BackgroundThe gut microbiota is an important modulator of immune, metabolic, psychological and cognitive mechanisms. Chemotherapy adversely affects the gut microbiota, inducing acute dysbiosis, and alters physiological and psychological function. Cancer among young adults has risen 38% in recent decades. Understanding chemotherapy’s long-term effects on gut microbiota and psycho-physiological function is critical to improve survivors’ physical and mental health, but remains unexamined. Restoration of the gut microbiota via targeted therapies (e.g. probiotics) could potentially prevent or reverse the psycho-physiological deficits often found in young survivors following chemotherapy, ultimately leading to reduced symptom burden and improved health.MethodsThis longitudinal study investigates chemotherapy induced long-term gut dysbiosis, and associations between gut microbiota, and immune, metabolic, cognitive and psychological parameters using data collected at < 2 month (T1), 3–4 months (T2), and 5–6 months (T3) post-chemotherapy. Participants will be 18–39 year old blood or solid tumor cancer survivors (n = 50), and a healthy sibling, partner or friend as a control (n = 50). Gut microbiota composition will be measured from fecal samples using 16 s RNA sequencing. Psychological and cognitive patient reported outcome measures will include depression, anxiety, post-traumatic stress disorder symptoms, pain, fatigue, and social and cognitive function. Dual-energy X-ray Absorptiometry (DXA) will be used to measure fat and lean mass, and bone mineral concentration. Pro-inflammatory cytokines, C-reactive protein (CRP), lipopolysaccharide (LPS), serotonin, and brain derived neurotrophic factor (BDNF) will be measured in serum, and long-term cortisol will be assayed from hair. Regression and linear mixed model (LMM) analyses will examine associations across time points (T1 – T3), between groups, and covariates with gut microbiota, cognitive, psychological, and physiological parameters.ConclusionKnowing what bacterial species are depleted after chemotherapy, how long these effects last, and the physiological mechanisms that may drive psychological and cognitive issues among survivors will allow for targeted, integrative interventions to be developed, helping to prevent or reverse some of the late-effects of treatment that many young cancer survivors face. This protocol has been approved by the Health Research Ethics Board of Alberta Cancer Committee (ID: HREBA.CC-19-0018).
Lower-extremity lymphedema (LEL) is a progressive, lifelong complication of cancer that places a substantial burden upon cancer survivors’ quality of life (QOL) and psychosocial well-being. Despite its prevalence, cancer-related LEL is inconsistently diagnosed, treated, and poorly recognized by health care professionals. The purpose of this systematic review was to summarize and appraise the quantitative literature evaluating the impact of cancer-related LEL on patients’ psychosocial well-being and QOL. Three databases (PubMed, PROQuest, and Scopus) were searched for observational research articles published before May 1st, 2020. Twenty-one articles were eligible (cross-sectional (n = 16), prospective cohort designs (n = 3), and retrospective cohort designs (n = 2)). The majority of studies reported a negative relationship between cancer-related LEL and global QOL and/or one or more psychosocial domains including (1) physical and functional; (2) psycho-emotional; (3) social, relational and financial. A greater number of LEL symptoms and higher LEL severity were associated with poorer QOL. Although the evidence to date suggests a negative relationship between cancer-related LEL and patients’ QOL and psychosocial well-being, there is a substantial need for longitudinal analyses to examine the directionality and temporality of this effect in order to inform cancer survivorship care modelling and improve patient outcomes after cancer.
Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLABbased tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/− 0.021 (p-value < 0.001), QRS interval 1+/− SD (p-value < 0.001), QT interval 0.981 +/− 0.023 p-value <0.001, and RR interval 1 +/− 0.001 p-value <0.001. Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease. INDEX TERMS Electrocardiogram, digitization, Matlab tool, image processing.
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