth in four groups: (I) roots without root canal filling, (II) roots with an inserted gutta-percha post without sealer, (III) roots with gutta-percha post and sealer, (IV) roots filled with sealer only, and (V) adhesively covered roots. Helium was used as the test gas, and its leakage rate was found by measuring the rising ion current using mass spectrometry. This system made it possible to differentiate between the leakage rates of tooth specimens with different fillings. Roots without filling showed the highest leakage values (p < 0.05). Specimens with a gutta-percha post without sealer showed statistically significantly higher leakage values than groups with a filling of gutta-percha and sealer or sealer alone (p < 0.05). This study shows that a standardized analysis system can be developed for periodontal-endodontic interfaces to prevent biomaterials and tissue degradation products from affecting the surrounding alveolar bone tissue.

Impact of thermal photodynamic disinfection on root dentin temperature in vitro.

BACKGROUND: Thermal photodynamic disinfection procedures have been proposed for adjunctive endodontic treatment. This study assessed photothermal disinfection relative to root dentin temperature and the thermal effects of simulated periodontal blood flow. METHODS: Thirty freshly extracted human single-rooted teeth were prepared endodontically using a 45/.02 master apical file. The root surfaces were coated with wax and covered with thermoforming sheets, leaving a circumferential space of 0.25 mm after wax removal. The sheets were perforated to allow fluid circulation through the simulated periodontal space. Irradiation was performed in two groups of 30: I, 810 nm laser (1.5 W, continuous wave), 4 × 5 s; II, photothermal group: indocyanine green and 810 nm laser (200 mW, continuous wave), 20 s. Thermographic measurements were performed at different water flow rates (6, 2.6, 0 mL/min) with a baseline temperature of 37 °C. Nonparametric statistical analysis was performed (Wilcoxon). RESULTS: The highest temperature change (median 7.52 °C, range 0.82-18.32 °C) was with 810 nm laser irradiation in group I, without any simulated blood flow. Fluid circulation resulted in a significant reduction in temperature changes in this group (median 2.14 °C, range 0.37-9.83 °C; p  < 0.05). The lowest temperature changes were in the photothermal group with a water flow rate of 6 mL/min (median 0.79 °C, range 0.00-3.88 °C; p < 0.05). CONCLUSION: Photothermal disinfection of root canals can increase root canal dentin temperatures, but periodontal fluid circulation has a cooling effect on the outer root surface, reducing the risk of potential thermal injury to periodontal tissue.

Integration of the ImageJ Ecosystem in the KNIME Analytics Platform.

Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances.

The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox(UKN). For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate into neuroepithelial cells for 6 days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox(UKN)) and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (1) the concentration-dependence, (2) the time dependence, and (3) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This 'RoFA predictor gene set' may be used for a simplified and less costly setup of the STOP-tox(UKN) assay.

Whither systems medicine?

New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.

Efficiency of soft tissue incision with a novel 445-nm semiconductor laser.

Using a 445-nm semiconductor laser for tissue incision, an effective cut is expected due to the special absorption properties of blue laser light in soft tissues. The aim of the present study was the histological evaluation of tissue samples after incision with a 445-nm diode laser. Forty soft tissue specimens were obtained from pork oral mucosa and mounted on a motorized linear translation stage. The handpiece of a high-frequency surgery device, a 970-nm semiconductor laser, and a 445-nm semiconductor laser were connected to the slide, allowing a constant linear movement (2 mm/s) and the same distance of the working tip to the soft tissue's surface. Four incisions were made each: (I) 970-nm laser with conditioned fiber tip, contact mode at 3-W cw; (II-III): 445-nm laser with non-conditioned fiber tip, contact mode at 2-W cw, and non-contact mode (1 mm) at 2 W; and (IV): high-frequency surgery device with straight working tip, 90° angulation, contact mode at 50 W. Histological analysis was performed after H&E staining of the embedded specimens at 35-fold magnification. The comparison of the incision depths showed a significant difference depending on the laser wavelength and the selected laser parameters. The highest incision depth was achieved with the 445-nm laser contact mode (median depth 0.61 mm, min 0.26, max 1.17, interquartile range 0.58) (p < 0.05) with the lowest amount of soft tissue denaturation (p < 0.05). The lowest incision depth was measured for the high-frequency surgical device (median depth 0.36 mm, min 0.12, max 1.12, interquartile range 0.23) (p < 0.05). Using a 445-nm semiconductor laser, a higher cutting efficiency can be expected when compared with a 970-nm diode laser and high-frequency surgery. Even the 445-nm laser application in non-contact mode shows clinically acceptable incision depths without signs of extensive soft tissue denaturation.

KNIME for reproducible cross-domain analysis of life science data.

Experiments in the life sciences often involve tools from a variety of domains such as mass spectrometry, next generation sequencing, or image processing. Passing the data between those tools often involves complex scripts for controlling data flow, data transformation, and statistical analysis. Such scripts are not only prone to be platform dependent, they also tend to grow as the experiment progresses and are seldomly well documented, a fact that hinders the reproducibility of the experiment. Workflow systems such as KNIME Analytics Platform aim to solve these problems by providing a platform for connecting tools graphically and guaranteeing the same results on different operating systems. As an open source software, KNIME allows scientists and programmers to provide their own extensions to the scientific community. In this review paper we present selected extensions from the life sciences that simplify data exploration, analysis, and visualization and are interoperable due to KNIME's unified data model. Additionally, we name other workflow systems that are commonly used in the life sciences and highlight their similarities and differences to KNIME.

Automated workflows for modelling chemical fate, kinetics and toxicity.

Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively.

KNIME for Open-Source Bioimage Analysis: A Tutorial.

The open analytics platform KNIME is a modular environment that enables easy visual assembly and interactive execution of workflows. KNIME is already widely used in various areas of research, for instance in cheminformatics or classical data analysis. In this tutorial the KNIME Image Processing Extension is introduced, which adds the capabilities to process and analyse huge amounts of images. In combination with other KNIME extensions, KNIME Image Processing opens up new possibilities for inter-domain analysis of image data in an understandable and reproducible way.

Complications with allogeneic, cancellous bone blocks in vertical alveolar ridge augmentation: prospective clinical case study and review of the literature.

OBJECTIVES: Vertical bone augmentation in dental implantology is an indication for cancellous allogeneic bone blocks (ABB). However, these materials may lead to adverse reactions, which are known well in orthopedics but rarely published. Therefore, in this study, we performed an evaluation of the use of ABB in vertical bone augmentation in clinical dental implantology. STUDY DESIGN: The prospective clinical study included 20 cases with vertical augmentation using ABB and subsequent or simultaneous placement of implants in the lateral maxilla and mandible. Follow-up included panoramic radiography, tissue healing, and peri-implantitis. Because of the limited number of patients, the report was planned to be descriptive only. Loss of ABB or peri-implantitis of more than 30% of the intraosseous implant length was deemed to indicate failure. RESULTS: The study was cancelled after six cases because of an unexpectedly high number of complications (5 of 6; 83%). The average surveillance time was 1460 days. Three types of unsatisfying outcome were observed: type I, early complete loss of the augmentation with soft tissue defects after 3 to 8 weeks (n = 2); type II, early soft tissue maceration (up to 8 weeks) without loss of coverage and complete early bone healing with later peri-implantitis and bone loss after prosthetic loading (6 months or later; n = 2); and type III, complication-free bone healing with subsequent peri-implantitis after prosthetic loading (6 months or later; n = 1). CONCLUSIONS: Complications were observed in vertical augmentation with ABB and implant placement. After careful consideration, literature data were found to support these results and also suggest that tissue level implants may be advantageous in vertical bone augmentation with ABB.

A KNIME-Based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules.

Recently, the photomotor response (PMR) of zebrafish embryos was reported as a robust behavior that is useful for high-throughput neuroactive drug discovery and mechanism prediction. Given the complexity of the PMR, there is a need for rapid and easy analysis of the behavioral data. In this study, we developed an automated analysis workflow using the KNIME Analytics Platform and made it freely accessible. This workflow allows us to simultaneously calculate a behavioral fingerprint for all analyzed compounds and to further process the data. Furthermore, to further characterize the potential of PMR for mechanism prediction, we performed PMR analysis of 767 neuroactive compounds covering 14 different receptor classes using the KNIME workflow. We observed a true positive rate of 25% and a false negative rate of 75% in our screening conditions. Among the true positives, all receptor classes were represented, thereby confirming the utility of the PMR assay to identify a broad range of neuroactive molecules. By hierarchical clustering of the behavioral fingerprints, different phenotypical clusters were observed that suggest the utility of PMR for mechanism prediction for adrenergics, dopaminergics, serotonergics, metabotropic glutamatergics, opioids, and ion channel ligands.

Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME.

Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to custom scripting for informatics and data analysis. Here, we illustrate how the large protein-ligand database BindingDB may be incorporated into KNIME workflows as a step toward the integration of pharmacological data with broader biomolecular analyses. Thus, we describe a collection of KNIME workflows that access BindingDB data via RESTful webservices and, for more intensive queries, via a local distillation of the full BindingDB dataset. We focus in particular on the KNIME implementation of knowledge-based tools to generate informed hypotheses regarding protein targets of bioactive compounds, based on notions of chemical similarity. A number of variants of this basic approach are tested for seven existing drugs with relatively ill-defined therapeutic targets, leading to replication of some previously confirmed results and discovery of new, high-quality hits. Implications for future development are discussed. Database URL: www.bindingdb.org.

Grouping of histone deacetylase inhibitors and other toxicants disturbing neural crest migration by transcriptional profiling.

Functional assays, such as the "migration inhibition of neural crest cells" (MINC) developmental toxicity test, can identify toxicants without requiring knowledge on their mode of action (MoA). Here, we were interested, whether (i) inhibition of migration by structurally diverse toxicants resulted in a unified signature of transcriptional changes; (ii) whether statistically-identified transcript patterns would inform on compound grouping even though individual genes were little regulated, and (iii) whether analysis of a small group of biologically-relevant transcripts would allow the grouping of compounds according to their MoA. We analyzed transcripts of 35 'migration genes' after treatment with 16 migration-inhibiting toxicants. Clustering, principal component analysis and correlation analyses of the data showed that mechanistically related compounds (e.g. histone deacetylase inhibitors (HDACi), PCBs) triggered similar transcriptional changes, but groups of structurally diverse toxicants largely differed in their transcriptional effects. Linear discriminant analysis (LDA) confirmed the specific clustering of HDACi across multiple separate experiments. Similarity of the signatures of the HDACi trichostatin A and suberoylanilide hydroxamic acid to the one of valproic acid (VPA), suggested that the latter compound acts as HDACi when impairing neural crest migration. In conclusion, the data suggest that (i) a given functional effect (e.g. inhibition of migration) can be associated with highly diverse signatures of transcript changes; (ii) statistically significant grouping of mechanistically-related compounds can be achieved on the basis of few genes with small regulations. Thus, incorporation of mechanistic markers in functional in vitro tests may support read-across procedures, also for structurally un-related compounds.

MARK-AGE data management: Cleaning, exploration and visualization of data.

Databases are an organized collection of data and necessary to investigate a wide spectrum of research questions. For data evaluation analyzers should be aware of possible data quality problems that can compromise results validity. Therefore data cleaning is an essential part of the data management process, which deals with the identification and correction of errors in order to improve data quality. In our cross-sectional study, biomarkers of ageing, analytical, anthropometric and demographic data from about 3000 volunteers have been collected in the MARK-AGE database. Although several preventive strategies were applied before data entry, errors like miscoding, missing values, batch problems etc., could not be avoided completely. Such errors can result in misleading information and affect the validity of the performed data analysis. Here we present an overview of the methods we applied for dealing with errors in the MARK-AGE database. We especially describe our strategies for the detection of missing values, outliers and batch effects and explain how they can be handled to improve data quality. Finally we report about the tools used for data exploration and data sharing between MARK-AGE collaborators.

The MARK-AGE phenotypic database: Structure and strategy.

In the context of the MARK-AGE study, anthropometric, clinical and social data as well as samples of venous blood, buccal mucosal cells and urine were systematically collected from 3337 volunteers. Information from about 500 standardised questions and about 500 analysed biomarkers needed to be documented per individual. On the one hand handling with such a vast amount of data necessitates the use of appropriate informatics tools and the establishment of a database. On the other hand personal information on subjects obtained as a result of such studies has, of course, to be kept confidential, and therefore the investigators must ensure that the subjects' anonymity will be maintained. Such secrecy obligation implies a well-designed and secure system for data storage. In order to fulfil the demands of the MARK-AGE study we established a phenotypic database for storing information on the study subjects by using a doubly coded system.

Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry.

MS-based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom-tailored and flexible data-processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open-source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high-quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label-free quantitation and identification of metabolites, and quality control for proteomics experiments.

Effect of simulated pulpal fluid circulation on intrapulpal temperature following irradiation with an Nd:YVO4 laser.

It is suggested that pulpal fluid circulation has an impact on pulp temperature increase during heat-generating dental treatment procedures. Thus, the aim of the study was to assess the effect of a simulated pulpal fluid circulation on temperature changes inside the pulp chamber following laser irradiation of the tooth surface. Twenty freshly extracted human multirooted teeth were included and cross-sectioned along the long axis exposing two root canals each. The pulp chamber and root canals were cleaned from remaining soft tissues to achieve access for a temperature sensor and two cannulas to allow fluid circulation. Cross sections were glued together, and the roots were encased with silicone impression material to ensure the position of the connected devices. Each tooth was irradiated by employing a neodymium-doped yttrium orthovanadate (Nd:YVO4) laser at 1,064 nm with a pulse duration of 9 ps and a repetition rate of 500 kHz. A commercially available scanning system (SCANcube 7, SCANLAB) deflected the beam by providing rectangular irradiated areas of 0.5 mm edge length. Measurements were performed with four different settings for fluid circulation: without any water and with water (23 °C) at a flow rate of 6, 3, and 0 ml/min. The primary outcome measure was the maximum temperature difference (ΔT) after laser irradiation. Highest temperature changes (median 3.6 K, range 0.5-7.1 K) could be observed without any fluid inside the pulp chamber. Water without circulation decreased ΔT values statistically significantly (median 1.4 K, range 0.2-4.9 K) (p < 0.05). Lowest temperature changes could be observed with a water flow rate of 6 ml/min (median 0.8 K, range 0.2-3.7 K) (p < 0.05). Pulpal fluid circulation has a cooling effect on temperature increase caused by laser irradiation of dental hard tissues. Studies on heat generation during dental treatment procedures should include this aspect to assess a potential thermal injury of pulp tissue.

From transient transcriptome responses to disturbed neurodevelopment: role of histone acetylation and methylation as epigenetic switch between reversible and irreversible drug effects.

The superordinate principles governing the transcriptome response of differentiating cells exposed to drugs are still unclear. Often, it is assumed that toxicogenomics data reflect the immediate mode of action (MoA) of drugs. Alternatively, transcriptome changes could describe altered differentiation states as indirect consequence of drug exposure. We used here the developmental toxicants valproate and trichostatin A to address this question. Neurally differentiating human embryonic stem cells were treated for 6 days. Histone acetylation (primary MoA) increased quickly and returned to baseline after 48 h. Histone H3 lysine methylation at the promoter of the neurodevelopmental regulators PAX6 or OTX2 was increasingly altered over time. Methylation changes remained persistent and correlated with neurodevelopmental defects and with effects on PAX6 gene expression, also when the drug was washed out after 3-4 days. We hypothesized that drug exposures altering only acetylation would lead to reversible transcriptome changes (indicating MoA), and challenges that altered methylation would lead to irreversible developmental disturbances. Data from pulse-chase experiments corroborated this assumption. Short drug treatment triggered reversible transcriptome changes; longer exposure disrupted neurodevelopment. The disturbed differentiation was reflected by an altered transcriptome pattern, and the observed changes were similar when the drug was washed out during the last 48 h. We conclude that transcriptome data after prolonged chemical stress of differentiating cells mainly reflect the altered developmental stage of the model system and not the drug MoA. We suggest that brief exposures, followed by immediate analysis, are more suitable for information on immediate drug responses and the toxicity MoA.

KNIME-CDK: Workflow-driven cheminformatics.

BACKGROUND: Cheminformaticians have to routinely process and analyse libraries of small molecules. Among other things, that includes the standardization of molecules, calculation of various descriptors, visualisation of molecular structures, and downstream analysis. For this purpose, scientific workflow platforms such as the Konstanz Information Miner can be used if provided with the right plug-in. A workflow-based cheminformatics tool provides the advantage of ease-of-use and interoperability between complementary cheminformatics packages within the same framework, hence facilitating the analysis process. RESULTS: KNIME-CDK comprises functions for molecule conversion to/from common formats, generation of signatures, fingerprints, and molecular properties. It is based on the Chemistry Development Toolkit and uses the Chemical Markup Language for persistence. A comparison with the cheminformatics plug-in RDKit shows that KNIME-CDK supports a similar range of chemical classes and adds new functionality to the framework. We describe the design and integration of the plug-in, and demonstrate the usage of the nodes on ChEBI, a library of small molecules of biological interest. CONCLUSIONS: KNIME-CDK is an open-source plug-in for the Konstanz Information Miner, a free workflow platform. KNIME-CDK is build on top of the open-source Chemistry Development Toolkit and allows for efficient cross-vendor structural cheminformatics. Its ease-of-use and modularity enables researchers to automate routine tasks and data analysis, bringing complimentary cheminformatics functionality to the workflow environment.