Increasing student engagement with course content in graduate public health education: A pilot randomized trial of behavioral nudges.

Digital advances in the learning space have changed the contours of student engagement as well as how it is measured. Learning management systems and other learning technologies now provide information about student behaviors with course materials in the form of learning analytics. In the context of a large, integrated and interdisciplinary Core curriculum course in a graduate school of public health, this study undertook a pilot randomized controlled trial testing the effect of providing a "behavioral nudge" in the form of digital images containing specific information derived from learning analytics about past student behaviors and performance. The study found that student engagement varied significantly from week to week, but nudges linking coursework completion to assessment grade performance did not significantly change student engagement. While the a priori hypotheses of this pilot trial were not upheld, this study yielded significant findings that can guide future efforts to increase student engagement. Future work should include a robust qualitative assessment of student motivations, testing of nudges that tap into these motivations and a richer examination of student learning behaviors over time using stochastic analyses of data from the learning management system.

A Real-World Prospective Study of the Safety and Effectiveness of the Loop Open Source Automated Insulin Delivery System.

Objective: To evaluate the safety and effectiveness of the Loop Do-It-Yourself automated insulin delivery system. Research Design and Methods: A prospective real-world observational study was conducted, which included 558 adults and children (age range 1-71 years, mean HbA1c 6.8% ± 1.0%) who initiated Loop either on their own or with community-developed resources and provided data for 6 months. Results: Mean time-in-range 70-180 mg/dL (TIR) increased from 67% ± 16% at baseline (before starting Loop) to 73% ± 13% during the 6 months (mean change from baseline 6.6%, 95% confidence interval [CI] 5.9%-7.4%; P < 0.001). TIR increased in both adults and children, across the full range of baseline HbA1c, and in participants with both high- and moderate-income levels. Median time <54 mg/dL was 0.40% at baseline and changed by -0.05% (95% CI -0.09% to -0.03%, P < 0.001). Mean HbA1c was 6.8% ± 1.0% at baseline and decreased to 6.5% ± 0.8% after 6 months (mean difference = -0.33%, 95% CI -0.40% to -0.26%, P < 0.001). The incidence rate of reported severe hypoglycemia events was 18.7 per 100 person-years, a reduction from the incidence rate of 181 per 100 person-years during the 3 months before the study. Among the 481 users providing Loop data at 6 months, median continuous glucose monitoring use was 96% (interquartile range [IQR] 91%-98%) and median time Loop modulating basal insulin was at least 83% (IQR 73%-88%). Conclusions: The Loop open source system can be initiated with community-developed resources and used safely and effectively by adults and children with type 1 diabetes.

A molecular analysis of sand fly blood meals in a visceral leishmaniasis endemic region of northwestern Ethiopia reveals a complex host-vector system.

BACKGROUND: Visceral leishmaniasis (VL, or "kala-azar") is a major cause of disability and death, especially in East Africa. Its vectors, sand flies (Diptera: Psychodidae: Phlebotominae), are poorly controlled and guarded against in these regions, owing in part to a lack of understanding about their feeding behavior. METHODS: A total of 746 freshly fed female sand flies were collected in five population centers in Kafta Humera (northwestern Ethiopia), where VL is endemic. Flies were collected from habitats that ranged from inside houses to open fields, using light traps and sticky traps. Sources of sand fly blood meals were identified using enzyme-linked immunosorbent assays (ELISA) and DNA amplification with reverse-line blot analysis (PCR-RLB); 632 specimens were screened using ELISA, 408 of which had identifiable blood meals, and 114 were screened using PCR-RLB, 53 of which yielded identifications. Fly species determinations were based on morphology, and those specimens subjected to PCR-RLB were also screened for Leishmania parasites using conventional PCR to amplify the nuclear marker ITS1 (internal transcribed spacer 1) with Leishmania-specific primers. RESULTS: More than three-fourths of all sand flies collected were Phlebotomus orientalis, and the remaining portion was comprised of nine other species. Nearly two-thirds of P. orientalis specimens were collected at village peripheries. The most common blood source for all flies was donkey (33.9% of all identifications), followed by cow (24.2%), human (17.6%), dog (11.8%), and goat or sheep (8.6%); mixtures of blood meals from different sources were found in 28.2% of all flies screened. Unidentified blood meals, presumably from wildlife, not domestic animals, were significantly higher in farm fields. Leishmania parasites were not detected in any of the 114 flies screened, not surprising given an expected infection rate of 1-5 out of 1,000. Meals that included a mixture of human and cow blood were significantly more frequent relative to all cow meals than human blood meals were to non-cow meals, suggesting a zoopotentiative interaction between cows and humans in this system. CONCLUSIONS: Habitat and host preferences of sand fly vectors in Kafta Humera confirmed the finding of previous reports that the main vector in the region, Phlebotomus orientalis, is a highly opportunistic feeder that prefers large animals and is most commonly found at village peripheries. These results were similar to those of a previous study conducted in a nearby region (Tahtay Adiabo), except for the role of cattle on the prevalence of human blood meals. Preliminary examinations of blood meal data from different settings point to the need for additional surveys and field experiments to understand the role of livestock on biting risks.

Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials.

OBJECTIVE: This study evaluated the association of time in range (TIR) of 70-180 mg/dL (3.9-10 mmol/L) with the development or progression of retinopathy and development of microalbuminuria using the Diabetes Control and Complications Trial (DCCT) data set in order to validate the use of TIR as an outcome measure for clinical trials. RESEARCH DESIGN AND METHODS: In the DCCT, blood glucose concentrations were measured at a central laboratory from seven fingerstick samples (seven-point testing: pre- and 90-min postmeals and at bedtime) collected during 1 day every 3 months. Retinopathy progression was assessed every 6 months and urinary microalbuminuria development every 12 months. Proportional hazards models were used to assess the association of TIR and other glycemic metrics, computed from the seven-point fingerstick data, with the rate of development of microvascular complications. RESULTS: Mean TIR of seven-point profiles for the 1,440 participants was 41 ± 16%. The hazard rate of development of retinopathy progression was increased by 64% (95% CI 51-78), and development of the microalbuminuria outcome was increased by 40% (95% CI 25-56), for each 10 percentage points lower TIR (P < 0.001 for each). Results were similar for mean glucose and hyperglycemia metrics. CONCLUSIONS: Based on these results, a compelling case can be made that TIR is strongly associated with the risk of microvascular complications and should be an acceptable end point for clinical trials. Although hemoglobin A1c remains a valuable outcome metric in clinical trials, TIR and other glycemic metrics-especially when measured with continuous glucose monitoring-add value as outcome measures in many studies.

Publisher Correction: Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring.

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

Does Time-in-Range Matter? Perspectives From People With Diabetes on the Success of Current Therapies and the Drivers of Improved Outcomes.

IN BRIEF After assessing patient perspectives on the success of current diabetes therapies and the factors that have the greatest impact on daily life, we show that time-in-range is a crucial outcome for people with diabetes and that current therapies are falling short on this metric. We also show that patients feel significant stress and worry, and they believe they are falling short in diet, exercise, and weight maintenance. In addition, they believe diet and exercise and in-range blood glucose are the biggest drivers of improved diabetes management and mindset. Together, these findings support the need for therapies that improve outcomes including and beyond A1C.

Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma.

The computational repositioning of existing drugs represents an appealing avenue for identifying effective compounds to treat diseases with no FDA-approved pharmacotherapies. Here we present the largest meta-analysis to date of differential gene expression in human vestibular schwannoma (VS), a debilitating intracranial tumor, and use these data to inform the first application of algorithm-based drug repositioning for this tumor class. We apply an open-source computational drug repositioning platform to gene expression data from 80 patient tumors and identify eight promising FDA-approved drugs with potential for repurposing in VS. Of these eight, mifepristone, a progesterone and glucocorticoid receptor antagonist, consistently and adversely affects the morphology, metabolic activity, and proliferation of primary human VS cells and HEI-193 human schwannoma cells. Mifepristone treatment reduces VS cell viability more significantly than cells derived from patient meningiomas, while healthy human Schwann cells remain unaffected. Our data recommend a Phase II clinical trial of mifepristone in VS.

Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning.

Computational drug repositioning methods can scalably nominate approved drugs for new diseases, with reduced risk of unforeseen side effects. The majority of methods eschew individual-level phenotypes despite the promise of biomarker-driven repositioning. In this study, we propose a framework for discovering serendipitous interactions between drugs and routine clinical phenotypes in cross-sectional observational studies. Key to our strategy is the use of a healthy and nondiabetic population derived from the National Health and Nutrition Examination Survey, mitigating risk for confounding by indication. We combine complementary diagnostic phenotypes (fasting glucose and glucose response) and associate them with prescription drug usage. We then sought confirmation of phenotype-drug associations in unidentifiable member claims data from the Aetna Insurance company using a retrospective self-controlled case analysis approach. We identify bupropion as a plausible glucose lowering agent, suggesting that surveying otherwise healthy individuals in cross-sectional studies can discover new drug repositioning hypotheses that have applicability to longitudinal clinical practice.

A review of validation strategies for computational drug repositioning.

Repositioning of previously approved drugs is a promising methodology because it reduces the cost and duration of the drug development pipeline and reduces the likelihood of unforeseen adverse events. Computational repositioning is especially appealing because of the ability to rapidly screen candidates in silico and to reduce the number of possible repositioning candidates. What is unclear, however, is how useful such methods are in producing clinically efficacious repositioning hypotheses. Furthermore, there is no agreement in the field over the proper way to perform validation of in silico predictions, and in fact no systematic review of repositioning validation methodologies. To address this unmet need, we review the computational repositioning literature and capture studies in which authors claimed to have validated their work. Our analysis reveals widespread variation in the types of strategies, predictions made and databases used as 'gold standards'. We highlight a key weakness of the most commonly used strategy and propose a path forward for the consistent analytic validation of repositioning techniques.

Erratum: Stigma in People With Type 1 or Type 2 Diabetes. Clinical Diabetes 2017;35:27-34. DOI: 10.2337/cd16-0020.

[This corrects the article on p. 27 in vol. 35, PMID: 28144043.].

A standard database for drug repositioning.

Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).

Stigma in People With Type 1 or Type 2 Diabetes.

IN BRIEF This study quantitatively measures diabetes stigma and its associated psychosocial impact in a large population of U.S. patients with type 1 or type 2 diabetes using an online survey sent to 12,000 people with diabetes. A majority of respondents with type 1 (76%) or type 2 (52%) diabetes reported that diabetes comes with stigma. Perceptions of stigma were significantly higher among respondents with type 1 diabetes than among those with type 2 diabetes, with the highest rate in parents of children with type 1 diabetes (83%) and the lowest rate in people with type 2 diabetes who did not use insulin (49%). Our results suggest that a disturbingly high percentage of people with diabetes experience stigma, particularly those with type 1 or type 2 diabetes who are on intensive insulin therapy. The experience of stigma disproportionately affects those with a higher BMI, higher A1C, and poorer self-reported blood glucose control, suggesting that those who need the most help are also the most affected by stigma.

MeSHDD: Literature-based drug-drug similarity for drug repositioning.

OBJECTIVE: Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms. METHODS: We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs. RESULTS: We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin. CONCLUSION: Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).

Serotonin promotes exploitation in complex environments by accelerating decision-making.

BACKGROUND: Fast responses can provide a competitive advantage when resources are inhomogeneously distributed. The nematode Caenorhabditis elegans was shown to modulate locomotion on a lawn of bacterial food in serotonin (5-HT)-dependent manners. However, potential roles for serotonergic signaling in responding to food discovery are poorly understood. RESULTS: We found that 5-HT signaling in C. elegans facilitates efficient exploitation in complex environments by mediating a rapid response upon encountering food. Genetic or cellular manipulations leading to deficient serotonergic signaling resulted in gradual responses and defective exploitation of a patchy foraging landscape. Physiological imaging revealed that the NSM serotonergic neurons responded acutely upon encounter with newly discovered food and were key to rapid responses. In contrast, the onset of responses of ADF serotonergic neurons preceded the physical encounter with the food. The serotonin-gated chloride channel MOD-1 and the ortholog of mammalian 5-HT1 metabotropic serotonin receptors SER-4 acted in synergy to accelerate decision-making. The relevance of responding rapidly was demonstrated in patchy environments, where the absence of 5-HT signaling was detrimental to exploitation. CONCLUSIONS: Our results implicate 5-HT in a novel form of decision-making, demonstrate its fitness consequences, suggest that NSM and ADF act in concert to modulate locomotion in complex environments, and identify the synergistic action of a channel and a metabotropic receptor in accelerating C. elegans decision-making.

ksRepo: a generalized platform for computational drug repositioning.

BACKGROUND: Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema. RESULTS: ksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo's performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods. CONCLUSIONS: We present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.

aRrayLasso: a network-based approach to microarray interconversion.

UNLABELLED: Robust conversion between microarray platforms is needed to leverage the wide variety of microarray expression studies that have been conducted to date. Currently available conversion methods rely on manufacturer annotations, which are often incomplete, or on direct alignment of probes from different platforms, which often fail to yield acceptable genewise correlation. Here, we describe aRrayLasso, which uses the Lasso-penalized generalized linear model to model the relationships between individual probes in different probe sets. We have implemented aRrayLasso in a set of five open-source R functions that allow the user to acquire data from public sources such as Gene Expression Omnibus, train a set of Lasso models on that data and directly map one microarray platform to another. aRrayLasso significantly predicts expression levels with similar fidelity to technical replicates of the same RNA pool, demonstrating its utility in the integration of datasets from different platforms. AVAILABILITY AND IMPLEMENTATION: All functions are available, along with descriptions, at https://github.com/adam-sam-brown/aRrayLasso. CONTACT: chirag_patel@hms.harvard.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Patient Perspectives on Biosimilar Insulin.

Given that a new wave of biosimilar insulins will likely enter the market in coming years, it is important to understand patient perspectives on these biosimilars. A survey (N = 3214) conducted by the market research company dQ&A, which maintains a 10 000-patient panel of people with type 1 or type 2 diabetes in roughly equal measure, investigated these perspectives. The survey asked whether patients would switch to a hypothetical less expensive biosimilar insulin that was approved by their provider. Approximately 66% of respondents reported that they would "definitely" or "likely" use a biosimilar insulin, while 17% reported that they were "unlikely" to use or would "definitely not use" such a product. Type 2 diabetes patients demonstrated slightly more willingness to use biosimilars than type 1 diabetes patients. Common patient concerns included whether biosimilars would be as effective as reference products (~650 respondents), whether side effect profiles would deviate from those of reference products (~220 respondents), and the design of the delivery device (~50 respondents). While cost savings associated with biosimilar insulins could increase patient uptake, especially among patients without health insurance (some recent estimates suggest that biosimilars will come at a substantial discount), patients may still need assurance that a cheaper price tag is not necessarily associated with substandard quality. Overall, the dQ&A survey indicates that the majority of patients are willing to consider biosimilar insulins, but manufacturers will need to work proactively to address and assuage patient concerns regarding efficacy, safety, drug administration, and other factors.