Patient-based outcome assessment instruments in acupuncture research.

BACKGROUND: Outcome assessment can support the therapeutic process by providing a way to track symptoms and functionality over time, providing insights to clinicians and patients, as well as offering a common language to discuss patient behavior/functioning. OBJECTIVES: In this article, we examine the patient-based outcome assessment (PBOA) instruments that have been used to determine outcomes in acupuncture clinical research and highlight measures that are feasible, practical, economical, reliable, valid, and responsive to clinical change. The aims of this review were to assess and identify the commonly available PBOA measures, describe a framework for identifying appropriate sets of measures, and address the challenges associated with these measures and acupuncture. Instruments were evaluated in terms of feasibility, practicality, economy, reliability, validity, and responsiveness to clinical change. METHODS: This study was a systematic review. A total of 582 abstracts were reviewed using PubMed (from inception through April 2009). RESULTS: A total of 582 citations were identified. After screening of title/abstract, 212 articles were excluded. From the remaining 370 citations, 258 manuscripts identified explicit PBOA; 112 abstracts did not include any PBOA. The five most common PBOA instruments identified were the Visual Analog Scale, Symptom Diary, Numerical Pain Rating Scales, SF-36, and depression scales such as the Beck Depression Inventory. CONCLUSIONS: The way a questionnaire or scale is administered can have an effect on the outcome. Also, developing and validating outcome measures can be costly and difficult. Therefore, reviewing the literature on existing measures before creating or modifying PBOA instruments can significantly reduce the burden of developing a new measure.

Thermodynamic analysis of ligand-induced changes in protein thermal unfolding applied to high-throughput determination of ligand affinities with extrinsic fluorescent dyes.

The quantification of protein-ligand interactions is essential for systems biology, drug discovery, and bioengineering. Ligand-induced changes in protein thermal stability provide a general, quantifiable signature of binding and may be monitored with dyes such as Sypro Orange (SO), which increase their fluorescence emission intensities upon interaction with the unfolded protein. This method is an experimentally straightforward, economical, and high-throughput approach for observing thermal melts using commonly available real-time polymerase chain reaction instrumentation. However, quantitative analysis requires careful consideration of the dye-mediated reporting mechanism and the underlying thermodynamic model. We determine affinity constants by analysis of ligand-mediated shifts in melting-temperature midpoint values. Ligand affinity is determined in a ligand titration series from shifts in free energies of stability at a common reference temperature. Thermodynamic parameters are obtained by fitting the inverse first derivative of the experimental signal reporting on thermal denaturation with equations that incorporate linear or nonlinear baseline models. We apply these methods to fit protein melts monitored with SO that exhibit prominent nonlinear post-transition baselines. SO can perturb the equilibria on which it is reporting. We analyze cases in which the ligand binds to both the native and denatured state or to the native state only and cases in which protein:ligand stoichiometry needs to treated explicitly.

Multivalent benzoboroxole functionalized polymers as gp120 glycan targeted microbicide entry inhibitors.

Microbicides are women-controlled prophylactics for sexually transmitted infections. The most important class of microbicides target HIV-1 and contain antiviral agents formulated for topical vaginal delivery. Identification of new viral entry inhibitors that target the HIV-1 envelope is important because they can inactivate HIV-1 in the vaginal lumen before virions can come in contact with CD4+ cells in the vaginal mucosa. Carbohydrate binding agents (CBAs) demonstrate the ability to act as entry inhibitors due to their ability to bind to glycans and prevent gp120 binding to CD4+ cells. However, as proteins they present significant challenges in regard to economical production and formulation for resource-poor environments. We have synthesized water-soluble polymer CBAs that contain multiple benzoboroxole moieties. A benzoboroxole-functionalized monomer was synthesized and incorporated into linear oligomers with 2-hydroxypropylmethacrylamide (HPMAm) at different feed ratios using free radical polymerization. The benzoboroxole small molecule analogue demonstrated weak affinity for HIV-1BaL gp120 by SPR; however, the 25 mol % functionalized benzoboroxole oligomer demonstrated a 10-fold decrease in the K(D) for gp120, suggesting an increased avidity for the multivalent polymer construct. High molecular weight polymers functionalized with 25, 50, and 75 mol % benzoboroxole were synthesized and tested for their ability to neutralize HIV-1 entry for two HIV-1 clades and both R5 and X4 coreceptor tropism. All three polymers demonstrated activity against all viral strains tested with EC(50)s that decrease from 15000 nM (1500 microg mL(-1)) for the 25 mol % functionalized polymers to 11 nM (1 microg mL(-1)) for the 75 mol % benzoboroxole-functionalized polymers. These polymers exhibited minimal cytotoxicity after 24 h exposure to a human vaginal cell line.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.