Motivations of children and their parents to participate in drug research: a systematic review

Information on motivations for research participation, may enable professionals to better tailor the process of recruitment and informed consent to the perspective of parents and children. Therefore, this systematic review assesses motivating and discouraging factors for children and their parents to decide to participate in clinical drug research. Studies were identified from searches in 6 databases. Two independent reviewers screened and selected relevant articles. Results were aggregated and presented by use of qualitative metasummary. 38 studies fulfilled the selection criteria and were of sufficient quality for inclusion in the qualitative metasummary. Most mentioned motivating factors for parents were: health benefit for child, altruism, trust in research, and relation to researcher. Most mentioned motivating factors for children were: personal health benefit, altruism and increasing comfort. Fear of risks, distrust in research, logistical aspects and disruption of daily life were mentioned most by parents as discouraging factors. Burden and disruption of daily life, feeling like a “guinea pig” and fear of risks were most mentioned as discouraging factors by children. Conclusion: Paying attention to these motivating and discouraging factors of children and their parents during the recruitment and informed consent process in drug research increases the moral and instrumental value of informed consent.What is known:• This systematic review pools the existing empirical literature on motivations of minors and their parents to consent or dissent to participation in clinical drug research.• The most mentioned motivating and discouraging factors for children and their parents to consent to participation in clinical drug research are identified aggregated and presented by use of qualitative metasummary.What is new:• This information can be used to adapt the research protocol, recruitment, and informed consent/assent process to the needs of children and their parents

A Conversational Agent for Social Support: Validation of Supportive Dialogue Sequences

Recently, we proposed a dialogue model for social support. To validate this model, we analyzed 23 real world chat conversations. After some adjustments, the dialogue sequence patterns specified in the model cover 87.4% of the data. Based on this result, we conclude that the dialogue model accurately describes comforting conversations. Next, the model will be incorporated into a comforting ECA

ALK inhibition in two emblematic cases of pediatric inflammatory myofibroblastic tumor: Efficacy and side effects

There is an increasing interest for anaplastic lymphoma kinase (ALK) inhibitors in pediatric oncology for specific entities such as ALK-driven inflammatory myofibroblastic tumor (IMT). IMT treatment can be challenging due to localization of the tumor and in rare cases of metastasis. When standard surgical treatment is not feasible, ALK inhibitors may play an important role, as recently reported for the first-generation ALK inhibitors (crizotinib). However, data on the second-generation ALK inhibitors are limited.We report two emblematic cases of IMT in pediatric patients, treated with the second-generation ALK inhibitor ceritinib in the context of a clinical trial (NCT01742286)

Dose-related efficacy and toxicity of gemtuzumab ozogamicin in pediatric acute myeloid leukemia

Gemtuzumab ozogamicin, an anti-tumour antibiotic linked to an anti-CD33 antibody (Mylotarg®), has been well studied in AML in adults but to a lesser extent in children. No review has yet been published on the dose-related toxicity and efficacy of gemtuzumab ozogamicin in pediatric AML patients. Here we looked at 14 studies then scatterplots and linear regressions were used to estimate the relationship between the dose of gemtuzumab and its toxicity and efficacy. A non-significant increase in bilirubin level and in incidence of veno-occlusive disease was seen with higher doses of gemtuzumab ozogamicin when used as single-agent. In terms of efficacy, even a low dose of 3 mg/m2 of gemtuzumab ozogamicin can have antileukemic effect, but available data do not allow conclusions on its dose-dependency. Data indicate that higher doses of gemtuzumab ozogamicin account for more adverse events. The data do not show that a high dose is required for anti-leukemic efficacy of gemtuzumab ozogamicin. This study also indicates that there seems to be a role for gemtuzumab ozogamicin in the treatment of pediatric AML and further studies are required to assess its optimal dose, schedule and balance between efficacy and side-effects

Een asymmetrische snel progressieve tonsillaire tumor bij een kind van zes jaar

Het Burkitt-lymfoom is een slecht gedifferentieerd, zeldzaam en agressief type van het non-hodgkinlymfoom. In dit artikel beschrijven wij een casus van een meisje van zes jaar, die zich presenteerde in het Sophia Kinderziekenhuis van het Erasmus MC (Erasmus MC – Sophia) met een snel progressieve, inspiratoire stridor en een bedreigde luchtweg op basis van een forse asymmetrische suspecte zwelling van de tonsil rechts. Met een beenmergaspiraat werd de diagnose Burkitt-lymfoom bevestigd en behandeling met chemotherapie ingezet. Hierop slonk de tumor binnen enkele dagen aanzienlijk, zodat operatief ingrijpen om de luchtweg veilig te stellen, niet meer nodig was

The transcriptome in transition: global gene expression profiles of young adult fruit flies depend more strongly on developmental than adult diet

Developmental diet is known to exert long-term effects on adult phenotypes in many animal species as well as disease risk in humans, purportedly mediated through long-term changes in gene expression. However, there are few studies linking developmental diet to adult gene expression. Here, we use a full-factorial design to address how three different larval and adult diets interact to affect gene expression in 1-day-old adult fruit flies (Drosophila melanogaster) of both sexes. We found that the largest contributor to transcriptional variation in young adult flies is larval, and not adult diet, particularly in females. We further characterized gene expression variation by applying weighted gene correlation network analysis (WGCNA) to identify modules of co-expressed genes. In adult female flies, the caloric content of the larval diet associated with two strongly negatively correlated modules, one of which was highly enriched for reproduction-related processes. This suggests that gene expression in young adult female flies is in large part related to investment into reproduction-related processes, and that the level of expression is affected by dietary conditions during development. In males, most modules had expression patterns independent of developmental or adult diet. However, the modules that did correlate with larval and/or adult dietary regimes related primarily to nutrient sensing and metabolic functions, and contained genes highly expressed in the gut and fat body. The gut and fat body are among the most important nutrient sensing tissues, and are also the only tissues known to avoid histolysis during pupation. This suggests that correlations between larval diet and gene expression in male flies may be mediated by the carry-over of these tissues into young adulthood. Our results show that developmental diet can have profound effects on gene expression in early life and warrant future research into how they correlate with actual fitness related traits in early adulthood.Molecular Epidemiolog

Subtype prediction in pediatric acute myeloid leukemia: Classification using differential network rank conservation revisited

Background: One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family's simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. Results: In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. Conclusions: Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types