Health Resilience in Arabic-speaking Adult Refugees With Type 2 Diabetes: A Grounded Theory Study During the COVID-19 Pandemic.

OBJECTIVES: This qualitative study aimed to describe the lived experiences of Arabic-speaking refugees in managing their type 2 diabetes mellitus (T2DM) while resettling during the COVID-19 pandemic, and to generate a grounded theory of how resilience is used to facilitate living well while facing multiple health stressors. METHODS: A grounded theory approach was used to conceptualize the dynamic process of resilience in living well with diabetes. Five recently resettled adult refugees with T2DM (2 women and 3 men) participated in unstructured individual interviews in Arabic in New Brunswick, Canada, during the pandemic's second wave (October 2020 to March 2021). Interview data were transcribed and analyzed thematically using open, axial, and core category coding followed by member checking. RESULTS: Participants identified self-reliance as the core driver for decision-making, actions, and interpretations in health management while experiencing unplanned instability. The process was found to be facilitated by 4 distinct constructs: knowledge seeking, positive outlook, self-care, and creativity. CONCLUSIONS: The substantive model derived from this study supports a strengths-based approach to clinical assessment and care of refugees with T2DM, notably during disrupted access to primary and preventive services due to forced resettlement and pandemic mitigation measures. More research is needed to increase understanding of how self-reliance can be optimized in resilience-promoting interventions to facilitate diabetes management among populations in posttraumatic circumstances.

Concurrent Identification and Characterization of Protein Structure and Continuous Internal Dynamics with REDCRAFT.

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.

Structure Calculation and Reconstruction of Discrete-State Dynamics from Residual Dipolar Couplings.

Residual dipolar couplings (RDCs) acquired by nuclear magnetic resonance (NMR) spectroscopy are an indispensable source of information in investigation of molecular structures and dynamics. Here, we present a comprehensive strategy for structure calculation and reconstruction of discrete-state dynamics from RDC data that is based on the singular value decomposition (SVD) method of order tensor estimation. In addition to structure determination, we provide a mechanism of producing an ensemble of conformations for the dynamical regions of a protein from RDC data. The developed methodology has been tested on simulated RDC data with ±1 Hz of error from an 83 residue α protein (PDB ID 1A1Z ) and a 213 residue α/ß protein DGCR8 (PDB ID 2YT4 ). In nearly all instances, our method reproduced the structure of the protein including the conformational ensemble to within less than 2 Å. On the basis of our investigations, arc motions with more than 30° of rotation are identified as internal dynamics and are reconstructed with sufficient accuracy. Furthermore, states with relative occupancies above 20% are consistently recognized and reconstructed successfully. Arc motions with a magnitude of 15° or relative occupancy of less than 10% are consistently unrecognizable as dynamical regions within the context of ±1 Hz of error.