Usability and feasibility of a take-home methadone web-application for opioid treatment program patients: A Small Business Innovation Research mixed methods study.

BACKGROUND: Most patients in opioid treatment programs (OTPs) attend daily for observed dosing. A Stage IA (create and adapt) and a Stage IB (feasibility and pilot) mixed method studies tested a web-application (app) designed to facilitate access to take-home methadone. METHODS: A Stage IA, intervention development study, used qualitative interviews to assess the usability (ease of use) and feasibility (ability to implement) of a take-home methadone app. The Stage IA market research was a two-week test with 96 patient participants from four OTPs. Qualitative interviews were completed with 20 systematically selected individuals who used the take-home app and 20 OTP clinicians (five each from the four OTPs). The Stage IB Small Business Innovation Research (SBIR) study (24 patients and 8 clinicians in a single OTP) included quantitative assessments of the app's usability, acceptability, appropriateness, and feasibility. Thematic analysis coded participant and staff assessments of the take-home app. RESULTS: Stage IA patients (mean age = 41 years; 52 % men, 57 % White) and IB patients (mean age = 38 years, 54 % men, 79 % White) described the app as "easy to use." Compared to unsupervised take-homes, some patients preferred using the take-home app. In Stage IB, patients rated the app highly on standardized measures of usability, acceptability, appropriateness, and feasibility. Clinician ratings were more ambivalent. Patients rated in-clinic dosing as more disruptive than unsupervised take-homes and take-homes using the app. DISCUSSION: A Stage IA study informed the development and maturation of a Stage IB feasibility pilot study. Overall, the take-home app's usability, acceptability, appropriateness, and feasibility were rated positively. Clinical staff ratings were less positive, but individuals commented that using the app a) enhanced patient quality of life, b) provided new tools for counselors, and c) offered competitive advantages. The SBIR award enhanced market research with more complete and systematic data collection and analysis.

Reward Behavior Disengagement, a Neuroeconomic Model-Based Objective Measure of Reward Pathology in Depression: Findings from the EMBARC Trial.

The probabilistic reward task (PRT) has identified reward learning impairments in those with major depressive disorder (MDD), as well as anhedonia-specific reward learning impairments. However, attempts to validate the anhedonia-specific impairments have produced inconsistent findings. Thus, we seek to determine whether the Reward Behavior Disengagement (RBD), our proposed economic augmentation of PRT, differs between MDD participants and controls, and whether there is a level at which RBD is high enough for depressed participants to be considered objectively disengaged. Data were gathered as part of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a double-blind, placebo-controlled clinical trial of antidepressant response. Participants included 195 individuals with moderate to severe MDD (Quick Inventory of Depressive Symptomatology (QIDS-SR) score ≥ 15), not in treatment for depression, and with complete PRT data. Healthy controls (n = 40) had no history of psychiatric illness, a QIDS-SR score < 8, and complete PRT data. Participants with MDD were treated with sertraline or placebo for 8 weeks (stage I of the EMBARC trial). RBD was applied to PRT data using discriminant analysis, and classified MDD participants as reward task engaged (n = 137) or reward task disengaged (n = 58), relative to controls. Reward task engaged/disengaged groups were compared on sociodemographic features, reward-behavior, and sertraline/placebo response (Hamilton Depression Rating Scale scores). Reward task disengaged MDD participants responded only to sertraline, whereas those who were reward task engaged responded to sertraline and placebo (F(1293) = 4.33, p = 0.038). Reward task engaged/disengaged groups did not differ otherwise. RBD was predictive of reward impairment in depressed patients and may have clinical utility in identifying patients who will benefit from antidepressants.

Advanced Drug Delivery Platforms for the Treatment of Oral Pathogens.

The oral cavity is a complex ecosystem accommodating various microorganisms (e.g., bacteria and fungi). Various factors, such as diet change and poor oral hygiene, can change the composition of oral microbiota, resulting in the dysbiosis of the oral micro-environment and the emergence of pathogenic microorganisms, and consequently, oral infectious diseases. Systemic administration is frequently used for drug delivery in the treatment of diseases and is associated with the problems, such as drug resistance and dysbiosis. To overcome these challenges, oral drug delivery systems (DDS) have received considerable attention. In this literature review, the related articles are identified, and their findings, in terms of current therapeutic challenges and the applications of DDSs, especially nanoscopic DDSs, for the treatment of oral infectious diseases are highlighted. DDSs are also discussed in terms of structures and therapeutic agents (e.g., antibiotics, antifungals, antiviral, and ions) that they deliver. In addition, strategies (e.g., theranostics, hydrogel, microparticle, strips/fibers, and pH-sensitive nanoparticles), which can improve the treatment outcome of these diseases, are highlighted.

Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.

The expected value of partial perfect information (EVPPI) provides an upper bound on the value of collecting further evidence on a set of inputs to a cost-effectiveness decision model. Standard Monte Carlo estimation of EVPPI is computationally expensive as it requires nested simulation. Alternatives based on regression approximations to the model have been developed but are not practicable when the number of uncertain parameters of interest is large and when parameter estimates are highly correlated. The error associated with the regression approximation is difficult to determine, while MC allows the bias and precision to be controlled. In this article, we explore the potential of quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) estimation to reduce the computational cost of estimating EVPPI by reducing the variance compared with MC while preserving accuracy. We also develop methods to apply QMC and MLMC to EVPPI, addressing particular challenges that arise where Markov chain Monte Carlo (MCMC) has been used to estimate input parameter distributions. We illustrate the methods using 2 examples: a simplified decision tree model for treatments for depression and a complex Markov model for treatments to prevent stroke in atrial fibrillation, both of which use MCMC inputs. We compare the performance of QMC and MLMC with MC and the approximation techniques of generalized additive model (GAM) regression, Gaussian process (GP) regression, and integrated nested Laplace approximations (INLA-GP). We found QMC and MLMC to offer substantial computational savings when parameter sets are large and correlated and when the EVPPI is large. We also found that GP and INLA-GP were biased in those situations, whereas GAM cannot estimate EVPPI for large parameter sets.

Seasonal changes in diet and chemical defense in the Climbing Mantella frog (Mantella laevigata).

Poison frogs acquire chemical defenses from the environment for protection against potential predators. These defensive chemicals are lipophilic alkaloids that are sequestered by poison frogs from dietary arthropods and stored in skin glands. Despite decades of research focusing on identifying poison frog alkaloids, we know relatively little about how environmental variation and subsequent arthropod availability impacts alkaloid loads in poison frogs. We investigated how seasonal environmental variation influences poison frog chemical profiles through changes in the diet of the Climbing Mantella (Mantella laevigata). We collected M. laevigata females on the Nosy Mangabe island reserve in Madagascar during the wet and dry seasons and tested the hypothesis that seasonal differences in rainfall is associated with changes in diet composition and skin alkaloid profiles of M. laevigata. The arthropod diet of each frog was characterized into five groups (i.e. ants, termites, mites, insect larvae, or 'other') using visual identification and cytochrome oxidase 1 DNA barcoding. We found that frog diet differed between the wet and dry seasons, where frogs had a more diverse diet in the wet season and consumed a higher percentage of ants in the dry season. To determine if seasonality was associated with variation in frog defensive chemical composition, we used gas chromatography / mass spectrometry to quantify alkaloids from individual skin samples. Although the assortment of identified alkaloids was similar across seasons, we detected significant differences in the abundance of certain alkaloids, which we hypothesize reflects seasonal variation in the diet of M. laevigata. We suggest that these variations could originate from seasonal changes in either arthropod leaf litter composition or changes in frog behavioral patterns. Although additional studies are needed to understand the consequences of long-term environmental shifts, this work suggests that alkaloid profiles are relatively robust against short-term environmental perturbations.

Engineering Genetically-Encoded Mineralization and Magnetism via Directed Evolution.

Genetically encoding the synthesis of functional nanomaterials such as magnetic nanoparticles enables sensitive and non-invasive biological sensing and control. Via directed evolution of the natural iron-sequestering ferritin protein, we discovered key mutations that lead to significantly enhanced cellular magnetism, resulting in increased physical attraction of ferritin-expressing cells to magnets and increased contrast for cellular magnetic resonance imaging (MRI). The magnetic mutants further demonstrate increased iron biomineralization measured by a novel fluorescent genetic sensor for intracellular free iron. In addition, we engineered Escherichia coli cells with multiple genomic knockouts to increase cellular accumulation of various metals. Lastly to explore further protein candidates for biomagnetism, we characterized members of the DUF892 family using the iron sensor and magnetic columns, confirming their intracellular iron sequestration that results in increased cellular magnetization.

Extending the Multi-level Method for the Simulation of Stochastic Biological Systems.

The multi-level method for discrete-state systems, first introduced by Anderson and Higham (SIAM Multiscale Model Simul 10(1):146-179, 2012), is a highly efficient simulation technique that can be used to elucidate statistical characteristics of biochemical reaction networks. A single point estimator is produced in a cost-effective manner by combining a number of estimators of differing accuracy in a telescoping sum, and, as such, the method has the potential to revolutionise the field of stochastic simulation. In this paper, we present several refinements of the multi-level method which render it easier to understand and implement, and also more efficient. Given the substantial and complex nature of the multi-level method, the first part of this work reviews existing literature, with the aim of providing a practical guide to the use of the multi-level method. The second part provides the means for a deft implementation of the technique and concludes with a discussion of a number of open problems.

Comparison of preparative reversed phase liquid chromatography and countercurrent chromatography for the kilogram scale purification of crude spinetoram insecticide.

Reversed phase HPLC (RP-HPLC) and high performance countercurrent chromatography (HPCCC) were compared for the pilot scale purification of two semi-synthetic spinosyns, spinetoram-J and spinetoram-L, the major components of the commercial insecticide spinetoram. Two, independently performed, 1 kg, purification campaigns were compared. Each method resulted in the isolation of both components at a purity of >97% and yields for spinetoram-J and spinetoram-L of >93% and ≥ 63% of theoretical, respectively. The HPCCC process produced a 2-fold higher throughput and consumed approximately 70% less solvent than preparative scale RP-HPLC, the volume of product containing fractions from HPCCC amounted to 7% of that produced by HPLC and so required much less post-run processing.

On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods.

We present a case-study on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are self-contained parallel computational devices that can be housed in conventional desktop and laptop computers and can be thought of as prototypes of the next generation of many-core processors. For certain classes of population-based Monte Carlo algorithms they offer massively parallel simulation, with the added advantage over conventional distributed multi-core processors that they are cheap, easily accessible, easy to maintain, easy to code, dedicated local devices with low power consumption. On a canonical set of stochastic simulation examples including population-based Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we nd speedups from 35 to 500 fold over conventional single-threaded computer code. Our findings suggest that GPUs have the potential to facilitate the growth of statistical modelling into complex data rich domains through the availability of cheap and accessible many-core computation. We believe the speedup we observe should motivate wider use of parallelizable simulation methods and greater methodological attention to their design.

An Experimental Evaluation of Remote Sensing-Based Hydrocarbon Measurements: A Comparison to FID Measurements.

The purpose of this study was to intercompare hydrocarbon (HC) measurements performed by a number of different instruments: a gas chromatograph (GC), a flame ionization detector (FID), a fourier transform infrared spectrometer (FTIR), a commercially produced non-dispersive infrared analyzer (NDIR), and two remote sensors. These instruments were used to measure total HC concentrations in a variety of samples, including (1) ten different individual HC species, (2) 12 different vehicle exhaust samples, and (3) three different volatilized fuel samples. The 12 exhaust samples were generated by operating two different vehicles on a dynamometer. Each vehicle was operated at different times with three different fuels. The vehicles were operated fuel rich, i.e., with low air/fuel ratios to encourage elevated exhaust HC levels. Some of the exhaust samples were obtained while operating each vehicle at a stoichiometric air/fuel ratio with one spark plug wire disconnected. To quantify the degree to which the various instruments agreed with the FID, a parameter called the response factor was used, where the response factor was defined as the HC/CO2 ratio measured by each instrument divided by the HC/CO2 ratio measured by the dynamometer bench. Of the various instruments, only the GC yielded response factors that were consistently at or close to one. The other instruments typically had values at or below one. For the ten individual HC species studied, the NDIR and remote sensors obtained response factors between 0.05 and 1.0, with the highest response factors being obtained for the alkanes and the lowest response factors obtained for toluene and ethylene. For the exhaust samples, the NDIR and remote sensors obtained response factors between 0.23 and 0.68. For raw fuel samples, the response factors were between 0.44 and 0.68. NDIR and remote sensor measurements correlated very poorly with total HC in exhaust.