Browsing by Subject "Cancer research"

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    Evaluation of the burdens and benefits of participation in research by parents of children with life-limiting illnesses
    (RCN Publishing, 2019-09-16) Hopper, Audrey; School of Nursing
    BACKGROUND: Research is needed to improve care and diminish suffering for children with life-limiting illnesses and their parents. However, there are doubts about whether it is possible to conduct paediatric end of life research safely and ethically, as it may unduly burden or inadvertently harm participants. AIM: To compare and evaluate responses from participants to the assessments of burdens and benefits that were conducted at two timepoints during a phenomenological study that investigated parents' experiences of having a child with life-limiting cancer participate in a Phase I clinical trial. DISCUSSION: Parents reported that participating in the study was beneficial and resulted in minimal burden or distress. The assessment of benefits and burdens at the first timepoint appeared sufficient to understand participants' experiences. CONCLUSION: This study adds to the evidence that research may be safely and effectively conducted with parents of children who are deceased or have life-limiting illnesses. Further research is needed to evaluate the most effective timing of assessments of the burdens and benefits of their participation in research. IMPLICATIONS FOR PRACTICE: It is important when conducting research with people with life-limiting illnesses or their family members to assess the burdens and benefits of their participation, to understand their experiences and assist in its conduct.
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    SCIPAC: quantitative estimation of cell-phenotype associations
    (Springer Nature, 2024-05-13) Gan, Dailin; Zhu, Yini; Lu, Xin; Li, Jun; Medicine, School of Medicine
    Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.