Discovering the Association between Gut Microbiome and Occurrence of Chemotherapy-Induced Nausea Among Breast Cancer Patients

Description

Humans and their microbiota are in a symbiotic relationship. It is known that microbiale residents within and on human bodies have the potential to impact host physiology in both healthy and disease states. To date, little is known about the…

Humans and their microbiota are in a symbiotic relationship. It is known that microbiale residents within and on human bodies have the potential to impact host physiology in both healthy and disease states. To date, little is known about the potential influence of the gut microbiome on the onset of nausea symptoms among cancer patients undergoing chemotherapy treatment. Chemotherapy-induced nausea (CIN) is a serious and common side effect. The CIN presentation is often coupled with other symptoms such as fatigue, sleep disturbance, depression, and anxiety. These symptoms both on an individual and collective level, cause negative impacts on patients’ health outcome as they challenge patients’ ability to tolerate and comply with chemotherapy. To understand the association between gut microbiome and CIN, we applied 16S rRNA amplicon sequencing to characterize the gut microbiome of breast cancer patients who reported nausea symptoms and those who reported no nausea symptoms. We hypothesize that the gut microbiome of patients who reported nausea symptoms is distinct from patients who reported no nausea. Our findings support this hypothesis, as the gut microbiome of nausea case was distinct from the no nausea cases. Specifically, we observed decreased abundance of Bacteroidetes in patients who reported nausea, while patients who reported no CIN had constant or increased abundance of Bacteroidetes. Overall, we showed that changes in the gut microbiota have an association with the occurrence of CIN symptoms among breast cancer patients receiving chemotherapy. These findings provide preliminary data for extensive research on the role of gut microbiome in CIN in the future.

Date Created
2023-05
Agent

Factors Affecting Compassion Fatigue Among Nurses During the Global COVID-19 Pandemic: Through a Socio-Ecological Model

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Description
Background: During the Coronavirus disease (COVID-19) pandemic, nurses experienced increased workloads which affected their compassion fatigue (CF). High levels of CF affect quality of care. However, little is known about what factors are associated with CF among nurses during the…

Background: During the Coronavirus disease (COVID-19) pandemic, nurses experienced increased workloads which affected their compassion fatigue (CF). High levels of CF affect quality of care. However, little is known about what factors are associated with CF among nurses during the pandemic. Aim: This study aims to examine the factors associated with CF using the socio-ecological model (SEM). Methods: This study is a cross-sectional correlational study which targeted nurses who are actively practicing and can speak English, Korean, Japanese, or French. Online websites for the recruitment including the study description and survey link were provided in each country. Survey data were collected from July 1, 2020 to January 25, 2021. CF, consisting of burnout and secondary traumatic stress (STS), was measured using Professional Quality of Life scale (ProQOL). Factors based on each level of the SEM were measured: intrapersonal factors (demographic factors, resilience), fear of infection, intention to leave their job, care of COVID-19 patients, developing policies, being asked to work at higher acuity levels, received training about COVID-19, and any COVID-19 test results); interpersonal factors (fear of bringing COVID-19 to family); organizational factors (provision of personal protective equipment [PPE] or masks, organizational support to prevent COVID-19, type of organization, and accommodational support); community factors (country of practice and incidence rate); and policy factor (mask policy). These data were analyzed using multiple regression using maximum likelihood estimation with robust standard errors. Results: Intrapersonal factors (resilience, age, being bedside staff, fear of infection, intention to leave their job, being asked to work at higher acuity levels, and receiving the positive COVID-19 results), organizational factors (provision of PPE, organizational support for COVID-19, and accommodational support), community factors (incidence rate when the mask policy was not in effect, and country of practice), and policy factor (mask policy under a high incidence rate) were the associated factors. The interaction between incidence rate and mask policy was significant. Conclusion: To prepare for future emerging infectious disease crises, organizational support with proper PPE supplies, continuing education on emerging infectious diseases, and providing interventions to increase resilience are suggested.
Date Created
2021
Agent
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