Orchestrating Complex Application Architectures in Heterogeneous Clouds

[EN] Private cloud infrastructures are now widely deployed and adopted across technology industries and research institutions. Although cloud computing has emerged as a reality, it is now known that a single cloud provider cannot fully satisfy complex user requirements. This has resulted in a growing interest in developing hybrid cloud solutions that bind together distinct and heterogeneous cloud infrastructures. In this paper we describe the orchestration approach for heterogeneous clouds that has been implemented and used within the INDIGO-DataCloud project. This orchestration model uses existing open-source software like OpenStack and leverages the OASIS Topology and Specification for Cloud Applications (TOSCA) open standard as the modeling language. Our approach uses virtual machines and Docker containers in an homogeneous and transparent way providing consistent application deployment for the users. This approach is illustrated by means of two different use cases in different scientific communities, implemented using the INDIGO-DataCloud solutions.The authors want to acknowledge the support of the INDIGO-Datacloud (grant number 653549) project, funded by the European Commission's Horizon 2020 Framework Program.Caballer Fernández, M.; Zala, S.; López, Á.; Moltó, G.; Orviz, P.; Velten, M. (2018). Orchestrating Complex Application Architectures in Heterogeneous Clouds. Journal of Grid Computing. 16(1):3-18. https://doi.org/10.1007/s10723-017-9418-yS318161Aguilar Gómez, F., de Lucas, J.M., García, D., Monteoliva, A.: Hydrodynamics and water quality forecasting over a cloud computing environment: indigo-datacloud. In: EGU General Assembly Conference Abstracts, vol. 19, p 9684 (2017)de Alfonso, C., Caballer, M., Alvarruiz, F., Hernández, V.: An energy management system for cluster infrastructures. Comput. Electr. Eng. 39(8), 2579–2590 (2013). http://www.sciencedirect.com/science/article/pii/S0045790613001365Amazon Web Services (AWS): Amazon Web Services (AWS). https://aws.amazon.com/ (2017)Amazon Web Services (AWS): CloudFormation. https://aws.amazon.com/cloudformation/ (2017)Apache Software Foundation: Apache Mesos. http://mesos.apache.org/ (2017)ARIA, ARIA. http://ariatosca.incubator.apache.org/ (2017)Bumpus, W.: NIST Cloud Computing Standards Roadmap. Tech. rep., National Institute of Standards and Technology (NIST). https://doi.org/10.6028/NIST.SP.500-291r2 (2013)Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C.: Dynamic management of virtual infrastructures. J Grid Comput. 13(1), 53–70 (2015). https://doi.org/10.1007/s10723-014-9296-5Campos Plasencia, I., Fernández-del Castillo, E., Heinemeyer, S., López García, Á., Pahlen, F., Borges, G.: Phenomenology tools on cloud infrastructures using OpenStack. Eur. Phys. J. C 73(4), 2375 (2013). https://doi.org/10.1140/epjc/s10052-013-2375-0Celar: Celar. http://www.cloudwatchhub.eu/celar (2017)Chen, Y., de Lucas, J.M., Aguilar, F., Fiore, S., Rossi, M., Ferrari, T.: Indigo: building a datacloud framework to support open science. In: EGU General Assembly Conference Abstracts, vol. 18, p 16610 (2016)Chronos: Chronos. https://mesos.github.io/chronos/ (2017)Cloudify: Cloudify. http://getcloudify.org (2017)Davidović, D., Cetinić, E., Skala, K.: European research area and digital humanitiesDistefano, S., Serazzi, G.: Performance driven WS orchestration and deployment in service oriented infrastructure. J Grid Comput. 12(2), 347–369 (2014). https://doi.org/10.1007/s10723-014-9293-8EGI FedCloud: EGI FedCloud. https://www.egi.eu/federation/egi-federated-cloud/ (2017)Eucalyptus: Eucalyptus. https://www.eucalyptus.com/ (2017)Fiore, S., D’Anca, A., Palazzo, C., Foster, I., Williams, D.N., Aloisio, G.: Ophidia: toward big data analytics for eScience. Procedia Comput. Sci. 18, 2376–2385 (2013). https://doi.org/10.1016/j.procs.2013.05.409Fiore, S., Palazzo, C., D’Anca, A., Elia, D., Londero, E., Knapic, C., Monna, S., Marcucci, N.M., Aguilar, F., Płóciennik, M., et al.: Big data analytics on large-scale scientific datasets in the indigo-datacloud project. In: Proceedings of the Computing Frontiers Conference, pp 343–348. ACM (2017)Fiore, S., Płóciennik, M., Doutriaux, C., Palazzo, C., Boutte, J., żok, T., Elia, D., Owsiak, M., D’Anca, A., Shaheen, Z., et al.: Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system. In: 2016 IEEE International Conference on Big Data (Big Data), pp 2911–2918. IEEE (2016)Galante, G., Erpen de Bona, L.C., Mury, A.R., Schulze, B., da Rosa Righi, R.: An analysis of public clouds elasticity in the execution of scientific applications: a survey. J Grid Comput.,1–24. https://doi.org/10.1007/s10723-016-9361-3 (2016)Google Cloud Platform (GCP): Google Cloud Platform (GCP). https://cloud.google.com/ (2017)Hochstein, L. (ed.): Ansible: Up and Running, Automating Configuration Management and Deployment the Easy Way. O’Reilly Media (2014)Idabc: European Interoperability Framework for pan-European eGovernment Services. European Commission version 1, 1–25. https://doi.org/10.1109/HICSS.2007.68 (2004)IM: IM. http://www.grycap.upv.es/im (2017)INDIGO-DataCloud: D1.8 - General Architecture. Tech. rep., INDIGO-DataCloud Consortium (2015)INDIGO-DataCloud: Ansible Galaxy repository for INDIGO-DataCloud. https://galaxy.ansible.com/indigo-dc/ (2017)INDIGO-DataCloud: Disvis/Powerfit Ansible Role in Ansible Galaxy. https://galaxy.ansible.com/indigo-dc/disvis-powerfit/ (2017)INDIGO-DataCloud: INDIGO-DataCloud. https://www.indigo-datacloud.eu/ (2017)INDIGO-DataCloud: INDIGO-DataCloud DockerHub application repository. https://hub.docker.com/u/indigodatacloudapps/ (2017)INDIGO-DataCloud: INDIGO-DataCloud PaaS Orchestrator. https://github.com/indigo-dc/orchestrator (2017)INDIGO-DataCloud: INDIGO-DataCloud RepoSync. https://github.com/indigo-dc/java-reposync (2017)INDIGO-DataCloud: INDIGO-DataCloud TOSCA templates. https://github.com/indigo-dc/tosca-templates (2017)INDIGO-DataCloud: TOSCA Across Clouds. https://github.com/indigo-dc/tosca-types/blob/master/examples/web_mysql_tosca_across_clouds.yaml (2017)INDIGO-DataCloud: TOSCA template for deploying an Elastic Mesos Cluster. http://github.com/indigo-dc/tosca-types/blob/master/examples/mesos_elastic_cluster.yaml (2017)INDIGO-DataCloud: TOSCA template for Powerfit application. https://github.com/indigo-dc/tosca-types/blob/master/examples/powerfit.yaml (2017)Kacsuk, P., Kecskemeti, G., Kertesz, A., Nemeth, Z., Kovȧcs, J., Farkas, Z.: Infrastructure aware scientific workflows and infrastructure aware workflow managers in science gateways. J Grid Comput., 641–654. https://doi.org/10.1007/s10723-016-9380-0 (2016)Korambath, P., Wang, J., Kumar, A., Hochstein, L., Schott, B., Graybill, R., Baldea, M., Davis, J.: Deploying kepler workflows as services on a cloud infrastructure for smart manufacturing. Procedia Comput. Sci. 29, 2254–2259 (2014)Koski, K., Hormia-Poutanen, K., Chatzopoulos, M., Legrė, Y., Day, B.: Position Paper: European Open Science Cloud for Research. Tech. Rep. october, EUDAT, LIBER, OpenAIRE, EGI, GĖANT Bari (2015)Krieger, M.T., Torreno, O., Trelles, O., Kranzlmüller, D.: Building an open source cloud environment with auto-scaling resources for executing bioinformatics and biomedical workflows. Futur. Gener. Comput. Syst. 67, 329–340 (2017). https://doi.org/10.1016/j.future.2016.02.008Kurkcuoglu Soner, Z., Bonvin, A.: Science in the clouds: virtualizing haddock powerfit and disvis using indigo-datacloud solutions (2016)Lipton, P.C.T., Moser, S.I., Palma, D.V., Spatzier, T.I.: Topology and Orchestration Specification for Cloud Applications. Tech. rep., OASIS Standard (2013)Liu, C., Mao, Y., Van der Merwe, J., Fernandez, M.: Cloud Resource Orchestration: a Data-Centric Approach. In: Proceedings of the Biennial Conference on Innovative Data Systems Research (CIDR), pp 1–8. Citeseer (2011)López García, Á., Fernández-del Castillo, E.: Analysis of scientific cloud computing requirements. In: Proceedings of the IBERGRID 2013 Conference, p 147 158 (2013)López García, Á., Fernández-del Castillo, E., Orviz Fernández, P.: Standards for enabling heterogeneous IaaS cloud federations. Comput. Standard Inter. 47, 19–23 (2016). https://doi.org/10.1016/j.csi.2016.02.002López García, Á., Zangrando, L., Sgaravatto, M., Llorens, V., Vallero, S., Zaccolo, V., Bagnasco, S., Taneja, S., Pra, S.D., Salomoni, D., Donvito G.: Improved cloud resource allocation: how INDIGO-datacloud is overcoming the current limitations in cloud schedulers. arXiv: 1707.06403 (2017)Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J Grid Comput. 12(4), 559–592 (2014). https://doi.org/10.1007/s10723-014-9314-7Marathon: Marathon. https://mesosphere.github.io/marathon/ (2017)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-Infrastructure. Tech. rep., Open Grid Forum (2010)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-RESTful HTTP Rendering. Tech. rep., Open Grid Forum (2011)Microsoft Azure: Microsoft Azure. https://azure.microsoft.com/ (2017)Moltó, G., Caballer, M., Pérez, A., Alfonso, D.C., Blanquer, I.: Coherent application delivery on hybrid distributed computing infrastructures of virtual machines and docker containers. In: 2017 25Th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). https://doi.org/10.1109/PDP.2017.29 , pp 486–490 (2017)Monna, S., Marcucci, N.M., Marinaro, G., Fiore, S., D’Anca, A., Antonacci, M., Beranzoli, L., Favali, P.: An Emso data case study within the indigo-Dc project. In: EGU General Assembly Conference Abstracts, vol. 19, p 12493 (2017)Nyrén, R., Metsch, T., Edmonds, A., Papaspyrou, A.: Open Cloud Computing Interface–Core. Tech. rep., Open Grid Forum (2010)OASIS: Organization for the Advancement of Structured Information Standards (OASIS). https://www.oasis-open.org (2015)Open Telekom Cloud (OTC): Open Telekom Cloud (OTC). https://cloud.telekom.de/en/ (2017)OpenNebula: OneFlow. http://docs.opennebula.org/5.2/advanced_components/application_flow_and_auto-scaling/index.html (2017)OpenNebula Project: OpenNebula. https://www.opennebula.org (2017)OpenStack Foundation: Heat Orchestration Template (HOT) Guide. https://docs.openstack.org/heat/latest/template_guide/hot_guide.html (2017)OpenStack Foundation: OpenStack. https://www.openstack.org (2017)OpenStack Foundation: Openstack Heat. http://wiki.openstack.org/wiki/Heat (2017)OpenStack Foundation: OpenStack Heat Translator. https://github.com/openstack/heat-translator (2017)OpenStack Foundation: OpenStack heat-translator project contribution statistics. http://stackalytics.com/?release=all&metric=commits&module=heat-translator (2017)OpenStack Foundation: OpenStack Tacker. https://wiki.openstack.org/wiki/Tacker (2017)OpenStack Foundation: OpenStack tosca-parser project contribution statistics. http://stackalytics.com/?release=all&metric=commits&module=tosca-parser (2017)OpenStack Foundation: TOSCA Parser. https://github.com/openstack/tosca-parser (2017)OpenTOSCA: OpenTOSCA. http://www.opentosca.org/ (2017)Owsiak, M., Plociennik, M., Palak, B., Zok, T., Reux, C., Di Gallo, L., Kalupin, D., Johnson, T., Schneider, M.: Running simultaneous kepler sessions for the parallelization of parametric scans and optimization studies applied to complex workflows. J Comput. Sci. 20, 103–111 (2017)Palma, D., Rutkowski, M., Spatzier T.: TOSCA Simple Profile in YAML Version 1.1. Tech. rep., OASIS Standard. http://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.1/TOSCA-Simple-Profile-YAML-v1.1.html (2016)Petcu, D.: Consuming resources and services from multiple clouds: from terminology to cloudware support. J Grid Comput. 12(2), 321–345 (2014). https://doi.org/10.1007/s10723-013-9290-3Plóciennik, M., Fiore, S., Donvito, G., Owsiak, M., Fargetta, M., Barbera, R., Bruno, R., Giorgio, E., Williams, D.N., Aloisio, G.: Two-level dynamic workflow orchestration in the INDIGO DataCloud for large-scale, climate change data analytics experiments. Procedia Comput. Sci. 80, 722–733 (2016). https://doi.org/10.1016/j.procs.2016.05.359Płóciennik, M., Fiore, S., Donvito, G., Owsiak, M., Fargetta, M., Barbera, R., Bruno, R., Giorgio, E., Williams, D.N., Aloisio, G.: Two-level dynamic workflow orchestration in the indigo datacloud for large-scale, climate change data analytics experiments. Procedia Comput. Sci. 80, 722–733 (2016)Python: Python Package Index (PyPI). https://pypi.python.org/pypi (2017)Ramakrishnan, L., Jackson, K.R., Canon, S., Cholia, S., Shalf, J.: Defining future platform requirements for e-Science clouds. In: Proceedings of the 1st ACM Symposium on Cloud Computing - SoCC ’10. https://doi.org/10.1145/1807128.1807145 , p 101 (2010)Ramakrishnan, L., Zbiegel, P.T.T.T.: Magellan: experiences from a science cloud. In: Proceedings of the 2Nd International Workshop on Scientific Cloud Computing. http://dl.acm.org/citation.cfm?id=1996119 , pp 49–58 (2011)Salomoni, D., Campos, I., Gaido, L., Donvito, G., Antonacci, M., Fuhrman, P., Marco, J., Lopez-Garcia, A., Orviz, P., Blanquer, I., et al.: Indigo-datacloud: foundations and architectural description of a platform as a service oriented to scientific computing. arXiv: http://arXiv.org/abs/1603.09536 (2016)Sánchez-Expósito, S., Martín, P., Ruiz, J.E., Verdes-Montenegro, L., Garrido, J., Sirvent, R., Falcó, A.R., Badia, R.M., Lezzi, D.: Web services as building blocks for science gateways in astrophysics. J Grid Comput. 14(4), 673–685 (2016). https://doi.org/10.1007/s10723-016-9382-ySlipStream: SlipStream. http://sixsq.com/products/slipstream/ (2017)Stockton, D.B., Santamaria, F.: Automating NEURON simulation deployment in cloud resources. Neuroinformatics 15(1), 51–70 (2017). https://doi.org/10.1007/s12021-016-9315-8Teckelmann, R., Reich, C., Sulistio, A.: Mapping of Cloud Standards to the Taxonomy of Interoperability in Iaas. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science (Cloudcom), pp 522–526. IEEE (2011)Toor, S., Osmani, L., Eerola, P., Kraemer, O., Lindén, T., Tarkoma, S., White, J.: A scalable infrastructure for CMS data analysis based on OpenStack Cloud and Gluster file system. J Phys.: Conf. Ser. 513(6), 062,047 (2014). https://doi.org/10.1088/1742-6596/513/6/062047 . http://stacks.iop.org/1742-6596/513/i=6/a=062047?key=crossref.84033a04265ce343371c7f38064e7143UK Government Cabinet Office: Open Standards Principles. https://www.gov.uk/government/publications/open-standards-principles/open-standards-principles (2015)Yangui, S., Marshall, I.J., Laisne, J.P., Tata, S.: Compatibleone: the open source cloud broker. J Grid Comput. 12(1), 93–109 (2014)Zhao, Y., Li, Y., Raicu, I., Lu, S., Tian, W., Liu, H.: Enabling scalable scientific workflow management in the cloud. Futur. Gener. Comput. Syst. 46, 3–16 (2015). https://doi.org/10.1016/j.future.2014.10.023van Zundert, G., Trellet, M., Schaarschmidt, J., Kurkcuoglu, Z., David, M., Verlato, M., Rosato, A., Bonvin, A.: The DisVis and PowerFit web servers: explorative and integrative modeling of biomolecular complexes. J. Mol. Biol. 429(3), 399–407 (2013). http://www.sciencedirect.com/science/article/pii/S002228361630527

Containers and Orchestration in the CERN Cloud

Docker Swarm, Docker Compose, Kubernetes, Mesos... containers remain a hot topic in computing, with new use cases and tools appearing every day. Basic functionality such as spawning containers seems to have settled, but topics like volume support or networking are still evolving. In this presentation we will describe and demo how we're integrating OpenStack Magnum as a solution to provide container support and orchestration in the CERN cloud, without having to bet on a single container product. We will describe some of the work we've been doing upstream as part of an OpenLab collaboration with Rackspace, building on feedback from some of our early pilot service users. We will cover Magnum's main functionality and some advanced use cases using Docker Swarm and Kubernetes, and how they can be used to improve common use cases in HEP, European projects like Indigo DataCloud and several of our infrastructure services. Finally, we'll summarize the most relevant differences between existing container solutions, how we're integrating storage services like Cinder and CVMFS, and future plans for more advanced functionality.</p

Integrating containers in the CERN private cloud

Containers remain a hot topic in computing, with new use cases and tools appearing every day. Basic functionality such as spawning containers seems to have settled, but topics like volume support or networking are still evolving. Solutions like Docker Swarm, Kubernetes or Mesos provide similar functionality but target different use cases, exposing distinct interfaces and APIs. The CERN private cloud is made of thousands of nodes and users, with many different use cases. A single solution for container deployment would not cover every one of them, and supporting multiple solutions involves repeating the same process multiple times for integration with authentication services, storage services or networking. In this paper we describe OpenStack Magnum as the solution to offer container management in the CERN cloud. We will cover its main functionality and some advanced use cases using Docker Swarm and Kubernetes, highlighting some relevant differences between the two. We will describe the most common use cases in HEP and how we integrated popular services like CVMFS or AFS in the most transparent way possible, along with some limitations found. Finally we will look into ongoing work on advanced scheduling for both Swarm and Kubernetes, support for running batch like workloads and integration of container networking technologies with the CERN infrastructure

Orchestrating complex application architectures in heterogeneous clouds: the INDIGO-DataCloud case

Cloud infrastructures are now widely adopted across technology industries and research institutions. However, single cloud providers may not fully satisfy more complex user requirements, and to cope with them, a solution that do not rely on a single cloud environment but instead allow resource provisioning from external providers is required. As a result, in the recent years there has been a growing interest in developing hybrid cloud solutions that bind together distinct and heterogeneous cloud infrastructures. In this paper we will describe the orchestration approach for heterogeneous clouds being implemented within the INDIGO-DataCloud project, based on the OASIS Topology and Specification for Cloud Applications (TOSCA) standard

Effects of on-Table Extubation after Pediatric Cardiac Surgery

Background: Enhanced recovery after surgery (ERAS) protocols are utilizing a multidisciplinary approach, reassessing physiology to improve clinical outcomes, reducing length of hospital stay (LOS) stay, resulting in cost reduction. Since its introduction in colorectal surgery. the concept has been utilized in various fields and benefits have been recognized also in adult cardiac surgery. However, ERAS concepts in pediatric cardiac surgery are not yet widely established. Therefore, the aim of the present study was to assess the effects of on-table extubation (OTE) after pediatric cardiac surgery compared to the standard approach of delayed extubation (DET) during intensive care treatment. Study Design and Methods: We performed a retrospective analysis of all pediatric cardiac surgery cases performed in children below the age of two years using cardiopulmonary bypass at our institution in 2021. Exclusion criteria were emergency and off pump surgeries as well as children already ventilated preoperatively. Results: OTE children were older (267.3 days vs. 126.7 days, p p p p = 0.001) compared to DET group. Furthermore, OTE children had significantly fewer catecholamine dependencies at 12-, 24-, 48-, and 72-h post-surgery, while DET children showed a significantly increased intrafluid shift relative to body weight (109.1 ± 82.0 mL/kg body weight vs. 63.0 ± 63.0 mL/kg body weight, p < 0.001). After propensity score matching considering age, weight, bypass duration, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Mortality (STATS)-Score, and the outcome variables, including duration of ICU treatment, catecholamine dependencies, and hospital LOS, findings significantly favored the OTE group. Conclusion: Our results suggest that on-table extubation after pediatric cardiac surgery is feasible and in our cohort was associated with a favorable postoperative course

The ongoing French metastatic breast cancer (MBC) cohort: the example-based methodology of the Epidemiological Strategy and Medical Economics (ESME)

International audiencePurpose The currently ongoing Epidemiological Strategy and Medical Economics (ESME) research programme aims at centralising real-life data on oncology care for epidemiological research purposes. We draw on results from the metastatic breast cancer (MBC) cohort to illustrate the methodology used for data collection in the ESME research programme. Participants All consecutive ≥18 years patients with MBC treatment initiated between 2008 and 2014 in one of the 18 French Comprehensive Cancer Centres were selected. Diagnostic, therapeutic and follow-up data (demographics, primary tumour, metastatic disease, treatment patterns and vital status) were collected through the course of the disease. Data collection is updated annually. Finding to date With a recruitment target of 30 000 patients with MBC by 2019, we currently screened a total of 45 329 patients, and >16 700 patients with a metastatic disease treatment initiated after 2008 have been selected. 20.7% of patients had an hormone receptor (HR)-negative MBC, 73.7% had a HER2-negative MBC and 13.9% were classified as triple-negative BC (ie, HER2 and HR status both negative). Median follow-up duration from MBC diagnosis was 48.55 months for the whole cohort. Future plans These real-world data will help standardise the management of MBC and improve patient care. A dozen of ancillary research projects have been conducted and some of them are already accepted for publication or ready to be issued. The ESME research programme is expanding to ovarian cancer and advanced/metastatic lung cancer. Our ultimate goal is to achieve a continuous link to the data of the cohort to the French national Health Data System for centralising data on healthcare reimbursement (drugs, medical procedures), inpatient/outpatient stays and visits in primary/secondary care settings

The hidden Niemann-Pick type C patient:Clinical niches for a rare inherited metabolic disease

Background: Niemann-Pick disease type C (NP-C) is a rare, inherited neurodegenerative disease of impaired intracellular lipid trafficking. Clinical symptoms are highly heterogeneous, including neurological, visceral, or psychiatric manifestations. The incidence of NP-C is under-estimated due to under-recognition or misdiagnosis across a wide range of medical fields. New screening and diagnostic methods provide an opportunity to improve detection of unrecognized cases in clinical sub-populations associated with a higher risk of NP-C. Patients in these at-risk groups (clinical niches) have symptoms that are potentially related to NP-C, but go unrecognized due to other, more prevalent clinical features, and lack of awareness regarding underlying metabolic causes. Methods: Twelve potential clinical niches identified by clinical experts were evaluated based on a comprehensive, non-systematic review of literature published to date. Relevant publications were identified by targeted literature searches of EMBASE and PubMed using key search terms specific to each niche. Articles published in English or other European languages up to 2016 were included. Findings: Several niches were found to be relevant based on available data: movement disorders (early-onset ataxia and dystonia), organic psychosis, early-onset cholestasis/(hepato)splenomegaly, cases with relevant antenatal findings or fetal abnormalities, and patients affected by family history, consanguinity, and endogamy. Potentially relevant niches requiring further supportive data included: early-onset cognitive decline, frontotemporal dementia, parkinsonism, and chronic inflammatory CNS disease. There was relatively weak evidence to suggest amyotrophic lateral sclerosis or progressive supranuclear gaze palsy as potential niches. Conclusions: Several clinical niches have been identified that harbor patients at increased risk of NP-C

The hidden Niemann-Pick type C patient : clinical niches for a rare inherited metabolic disease

BACKGROUND : Niemann-Pick disease type C (NP-C) is a rare, inherited neurodegenerative disease of impaired intracellular lipid trafficking. Clinical symptoms are highly heterogeneous, including neurological, visceral, or psychiatric manifestations. The incidence of NP-C is under-estimated due to under-recognition or misdiagnosis across a wide range of medical fields. New screening and diagnostic methods provide an opportunity to improve detection of unrecognized cases in clinical sub-populations associated with a higher risk of NP-C. Patients in these at-risk groups (“clinical niches”) have symptoms that are potentially related to NP-C, but go unrecognized due to other, more prevalent clinical features, and lack of awareness regarding underlying metabolic causes. METHODS : Twelve potential clinical niches identified by clinical experts were evaluated based on a comprehensive, non-systematic review of literature published to date. Relevant publications were identified by targeted literature searches of EMBASE and PubMed using key search terms specific to each niche. Articles published in English or other European languages up to 2016 were included. FINDINGS : Several niches were found to be relevant based on available data: movement disorders (early-onset ataxia and dystonia), organic psychosis, early-onset cholestasis/(hepato)splenomegaly, cases with relevant antenatal findings or fetal abnormalities, and patients affected by family history, consanguinity, and endogamy. Potentially relevant niches requiring further supportive data included: early-onset cognitive decline, frontotemporal dementia, parkinsonism, and chronic inflammatory CNS disease. There was relatively weak evidence to suggest amyotrophic lateral sclerosis or progressive supranuclear gaze palsy as potential niches. CONCLUSIONS : Several clinical niches have been identified that harbor patients at increased risk of NP-C.Actelion Pharmaceuticals Ltd., Allschwil, Switzerland. From Actelion Pharmaceuticals Ltd.: travel expenses AC, AD, AP, CD-V, CJH, CL, HHK, MT, MW, MP, MS, PB, OB, SD, SL; research funding AP, CL, CD-V, CJH, FT-B, J-CC, MW, OB, PB, RI, SD, TD, TdK; consultancy fees AP, CJH, CL, HHK, MT, MW, OB, PB, SL; speaker honoraria CD-V, MP, MS, PB, SD. MA has received speaker honoraria and travel expenses from Abbvie, TEVA, and UCB. J-CC has received speaker honoraria from Abbvie, travel grants from Abbvie, research funding from, Ipsen, and the Michael J Fox Foundation, and consultancy fees from BMS, Zambon, Pfizer, Amarantus, Clevexel, and Abbvie. CD-V has received research grants, investigator fees, speaker honoraria, and travel expenses from Sanofi Genzyme, Orphan Europe, and Nutricia. SD has received research funding from TEVA. CJH is Director of FYMCA Medical Ltd., has received consultancy fees and travel expenses from Alexion, Amicus, Biomarin, Inventiva, Sanofi Genzyme, and Shire, and has undertaken paid research on behalf of Amicus, Biomarin, Sanofi Genzyme and Shire. SL has received consultancy fees and travel expenses from TEVA, Boehringer, Gruenenthal, and UCB6e. AP has received research funding, consultancy fees and travel expenses from Eli-Lilly, GE Health, and Lundbeck. CT has received speaker honoraria and travel expenses from Abbvie, Zambon, TEVA, and UCB. CV and SK are employees of Actelion Pharmaceuticals Ltd.http://www.tandfonline.com/loi/icmo202018-03-02hj2018Paediatrics and Child Healt