ABSTRACT
INTRODUCTION: We conducted a secondary, real-world clinical assessment of a randomized controlled trial to determine how a glaucoma medication adherence intervention impacted the clinical outcomes of participants at 12 months post randomization. Participants included veterans at a VA eye clinic with medically treated glaucoma who reported poor adherence, and their companions if applicable. METHODS: The treatment group received a glaucoma education session with drop administration instruction, and virtual reminders from a "smart bottle" (AdhereTech) for their eye drops. The control group received a general eye health class, and the smart bottle with the reminder function turned off. Medical chart extraction determined if participants in each group experienced visual field progression, additional glaucoma medications, or a recommendation for surgery or laser due to inadequate intraocular pressure (IOP) control over the 12 months following randomization. The main outcome measure was disease progression, defined as visual field progression or escalation of glaucoma therapy, in the 12 months following randomization. RESULTS: Thirty-six vs. 32% of the intervention (n=100) vs. control (n=100) group, respectively experienced disease intensification. There was no difference between the intervention and control groups in terms of intensification, [Intervention vs. Control Group Odds Ratio: 1.20, 95% Confidence Interval: (0.67, 2.15)], including when age, race, and disease severity were accounted for in the logistic regression model. Those whose study dates included time during the COVID-19 pandemic were evenly distributed between groups. CONCLUSIONS: A multi-faceted intervention that improved medication adherence for glaucoma for 6 months did not affect the clinical outcomes measured at 12 months post randomization. Twelve months may not be long enough to see the clinical effect of this intervention or more than 6 months of intervention are needed.
ABSTRACT
Internal dynamics of proteins can play a critical role in the biological function of some proteins. Several well documented instances have been reported such as MBP, DHFR, hTS, DGCR8, and NSP1 of the SARS-CoV family of viruses. Despite the importance of internal dynamics of proteins, there currently are very few approaches that allow for meaningful separation of internal dynamics from structural aspects using experimental data. Here we present a computational approach named REDCRAFT that allows for concurrent characterization of protein structure and dynamics. Here, we have subjected DHFR (PDB-ID 1RX2), a 159-residue protein, to a fictitious, mixed mode model of internal dynamics. In this simulation, DHFR was segmented into 7 regions where 4 of the fragments were fixed with respect to each other, two regions underwent rigid-body dynamics, and one region experienced uncorrelated and melting event. The two dynamical and rigid-body segments experienced an average orientational modification of 7° and 12° respectively. Observable RDC data for backbone C'-N, N-HN, and C'-HN were generated from 102 uniformly sampled frames that described the molecular trajectory. The structure calculation of DHFR with REDCRAFT by using traditional Ramachandran restraint produced a structure with 29 Å of structural difference measured over the backbone atoms (bb-rmsd) over the entire length of the protein and an average bb-rmsd of more than 4.7 Å over each of the dynamical fragments. The same exercise repeated with context-specific dihedral restraints generated by PDBMine produced a structure with bb-rmsd of 21 Å over the entire length of the protein but with bb-rmsd of less than 3 Å over each of the fragments. Finally, utilization of the Dynamic Profile generated by REDCRAFT allowed for the identification of different dynamical regions of the protein and the recovery of individual fragments with bb-rmsd of less than 1 Å. Following the recovery of the fragments, our assembly procedure of domains (larger segments consisting of multiple fragments with a common dynamical profile) correctly assembled the four fragments that are rigid with respect to each other, categorized the two domains that underwent rigid-body dynamics, and identified one dynamical region for which no conserved structure could be defined. In conclusion, our approach was successful in identifying the dynamical domains, recovery of structure where it is meaningful, and relative assembly of the domains when possible.