Cecilia Astorga

Bio: Cecilia Astorga is an academic researcher. The author has contributed to research in topics: Health literacy. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

Barriers and facilitators to accessing insulin pump therapy by adults with type 1 diabetes mellitus: a qualitative study.

Abstract: Uptake of continuous subcutaneous insulin infusion (CSII) by people with diabetes (PwD) in Ireland is low and exhibits regional variation. This study explores barriers and facilitators to accessing CSII by adults with Type 1 diabetes mellitus. A qualitative study employing focus groups with adults with Type 1 diabetes mellitus (n = 26) and semi-structured interviews with health care professionals (HCP) and other key stakeholders (n = 21) was conducted. Reflexive thematic analysis was used to analyze data, using NVivo. Four main themes comprising barriers to or facilitators of CSII uptake were identified. These included: (1) awareness of CSII and its benefits, (2) the structure of diabetes services, (3) the capacity of the diabetes service to deliver the CSII service, and (4) the impact of individuals’ attitudes and personal characteristics—both PwD, and HCP. Each of these themes was associated with a number of categories, of which 18 were identified and explored. If the structure of the health-service is insufficient and capacity is poor (e.g., under-resourced clinics), CSII uptake appears to be impacted by individuals’: interest, attitude, willingness and motivation, which may intensify the regional inequality in accessing CSII. This study identified factors that contribute to gaps in the delivery of diabetes care that policy-makers may use to improve access to CSII for adult PwD.

Systemic Solutions for Addressing Non-Communicable Diseases in Low- and Middle-Income Countries

Abstract: Non-communicable diseases (NCDs) have been on the rise in low- and middle-income countries (LMICs) over the last few decades and represent a significant healthcare concern. Over 85% of "premature" deaths worldwide due to NCDs occur in the LMICs. NCDs are an economic burden on these countries, increasing their healthcare expenditure. However, targeting NCDs in LMICs is challenging due to evolving health systems and an emphasis on acute illness. The major issues include limitations with universal health coverage, regulations, funding, distribution and availability of the healthcare workforce, and availability of health data. Experts from across the health sector in LMICs formed a Think Tank to understand and examine the issues, and to offer potential opportunities that may address the rising burden of NCDs in these countries. This review presents the evidence and posits pragmatic solutions to combat NCDs.

SIRT1 Activation by Equisetum Arvense L. (Horsetail) Modulates Insulin Sensitivity in Streptozotocin Induced Diabetic Rats.

Abstract: BACKGROUND: Equisetum arvense L., commonly known as field horsetail is a perennial fern of which extracts are rich sources of phenolic compounds, flavonoids, and phenolic acids. Activation of SIRT1 that was shown to be involved in well-known signal pathways of diabetic cardiomyopathy has a protective effect against oxidative stress, inflammatory processes, and apoptosis that are the basis of diseases such as obesity, diabetes mellitus, or cardiovascular diseases. The aim of our study was to evaluate the antidiabetic and cardioprotective effects of horsetail extract in streptozotocin induced diabetic rats. METHODS: Diabetes was induced by a single intraperitoneal injection of 45 mg/kg streptozotocin. In the control groups (healthy and diabetic), rats were administered with vehicle, whilst in the treated groups, animals were administered with 50, 100, or 200 mg/kg horsetail extract, respectively, for six weeks. Blood glucose levels, glucose tolerance, and insulin sensitivity were determined, and SIRT1 levels were measured from the cardiac muscle. RESULTS: The horsetail extract showed moderate beneficial changes in blood glucose levels and exhibited a tendency to elevate SIRT1 levels in cardiomyocytes, furthermore a 100 mg/kg dose also improved insulin sensitivity. CONCLUSIONS: Altogether our results suggest that horsetail extract might have potential in ameliorating manifested cardiomyopathy acting on SIRT1.

Predictive analytics for blood glucose concentration: an empirical study using the tree-based ensemble approach

Abstract: The primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction performance of four tree-based ensemble models, i.e. bagging with tree regressors (bagging-decision tree [Bagging-DT]), AdaBoost with tree regressors (Adaboost-DT), random forest (RF) and gradient boosting decision tree (GBDT).,This study proposed a majority voting feature selection method by combining lasso regression with the Akaike information criterion (AIC) (LR-AIC), lasso regression with the Bayesian information criterion (BIC) (LR-BIC) and RF to select indicators with excellent predictive performance from initial 38 indicators in 5,642 samples. The selected features were deployed to build the tree-based ensemble models. The 10-fold cross-validation (CV) method was used to evaluate the performance of each ensemble model.,The results of feature selection indicated that age, corpuscular hemoglobin concentration (CHC), red blood cell volume distribution width (RBCVDW), red blood cell volume and leucocyte count are five most important clinical/physical indicators in BG prediction. Furthermore, this study also found that the GBDT ensemble model combined with the proposed majority voting feature selection method is better than other three models with respect to prediction performance and stability.,This study proposed a novel BG prediction framework for better predictive analytics in health care.,This study incorporated medical background and machine learning technology to reduce diabetes morbidity and formulate precise medical schemes.,The majority voting feature selection method combined with the GBDT ensemble model provides an effective decision-making tool for predicting BG and detecting diabetes risk in advance.