Fibroblast Growth Factor 21 (FGF21) Protects against High Fat Diet Induced Inflammation and Islet Hyperplasia in Pancreas

Fibroblast growth factor 21 (FGF21) is an important endocrine metabolic regulator expressed in multiple tissues including liver and adipose tissue. Although highest levels of expression are in pancreas, little is known about the function of FGF21 in this tissue. In order to understand the physiology of FGF21 in the pancreas, we analyzed its expression and regulation in both acinar and islet tissues. We found that acinar tissue express 20-fold higher levels than that observed in islets. We also observed that pancreatic FGF21 is nutritionally regulated; a marked reduction in FGF21 expression was noted with fasting while obesity is associated with 3–4 fold higher expression. Acinar and islet cells are targets of FGF21, which when systemically administered, leads to phosphorylation of the downstream target ERK 1/2 in about half of acinar cells and a small subset of islet cells. Chronic, systemic FGF21 infusion down-regulates its own expression in the pancreas. Mice lacking FGF21 develop significant islet hyperplasia and periductal lymphocytic inflammation when fed with a high fat obesogenic diet. Inflammatory infiltrates consist of TCRb+ Thy1+ T lymphocytes with increased levels of Foxp3+ regulatory T cells. Increased levels of inflammatory cells were coupled with elevated expression of cytokines such as TNFα, IFNγ and IL1β. We conclude that FGF21 acts to limit islet hyperplasia and may also prevent pancreatic inflammation.     

Who Are the High-Cost Users? A Method for Person-Centred Attribution of Health Care Spending

Objective

To develop person-centered episodes of care (PCE) for community-dwelling individuals in the top fifth percentile of Ontario health care expenditures in order to: (1) describe the main clinical groupings for spending; and (2) identify patterns of spending by health sector (e.g. acute care, home care, physician billings) within and across PCE.

Data sources

Data were drawn from population-based administrative databases for all publicly funded health care in Ontario, Canada in 2010/11.

Study design

This study is a retrospective cohort study.

Data collection/extraction methods

A total of 587,982 community-dwelling individuals were identified among those accounting for the top 5% of provincial health care expenditures between April 1, 2010 and March 31, 2011. PCE were defined as starting with an acute care admission and persisting through subsequent care settings and providers until individuals were without health system contact for 30 days. PCE were classified according to the clinical grouping for the initial admission. PCE and non-PCE costs were calculated and compared to provide a comprehensive measurement of total health system costs for the year.

Principal findings

Among this community cohort, 697,059 PCE accounted for nearly 70% ($11,815.3 million (CAD)) of total annual publicly-funded expenditures on high-cost community-dwelling individuals. The most common clinical groupings to start a PCE were Acute Planned Surgical (35.2%), Acute Unplanned Medical (21.0%) and Post-Admission Events (10.8%). Median PCE costs ranged from $3,865 (IQR = $1,712-$10,919) for Acute Planned Surgical to $20,687 ($12,207-$39,579) for Post-Admission Events. Inpatient acute ($8,194.5 million) and inpatient rehabilitation ($434.6 million) health sectors accounted for the largest proportions of allocated PCE spending over the year.

Conclusions

Our study provides a novel methodological approach to categorize high-cost health system users into meaningful person-centered episodes. This approach helps to explain how costs are attributable within individuals across sectors and has applications in episode-based payment formulas and quality monitoring.     

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.