The usefulness of the Korean version of modified Mini-Mental State Examination (K-mMMSE) for dementia screening in community dwelling elderly people

BACKGROUND: We assessed whether the Korean version of modified Mini-Mental State Examination (K-mMMSE) has improved performance as a screening test for cognitive impairment or dementia in a general population compared with the Korean Mini-Mental State Examination (K-MMSE). METHODS: Screening interviews were conducted with people aged 65 and over in Noam-dong, Namwon-city, Jeonbuk province. There were 522 community participants, of whom 235 underwent clinical and neuropsychological examination for diagnosis of dementia and Cognitive Impairment No Dementia (CIND). Sensitivity, specificity and areas under the receiver operating characteristic (ROC) curves for the K-mMMSE and the K-MMSE were the main outcome measures. RESULTS: Cronbach's alpha for the K-mMMSE was 0.91, compared with 0.84 for the K-MMSE. The areas under the ROC curves in identifying all levels of CIND or dementia were 0.91 for the K-mMMSE and 0.89 for the K-MMSE (P < 0.05). For the K-mMMSE, the optimal cut-off score for a diagnosis of CIND was 69/70, which had a sensitivity of 0.86 and a specificity of 0.79, while, for a diagnosis of dementia, the optimal cut-off score of 59/60 had a sensitivity of 0.91 and a specificity of 0.78. The K-mMMSE also had a high test-retest reliability (r = 0.89). CONCLUSION: Our findings indicate that the K-mMMSE is more reliable and valid than the K-MMSE as a cognitive screen in a population based study of dementia. Considering the test characteristics, the K-MMSE and modified version are expected to be optimally used in clinical and epidemiologic fields

Finding Kinematics-Driven Latent Neural States From Neuronal Population Activity for Motor Decoding

While intracortical brain-machine interfaces (BMIs) demonstrate feasibility to restore mobility to people with paralysis, it is still challenging to maintain high-performance decoding in clinical BMIs. One of the main obstacles for high-performance BMI is the noise-prone nature of traditional decoding methods that connect neural response explicitly with physical quantity, such as velocity. In contrast, the recent development of latent neural state model enables a robust readout of large-scale neuronal population activity contents. However, these latent neural states do not necessarily contain kinematic information useful for decoding. Therefore, this study proposes a new approach to finding kinematics-dependent latent factors by extracting latent factors&apos; kinematics-dependent components using linear regression. We estimated these components from the population activity through nonlinear mapping. The proposed kinematics-dependent latent factors generate neural trajectories that discriminate latent neural states before and after the motion onset. We compared the decoding performance of the proposed analysis model with the results from other popular models. They are factor analysis (FA), Gaussian process factor analysis (GPFA), latent factor analysis via dynamical systems (LFADS), preferential subspace identification (PSID), and neuronal population firing rates. The proposed analysis model results in higher decoding accuracy than do the others (&gt;17% improvement on average). Our approach may pave a new way to extract latent neural states specific to kinematic information from motor cortices, potentially improving decoding performance for online intracortical BMIs

Association between the Delta Estimated Glomerular Filtration Rate and the Prevalence of Monoclonal Gammopathy of Undetermined Significance in Korean Males

Background. We investigated the association between the reduction in the estimated glomerular filtration rate (eGFR) and the prevalence of monoclonal gammopathy of undetermined significance (MGUS) in Korean males. Methods. We enrolled 723 healthy Korean males. Serum creatinine concentration, serum electrophoresis, serum immunofixation, and the serum free light chain assay were performed. We calculated delta eGFR per year (ΔeGFR/yr). The prevalence of MGUS was compared based on the ΔeGFR/yr and age group. Results. Thirteen (1.8%) of 723 participants exhibited the monoclonal band on serum immunofixation. Prevalence of MGUS by age group was 0.00% (0/172 for 40 years), 1.63% (6/367 for 60 years), and 3.80% (7/184 for &gt;60 years). The median decrease in ΔeGFR/yr was 5.3%. The prevalence of MGUS in participants in their 50s with &gt;5.3% decline in ΔeGFR/yr was significantly higher than those with &lt;5.3% decrease in ΔeGFR/yr (3.16% versus 0.00%; = 0.049). The prevalence of MGUS in participants in their 50s with &gt;5.3% decrease in ΔeGFR/yr was similar to that of healthy males in their 60s. Conclusion. Using the rate of reduction in ΔeGFR/yr in healthy Korean males who had their serum creatinine level checked regularly may increase the MGUS detection rate in clinical practice

Decoding Kinematic Information From Primary Motor Cortex Ensemble Activities Using a Deep Canonical Correlation Analysis

The control of arm movements through intracortical brain-machine interfaces (BMIs) mainly relies on the activities of the primary motor cortex (M1) neurons and mathematical models that decode their activities. Recent research on decoding process attempts to not only improve the performance but also simultaneously understand neural and behavioral relationships. In this study, we propose an efficient decoding algorithm using a deep canonical correlation analysis (DCCA), which maximizes correlations between canonical variables with the non-linear approximation of mappings from neuronal to canonical variables via deep learning. We investigate the effectiveness of using DCCA for finding a relationship between M1 activities and kinematic information when non-human primates performed a reaching task with one arm. Then, we examine whether using neural activity representations from DCCA improves the decoding performance through linear and non-linear decoders: a linear Kalman filter (LKF) and a long short-term memory in recurrent neural networks (LSTM-RNN). We found that neural representations of M1 activities estimated by DCCA resulted in more accurate decoding of velocity than those estimated by linear canonical correlation analysis, principal component analysis, factor analysis, and linear dynamical system. Decoding with DCCA yielded better performance than decoding the original FRs using LSTM-RNN (6.6 and 16.0% improvement on average for each velocity and position, respectively; Wilcoxon rank sum test, p &lt; 0.05). Thus, DCCA can identify the kinematics-related canonical variables of M1 activities, thus improving the decoding performance. Our results may help advance the design of decoding models for intracortical BMIs

Deficiency of peroxiredoxin 2 exacerbates angiotensin II-induced abdominal aortic aneurysm

Abdominal aortic aneurysm: Potential enzyme biomarker identified An enzyme with antioxidant properties may provide a biomarker and therapeutic agent to help treat abdominal aortic aneurysm (AAA). AAA involves the structural deterioration of the aorta through chronic inflammation and oxidative stress, and can trigger life-threatening artery rupture. An antioxidant enzyme called peroxiredoxin 2 (PRDX2) is increased in patients with ruptures, but whether its role in AAA is beneficial or detrimental is unclear. Goo Taeg Oh at the Ewha Womans University in Seoul, Jong-Gil Park at the Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea, and co-workers examined the effect of PRDX2 on AAA progression. PRDX2 suppressed structural damage in mice, limiting artery dilation and protein degradation. Loss of PRDX2 accelerated AAA development. Measuring levels of PRDX2 may indicate AAA severity in patients, while boosting the enzyme could repair aortic damage