Glucagon-like peptide 1 receptor is a T cell-negative costimulatory molecule.

Glucagon-like peptide-1 receptor (GLP-1R) is a key regulator of glucose metabolism known to be expressed by pancreatic ß cells. We herein investigated the role of GLP-1R on T lymphocytes during immune response. Our data showed that a subset of T lymphocytes expresses GLP-1R, which is upregulated during alloimmune response, similarly to PD-1. When mice received islet or cardiac allotransplantation, an expansion of GLP-1Rpos T cells occurred in the spleen and was found to infiltrate the graft. Additional single-cell RNA sequencing (scRNA-seq) analysis conducted on GLP-1Rpos and GLP-1Rneg CD3+ T cells unveiled the existence of molecular and functional dissimilarities between both subpopulations, as the GLP-1Rpos are mainly composed of exhausted CD8 T cells. GLP-1R acts as a T cell-negative costimulatory molecule, and GLP-1R signaling prolongs allograft survival, mitigates alloimmune response, and reduces T lymphocyte graft infiltration. Notably, GLP-1R antagonism triggered anti-tumor immunity when tested in a preclinical mouse model of colorectal cancer.

Reversal of Experimental Autoimmune Diabetes With an sCD39/Anti-CD3 Treatment.

Extracellular (e)ATP, a potent proinflammatory molecule, is released by dying/damaged cells at the site of inflammation and is degraded by the membrane ectonucleotidases CD39 and CD73. In this study, we sought to unveil the role of eATP degradation in autoimmune diabetes. We then assessed the effect of soluble CD39 (sCD39) administration in prevention and reversal studies in NOD mice as well as in mechanistic studies. Our data showed that eATP levels were increased in hyperglycemic NOD mice compared with prediabetic NOD mice. CD39 and CD73 were found expressed by both α- and ß-cells and by different subsets of T cells. Importantly, prediabetic NOD mice displayed increased frequencies of CD3+CD73+CD39+ cells within their pancreata, pancreatic lymph nodes, and spleens. The administration of sCD39 into prediabetic NOD mice reduced their eATP levels, abrogated the proliferation of CD4+- and CD8+-autoreactive T cells, and increased the frequency of regulatory T cells, while delaying the onset of T1D. Notably, concomitant administration of sCD39 and anti-CD3 showed a strong synergism in restoring normoglycemia in newly hyperglycemic NOD mice compared with monotherapy with anti-CD3 or with sCD39. The eATP/CD39 pathway plays an important role in the onset of T1D, and its targeting might represent a potential therapeutic strategy in T1D.

Nanotargeted Delivery of Immune Therapeutics in Type 1 Diabetes.

Immune therapeutics holds great promise in the treatment of type 1 diabetes (T1D). Nonetheless, their progress is hampered by limited efficacy, equipoise, or issues of safety. To address this, a novel and specific nanodelivery platform for T1D that targets high endothelial venules (HEVs) presented in the pancreatic lymph nodes (PLNs) and pancreas is developed. Data indicate that the pancreata of nonobese diabetic (NOD) mice and patients with T1D are unique in their expression of newly formed HEVs. Anti-CD3 mAb is encapsulated in poly(lactic-co-glycolic acid)-poly(ethylene glycol) nanoparticles (NPs), the surfaces of which are conjugated with MECA79 mAb that recognizes HEVs. Targeted delivery of these NPs improves accumulation of anti-CD3 mAb in both the PLNs and pancreata of NOD mice. Treatment of hyperglycemic NOD mice with MECA79-anti-CD3-NPs results in significant reversal of T1D compared to those that are untreated, treated with empty NPs, or provided free anti-CD3. This effect is associated with a significant reduction of T effector cell populations in the PLNs and a decreased production of pro-inflammatory cytokine in the mice treated with MECA79-anti-CD3-NPs. In summary, HEV-targeted therapeutics may be used as a means by which immune therapeutics can be delivered to PLNs and pancreata to suppress autoimmune diabetes effectively.

Broadening horizons in mechanisms, management, and treatment of diabetic kidney disease.

Diabetic kidney disease (DKD) is the first cause of end-stage kidney disease in patients with diabetes and its prevalence is increasing worldwide. It encompasses histological alterations that mainly affect the glomerular filtration unit, which include thickening of the basement membrane, mesangial cell proliferation, endothelial alteration, and podocyte injury. These morphological abnormalities further result in a persistent increase of urinary albumin-to-creatinine ratio and in a reduction of the estimated glomerular filtration rate. Several molecular and cellular mechanisms have been recognized, up to date, as major players in mediating such clinical and histological features and many more are being under investigation. This review summarizes the most recent advances in understanding cell death mechanisms, intracellular signaling pathways and molecular effectors that play a role in the onset and progression of diabetic kidney damage. Some of those molecular and cellular mechanisms have been already successfully targeted in preclinical models of DKD and, in some cases, strategies have been tested in clinical trials. Finally, this report sheds light on the relevance of novel pathways that may become therapeutic targets for future applications in DKD.

eATP and autoimmune diabetes.

PURPOSE OF REVIEW: The purine nucleotide adenosine triphosphate (ATP) is released into extracellular spaces as extracellular ATP (eATP) as a consequence of cell injury or death and activates the purinergic receptors. Once released, eATP may facilitate T-lymphocyte activation and differentiation. The purpose of this review is to elucidate the role of ATP-mediated signaling in the immunological events related to type 1 diabetes (T1D). RECENT FINDINGS: T lymphocytes mediate immune response during the onset of T1D and promote pancreatic islet or whole pancreas rejection in transplantation. Recent data suggest a potential role for eATP in early steps of T1D onset and of allograft rejection. In different preclinical experimental models and clinical trials, several drugs targeting purinergic signaling have been employed to abrogate lymphocyte activation and differentiation, thus representing an achievable treatment to prevent/revert T1D or to induce long-term islet allograft function. SUMMARY: In preclinical and clinical settings, eATP-signaling inhibition induces immune tolerance in autoimmune disease and in allotransplantation. In this view, the purinergic system may represent a novel therapeutic target for auto- and allo-immunity.

A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections.

Background: A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention. In the United States, sexually transmitted infections (STIs) are a huge economic burden, with a large fraction of the burden attributed to HIV. Data also show interactions between HIV and other sexually transmitted infections (STIs), such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without. However, given the wide range in prevalence and incidence burdens of STIs, current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling. Further, causal factors for higher risk of coinfection could be both behavioral (i.e., compounding effects of individual behaviors, network structures, and care behaviors) and biological (i.e., presence of one disease can biologically increase the risk of another). However, the data on the fraction attributed to each are limited. Methods: We present a new mixed agent-based compartmental (MAC) framework for jointly modeling STIs. It uses a combination of a new agent-based evolving network modeling (ABENM) technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases. As a demonstration, we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2, using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs. We simulated sexual transmissions among heterosexual males, heterosexual females, and men who have sex with men (men only and men and women). Setting the biological risk of co-infection to zero, we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics. Results: The contribution of behavioral factors to risk of coinfection was sensitive to disease burden, care access, and population heterogeneity and mixing. The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here, suggesting potential role of biological factors, that should be investigated further specific to an STI. Conclusions: The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features. The numerical analysis serves as proof-of-concept for the application to STIs. Interactions between diseases are influenced by behavioral factors, are sensitive to care access and population features, and are likely exacerbated by biological factors. Social and economic conditions are among key drivers of behaviors that increase STI transmission, and thus, structural interventions are a key part of behavioral interventions. Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention. The new simulation framework is especially suited to simulate behavior as a function of social determinants, and further, to identify optimal combinations of common structural and disease-specific interventions.

Prediction of water quality extremes with composite quantile regression neural network.

Water quality extremes, which water quality models often struggle to predict, are a grave concern to water supply facilities. Most existing water quality models use mean error functions to maximize the predictability of water quality mean value. This paper describes a composite quantile regression neural network (CQRNN) model, which simultaneously estimates non-crossing regression quantiles by minimizing the composite quantile regression error function. This method can improve the prediction of extremes. This paper evaluates the performance of CQRNN for predicting extreme values of turbidity and total organic carbon (TOC) and compares with quantile regression (QR), linear regression (LR), and k-nearest neighbors (KNN) in an application to the Hetch Hetchy Regional Water System, which is the primary water supply for San Francisco, CA. CQRNN is superior to QR, LR, and KNN for predicting the mean trend and extremes of turbidity and TOC, especially for the non-Gaussian turbidity data. The performance of CQRNN is the most stable relative to other methods over different training sample sizes.

Simple Heuristics for Near-Optimal Appointment Scheduling in Primary Care.

In primary care allocating appointments to sequential requests can result in sub-optimal scheduling. Optimal scheduling requires hiring of consultants to analyze historical patterns. Many practices focus their resources on larger problems instead of optimizing appointment schedules. We simulate simple heuristics to compare their performance with optimal schedules uncovered using offline optimization models. We use uncapacitated appointment calendars for a nationally representative heterogeneous primary care panel to meet all patients' requests. The stochastic nature of appointment requests gives a distribution for daily appointments and for the uncovered optimal capacity. The First Minimum heuristic gives near-optimal schedules and can be easily implemented in small practices using pen-and-paper, without any investment in computer-systems.

Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases.

Agent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.

Hypertension management in rural western Kenya: a needs-based health workforce estimation model.

BACKGROUND: Elevated blood pressure is the leading risk for mortality in the world. Task redistribution has been shown to be efficacious for hypertension management in low- and middle-income countries. However, the workforce requirements for such a task redistribution strategy are largely unknown. Therefore, we developed a needs-based workforce estimation model for hypertension management in western Kenya, using need and capacity as inputs. METHODS: Key informant interviews, focus group discussions, a Delphi exercise, and time-motion studies were conducted among administrative leadership, clinicians, patients, community leaders, and experts in hypertension management. These results were triangulated to generate the best estimates for the inputs into the health workforce model. The local hypertension clinical protocol was used to derive a schedule of encounters with different levels of clinician and health facility staff. A Microsoft Excel-based spreadsheet was developed to enter the inputs and generate the full-time equivalent workforce requirement estimates over 3 years. RESULTS: Two different scenarios were modeled: (1) "ramp-up" (increasing growth of patients each year) and (2) "steady state" (constant rate of patient enrollment each month). The ramp-up scenario estimated cumulative enrollment of 7000 patients by year 3, and an average clinical encounter time of 8.9 min, yielding nurse full-time equivalent requirements of 4.8, 13.5, and 30.2 in years 1, 2, and 3, respectively. In contrast, the steady-state scenario assumed a constant monthly enrollment of 100 patients and yielded nurse full-time equivalent requirements of 5.8, 10.5, and 14.3 over the same time period. CONCLUSIONS: A needs-based workforce estimation model yielded health worker full-time equivalent estimates required for hypertension management in western Kenya. The model is able to provide workforce projections that are useful for program planning, human resource allocation, and policy formulation. This approach can serve as a benchmark for chronic disease management programs in low-resource settings worldwide.

Panel Size, Office Visits, and Care Coordination Events: A New Workload Estimation Methodology Based on Patient Longitudinal Event Histories.

Background. Panel size, or the number of patients a primary care physician (PCP) and her care team can feasibly manage as part of a practice, remains a vital question in primary care. Objective. To Illustrate a new methodology for quantifying two types of workload associated with a panel size: 1) the PCP weekly office visit distribution and 2) the weekly distribution of non-PCP events (subspecialty visits, emergency room visits, hospitalizations) that potentially require non-face-to-face coordination. Methods. We assemble granular individual-level histories of events in the health system using the Medical Expenditure Panel Survey from 2011. Using the date on which each event occurred, we create weekly utilization estimates as a function of panel size for the general population and Medicare patients. Results. A PCP with a panel of 2,000 adults approximately representative of the US population can expect to have 93.54 office visits on average each week. A simple model quantifying demand-capacity mismatch suggests that a PCP with a weekly capacity of 80 to 90 appointments will struggle to satisfy this office-visit demand in a timely manner. Furthermore, each week the PCP can expect the same panel to have 9.08 visits to the emergency room, 4.69 hospital inpatient events, and 131.29 office-based visits to non-primary care subspecialists; these events contribute to the non-face-to-face coordination workload, increasing the probability of an overburdened workweek. Both PCP office visit and coordination events are highly concentrated in less than 200 individuals (<10% of the 2,000). Conclusion. Patient-level longitudinal event histories can be retrospectively assembled to quantify patterns of face-to-face office visits and coordination workload associated with a primary care panel.

Neonatal Cerebral Air Embolism.

BACKGROUND: Vascular air embolism (VAE) is rare but potentially lethal condition, and survival is rarely reported in newborn. CHARACTERISTICS: A preterm (27+1 weeks) neonate on Continuous positive airway pressure developed sudden cardiac asystole on day 3 of life and required 30 minutes of cardiopulmonary resuscitation. OBSERVATION: Infant had air embolism in liver and brain. He survived but developed cystic encephalomalcia requiring extensive neuro-rehabilitation. MESSAGE: Air embolism should be considered as differential diagnosis of sudden unexplained cardiac deterioration in well neonate.

Meconium Evacuation for Facilitating Feed Tolerance in Preterm Neonates: A Systematic Review and Meta-Analysis.

BACKGROUND: A delayed passage of meconium is considered as a risk factor for feed intolerance in preterm neonates. OBJECTIVES: The aim of this study was to review the effects of different therapeutic agents for meconium evacuation on feed tolerance in preterm neonates. METHODS: A systematic review of randomised controlled trials (RCTs) of different therapeutic agents for meconium evacuation in preterm neonates (gestation <32 weeks and/or birth weight <1,500 g) using the Cochrane systematic review methodology was undertaken. Databases including Google Scholar were searched in January 2016. The primary outcome was the time to reach full feeds (TFF; ≥120 ml/kg feeds with stoppage of parenteral nutrition >24 h). Secondary outcomes included necrotising enterocolitis (NEC), weight at discharge and adverse effects. The results were summarised as per the GRADE guidelines. RESULTS: Six RCTs (2 each of glycerine suppository and enema, 1 normal saline enema and 1 oral osmotic contrast agent; n = 442) with a low or unclear risk of bias were included. The pooled estimate (random effects model) showed no reduction in TFF [mean difference (MD) -0.03, 95% CI -2.47, 2.41, p = 0.98; level of evidence: low]. No differences in NEC [risk ratio (RR) 1.71, 95% CI 0.63, 4.65, p = 0.30; level of evidence: low] and weight at discharge (MD -0.08, 95% CI -0.30, 0.15, p = 0.50; level of evidence: low) were found. The trial assessing oral osmotic contrast agents reported a trend towards a higher incidence of NEC ≥ stage II. There were no other adverse effects. CONCLUSION: Limited low-quality evidence indicates that prophylactic glycerine suppository, small volume glycerine/normal saline enema or oral osmotic contrast agents to evacuate meconium did not reduce TFF in preterm neonates. Large, well-designed trials are essential to study this clinically significant issue.

Using Computer Simulation to Study Nurse-to-Patient Ratios in an Emergency Department.

OBJECTIVE: To study the impact of nurse-to-patient ratios on patient length of stay (LOS) in computer simulations of emergency department (ED) care. METHODS: Multiple 24-hour computer simulations of emergency care were used to evaluate the impact of different minimum nurse-to-patient ratios related to ED LOS, which is composed of wait (arrival to bed placement) and bedtime (bed placement to leave bed). RESULTS: Increasing the number of patients per nurse resulted in increased ED LOS. Mean bedtimes in minutes were impacted by nurse-to-patient ratios. CONCLUSIONS: In computer simulation of ED care, increasing the number of patients per nurse resulted in increasing delays in care (ie, increasing bedtime).

The impact of age and gender on resource utilization and profitability in ED patients seen and released.

OBJECTIVE: To determine how age and gender impact resource utilization and profitability in patients seen and released from an Emergency Department (ED). METHODS: Billing data for patients seen and released from an Emergency Department (ED) with >100,000 annual visits between 2003 and 2009 were collected. Resource utilization was measured by length of stay (placement in ED bed to leaving the bed) and direct clinical costs (e.g., ED nursing salary and benefits, pharmacy and supply costs, etc.) estimated using relative value unit cost accounting. The primary outcome of profitability was defined as contribution margin per hour. A patient's contribution margin by insurance type (excluding self-pay) was determined by subtracting direct clinical costs from facility contractual revenue. Results are expressed as medians and US dollars. RESULTS: In 523 882 outpatient ED encounters, as patients' aged, length of stay and direct clinical cost increased while the contribution margin and contribution margin by hour decreased. Women of childbearing age (15-44) had higher median length of stay (2.1 hours), direct clinical cost ($149), and contribution margin per hour ($103/hour) than men of same age (1.7, $131, $85/hour, respectively). Resource utilization and profitability by gender were similar in children and adults over 45. CONCLUSION: Resource utilization increased and profitability decreased with increasing age in patients seen and released from an ED. The care of women of childbearing age resulted in higher resource utilization and higher profitability than men of the same age. No differences in resource utilization or profitability by gender were observed in children and adults over 45.

Balancing clinical experience in outpatient residency training.

BACKGROUND: To receive adequate training experience, resident panels in teaching clinics must have a sufficiently diverse patient case-mix. However, case-mix can differ from one resident panel to another, resulting in inconsistent training. METHOD: Encounter data from primary care residency clinics at Massachusetts General Hospital from July 2008 to May 2010 (64 residents and ~3800 patients) were used to characterize patients by gender, age, major disease category (both acute and chronic, e.g., Cardio Acute, Cardio Chronic, etc., for a total of 44 disease categories), and number of disease categories. Imbalance across resident panels was characterized by the standard deviation for disease category, patient panel size, and annual visit frequency. To balance case-mix in resident panels, patient reassignment algorithms were proposed. First, patients were sorted by complexity; then patients were allocated sequentially to the panel with the least overall complexity. Patient reassignment across resident panels was considered under 3 scenarios: 1) within preceptor, 2) within a group of preceptors, and 3) across the entire practice annually. RESULTS: were compared with case-mix (pre-July 2012) and post-July 2012. Results. All 3 reassignment algorithms produced significant reductions in standard deviation of either number of disease categories or diagnoses across residents when compared with baseline (pre-July 2012) and actual July 2012 reassignment. Reassignment across the clinic and group provided the best and second best scenarios, respectively, although both came at the cost of initially reduced patient-preceptor continuity. CONCLUSION: Systematically reallocating patient panels in teaching clinics potentially can improve the consistency and breadth of the educational experience. The method in principle can be extended to any target of health care system reform where there is patient or clinician turnover.

Dynamic allocation of same-day requests in multi-physician primary care practices in the presence of prescheduled appointments.

Appointments in primary care are of two types: 1) prescheduled appointments, which are booked in advance of a given workday; and 2) same-day appointments, which are booked as calls come during the workday. The challenge for practices is to provide preferred time slots for prescheduled appointments and yet see as many same-day patients as possible during regular work hours. It is also important, to the extent possible, to match same-day patients with their own providers (so as to maximize continuity of care). In this paper, we present a mathematical framework (a stochastic dynamic program) for same-day patient allocation in multi-physician practices in which calls for same-day appointments come in dynamically over a workday. Allocation decisions have to be made in the presence of prescheduled appointments and without complete demand information. The objective is to maximize a weighted measure that includes the number of same-day patients seen during regular work hours as well as the continuity provided to these patients. Our experimental design is motivated by empirical data we collected at a 3-provider family medicine practice in Massachusetts. Our results show that the location of prescheduled appointments - i.e. where in the day these appointments are booked - has a significant impact on the number of same-day patients a practice can see during regular work hours, as well as the continuity the practice is able to provide. We find that a 2-Blocks policy which books prescheduled appointments in two clusters - early morning and early afternoon - works very well. We also provide a simple, easily implementable policy for schedulers to assign incoming same-day requests to appointment slots. Our results show that this policy provides near-optimal same-day assignments in a variety of settings.

The impact of case mix on timely access to appointments in a primary care group practice.

At the heart of the practice of primary care is the concept of a physician panel. A panel refers to the set of patients for whose long term, holistic care the physician is responsible. A physician's appointment burden is determined by the size and composition of the panel. Size refers to the number of patients in the panel while composition refers to the case-mix, or the type of patients (older versus younger, healthy versus chronic patients), in the panel. In this paper, we quantify the impact of the size and case-mix on the ability of a multi-provider practice to provide adequate access to its empanelled patients. We use overflow frequency, or the probability that the demand exceeds the capacity, as a measure of access. We formulate problem of minimizing the maximum overflow for a multi-physician practice as a non-linear integer programming problem and establish structural insights that enable us to create simple yet near optimal heuristic strategies to change panels. This optimization framework helps a practice: (1) quantify the imbalances across physicians due to the variation in case mix and panel size, and the resulting effect on access; and (2) determine how panels can be altered in the least disruptive way to improve access. We illustrate our methodology using four test practices created using patient level data from the primary care practice at Mayo Clinic, Rochester, Minnesota. An important advantage of our approach is that it can be implemented in an Excel Spreadsheet and used for aggregate level planning and panel management decisions.