Purpose: This study assessed the feasibility of a meditation-based program called Cognitively-Based Compassion Training (CBCT) with breast cancer survivors. Enrollment and participant satisfaction with a novel intervention, adherence to program requirements, and differences between the intervention group and wait list controls on self-report measures were also assessed. Additionally, cortisol, a stress-related endocrine biomarker, was assessed.
Methods: Participants (n = 33) were randomly assigned to CBCT or the wait list. CBCT provided eight weekly, 2-h classes and a "booster" CBCT session 4 weeks later. CBCT participants were expected to attend classes and meditate between classes at least three times per week. Pre-/post-intervention and follow-up questionnaires measured symptom change (depression, intrusive thoughts, perceived stress, fear of cancer recurrence, fatigue/vitality, loneliness, and quality of life). Saliva samples were collected at the same periods to assess the slope of diurnal cortisol activity.
Results: Enrollment, class attendance, home practice time, and patient satisfaction exceeded expectations. Compared to controls, post-intervention, the CBCT group showed suggestions of significant improvements in depression, avoidance of intrusive thoughts, functional impairment associated with fear of recurrence, mindfulness, and vitality/fatigue. At follow-up, less perceived stress and higher mindfulness were also significant in the CBCT group. No significant changes were observed on any other measure including diurnal cortisol activity.
Conclusions: Within the limits of a pilot feasibility study, results suggest that CBCT is a feasible and highly satisfactory intervention potentially beneficial for the psychological well-being of breast cancer survivors. However, more comprehensive trials are needed to provide systematic evidence.
Relevance: CBCT may be very beneficial for improving depression and enhancing well-being during breast cancer survivorship.
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http://dx.doi.org/10.1007/s00520-015-2888-1 | DOI Listing |
East Mediterr Health J
December 2024
Department of Radiology, King Abdulaziz University, Jeddah, Saudi Arabia.
Background: Breast cancer is often thought to occur at a younger age among Arab women based on the mean or median age at diagnosis, or the proportion of women diagnosed with breast cancer at a young age.
Objective: To compare age-specific breast cancer incidence rates among women from selected Arab countries with selected high- and middle-income countries.
Methods: We examined population-based, age-specific, national or regional breast cancer incidence data for 2008-2012 and 2013-2017 from Australia, Brazil, Canada, Germany, Japan, United Kingdom, and United States of America, and compared them with data from Algeria, Bahrain, Jordan, Kuwait, Morocco, Qatar, and Saudi Arabia.
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
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