Educating Outpatient Clinic Nurses in the Management of Diabetes

Diabetes mellitus must be carefully managed and cared for to reduce complications and enhance quality of life. Nurses are essential to the treatment of people with diabetes, so they must have up- to-date knowledge and expertise in diabetic education. The significance of diabetic education for nurses, its effect on patient outcomes, and techniques for boosting nurses\u27 competency in diabetes care are critical to positive patient outcomes and are all explored in this academic research. The results suggest a notable disparity between the participants\u27 knowledge ratings before and after the intervention, confirming that all 13 participants benefited from the diabetic self-management education program

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Daily Vitamin D3 Supplementation as a Treatment for Health Disparities

There is discordance in the vitamin D literature between correlational studies, which show an association between higher vitamin D exposure and good health, and randomized controlled trials of vitamin D supplementation, which are inconclusive. I test the theory that this discordance is due to false assumptions about how vitamin D affects human health. I use the method of systematic review and meta-analysis—accepting only experimental studies that supplement with the animal version of vitamin D, D3, not D2 or vitamin D metabolites or analogues, as well as accepting only studies with daily rather than less-frequent but larger doses—to show that daily vitamin D3 supplementation has a statistically-significant beneficial effect on blood pressure and markers of diabetes. Using nationally-representative correlational data, I also demonstrate that the health disparities in blood pressure, if not diabetes, will be eliminated only with new health policies dedicated to health education on the vast nutritional difference in vitamin D status between black and white Americans. As a part of this dissertation, I also developed an online multi-user web application to facilitate systematic reviews and meta-analyses

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Skeletal muscle responses to physical activity in health and metabolic disease

Sedentary lifestyles, characterised by a lack of physical activity and prolonged periods of sitting, have been linked to reductions in whole-body metabolic flexibility and the increased risk of metabolic diseases, including type 2 diabetes. This can be attributed, at least partially, to the direct negative effects of physical inactivity on skeletal muscle insulin sensitivity, oxidative capacity, and overall metabolic health. In addition, sedentary behaviour can lead to anabolic resistance, resulting in losses of skeletal muscle mass and strength, which can further contribute to conditions like sarcopenic obesity, impairing physical performance and overall quality of life. Conversely, physical activity plays a crucial role in maintaining and improving skeletal muscle health. Exercise is associated with various adaptations in skeletal muscle that enhance tissue oxidative capacity, substrate handling, insulin sensitivity, as well as skeletal muscle mass and strength. These positive changes in skeletal muscle contribute to improvements in systemic metabolic wellbeing. The molecular mechanisms underlying skeletal muscle adaptation to the perturbations caused by physical activity are complex and involve intrinsic processes within the muscle fibre itself, as well as communication between different cell populations in composite skeletal muscle tissue. However, our understanding of the intricate details of these mechanisms remains incomplete. Gaining deeper insights into the regulation of skeletal muscle adaptation could not only facilitate personalised exercise recommendations but also uncover novel opportunities for drug discovery, ultimately leading to improvements in human health. Despite the well-known benefits of exercise, physical activity guidelines are often not met by the general population. Therefore, there is a pressing need for low-level entry paradigms that can promote physical activity and reduce sedentary behaviour for the betterment of individual and public health. One such approach is the incorporation of frequent activity breaks or ‘exercise snacks’ into daily routines, which involves short-duration physical activity breaks throughout the day to disrupt prolonged periods of sitting. These interventions have demonstrated efficacy for cardiometabolic health in controlled settings, such as laboratory-based clinical trials. However, it is essential to evaluate the benefits of breaking sedentary time using strategies that better mimic real-world scenarios to inform practical public health guidelines. In this thesis, the following objectives were pursued: (1) To assess the translational efficacy of interrupting sedentary time in improving cardiometabolic health. (2) To investigate the skeletal muscle transcriptome following exercise or physical inactivity in the context of health and metabolic diseases. (3) To determine the metabolic effects of physical activity- responsive transcription factors and signalling molecules in skeletal muscle. Study I revealed that even a minor addition of ≈750 steps dispersed throughout the day, equivalent to ≈10 minutes of extra walking time, improved dynamic glucose control in individuals with obesity and insulin resistance. Notably, those who engaged in higher levels of physical activity while interrupting sedentary time experienced greater benefits, indicating that more breaks from sedentary behaviour lead to better metabolic health outcomes. Nevertheless, adherence to the intervention, which involved 3-min activity bouts every 30 min between 08:00-18:00, was lower than anticipated. This raises questions about the long- term feasibility of such approaches when considered in isolation from other modifiable lifestyle factors, including changes in dietary habits or structured exercise routines. Study II employed a comprehensive meta-analytical approach to compare the skeletal muscle transcriptomic response to acute aerobic or resistance exercise, exercise training, and physical inactivity. This analysis revealed distinct gene signatures in skeletal muscle after a single bout of exercise in the naïve state, which differed from those observed after training of the same exercise modality. Interestingly, there was greater overlap in the skeletal muscle transcriptome between acute aerobic and resistance exercise than there was between acute exercise and exercise training. These findings highlight the refinement of the adaptive response in skeletal muscle over time through dedicated training to a specific exercise modality. Study II identified the transcription factor nuclear receptor subfamily 4 group A member 3 (NR4A3) as a gene that is upregulated in skeletal muscle after exercise but downregulated in response to physical inactivity. Study III delved deeper into the role of NR4A3 in the context of physical inactivity and revealed its regulatory role in translation within skeletal muscle. Depletion of NR4A3 resulted in skeletal muscle atrophy and compromised glucose oxidation, instead favouring increased lactate production. Therefore, decreased levels of NR4A3 during physical inactivity may directly contribute to muscle disuse atrophy and impaired skeletal muscle metabolism. Furthermore, study II identified that individuals with obesity and/or type 2 diabetes exhibit an altered skeletal muscle transcriptional response to exercise training compared to healthy individuals. Study IV uncovered a heightened inflammatory response during the recovery period after exercise in individuals with type 2 diabetes. This response was attributed to an increased influx of immune cells into skeletal muscle tissue, potentially facilitating crosstalk between different cell types within the skeletal muscle interstitial space. Notably, the cytokine stromal cell-derived factor 1 (CXCL12/SDF-1) was found to be expressed by macrophages or endothelial cells in response to factors released by skeletal muscle fibres or hypoxia, respectively. CXCL12 activation, in turn, promoted the proliferation of skeletal muscle satellite cells, which could be integral for adaptive remodelling following exercise. In conclusion, the research presented in this thesis emphasises the central role of physical activity in improving human health, with a specific focus on the ability of exercise to induce adaptations in skeletal muscle. The findings herein shed light on the intricate molecular mechanisms underlying skeletal muscle responses to physical activity, which contribute to the metabolic fitness of this tissue and of the human body as a whole

Acute lung injury in paediatric intensive care: course and outcome

Introduction: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) carry a high morbidity and mortality (10-90%). ALI is characterised by non-cardiogenic pulmonary oedema and refractory hypoxaemia of multifactorial aetiology [1]. There is limited data about outcome particularly in children. Methods This retrospective cohort study of 85 randomly selected patients with respiratory failure recruited from a prospectively collected database represents 7.1% of 1187 admissions. They include those treated with High Frequency Oscillation Ventilation (HFOV). The patients were admitted between 1 November 1998 and 31 October 2000. Results: Of the 85, 49 developed acute lung injury and 47 had ARDS. There were 26 males and 23 females with a median age and weight of 7.7 months (range 1 day-12.8 years) and 8 kg (range 0.8-40 kg). There were 7 deaths giving a crude mortality of 14.3%, all of which fulfilled the Consensus I [1] criteria for ARDS. Pulmonary occlusion pressures were not routinely measured. The A-a gradient and PaO2/FiO2 ratio (median + [95% CI]) were 37.46 [31.82-43.1] kPa and 19.12 [15.26-22.98] kPa respectively. The non-survivors had a significantly lower PaO2/FiO2 ratio (13 [6.07-19.93] kPa) compared to survivors (23.85 [19.57-28.13] kPa) (P = 0.03) and had a higher A-a gradient (51.05 [35.68-66.42] kPa) compared to survivors (36.07 [30.2-41.94]) kPa though not significant (P = 0.06). Twenty-nine patients (59.2%) were oscillated (Sensormedics 3100A) including all 7 non-survivors. There was no difference in ventilation requirements for CMV prior to oscillation. Seventeen of the 49 (34.7%) were treated with Nitric Oxide including 5 out of 7 non-survivors (71.4%). The median (95% CI) number of failed organs was 3 (1.96-4.04) for non-survivors compared to 1 (0.62-1.62) for survivors (P = 0.03). There were 27 patients with isolated respiratory failure all of whom survived. Six (85.7%) of the non-survivors also required cardiovascular support.Conclusion: A crude mortality of 14.3% compares favourably to published data. The A-a gradient and PaO2/FiO2 ratio may be of help in morbidity scoring in paediatric ARDS. Use of Nitric Oxide and HFOV is associated with increased mortality, which probably relates to the severity of disease. Multiple organ failure particularly respiratory and cardiac disease is associated with increased mortality. ARDS with isolated respiratory failure carries a good prognosis in children

Understanding the Determinants of Sustained Beta Cell Function in Type 1 Diabetes

Type 1 diabetes is a disease defined by the inexorable autoimmune destruction of pancreatic beta cells, leading to endogenous insulin deficiency. C-peptide, a 31 amino acid protein that joins the alpha and beta chains of insulin in the proinsulin molecule, is well established as a marker of endogenous insulin secretion. Circulating levels within people with type 1 diabetes demonstrate persistence of insulin secretion, in some cases, for many years after diagnosis. Additionally, histological analyses of donor pancreata have provided evidence for persistent immunoreactive insulin-positive beta cells. These findings have challenged the dogma that all beta cells are destroyed at, or soon after, onset of type 1 diabetes. Although it is clear there is some relationship between residual C-peptide and preserved beta cell mass, residual C-peptide alone cannot distinguish between loss of beta cell mass and reduced functionality. As such, C-peptide level remains a contested surrogate for the aetiopathological definition of type 1 diabetes, which is used across disease classification and as the end point in many intervention trials designed to preserve beta cell function. A fundamental difference between type 1 diabetes and type 2 diabetes is that the former is characterised by rapid progression to endogenous insulin deficiency due to autoimmune beta-cell destruction. Since histological classification is impossible in living humans with type 1 diabetes, C-peptide-defined type 1 and type 2 diabetes have been used as the endpoint in the development and validation of classification models which combine clinical features and biomarkers to improve classification of disease at diagnosis. In Chapter 2, I aimed to I validate a classification model that was previously developed on a C-peptide outcome in a clinical cohort, against a histological definition of type 1 diabetes. This classification model combined age, body mass index (BMI), autoantibody status and type 1 diabetes genetic risk score (T1D GRS), with its predictive performance tested on samples defined histologically as having type 1 diabetes and non-type 1 diabetes from the Network for Pancreatic Organ Donors with Diabetes (nPOD) biobank. Strong predictive performance of the model in this setting demonstrated that the C-peptide outcome, used in its development, is representative of histologically defined disease, confirming that C-peptide is a robust, appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. In the 1970s it was crystallised that type 1 diabetes is a disease mediated by the autoimmune destruction of insulin producing beta cells. Since then, the centrepiece of many disease modifying intervention trials has been to augment the survival of functional beta cells, assessed via measures of preserved C-peptide secretion. However, there are clear differences in disease progression between children and adults with recent suggestions that, even within children, differences are driven by underlying endotypes. In Chapter 3, across disease duration, I compared the trends of decline of C-peptide in a cohort of living children from the UK Genetic Resource Investigating Diabetes, to the trends of decline of pancreatic beta cells in organ donors from the combined nPOD and Exeter Archival Diabetes Biobank (EADB), through stratifying by newly described age at diagnosis associated endotypes. I demonstrated that C-peptide loss and beta cell loss, in all age at diagnoses studied, mirror one another across duration of disease. I demonstrated that proportionally fewer children diagnosed <7 retained C-peptide after one year of diagnosis, with the levels of retained C-peptide being lower at diagnosis that those diagnosed at older ages. I showed these trends of loss are almost identical in pancreas donors, with proportionally fewer children retaining islets containing insulin positive beta cells after one year of diagnosis, with fewer insulin positive beta cells at diagnosis as compared to donors diagnosed at older ages. The results in this chapter are indicative of the differences in disease progression in children. The rapid depletion of C-peptide and beta cells in children diagnosed < 7 years is suggestive that early intervention close to or before diagnosis may be most time critical, and should additionally be considered in planning and interpretation of intervention trials. Preserving C-peptide is unequivocally beneficial to a person diagnosed with type 1 diabetes, associating with reduced frequency and severity of self-reported hypoglycaemia and fewer long term microvascular complications, as evidenced originally from DCCT. In Chapter 4, using insights from continuous glucose monitoring (CGM) technology, I demonstrated that higher levels of endogenous insulin secretion around the time of diagnosis impact glycaemic variability, but are not associated with hypoglycaemia. The work in this chapter adds to findings from previous studies of longer duration diabetes to offer a more complete picture of the impact that variation in C-peptide levels have on glucose control in people with type 1 diabetes. Increased use of flash and continuous glucose monitoring has enabled more detailed, daily insights into glycaemic control within type 1 diabetes, the relationships of such with C-peptide have been explored within this thesis. This technology however offers a wealth of opportunity for exploring the lived experience type 1 diabetes, in relation to daily glucose control. In Chapter 5 I developed upon the skills I had refined in CGM data analysis, exploring the impact that free-lived high and moderate intensity exercise have on glycaemic control in type 1 diabetes, as compared to an individual’s non-exercise “normal”. I compare monitored glucose traces from 10 adults with type 1 diabetes that each completed three, 14-day intervention periods of: home-based high intensity interval exercise, home-based moderate intensity continuous exercise and a free-living non-exercise control period. A key part of this analysis was the careful comparison of the glucose traces in each exercise intervention period to the glucose traces within the non-exercise of control period, in order to understand how much exercise perturbed an individual from their “normal” . In this analysis I found that the exercise modes assessed increase glycaemic variability and hypoglycaemia in the 4 hours after exercise, had a modest effect on glycaemic variability overnight, but increased glycaemic variability and hypoglycaemia the day after exercise. The findings in this chapter suggest that developing focused clinical guidance around time periods post-exercise, and accounting for “everyday life”, may improve the management of blood glucose in type 1 diabetes and ultimately reduce barriers to exercise. In the majority of endocrine conditions, the hormone in question is measured as part of routine usual care. In diabetes this is not yet the case. In this thesis I provide evidence that C-peptide is a robust surrogate marker of functional beta cells in clinical settings and demonstrate how an estimate of a patients C-peptide reserve could benefit clinical management. In addition to C-peptide level, I explore how exercise is another influential factor on glucose control in type 1 diabetes. Throughout this thesis I aim to place findings within the context of the lived experience of type 1 diabetes. After all, it is the people living with type 1 diabetes that are the reason we continue our research

Dynamic Contrast Enhanced Computed Tomography Measurement of Perfusion in Hepatic Cancer

ABSTRACT In recent years, the incidence and mortality rate for hepatocellular carcinoma (HCC) have increased due to the emergence of hepatitis B, C and other diseases that cause cirrhosis. The progression from cirrhosis to HCC is characterized by abnormal vascularization and by a shift from a venous to an arterial blood supply. A knowledge of HCC vascularity which is manifested as alterations in liver blood flow may distinguish among different stages of liver disease and can be used to monitor response to treatment. Unfortunately, conventional diagnostic imaging techniques lack the ability to accurately quantify HCC vascularity. The purpose of this thesis was to validate and assess the diagnostic capabilities of dynamic contrast enhanced computed tomography (DCE-CT) and perfusion software designed to measure hepatic perfusion. Chapter 2 described a study designed to evaluate the accuracy and precision of hepatic perfusion measurement. The results showed a strong correlation between hepatic artery blood flow measurement with DCE-CT and radioactive microspheres under steady state in a rabbit model for HCC (VX2 carcinoma). Using repeated measurements and Monte Carlo simulations, DCE-CT perfusion measurements were found to be precise; with the highest precision in the tumor rim. In Chapter 3, we used fluorine-18 fluoro-2-deoxy-D-glucose (FDG) positron emission tomography and DCE-CT perfusion to determined an inverse correlation between glucose utilization and tumor blood flow; with an R of 0.727 (P \u3c 0.05). This suggests a limited supply of oxygen (possibly hypoxia) and that the tumor cells were surviving via anaerobic glycolysis. in In Chapter 4, hepatic perfusion data showed that thalidomide caused a reduction of tumor perfusion in the responder group during the first 8 days after therapy, P \u3c 0.05; while perfusion in the partial responder and control group remained unchanged, P \u3e 0.05. These changes were attributed to vascular remodeling and maturation resulting in a more functional network of endothelial tubes lined with pericytes. The results of this thesis demonstrate the accuracy and precision of DCE-CT hepatic perfusion measurements. It also showed that DCE-CT perfusion has the potential to enhance the functional imaging ability of hybrid PET/CT scanners and evaluate the efficacy of anti-angiogenesis therapy

Build-Ups in the Supply Chain of the Brain: on the Neuroenergetic Cause of Obesity and Type 2 Diabetes Mellitus

Obesity and type 2 diabetes have become the major health problems in many industrialized countries. A few theoretical frameworks have been set up to derive the possible determinative cause of obesity. One concept views that food availability determines food intake, i.e. that obesity is the result of an external energy “push” into the body. Another one views that the energy milieu within the human organism determines food intake, i.e. that obesity is due to an excessive “pull” from inside the organism. Here we present the unconventional concept that a healthy organism is maintained by a “competent brain-pull” which serves systemic homeostasis, and that the underlying cause of obesity is “incompetent brain-pull”, i.e. that the brain is unable to properly demand glucose from the body. We describe the energy fluxes from the environment, through the body, towards the brain with a mathematical “supply chain” model and test whether its predictions fit medical and experimental data sets from our and other research groups. In this way, we show data-based support of our hypothesis, which states that under conditions of food abundance incompetent brain-pull will lead to build-ups in the supply chain culminating in obesity and type 2 diabetes. In the same way, we demonstrate support of the related hypothesis, which states that under conditions of food deprivation a competent brain-pull mechanism is indispensable for the continuance of the brain´s high energy level. In conclusion, we took the viewpoint of integrative physiology and provided evidence for the necessity of brain-pull mechanisms for the benefit of health. Along these lines, our work supports recent molecular findings from the field of neuroenergetics and continues the work on the “Selfish Brain” theory dealing with the maintenance of the cerebral and peripheral energy homeostasis

Forecasting glycaemia for type 1 diabetes mellitus patients by means of IoMT devices

The chronic metabolic condition, Type 1 diabetes mellitus (DM1), is marked by consistent hyperglycemia due to the body's inability to produce sufficient insulin. This necessitates the patient's daily monitoring of blood glucose fluctuations to discern a trend and predict future glycemia, subsequently dictating the amount of external insulin needed to regulate glycemia effectively. However, this technique often grapples with a degree of inaccuracy, presenting potential hazards. Nonetheless, contemporary advancements in information and communication technologies (ICT) coupled with novel biological signal sensors offer a refreshing perspective for DM1 management by enabling comprehensive, continual patient health evaluation. Herein, burgeoning technological disruptions such as Big Data, the internet of medical things (IoMT), cloud computing, and machine learning algorithms (ML) could serve pivotal roles in the effective control of DM1. This paper delves into the exploration of the latest IoMT-based methodologies for the unbroken surveillance of DM1 management, facilitating a profound characterization of diabetic patients. The fusion of wearable technologies with machine learning strategies has the potential to yield robust models for short-term blood glucose prediction. The ambition of this study is to develop precise, individual-centric prediction models harnessing an array of pertinent factors. The study applied modeling techniques to a comprehensive dataset comprising glycaemia-associated biological attributes, sourced from an expansive passive monitoring campaign involving 40 DM1 patients. Leveraging the Random Forest method, the resulting models can predict glucose levels over a 30-min time span with an average error as minimal as 18.60 mg/dL for six-hour data and 26.21 mg/dL for a 45-minute prediction horizon, offering also a good performance in the prediction delay.Funding for open Access charge: Universidad de Málaga / CBUA