Outlooks on using a mobile health intervention for supportive pain management for children and adolescents with cancer: a qualitative study.

BACKGROUND: Considerable improvements in the prognosis of pediatric cancer patients have been achieved over recent decades due to advances in treatment. Nevertheless, as the most common and distressing health issue for pediatrics with cancer, cancer-related pain is still a significant hurdle that impedes patients' journey to recovery, compromises their quality of life, and delays the positive outcome and effectiveness of their treatments. PURPOSE: Taking into consideration that acceptability studies are imperative for the design, evaluation, and implementation of healthcare interventions, this study aims to explore pediatric oncology patients' readiness to use a mobile health application that emphasizes social assistance and peer support in addition to conventional pain management methods. DESIGN AND METHODS: This study followed the Qualitative description approach. Twelve participants were chosen based on purposive sampling and maximum variation sampling. Interviews were analyzed using the conventional content analysis. RESULTS: Analysis of the interviews revealed four major categories: (A) The need for connectedness; (B) An innovative way to connect yet fearful; (C) A 3D approach; (D) Fears of the unfamiliar. CONCLUSIONS: This study is the first in Lebanon and the region to undertake an initiative towards introducing technology for pain assessment and management of children with cancer through a dedicated digital platform. The study results attested to the acceptability and potential utilization of this platform by children with cancer. PRACTICE IMPLICATIONS: Nurses need to be trained to play an essential role in teaching children with cancer about the significance of social support and assisting them to establish their social support network. Children with cancer are encouraged to voice out their need for help. Our proposed application can create an enabling environment to harness the power of social support and provide children with cancer the opportunity to connect on a deeper level in a supportive and pity-free space.

Phenotypic and transcriptomic impact of expressing mammalian TET2 in the Drosophila melanogaster model.

Ten-Eleven Translocation (TET) proteins have recently come to light as important epigenetic regulators conserved in multicellular organisms. TET knockdown studies in rodents have highlighted the critical role of these proteins for proper brain development and function. Mutations in mammalian mTET proteins and mTET2 specifically are frequent and deregulated in leukaemia and glioma respectively. Accordingly, we examined the role of mTET2 in tumorigenesis in larval haemocytes and adult heads in Drosophila melanogaster. Our findings showed that expression of mutant and wild type mTET2 resulted in general phenotypic defects in adult flies and accumulation of abdominal melanotic masses. Notably, flies with mTET2-R43G mutation at the N-terminus of mTET2 exhibited locomotor and circadian behavioural deficits, as well as reduced lifespan. Flies with mTET2-R1261C mutation in the catalytic domain, a common mutation in acute myeloid leukaemia (AML), displayed alterations affecting haemocyte haemostasis. Using transcriptomic approach, we identified upregulated immune genes in fly heads that were not exclusive to TET2 mutants but also found in wild type mTET2 flies. Furthermore, inhibiting expression of genes that were found to be deregulated in mTET2 mutants, such as those involved in immune pathways, autophagy, and transcriptional regulation, led to a rescue in fly survival, behaviour, and hemocyte number. This study identifies the transcriptomic profile of wild type mTET2 versus mTET2 mutants (catalytic versus non-catalytic) with indications of TET2 role in normal central nervous system (CNS) function and innate immunity.

A temporal in vivo catalog of chromatin accessibility and expression profiles in pineoblastoma reveals a prevalent role for repressor elements.

Pediatric pineoblastomas (PBs) are rare and aggressive tumors of grade IV histology. Although some oncogenic drivers are characterized, including germline mutations in RB1 and DICER1, the role of epigenetic deregulation and cis-regulatory regions in PB pathogenesis and progression is largely unknown. Here, we generated genome-wide gene expression, chromatin accessibility, and H3K27ac profiles covering key time points of PB initiation and progression from pineal tissues of a mouse model of CCND1-driven PB. We identified PB-specific enhancers and super-enhancers, and found that in some cases, the accessible genome dynamics precede transcriptomic changes, a characteristic that is underexplored in tumor progression. During progression of PB, newly acquired open chromatin regions lacking H3K27ac signal become enriched for repressive state elements and harbor motifs of repressor transcription factors like HINFP, GLI2, and YY1. Copy number variant analysis identified deletion events specific to the tumorigenic stage, affecting, among others, the histone gene cluster and Gas1, the growth arrest specific gene. Gene set enrichment analysis and gene expression signatures positioned the model used here close to human PB samples, showing the potential of our findings for exploring new avenues in PB management and therapy. Overall, this study reports the first temporal and in vivo cis-regulatory, expression, and accessibility maps in PB.

TempoMAGE: a deep learning framework that exploits the causal dependency between time-series data to predict histone marks in open chromatin regions at time-points with missing ChIP-seq datasets.

MOTIVATION: Identifying histone tail modifications using ChIP-seq is commonly used in time-series experiments in development and disease. These assays, however, cover specific time-points leaving intermediate or early stages with missing information. Although several machine learning methods were developed to predict histone marks, none exploited the dependence that exists in time-series experiments between data generated at specific time-points to extrapolate these findings to time-points where data cannot be generated for lack or scarcity of materials (i.e. early developmental stages). RESULTS: Here, we train a deep learning model named TempoMAGE, to predict the presence or absence of H3K27ac in open chromatin regions by integrating information from sequence, gene expression, chromatin accessibility and the estimated change in H3K27ac state from a reference time-point. We show that adding reference time-point information systematically improves the overall model's performance. In addition, sequence signatures extracted from our method were exclusive to the training dataset indicating that our model learned data-specific features. As an application, TempoMAGE was able to predict the activity of enhancers from pre-validated in-vivo dataset highlighting its ability to be used for functional annotation of putative enhancers. AVAILABILITY AND IMPLEMENTATION: TempoMAGE is freely available through GitHub at https://github.com/pkhoueiry/TempoMAGE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.