Unlabelled: Advancements in paediatric oncology have made quality of life after cancer increasingly clinically important. Little is currently known about children's experiences of treatment completion and its management.
Aim: The current study explores children's experience of ending treatment for Acute Lymphoblastic Leukaemia (ALL), and the meaning it is given, particularly how endings are signified and marked.
Method: Semi-structured interviews were conducted with seven children who had completed cancer treatment for ALL with good prognoses. Interviews were analysed using Interpretative Phenomenological Analysis.
Results: Five superordinate themes were generated: 'the end is always there', 'the punctuation of endings', 'that which is remembered, that which is forgotten', 'the voiced and the unvoiced', and 'freedom from cancer.'
Conclusion: Children highlighted the importance of punctuating and celebrating the end of their treatment, and the need for doing this in ways that helped them process the complexity of ending active treatment and provides space for their voices.
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http://dx.doi.org/10.1016/j.jcpo.2023.100442 | DOI Listing |
Clin Exp Med
January 2025
Department of Clinical Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Krakow Branch, Poland.
Immune checkpoint inhibitors have improved the treatment of metastatic renal cell carcinoma (RCC), with the combination of nivolumab (NIVO) and ipilimumab (IPI) showing promising results. However, not all patients benefit from these therapies, emphasizing the need for reliable, easily assessable biomarkers. This multicenter study involved 116 advanced RCC patients treated with NIVO + IPI across nine oncology centers in Poland.
View Article and Find Full Text PDFSupport Care Cancer
January 2025
Department of Nursing, Nanfang Hosptial of Southern Medical University, Guangzhou, 510515, People's Republic of China.
Purpose: Our study aim was to understand the (human and organizational) factors influencing fall risk among people with hematological malignancies using the Reason model as a framework, providing insights that can inform the development of safe and effective fall management strategies.
Methods: Purposive sampling was employed to conduct semi-structured interviews with 13 people with hematological malignancies and 12 nurses from the hematology department of a tertiary grade A hospital in Guangzhou from December 2023 to February 2024. The topic analysis method was utilized to analyze the interview data.
J Cancer Educ
January 2025
Department of Pharmacy, Al Rafidain University College, 10001, Baghdad, Iraq.
Chemotherapy-drug interactions (CDIs) pose significant challenges in oncology, affecting treatment efficacy and patient safety. Despite their importance, there is a lack of validated tools to assess oncologists' knowledge of CDIs. This study aimed to develop and validate a comprehensive questionnaire to address this gap and ensure the reliability and validity of the instrument.
View Article and Find Full Text PDFLasers Med Sci
January 2025
Departamento de Biofísica e Biometria Instituto de Biologia Roberto Alcântara Gomes, Universidade do Estado do Rio de Janeiro, Avenida 28 de Setembro, 87, fundos, Vila Isabel, Rio de Janeiro, 20551030, Brazil.
In this article, we aim to evaluate the effects of photobiomodulation on mitochondria quantity, biogenesis, and mitophagy-associated genes in breast cancer (BC) cells. Both models were irradiated with a low-power infrared laser (880 nm, 150 mW) and amber LED (617 nm, 1500 mW), alone or simultaneously. We evaluated the mRNA expression of PINK1 and PGC-1α genes, and the mitochondrial number was assessed based on the ratio of mitochondrial DNA/genomic DNA (mtDNA/gDNA).
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
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