Binge eating disorders and psychotherapy: is it possible to systematize a psychodynamic formulation case?

The Core Conflictual Relationship Theme (CCRT) method establishes psychotherapeutic guidelines for a patient's relational patterns. The CCRT was used in the analysis of narratives coming from psychodynamic psychotherapy sessions (taped and transcribed) from a patient suffering from Binge Eating Episodes. The CCRT method made it possible to systematize the connection between psychological conflicts and symptoms, not always an easy task for psychiatrists and psychologists in training.O método Tema Central de Conflito nos Relacionamentos - CCRT sistematiza as inferências feitas por psicoterapeutas dos padrões de relacionamento de pacientes submetidos à psicoterapia. O CCRT foi utilizado na análise das narrativas de uma paciente com Compulsão Alimentar Periódica, submetida a sessões de psicoterapia (gravadas e transcritas). Com o método CCRT foi possível sistematizar a relação entre os conflitos psíquicos e o aparecimento de sintomas, uma tarefa nem sempre fácil para psiquiatras e psicólogos em formação.Universidade Federal de São Paulo (UNIFESP)Universidade de São Paulo Instituto de PsicologiaUniversidade de São Paulo Faculdade de Filosofia, Ciência e Letras de Ribeirão PretoUniversidade de São Paulo Faculdade de Medicina Hospital das ClínicasUNIFESPSciEL

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes

Multiple Myeloma Treatment in Real-world Clinical Practice : Results of a Prospective, Multinational, Noninterventional Study

Funding Information: The authors would like to thank all patients and their families and all the EMMOS investigators for their valuable contributions to the study. The authors would like to acknowledge Robert Olie for his significant contribution to the EMMOS study. Writing support during the development of our report was provided by Laura Mulcahy and Catherine Crookes of FireKite, an Ashfield company, a part of UDG Healthcare plc, which was funded by Millennium Pharmaceuticals, Inc, and Janssen Global Services, LLC. The EMMOS study was supported by research funding from Janssen Pharmaceutical NV and Millennium Pharmaceuticals, Inc. Funding Information: The authors would like to thank all patients and their families and all the EMMOS investigators for their valuable contributions to the study. The authors would like to acknowledge Robert Olie for his significant contribution to the EMMOS study. Writing support during the development of our report was provided by Laura Mulcahy and Catherine Crookes of FireKite, an Ashfield company, a part of UDG Healthcare plc, which was funded by Millennium Pharmaceuticals, Inc, and Janssen Global Services, LLC. The EMMOS study was supported by research funding from Janssen Pharmaceutical NV and Millennium Pharmaceuticals, Inc. Funding Information: M.M. has received personal fees from Janssen, Celgene, Amgen, Bristol-Myers Squibb, Sanofi, Novartis, and Takeda and grants from Janssen and Sanofi during the conduct of the study. E.T. has received grants from Janssen and personal fees from Janssen and Takeda during the conduct of the study, and grants from Amgen, Celgene/Genesis, personal fees from Amgen, Celgene/Genesis, Bristol-Myers Squibb, Novartis, and Glaxo-Smith Kline outside the submitted work. M.V.M. has received personal fees from Janssen, Celgene, Amgen, and Takeda outside the submitted work. M.C. reports honoraria from Janssen, outside the submitted work. M. B. reports grants from Janssen Cilag during the conduct of the study. M.D. has received honoraria for participation on advisory boards for Janssen, Celgene, Takeda, Amgen, and Novartis. H.S. has received honoraria from Janssen-Cilag, Celgene, Amgen, Bristol-Myers Squibb, Novartis, and Takeda outside the submitted work. V.P. reports personal fees from Janssen during the conduct of the study and grants, personal fees, and nonfinancial support from Amgen, grants and personal fees from Sanofi, and personal fees from Takeda outside the submitted work. W.W. has received personal fees and grants from Amgen, Celgene, Novartis, Roche, Takeda, Gilead, and Janssen and nonfinancial support from Roche outside the submitted work. J.S. reports grants and nonfinancial support from Janssen Pharmaceutical during the conduct of the study. V.L. reports funding from Janssen Global Services LLC during the conduct of the study and study support from Janssen-Cilag and Pharmion outside the submitted work. A.P. reports employment and shareholding of Janssen (Johnson & Johnson) during the conduct of the study. C.C. reports employment at Janssen-Cilag during the conduct of the study. C.F. reports employment at Janssen Research and Development during the conduct of the study. F.T.B. reports employment at Janssen-Cilag during the conduct of the study. The remaining authors have stated that they have no conflicts of interest. Publisher Copyright: © 2018 The AuthorsMultiple myeloma (MM) remains an incurable disease, with little information available on its management in real-world clinical practice. The results of the present prospective, noninterventional observational study revealed great diversity in the treatment regimens used to treat MM. Our results also provide data to inform health economic, pharmacoepidemiologic, and outcomes research, providing a framework for the design of protocols to improve the outcomes of patients with MM. Background: The present prospective, multinational, noninterventional study aimed to document and describe real-world treatment regimens and disease progression in multiple myeloma (MM) patients. Patients and Methods: Adult patients initiating any new MM therapy from October 2010 to October 2012 were eligible. A multistage patient/site recruitment model was applied to minimize the selection bias; enrollment was stratified by country, region, and practice type. The patient medical and disease features, treatment history, and remission status were recorded at baseline, and prospective data on treatment, efficacy, and safety were collected electronically every 3 months. Results: A total of 2358 patients were enrolled. Of these patients, 775 and 1583 did and did not undergo stem cell transplantation (SCT) at any time during treatment, respectively. Of the patients in the SCT and non-SCT groups, 49%, 21%, 14%, and 15% and 57%, 20%, 12% and 10% were enrolled at treatment line 1, 2, 3, and ≥ 4, respectively. In the SCT and non-SCT groups, 45% and 54% of the patients had received bortezomib-based therapy without thalidomide/lenalidomide, 12% and 18% had received thalidomide/lenalidomide-based therapy without bortezomib, and 30% and 4% had received bortezomib plus thalidomide/lenalidomide-based therapy as frontline treatment, respectively. The corresponding proportions of SCT and non-SCT patients in lines 2, 3, and ≥ 4 were 45% and 37%, 30% and 37%, and 12% and 3%, 33% and 27%, 35% and 32%, and 8% and 2%, and 27% and 27%, 27% and 23%, and 6% and 4%, respectively. In the SCT and non-SCT patients, the overall response rate was 86% to 97% and 64% to 85% in line 1, 74% to 78% and 59% to 68% in line 2, 55% to 83% and 48% to 60% in line 3, and 49% to 65% and 36% and 45% in line 4, respectively, for regimens that included bortezomib and/or thalidomide/lenalidomide. Conclusion: The results of our prospective study have revealed great diversity in the treatment regimens used to manage MM in real-life practice. This diversity was linked to factors such as novel agent accessibility and evolving treatment recommendations. Our results provide insight into associated clinical benefits.publishersversionPeer reviewe

Transtornos da compulsão alimentar periódica e psicoterapia: é possível sistematizar a formulação psicodinâmica de caso?

O método Tema Central de Conflito nos Relacionamentos - CCRT sistematiza as inferências feitas por psicoterapeutas dos padrões de relacionamento de pacientes submetidos à psicoterapia. O CCRT foi utilizado na análise das narrativas de uma paciente com Compulsão Alimentar Periódica, submetida a sessões de psicoterapia (gravadas e transcritas). Com o método CCRT foi possível sistematizar a relação entre os conflitos psíquicos e o aparecimento de sintomas, uma tarefa nem sempre fácil para psiquiatras e psicólogos em formação

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.We acknowledge the support of the developers of PhenoTips, which was used in the past by RD-Connect and NeurOmics as the primary tool to collate phenotypic data. We would also like to thank the leaders and members of the Instituto Nacional de Bioinformática (INB) and ELIXIR for their support and collaboration throughout the years. RD-Connect (RD-Connect, an integrated platform connecting registries, biobanks, and clinical bioinformatics) received funding from the Seventh Framework (FP7) Programme of the European Union under grant agreement No 305444. Data were analyzed using the RD-Connect GPAP, which received funding from EU projects Solve-RD, EJP-RD (grant numbers H2020 779257, H2020 825575), Instituto de Salud Carlos III (Grant numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB), ELIXIR-EXCELERATE (Grant number EU H2020 #676559) and ELIXIR Implementation Studies (Remote real-time visualization of human rare disease genomics data (RD-Connect) stored at the EGA ELIXIR. 2017-2018; ELIXIR IT-2017-INTEGRATION, Rare Disease Infrastructure ELIXIR, 2019-2020 and the Beacon ELIXIR, 2019-2021). The RD-Connect GPAP has leveraged developments funded through project VEIS (001-P-001647 co-financed by the European Regional Development Fund of the European Union in the framework of the Operational Program FEDER of Catalonia 2014-2020 with the support of the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya) and URD-Cat (PERIS SLT002/16/00174, Departament de Salut, Generalitat de Catalunya). The research leading to these results has received funding from Consequitur (Newton Fund UK/Turkey, MR/N027302/1), BBMRI-LPC (EU FP7 #313010), NeurOmics (EU FP7 #305121), the Economic Development Department of the Navarra Government (Grant number 001114112017), the European Reference Network for Rare Neurological Diseases (Project ID number 739510) and NIH, National Institute of Child Health and Human Development (1R01HD103805-01). We acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Program/Generalitat de Catalunya. We also acknowledge the support of the Generalitat de Catalunya through Departament de Salut and Departament d'Empresa i Coneixement and Co-financing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth Operating Program. HL receives support from the Canadian Institutes of Health Research (Foundation Grant FDN-167281), the Canadian Institutes of Health Research and Muscular Dystrophy Canada (Network Catalyst Grant for NMD4C), the Canada Foundation for Innovation (CFI-JELF 38412), and the Canada Research Chairs program (Canada Research Chair in Neuromuscular Genomics and Health, 950-232279)

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press