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"Nelson, Todd"
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Bringing Stalin back in : memory politics and the creation of a useable past in Putin's Russia
While Joseph Stalin is commonly reviled in the West as a murderous tyrant who committed egregious human rights abuses against his own people, in Russia he is often positively viewed as the symbol of Soviet-era stability and state power. How can there be such a disparity in perspectives? Utilizing an ethnographic approach, extensive interview data, and critical discourse analysis, this book examines the ways that the political elite in Russia are able to control and manipulate historical discourse about the Stalin period in order to advance their own political objectives. Appropriating the Stalinist discourse, they minimize or ignore outright crimes of the Soviet period, and instead focus on positive aspects of Stalin's rule, especially his role in leading the Soviet Union to victory in the Second World War. Advancing the concepts of \"preventive\" and \"complex\" co-optation, this book analyzes how elites in Russia inhibit the emergence of groups that espouse alternative narratives, while promoting message-friendly groups that are in line with the Kremlin's agenda. Bringing the resources of the state to bear, the Russian elite are able to co-opt multiple avenues of discourse formulation and dissemination. Elite-sponsored discourse positions Stalin as the symbol of a strong, centralized state that was capable of great achievements, despite great cost, enabling favorably portrayals of Stalin as part of a tradition of harsh but effective rulers in Russian history, such as Peter the Great. This strong state discourse is used to legitimize the return of authoritarianism in Russia today.
BOOK
Improved balance in middle-aged adults after 8 weeks of a modified version of Otago Exercise Program: A randomized controlled trial
The objective of this randomized controlled trial was to examine dynamic balance changes (reach distance) in middle-aged adults using the Y Balance Test (YBT) following 8 weeks of home-based exercise program adapted from the Otago Exercise Program (OEP). Fifty-two healthy middle-aged adults with mean age of 54.4±5.4 years and body mass index of 27.7±5.7 kg/m.sup.2 were randomly assigned into either the exercise group (a modification of the Otago Exercise Program, that consisted of home-based balance and strength exercises) or the non-exercise group (continuation of usual lifestyle) by having the participants select a paper from a sealed envelope. The YBT was used to measure participants' dynamic balance in the right and left anterior (RA, LA), posteromedial (RPM, LPM), and posterolateral (RPL, LPL) directions. The outcome in this trial was reach distance (cm). There was a significant group by time interaction in terms of reach distance for all directions (p<0.05, [eta].sup.2 ranged from 0.06 to 0.20). In the exercise group, results of the repeated measures analysis of variance (ANOVA) showed significant improvements in the reach distance in all the directions (p<0.001). In contrast, the non-exercise group had significant difference only in the left posterolateral direction (p = 0.009). Participants in the exercise group achieved significantly greater reach distance (cm) (95% confidence interval (CI)) for RA[(2.8, 0.4 to 5.2), p = 0.023]; LA[(3.2, 0.9 to 5.6), p = 0.008]; RPM[(4.0, 1.0 to 7.9), p = 0.046]; LPM[(5.8,1.3 to 10.3), p = 0.013]; RPL[(7.6, 2.6 to 12.6), p = 0.003]; and LPL[(4.2, 0.3 to 8.2), p = 0.035]. The modified version of OEP appears to be effective in improving parameters of dynamic balance in the middle-aged adult population. The improvements in YBT reach distance in the exercise group are indicative of the significance of performing balance and strength exercises regularly for this population.
Journal Article
Risk prediction of mild cognitive impairment using electronic health record data
INTRODUCTION Mild cognitive impairment (MCI) is underdiagnosed by primary care providers (PCPs), with detection rates as low as 6%–15%. Predictive models support the identification of individuals at risk, enabling timely intervention. METHODS This retrospective study was conducted on 271,054 cognitively unimpaired and 14,501 confirmed MCI cohorts from electronic health records. A machine learning model was developed with a data‐driven variable selection approach based on demographics and comorbidities. RESULTS From 101 variables, 26 were chosen for the final model, achieving an overall area under the curve (AUC) of 86%. Age‐stratified AUCs were 79.1% (40–49), 77.0% (50–64), 76.9% (65–79), and 74.4% (≥80), showing the highest predictive performance in younger age groups. DISCUSSION Demographic factors and comorbidities can serve as effective predictors for the risk of MCI. The model demonstrates strong predictive performance and assists as a triage tool for PCPs, facilitating the identification of individuals with MCI for early treatment. Highlights Predictive algorithms using electronic health records (EHRs) are useful for identifying individuals at risk for mild cognitive impairment (MCI) to triage for further clinical evaluation. A machine learning model was developed using EHR data to predict those at risk for MCI. The model identified 26 variables that were able to distinguish the MCI from non‐MCI cohorts. The model accurately detected MCI across the cohort (area under the curve [AUC] = 86%) and trended best for younger age groups (AUC was 77%, 77%, and 74% in 50–64, 65–79, and ≥80 age groups, respectively). Implementation of a triage tool could be used to detect MCI across aging patient populations sooner, leading to a timelier diagnosis, intervention, and treatment management.
Journal Article
A Multimodal Magnetic Resonance Imaging Study on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Feasibility and Clinical Correlation
Background/Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a neurological disorder characterized by post-exertional malaise. Despite its clinical relevance, the disease mechanisms of ME/CFS are not fully understood. The previous studies targeting brain function or metabolites have been inconclusive in understanding ME/CFS complexity. We combined single-voxel magnetic resonance spectroscopy (SV-MRS) and functional magnetic resonance imaging (fMRI). Our objectives were to examine the feasibility of the multimodal MRI protocol, identify possible differences between ME/CFS and healthy controls (HCs), and relate MRI findings with clinical symptoms. Methods: We enrolled 18 female ME/CFS participants (mean age: 39.7 ± 12.0 years) and five HCs (mean age: 45.6 ± 14.5 years). SV-MRS spectra were acquired from three voxels of interest: the anterior cingulate gyrus (ACC), brainstem (BS), and left dorsolateral prefrontal cortex (L-DLPFC). Whole-brain fMRI used n-back task testing working memory and executive function. The feasibility was assessed as protocol completion rate and time. Group differences in brain metabolites and fMRI activation between ME/CFS and HCs were compared and correlated with behavioral and symptom severity measurements. Results: The completion rate was 100% regardless of participant group without causing immediate fatigue. ME/CFS appeared to show a higher N-Acetylaspartate in L-DLPFC compared to HCs (OR = 8.49, p = 0.040), correlating with poorer fatigue, pain, and sleep quality scores (p’s = 0.001–0.015). An increase in brain activation involving the frontal lobe and the brainstem was observed in ME/CFS compared to HCs (Z > 3.4, p’s < 0.010). Conclusions: The study demonstrates the feasibility of combining MRS and fMRI to capture neurochemical and neurophysiological features of ME/CFS in female participants. Further research with larger cohorts of more representative sampling and follow-ups is needed to validate these apparent differences between ME/CFS and HCs.
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