[Osteoporotic vertebral fractures of the thoracic and lumbar spine]

The frequency of osteoporotic vertebral fractures in the clinical routine is increasing due to the demographic change. They are the most frequent fractures associated with osteoporosis and affect an especially morbid and vulnerable group of patients. These fractures often occur after minor trauma or spontaneously. Pain is the predominant symptom, whereas mechanical stability is mostly sufficient, in comparison to vertebral fractures after high-energy trauma, and is not a predominant indication for surgery. These fractures can be described using the classification for fractures associated with osteoporosis and the corresponding treatment recommendations are guided by them. Besides the specific treatment of osteoporotic vertebral fractures, a holistic treatment of patients taking pre-existing comorbidities into consideration is decisive. A mobilization as quickly as possible and treatment of the underlying osteoporosis are important to prevent further fractures.

Will it run?-A proof of concept for smoke testing decentralized data analytics experiments

The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution. Failing to detect such SPOFs can, for example, result in improper termination of the DA code, necessitating additional efforts from multiple stakeholders to resolve the malfunctions. Moreover, these malfunctions disrupt the seamless conduct of DA and entail several crucial consequences, including technical obstacles to resolve the issues, potential delays in research outcomes, and increased costs. In this study, we address this challenge by introducing a concept based on a method called Smoke Testing, an initial and foundational test run to ensure the operability of the analysis code. We review existing DA platforms and systematically extract six specific Smoke Testing criteria for DA applications. With these criteria in mind, we create an interactive environment called Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR), which allows researchers to perform Smoke Tests on their DA experiments. We conduct a user-study with 29 participants to assess our environment and additionally apply it to three real use cases. The results of our evaluation validate its effectiveness, revealing that 96.6% of the analyses created and (Smoke) tested by participants using our approach successfully terminated without any errors. Thus, by incorporating Smoke Testing as a fundamental method, our approach helps identify potential malfunctions early in the development process, ensuring smoother data-driven research within the scope of DA. Through its flexibility and adaptability to diverse real use cases, our solution enables more robust and efficient development of DA experiments, which contributes to their reliability.

Explainable AI for Bioinformatics: Methods, Tools and Applications

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.

A Privacy-Preserving Distributed Analytics Platform for Health Care Data

Background  In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest.

Objective  We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location.

Methods  In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers.

Results  We show that our infrastructure enables the training of data models based on distributed data sources.

Conclusion  Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.

Convolutional Embedded Networks for Population Scale Clustering and Bio-Ancestry Inferencing

The study of genetic variants (GVs) can help find correlating population groups and to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning techniques are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation, deep neural networks (DNNs) can learn non-linear mappings that allow transforming GVs data into more clustering and classification friendly representations than manual feature selection. In this paper, we propose convolutional embedded networks (CEN) in which we combine two DNN architectures called convolutional embedded clustering (CEC) and convolutional autoencoder (CAE) classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning to 95 million GVs from the '1000 genomes' (covering 2,504 individuals from 26 ethnic origins) and 'Simons genome diversity' (covering 279 individuals from 130 ethnic origins) projects. Quantitative and qualitative analyses with a focus on accuracy and scalability show that our approach outperforms state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index (ARI) of 0.915, the normalized mutual information (NMI) of 0.92, and the clustering accuracy (ACC) of 89 percent. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient (MCC) score of 0.9004 and 0.8245, respectively. Further, to provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees (GBT) and SHapley Additive exPlanations (SHAP). Overall, our approach is transparent and faster than the baseline methods, and scalable for 5 to 100 percent of the full human genome.

Molybdenum trioxide enhances viability, osteogenic differentiation and extracellular matrix formation of human bone marrow-derived mesenchymal stromal cells

BACKGROUND

Metals and their ions allow specific modifications of the biological properties of bioactive materials that are intended for application in bone tissue engineering. While there is some evidence about the impact of particles derived from orthopedic Cobalt-Chromium-Molybdenum (Co-Cr-Mo) alloys on cells, there is only limited data regarding the influence of the essential trace element Mo and its ions on the viability, osteogenic differentiation as well as on the formation and maturation of the primitive extracellular matrix (ECM) of primary human bone marrow-derived stromal cells (BMSCs) available so far.

METHODS

In this study, the influence of a wide range of molybdenum (VI) trioxide (MoO₃), concentrations on BMSC viability was evaluated via measurement of fluorescein diacetate metabolization. Thereafter, the impact of three non-cytotoxic concentrations of MoO₃ on the cellular osteogenic differentiation as well as on ECM formation and maturation of BMSCs was assessed.

RESULTS

MoO₃ had no negative influence on BMSC viability in most tested concentrations, as viability was in fact even enhanced. Only the highest concentration (10 mM) of MoO₃ showed cytotoxic effects. Cellular osteogenic differentiation, measured via the marker enzyme alkaline phosphatase was enhanced by the presence of MoO₃ in a concentration-dependent manner. Furthermore, MoO₃ showed a positive influence on the expression of relevant marker genes for osteogenic differentiation (osteopontin, osteocalcin and type I collagen alpha 1) and on the formation and maturation of the primitive ECM, as measured by collagen deposition and ECM calcification.

CONCLUSION

MoO₃ is considered as an attractive candidate for supplementation in biomaterials and qualifies for further research.

DAMS: A Distributed Analytics Metadata Schema

In recent years, implementations enabling Distributed Analytics (DA) have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data. These concepts propose privacy-enhancing alternatives to data centralisation approaches, which have restricted applicability in case of sensitive data due to ethical, legal or social aspects. Nevertheless, the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions, particularly the absence of basic metadata for data sets or analysis tasks. To approach the mentioned problems, we propose a metadata schema for DA infrastructures, which provides a vocabulary to enrich the involved entities with descriptive semantics. We initially perform a requirement analysis with domain experts to reveal necessary metadata items, which represents the foundation of our schema. Afterwards, we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content. In the final step, we enable machine-readability via RDF(S) and SHACL serialisations. We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures. Additionally, we evaluate the schema's compliance with the FAIR principles. The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles. Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures, our work lowers data access and analysis barriers. It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.

Deep learning-based clustering approaches for bioinformatics

Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.

[Minimally invasive stabilization of sacral fractures]

BACKGROUND

Sacral fractures can be of traumatic origin and can also occur as insufficiency fractures. While the therapeutic target of mechanically stable insufficiency fractures is mainly pain relief, mechanically unstable insufficiency fractures and traumatic sacral fractures following high-energy trauma require biomechanical stabilization. Various surgical strategies are available for this, whereby minimally invasive techniques are now preferred whenever possible.

OBJECTIVE

This article presents the clinical challenges and options for minimally invasive treatment of sacral fractures.

MATERIAL AND METHODS

Selected important study data are discussed and our own treatment approach is presented.

RESULTS

The most important minimally invasive techniques for operative treatment of sacral fractures are presented: sacroiliac screw osteosynthesis, lumbopelvic stabilization and sacroplasty. The selection of the surgical technique should be made on an individual basis. While sacroiliac screw osteosynthesis is the international gold standard, diverse authors have also published minimally invasive techniques for lumbopelvic stabilization. The latter enables a higher mechanical stability. In contrast, sacroplasty should only be used as an alternative treatment in insufficiency fractures. Comparative studies of the described techniques are still missing.

CONCLUSION

All surgical options have their indications. Nevertheless, the biomechanical stability which can be achieved differs widely. Therefore, an exact analysis should be carried out of what is necessary with respect to reduction and retention and what should be achieved when treating sacral fractures.

Enabling ad-hoc reuse of private data repositories through schema extraction

BACKGROUND

Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) impose strict requirements concerning the protection of personal and privacy sensitive data. Therefore new approaches, such as the Personal Health Train initiative, are emerging to utilize data right in their original repositories, circumventing the need to transfer data.

RESULTS

Circumventing limitations of previous systems, this paper proposes a configurable and automated schema extraction and publishing approach, which enables ad-hoc SPARQL query formulation against RDF triple stores without requiring direct access to the private data. The approach is compatible with existing Semantic Web-based technologies and allows for the subsequent execution of such queries in a safe setting under the data provider's control. Evaluation with four distinct datasets shows that a configurable amount of concise and task-relevant schema, closely describing the structure of the underlying data, was derived, enabling the schema introspection-assisted authoring of SPARQL queries.

CONCLUSIONS

Automatically extracting and publishing data schema can enable the introspection-assisted creation of data selection and integration queries. In conjunction with the presented system architecture, this approach can enable reuse of data from private repositories and in settings where agreeing upon a shared schema and encoding a priori is infeasible. As such, it could provide an important step towards reuse of data from previously inaccessible sources and thus towards the proliferation of data-driven methods in the biomedical domain.

Distributed Analytics on Sensitive Medical Data: The Personal Health Train

In recent years, as newer technologies have evolved around the healthcare ecosystem, more and more data have been generated. Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis of diseases; improve the care given to the patient; and empower citizens to participate in the decision-making process regarding their own health and well-being. However, the sensitive nature of the health data prohibits healthcare organizations from sharing the data. The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remain in their original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations. This paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.

A snapshot neural ensemble method for cancer-type prediction based on copy number variations

An accurate diagnosis and prognosis for cancer are specific to patients with particular cancer types and molecular traits, which needs to address carefully. The discovery of important biomarkers is becoming an important step toward understanding the molecular mechanisms of carcinogenesis in which genomics data and clinical outcomes need to be analyzed before making any clinical decision. Copy number variations (CNVs) are found to be associated with the risk of individual cancers and hence can be used to reveal genetic predispositions before cancer develops. In this paper, we collect the CNVs data about 8000 cancer patients covering 14 different cancer types from The Cancer Genome Atlas. Then, two different sparse representations of CNVs based on 578 oncogenes and 20,308 protein-coding genes, including genomic deletions and duplication across the samples, are prepared. Then, we train Conv-LSTM and convolutional autoencoder (CAE) networks using both representations and create snapshot models. While the Conv-LSTM can capture locally and globally important features, CAE can utilize unsupervised pretraining to initialize the weights in the subsequent convolutional layers against the sparsity. Model averaging ensemble (MAE) is then applied to combine the snapshot models in order to make a single prediction. Finally, we identify most significant CNVs biomarkers using guided-gradient class activation map plus (GradCAM++) and rank top genes for different cancer types. Results covering several experiments show fairly high prediction accuracies for the majority of cancer types. In particular, using protein-coding genes, Conv-LSTM and CAE networks can predict cancer types correctly at least 72.96% and 76.77% of the cases, respectively. Contrarily, using oncogenes gives moderately higher accuracies of 74.25% and 78.32%, whereas the snapshot model based on MAE shows overall 2.5% of accuracy improvement.

Transferring Knowledge from Monitored to Unmonitored Areas for Forecasting Parking Spaces

Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promising, indeed. However, existing approaches are limited by the high cost of sensors that need to be installed throughout the city in order to achieve an accurate estimation. This work investigates the extension of estimating parking information from areas equipped with sensors to areas where they are missing. To this end, the similarity between city neighborhoods is determined based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.

Design of Metadata Services for Clinical Data Interoperability in Germany

Secondary use of electronic health record (EHR) data requires a detailed description of metadata, especially when data collection and data re-use are organizationally and technically far apart. This paper describes the concept of the SMITH consortium that includes conventions, processes, and tools for describing and managing metadata using common standards for semantic interoperability. It deals in particular with the chain of processing steps of data from existing information systems and provides an overview of the planned use of metadata, medical terminologies, and semantic services in the consortium.

A targeted neuropsychological examination of children with a history of sport-related concussion

Experimental research suggests that sport-related concussion can lead to persistent alterations in children's neurophysiology and cognition. However, the search for neuropsychological tests with a similar ability to detect long-term deficits continues.

PRIMARY OBJECTIVE

The current study assessed whether a target battery of neuropsychological measures of higher cognition and academic achievement would detect lingering deficits in children 2 years after injury.

RESEARCH DESIGN

Cross-sectional.

METHODS AND PROCEDURE

A total of 32 pre-adolescent children (16 concussion history, 16 control) completed a targeted battery of neuropsychological and academic tests.

MAIN OUTCOMES AND RESULTS

Children with a history of concussion exhibited selective deficits during the Raven's Coloured Progressive Matrices, Comprehensive Trail-Making Test, and the mathematics sub-section of the WRAT-3. Deficit magnitude was significantly related to age at injury, but not time since injury.

CONCLUSIONS

The current results suggest that neuropsychological measures of higher cognition and academic achievement may be sensitive to lingering deficits, and that children injured earlier in life may exhibit worse neuropsychological and academic performance.

Smart Medical Information Technology for Healthcare (SMITH)

INTRODUCTION

This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. "Smart Medical Information Technology for Healthcare (SMITH)" is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH's goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach.

OBJECTIVES

To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented.

GOVERNANCE AND POLICIES

SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals' Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage.

METHODOLOGY AND ARCHITECTURAL FRAMEWORK

To share medical and research data, SMITH's information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM² for enterprise architecture modeling supports a consistent development process.The DIC reference architecture determines the services, applications and the standardsbased communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability.

USE CASES

The methodological use case "Phenotype Pipeline" (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case "Algorithmic Surveillance of ICU Patients" (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a "hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections" (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design.

DISCUSSION

SMITH's strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals' information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.

[Surgical treatment of acute mediastinitis]

Despite modern intensive care management, acute mediastinitis is still associated with a high morbidity and mortality (up to approximately 40 %). Effective antibiotic therapy, intensive care management, elimination of the causative sources of infection and drainage of the affected mediastinal compartments are the cornerstones of therapy in a multidisciplinary treatment concept. Early diagnosis, prompt and uncompromising initial therapy and planned computed tomography (CT) control after the first stages of therapy in order to decide on the necessity for surgical re-interventions are essential for achieving optimal results. Knowledge of the specific anatomical characteristics is crucial for the understanding of this disease and its treatment; therefore, the current knowledge on fascial layers and interstitial spaces from the neck to the mediastinum is described and discussed. A possible foudroyant spread of the infection, especially within the posterior mediastinum, has to be anticipated. The approach to the mediastinum depends on the mediastinal compartments affected, on the causative disease and on the patient's clinical situation. The surgical approach should be adapted to the particular clinical situation of the individual patient and to the surgical experience of the surgeon. When in doubt, the more invasive approach to the mediastinum, such as bilateral thoracotomy, is recommended. An ascending mediastinitis due to pancreatitis is a very rare condition; however, as chest pains are often the main clinical sign surgeons should be aware of this differential diagnosis. An intraoperative brown-black serous fluid in the mediastinal tissue is virtually pathognomonic. The treatment results of esophageal perforation as the most frequent cause of mediastinitis have been improved by integration of various interventional procedures. Hyperbaric oxygen therapy or immunoglobulin treatment can play an auxiliary role in selected patients with acute mediastinitis.

Lymphabstromszintigraphie bei einem artefiziellen Ödem des Unterschenkels

Es wird über eine junge Frau berichtet, die nach einem Trauma über ca. 2 Monate wiederholt zur stationären Diagnostik aufgenommen wurde, bevor die arte-fizielle Genese eines Ödems des linken Beines geklärt werden konnte. Die Lymphszintigraphie zeigte eine starke Beschleunigung des Lymphabstroms aus dem geschwollenen Bein.

[Limb salvage or amputation after severe trauma to the lower extremities : Evidence from the LEAP Study]

Deciding between reconstruction and primary amputation after severe high-energy trauma to the lower extremities is difficult and consequential. The Lower Extremity Assessment Project (LEAP) prospectively included and investigated patients with severe, limb-threatening injuries below the femur, with third-grade open fractures, defined soft-tissue damage and amputation wounds. This paper aims to review the key results of the LEAP study, which were published in several parts, in due consideration of the newer relevant literature, and to deduce the consequences for clinical practice. The main results are as follows: No score is sufficiently reliable to predict the success of reconstruction. Loss of muscle seems to be more momentous than loss of bone. Any accompanying injuries that should be taken into account in the individual treatment concepts are crucial to the results, in addition to comorbidities and other individual patient-related factors, such as alcoholism, smoking, insurance, and social background. Psychological impairment is frequent after these injuries and should therefore be addressed regularly with regard to rehabilitation.

The Sheep Grimace Scale as an indicator of post-operative distress and pain in laboratory sheep

The EU Directive 2010/63/EU changed the requirements regarding the use of laboratory animals and raised important issues related to assessing the severity of all procedures undertaken on laboratory animals. However, quantifiable parameters to assess severity are rare, and improved assessment strategies need to be developed. Hence, a Sheep Grimace Scale (SGS) was herein established by observing and interpreting sheep facial expressions as a consequence of pain and distress following unilateral tibia osteotomy. The animals were clinically investigated and scored five days before surgery and at 1, 3, 7, 10, 14 and 17 days afterwards. Additionally, cortisol levels in the saliva of the sheep were determined at the respective time points. For the SGS, video recording was performed, and pictures of the sheep were randomized and scored by blinded observers. Osteotomy in sheep resulted in an increased clinical severity score from days 1 to 17 post-surgery and elevated salivary cortisol levels one day post-surgery. An analysis of facial expressions revealed a significantly increased SGS on the day of surgery until day 3 post-surgery; this elevated level was sustained until day 17. Clinical severity and SGS scores correlated positively with a Pearson´s correlation coefficient of 0.47. Further investigations regarding the applicability of the SGS revealed a high inter-observer reliability with an intraclass correlation coefficient of 0.92 and an accuracy of 68.2%. In conclusion, the SGS represents a valuable approach for severity assessment that may help support and refine a widely used welfare assessment for sheep during experimental procedures, thereby meeting legislation requirements and minimizing the occurrence of unrecognized distress in animal experimentation.

BioFed: federated query processing over life sciences linked open data

Background

Biomedical data, e.g. from knowledge bases and ontologies, is increasingly made available following open linked data principles, at best as RDF triple data. This is a necessary step towards unified access to biological data sets, but this still requires solutions to query multiple endpoints for their heterogeneous data to eventually retrieve all the meaningful information. Suggested solutions are based on query federation approaches, which require the submission of SPARQL queries to endpoints. Due to the size and complexity of available data, these solutions have to be optimised for efficient retrieval times and for users in life sciences research. Last but not least, over time, the reliability of data resources in terms of access and quality have to be monitored. Our solution (BioFed) federates data over 130 SPARQL endpoints in life sciences and tailors query submission according to the provenance information. BioFed has been evaluated against the state of the art solution FedX and forms an important benchmark for the life science domain.

Methods

The efficient cataloguing approach of the federated query processing system ’BioFed’, the triple pattern wise source selection and the semantic source normalisation forms the core to our solution. It gathers and integrates data from newly identified public endpoints for federated access. Basic provenance information is linked to the retrieved data. Last but not least, BioFed makes use of the latest SPARQL standard (i.e., 1.1) to leverage the full benefits for query federation. The evaluation is based on 10 simple and 10 complex queries, which address data in 10 major and very popular data sources (e.g., Dugbank, Sider).

Results

BioFed is a solution for a single-point-of-access for a large number of SPARQL endpoints providing life science data. It facilitates efficient query generation for data access and provides basic provenance information in combination with the retrieved data. BioFed fully supports SPARQL 1.1 and gives access to the endpoint’s availability based on the EndpointData graph. Our evaluation of BioFed against FedX is based on 20 heterogeneous federated SPARQL queries and shows competitive execution performance in comparison to FedX, which can be attributed to the provision of provenance information for the source selection.

Conclusion

Developing and testing federated query engines for life sciences data is still a challenging task. According to our findings, it is advantageous to optimise the source selection. The cataloguing of SPARQL endpoints, including type and property indexing, leads to efficient querying of data resources over the Web of Data. This could even be further improved through the use of ontologies, e.g., for abstract normalisation of query terms.

[Sagittal Balance of the Spine--Clinical Importance and Radiographic Assessment]

Sagittal deformities of the spine frequently result in back pain, as patients have to expend much energy in compensation. The sagittal alignment of the spine is defined by its curvatures (lordosis and kyphosis) relative to the position of the pelvis. Diagnostic assessment is based on full spine a. p. and lateral X-rays. The sagittal balance is primarily described by different angles that can be measured, e.g. lumbar lordosis, pelvic incidence, pelvic tilt and thoracic kyphosis. The quality of life can best be estimated by subtracting lumbar lordosis from the pelvic incidence. However, initial evaluation of the sagittal balance can also be based on the sagittal vertical axis. The severity of imbalance can be described by the sagittal vertical axis and the pelvic tilt, but surgical therapy necessitates a more profound analysis, which can be based on the SRS-Schwab classification.

Percutaneous dilatational tracheostomy (PDT) in trauma patients: a safe procedure

PURPOSE

Percutaneous dilatational tracheostomy (PDT) is a standard procedure routinely performed on intensive care units. While complication rates and long-term outcomes have been studied in different patient populations, there are few studies known to these authors involving PDT in trauma patients and the complications which may result.

METHODS

Between March 2007 and August 2013, all instances and peri-procedural complications during PDT occurring on the trauma intensive care unit, a unit specialized in the care of injured patients and especially polytrauma patients, were documented. PDTs were performed by a surgeon with the assistance and supervision of another, using bronchoscopic guidance performed by the respiratory medicine department.

RESULTS

289 patients were included in the study, 225 men and 64 women with a mean age of 49 ± 21 years. Complications occurred in 37.4 % of cases. The most common complication, bleeding, occurred in 26.3 % of patients ranging from little to severe bleeding. Fracture of tracheal cartilage occurred in 6 % of PDT cases. Additional complications such as dislocation of the guidewire, hypotension, and oxygen desaturation were observed. Most complications did not require treatment. The second tracheal intercartilaginous space was successfully intubated in 82 % of cases.

CONCLUSIONS

PDT is a safe procedure in trauma patients. When considering the severity of complications such as major blood loss, pneumothorax, or death, this evidence suggests that PDT is safer in trauma patients compared to other patient cohorts.

TopFed: TCGA tailored federated query processing and linking to LOD

BACKGROUD

The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to catalogue genetic mutations responsible for cancer using genome analysis techniques. One of the aims of this project is to create a comprehensive and open repository of cancer related molecular analysis, to be exploited by bioinformaticians towards advancing cancer knowledge. However, devising bioinformatics applications to analyse such large dataset is still challenging, as it often requires downloading large archives and parsing the relevant text files. Therefore, it is making it difficult to enable virtual data integration in order to collect the critical co-variates necessary for analysis.

METHODS

We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed.

RESULTS

We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX.

CONCLUSION

With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.

[Infiltration of the superior vena cava in NSCLC: results of surgical intervention]

The benefits of surgical therapy of locally advanced non-small cell lung cancer (NSCLC) with infiltration of the superior vena cava (SVC) remains controversial. Here we describe our therapeutic approach and results of our intervention.A retrospective analysis of 22 patients with NSCLC who underwent SVC replacement (n = 17) or reconstruction (n = 5) between 1998 and 2013 was performed. Pneumonectomy was necessary in 16 patients, lobectomy in 8. Preoperative chemotherapy was administered to 3 patients, 16 received postoperative radiation treatment. The clinical course and survival were analyzed.Major postoperative morbidities were found in 13 patients. Graft thrombosis did not occur. Thirty-day mortality was 7 % in pneumonectomy patients and 0 % following lobectomy. Local recurrence was found in 4.5 %, distant metastases developed in 54.5 % of the patients (p = 0.0008). One- and five-year survival probabilities for all patients were 63.6 and 27.9 %. Five-year survival probability was 33 % for patients with SVC reconstruction and 25 % for patients with SVC replacement (p = 0.22). Five-year survival rates after pneumonectomy and lobectomy were 21.4 % and 37.5 %, respectively (p = 0.18).Radical resection involving the SVC in carefully selected patients with NSCLC results in excellent local tumor control. Due to the high rate of distant metastases, application of induction and adjuvant chemotherapy should be carefully assessed.

ReVeaLD: a user-driven domain-specific interactive search platform for biomedical research

Bioinformatics research relies heavily on the ability to discover and correlate data from various sources. The specialization of life sciences over the past decade, coupled with an increasing number of biomedical datasets available through standardized interfaces, has created opportunities towards new methods in biomedical discovery. Despite the popularity of semantic web technologies in tackling the integrative bioinformatics challenge, there are many obstacles towards its usage by non-technical research audiences. In particular, the ability to fully exploit integrated information needs using improved interactive methods intuitive to the biomedical experts. In this report we present ReVeaLD (a Real-time Visual Explorer and Aggregator of Linked Data), a user-centered visual analytics platform devised to increase intuitive interaction with data from distributed sources. ReVeaLD facilitates query formulation using a domain-specific language (DSL) identified by biomedical experts and mapped to a self-updated catalogue of elements from external sources. ReVeaLD was implemented in a cancer research setting; queries included retrieving data from in silico experiments, protein modeling and gene expression. ReVeaLD was developed using Scalable Vector Graphics and JavaScript and a demo with explanatory video is available at http://www.srvgal78.deri.ie:8080/explorer. A set of user-defined graphic rules controls the display of information through media-rich user interfaces. Evaluation of ReVeaLD was carried out as a game: biomedical researchers were asked to assemble a set of 5 challenge questions and time and interactions with the platform were recorded. Preliminary results indicate that complex queries could be formulated under less than two minutes by unskilled researchers. The results also indicate that supporting the identification of the elements of a DSL significantly increased intuitiveness of the platform and usability of semantic web technologies by domain users.

CXCR4-SERINE339 regulates cellular adhesion, retention and mobilization, and is a marker for poor prognosis in acute myeloid leukemia

The CXCR4 receptor is a major regulator of hematopoietic cell migration. Overexpression of CXCR4 has been associated with poor prognosis in acute myelogenous leukemia (AML). We have previously shown that ligand-mediated phosphorylation of the Serine339 (CXCR4-S339) residue of the intracellular domain by PIM1 is implicated in surface re-expression of this receptor. Here, we report that phosphorylation of CXCR4-S339 in bone marrow (BM) biopsies correlated with poor prognosis in a cohort of AML patients. To functionally address the impact of CXCR4-S339 phosphorylation, we generated cell lines-expressing CXCR4 mutants that mimic constitutive phosphorylation (S339E) or abrogate phosphorylation (S339A). Whereas the expression of CXCR4 significantly increased, both CXCR4-S339E and the CXCR4-S339A mutants significantly reduced the BM homing and engraftment of Kasumi-1 AML cells in immunodeficient mice. In contrast, only expression of the CXCR4-S339E mutant increased the BM retention of the cells and resistance to cytarabine treatment, and impaired detachment capacity and AMD3100-induced mobilization of engrafted leukemic cells. These observations suggest that the poor prognosis in AML patients displaying CXCR4-S339 phosphorylation can be the consequence of an increased retention to the BM associated with an enhanced chemoresistance of leukemic cells. Therefore, CXCR4-S339 phosphorylation could serve as a novel prognostic marker in human AML.

[Differential diagnosis of Parkinson's disease: current consensus criteria and outlook]

Parkinsonian-syndrome, clinically based on the combination of cardinal symptoms, could be the clinical manifestation of different, neuropathological defined entities. Because of the different prognostic, therapeutical and scientific implications a reliable differential diagnostic of the entities in early course of disease is desirable. For this purpose standardized clinical diagnostic criteria with sufficient validation against the gold standard of the neuropathological diagnostic are important. In this article, the clinical diagnostic criteria of atypical Parkinsonian-syndrome and their validity were discussed.

Representing and sharing folksonomies with semantics

Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using ‘tags’ or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. However, there are a number of issues in relation to data sharing and interoperability when processing tag data across heterogeneous tagging platforms. In this paper we introduce a semantic tag model that aims to explicitly offer the necessary structure, semantics and relationships between tags. This approach provides an improved opportunity for representing tag data in the form of reusable constructs at a semantic level. We also demonstrate a prototype that consumes and makes use of shared tag metadata across heterogeneous sources.

A URI is Worth a Thousand Tags

Although tagging is a widely accepted practice on the Social Web, it raises various issues like tags ambiguity and heterogeneity, as well as the lack of organization between tags. We believe that Semantic Web technologies can help solve many of these issues, especially considering the use of formal resources from the Web of Data in support of existing tagging systems and practices. In this article, we present the MOAT—Meaning Of A Tag—ontology and framework, which aims to achieve this goal. We will detail some motivations and benefits of the approach, both in an Enterprise 2.0 ecosystem and on the Web. As we will detail, our proposal is twofold: It helps solve the problems mentioned previously, and weaves user-generated content into the Web of Data, making it more efficiently interoperable and retrievable.

The hollow fibre model in cancer drug screening: the NCI experience

The in vivo hollow fibre model was developed by the National Cancer Institute (NCI) in the United States of America (USA) at a time when the number of potential anti-cancer drugs arising from in vitro screening efforts exceeded the available capacity for testing in traditional xenograft models. Updated analysis of the predictive value of the hollow fibre model continues to indicate that the greater the response in the hollow fibre assay, the more likely it is that activity will be seen in subsequent xenograft models. The original 12 cell line hollow fibre panel has been supplemented with histology-specific panels, and we begin here to analyse their utility in predicting activity in subsequent in vivo models. The key goal of using the hollow fibre model as a way to decrease the cost, both financial and in the number of animals used, to evaluate initial evidence of a compound's capacity to act across physiological barriers continues to be reinforced with our enlarging experience.

The small bovine amelogenin LRAP fails to rescue the amelogenin null phenotype

Amelogenins are the most abundant secreted proteins in developing dental enamel. These evolutionarily-conserved proteins have important roles in enamel mineral formation, as mutations within the amelogenin gene coding region lead to defects in enamel thickness or mineral structure. Because of extensive alternative splicing of the primary RNA transcript and proteolytic processing of the secreted proteins, it has been difficult to assign functions to individual amelogenins. To address the function of one of the amelogenins, we have created a transgenic mouse that expresses bovine leucine-rich amelogenin peptide (LRAP) in the enamel-secreting ameloblast cells of the dental organ. Our strategy was to breed this transgenic mouse with the recently generated amelogenin knockout mouse, which makes none of the amelogenin proteins and has a severe hypoplastic and disorganized enamel phenotype. It was found that LRAP does not rescue the enamel defect in amelogenin null mice, and enamel remains hypoplastic and disorganized in the presence of this small amelogenin. In addition, LRAP overexpression in the transgenic mouse (wildtype background) leads to pitting in the enamel surface, which may result from excess protein production or altered protein processing due to minor differences between the amino acid compositions of murine and bovine LRAP. Since introduction of bovine LRAP into the amelogenin null mouse does not restore normal enamel structure, it is concluded that other amelogenin proteins are essential for normal appearance and function.