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Background: Most smokers start smoking during their early adolescence, often with the idea that smoking is glamorous. Interventions that harness the broad availability of mobile phones as well as adolescents' interest in their appearance may be a novel way to improve school-based prevention. A recent study conducted in Germany showed promising results. However, the transfer to other cultural contexts, effects on different genders, and implementability remains unknown.
Objective: In this observational study, we aimed to test the perception and implementability of facial-aging apps to prevent smoking in secondary schools in Brazil in accordance with the theory of planned behavior and with respect to different genders.
Methods: We used a free facial-aging mobile phone app ("Smokerface") in three Brazilian secondary schools via a novel method called mirroring. The students’ altered three-dimensional selfies on mobile phones or tablets and images were "mirrored" via a projector in front of their whole grade. Using an anonymous questionnaire, we then measured on a 5-point Likert scale the perceptions of the intervention among 306 Brazilian secondary school students of both genders in the seventh grade (average age 12.97 years). A second questionnaire captured perceptions of medical students who conducted the intervention and its conduction per protocol.
Results: The majority of students perceived the intervention as fun (304/306, 99.3%), claimed the intervention motivated them not to smoke (289/306, 94.4%), and stated that they learned new benefits of not smoking (300/306, 98.0%). Only a minority of students disagreed or fully disagreed that they learned new benefits of nonsmoking (4/306, 1.3%) or that they themselves were motivated not to smoke (5/306, 1.6%). All of the protocol was delivered by volunteer medical students.
Conclusions: Our data indicate the potential for facial-aging interventions to reduce smoking prevalence in Brazilian secondary schools in accordance with the theory of planned behavior. Volunteer medical students enjoyed the intervention and are capable of complete implementation per protocol.
The Education Against Tobacco (EAT) network delivers smoking prevention advice in secondary schools, typically using the mirroring approach (i.e., a "selfie" altered with a face-aging app and shared with a class). In November 2017, however, the German assembly of EAT opted to expand its remit to include nursing students. To assess the transferability of the existing approach, we implemented it with the self-developed face-aging app "Smokerface" (=mixed − methods approach) in six nursing schools. Anonymous questionnaires were used to assess the perceptions of 197 students (age 18–40 years; 83.8% female; 26.4% smokers; 23.3% daily smokers) collecting qualitative and quantitative data for our cross-sectional study. Most students perceived the intervention to be fun (73.3%), but a minority disagreed that their own animated selfie (25.9%) or the reaction of their peers (29.5%) had motivated them to stop smoking. The impact on motivation not to smoke was considerably lower than experienced with seventh graders (63.2% vs. 42.0%; notably, more smokers also disagreed (45.1%) than agreed (23.5%) with this statement. Agreement rates on the motivation not to smoke item were higher in females than in males and in year 2–3 than in year 1 students. Potential improvements included greater focus on pathology (29%) and discussing external factors (26%). Overall, the intervention seemed to be appealing for nursing students
As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non–BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.
Chordomas are rare bone tumors with few therapeutic options. Here we show, using whole-exome and genome sequencing within a precision oncology program, that advanced chordomas (n = 11) may be characterized by genomic patterns indicative of defective homologous recombination (HR) DNA repair and alterations affecting HR-related genes, including, for example, deletions and pathogenic germline variants of BRCA2, NBN, and CHEK2. A mutational signature associated with HR deficiency was significantly enriched in 72.7% of samples and co-occurred with genomic instability. The poly(ADP-ribose) polymerase (PARP) inhibitor olaparib, which is preferentially toxic to HR-incompetent cells, led to prolonged clinical benefit in a patient with refractory chordoma, and whole-genome analysis at progression revealed a PARP1 p.T910A mutation predicted to disrupt the autoinhibitory PARP1 helical domain. These findings uncover a therapeutic opportunity in chordoma that warrants further exploration, and provide insight into the mechanisms underlying PARP inhibitor resistance.
Background: The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demanded the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Main body: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected in a consensus-based process by working groups of medical experts from the respective specialty to ensure that the contents are aligned with the research needs of the specialty. The selected data elements were mapped to international standardized vocabularies and data exchange specifications were created using HL7 FHIR profiles on the appropriate resources. All steps were performed in close interdisciplinary collaboration between medical domain experts, medical information scientists and FHIR developers. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. In that way, we defined dataset specifications for a total number of 23 (immunization), 59 (pediatrics), and 50 (cardiology) data elements that augment the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions: We here present extension modules for the GECCO core dataset that contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology. These extension modules were defined in an interdisciplinary, iterative, consensus-based approach that may serve as a blueprint for the development of further dataset definitions and GECCO extension modules. The here developed GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
Background The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Objective To (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research in immunization, pediatrics, and cardiology.
Methods We developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (immunization, pediatrics, and cardiology) to the select data elements most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications using HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information scientists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process.
Results We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions These here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.
Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.
Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.
Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data.
Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.
Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.
Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
Background: To evaluate optimal therapy and potential risk factors.
Methods: Data of DSRCT patients <40 years treated in prospective CWS trials 1997‐2015 were analyzed.
Results: Median age of 60 patients was 14.5 years. Male:female ratio was 4:1. Tumors were abdominal/retroperitoneal in 56/60 (93%). 6/60 (10%) presented with a localized mass, 16/60 (27%) regionally disseminated nodes, and 38/60 (63%) with extraperitoneal metastases. At diagnosis, 23/60 (38%) patients had effusions, 4/60 (7%) a thrombosis, and 37/54 (69%) elevated CRP. 40/60 (67%) patients underwent tumor resection, 21/60 (35%) macroscopically complete. 37/60 (62%) received chemotherapy according to CEVAIE (ifosfamide, vincristine, actinomycin D, carboplatin, epirubicin, etoposide), 15/60 (25%) VAIA (ifosfamide, vincristine, adriamycin, actinomycin D) and, 5/60 (8%) P6 (cyclophosphamide, doxorubicin, vincristine, ifosfamide, etoposide). Nine received high‐dose chemotherapy, 6 received regional hyperthermia, and 20 received radiotherapy. Among 25 patients achieving complete remission, 18 (72%) received metronomic therapies. Three‐year event‐free (EFS) and overall survival (OS) were 11% (±8 confidence interval [CI] 95%) and 30% (±12 CI 95%), respectively, for all patients and 26.7% (±18.0 CI 95%) and 56.9% (±20.4 CI 95%) for 25 patients achieving remission. Extra‐abdominal site, localized disease, no effusion or ascites only, absence of thrombosis, normal CRP, complete tumor resection, and chemotherapy with VAIA correlated with EFS in univariate analysis. In multivariate analysis, significant factors were no thrombosis and chemotherapy with VAIA. In patients achieving complete remission, metronomic therapy with cyclophosphamide/vinblastine correlated with prolonged time to relapse.
Conclusion: Pleural effusions, venous thrombosis, and CRP elevation were identified as potential risk factors. The VAIA scheme showed best outcome. Maintenance therapy should be investigated further.
Gene-modified autologous hematopoietic stem cells (HSC) can provide ample clinical benefits to subjects suffering from X-linked chronic granulomatous disease (X-CGD), a rare inherited immunodeficiency characterized by recurrent, often life-threatening bacterial and fungal infections. Here we report on the molecular and cellular events observed in two young adults with X-CGD treated by gene therapy in 2004. After the initial resolution of bacterial and fungal infections, both subjects showed silencing of transgene expression due to methylation of the viral promoter, and myelodysplasia with monosomy 7 as a result of insertional activation of ecotropic viral integration site 1 (EVI1). One subject died from overwhelming sepsis 27 months after gene therapy, whereas a second subject underwent an allogeneic HSC transplantation. Our data show that forced overexpression of EVI1 in human cells disrupts normal centrosome duplication, linking EVI1 activation to the development of genomic instability, monosomy 7 and clonal progression toward myelodysplasia.