No abstract

We compared the sleep quality indices between patients with mild cognitive impairment (MCI) and normal elderly subjects and analyzed the effects of sleep characteristics on cognitive functions. Cases of MCI patients (320 persons, MCI group) and 630 normal elderly with matched age, gender, and level of education (control group) were enrolled in this study. The Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep characteristics. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess cognitive function. There were 110 (34.3%) and 170 (27%) cases with sleep disorders in the MCI and control groups, respectively (P < 0.01). There was a significant difference of total PQSI scores between the two groups, and the scores of sleep duration (factor III) and habitual sleep efficiency (factor IV) in the MCI group were significantly lower than those in the control group. Total PQSI scores negatively correlated with MoCA scores and MMSE scores. MoCA scores negatively correlated with scores of the sleep latency (factor II), sleep duration (factor III), and habitual sleep efficiency (factor IV), while MMSE scores negatively correlated with scores of factor III and factor IV. The scores of attention and calculation, reading and language understanding, and visuospatial function (MMSE), and also of visuospatial/executive function, attention, and clock-drawing test (MoCA) in MCI patients without sleep disorders were significantly higher than those in MCI patients with such disorders. The incidence of sleep disorders is higher in patients with MCI, compared with normal elderly. Effects of sleep disorders on cognitive functions are mainly reflected in the state of attention, and visuospatial/executive function.

Background and purposeThe diagnosis of tuberculous meningitis (TBM) is difficult due to the lack of sensitive methods. Identification of TBM-specific biomarkers in the cerebrospinal fluid (CSF) may help diagnose and improve our understanding of TBM pathogenesis.Patients and methodsOf the 112 suspected patients with TBM prospectively enrolled in the study, 32 patients with inconclusive diagnosis, non-infectious meningitis, and long-term treatment with hormones and immunosuppressants were excluded. The expression of 8 proteins in the CSF was analyzed using ELISA in 22 patients with definite TBM, 18 patients with probable TBM, and 40 patients with non-TBM.ResultsSignificant differences in the expression of 7 proteins were detected between the TBM and non-TBM groups (P &lt; 0.01). Unsupervised hierarchical clustering (UHC) analysis revealed a disease-specific profile consisting of 7 differentially expressed proteins for TBM diagnosis, with an accuracy of 82.5% (66/80). Logistic regression with forward stepwise analysis indicated that a combination of 3 biomarkers (APOE_APOAI_S100A8) showed a better ability to discriminate TBM from patients with non-TBM [area under the curve (AUC) = 0.916 (95%CI: 0.857–0.976)], with a sensitivity of 95.0% (95%CI: 83.1–99.4%) and a specificity of 77.5% (95%CI: 61.5–89.2%).ConclusionOur results confirmed the potential ability of CSF proteins to distinguish TBM from patients with non-TBM and provided a useful panel for the diagnosis of TBM.