Reliability and validity of the quality of life-Alzheimer disease Chinese version.

OBJECTIVES: To evaluate the psychometric properties of the quality of life-Alzheimer disease (QOL-AD) Chinese version in patients with dementia in mainland, China and to compare patient and caregiver reports of patient QOL. METHODS: The QOL-AD Chinese version was established following standard guidelines for cross-cultural adaptation of measures. The reliability was assessed by internal consistency and test-retest reliability. The validity included construct and convergent validity. A paired Student t test was performed to determine differences between patient reports and caregiver reports. RESULTS: The reliability for both patient and caregiver reports on the QOL-AD was good (Cronbach α values of 0.66 and 0.87; intraclass correlation coefficients of 0.84 and 0.90 for patient and caregiver reports, respectively). The validity of patient and caregiver reports was supported by correlation with domain measures. All of the multitrait-multimethod correlations demonstrated a high relationship between patient reports and caregiver reports, ranging from 0.26 to 0.55. The caregivers rated patient QOL significantly higher than did the patients. CONCLUSION: The findings support the use of the Chinese version of the QOL-AD as a generic instrument to measure QOL of AD in mainland China. Further research is needed to clarify the relationship between patient and caregiver reports of patient QOL.

Multi-state Markov model in outcome of mild cognitive impairments among community elderly residents in Mainland China.

BACKGROUND: Although knowledge of established risk factors for Alzheimer's disease (AD) can logically contribute to the search for predictors of the progression of cognitive impairment, it has not yet been firmly established where in the cognitive impairment process these risk factors exert their effects and how to predict quantitatively for the progression of mild cognitive impairments (MCI) to AD. This study aimed to determine whether known risk factors increased the risk of progression from MCI to AD and to make prediction based on transition probabilities. METHODS: Based on ten examinations of 600 community-dwelling MCI residents and cognitive assessments to classify individuals into MCI, global impairment, and AD, a multi-state Markov Cox's regression model was used and the hazard ratios with their confidence intervals and transition probabilities were estimated. RESULTS: Multivariate analysis showed that gender, age, and hypertension were statistically significant predictors of transition from MCI to global impairment; age, education, and reading statistically influenced transition from global impairment to MCI; gender, age, hypertension, diabetes, and apolipoprotein E geneε4 status were statistically associated with transition from global impairment to AD. Subjects at MCI were more likely (67%) to remain in that cognitive state at the next cognitive assessment than to transition to cognitive deterioration. For global impairment, probability of remaining in the same state was only 18% and that of forward transition was three times more likely than that of backward transition. CONCLUSIONS: Known risk factors influenced differently for different transitions. Transition from global impairment was more likely to worsen to severe cognitive deterioration than transition from MCI.

[Determination of microelements from different types of grass in the habitat of Przewalski's gazelle by sealed microwave digestion ICP-AES].

For the mixture forge from different types of grassland, trace elements including copper, manganese, iron, zinc and molybdenum were separately determined by ICP-AES using high pressure system-sealed microwave digestion in the habitat of the Przewalski's gazelle in Qinghai Hudong in summer (mid-June), autumn (mid-September) and winter (mid-December). The samples of mixture forage were digested with HNO3-H2O2 acids system. The detection limits of the method for the elements varied from 0.002 to 0.008 microg x g(-1), with relative standard deviations between 0.13% and 4.29%, and the spike recovery ratios for them were in the range from 94.0% to 101.30%. This method was simple, sensitive and precise compared with conventional method, which will provide scientific basis for the research on gazelles habitat condition.

The long noncoding RNA MIR210HG promotes tumor metastasis by acting as a ceRNA of miR-1226-3p to regulate mucin-1c expression in invasive breast cancer.

BACKGROUND: Long noncoding RNAs have been known to be involved in multiple types of malignancies, including invasive breast cancer (IBC). This study aimed to explore the role of long noncoding RNAs in IBC and elucidate the potential molecular mechanisms. METHODS: Using TCGA microarray data analysis, we identified a long noncoding RNA, MIR210HG, highly expressed in IBC. Kaplan-Meier method and the log-rank test were used for survival analysis. The gain-of-function experiments were performed to assess the function of MIR210HG in IBC invasion and migration in both in vitro and in vivo settings. Bioinformatic analysis as well as luciferase reporter assay, rescue experiments and western blot assay revealed the mode of action of MIR210HG. RESULTS: The aberrantly enhanced MiR210HG expression predicted poor prognosis and lower survival rate. Knockdown of MiR210HG suppressed IBC cell invasion and metastasis both in vitro and in vivo. MiR-1226-3p was identified and validated to be the target miRNA of MiR210HG. Furthermore, MiR210HG functions as a competing endogenous RNAs (ceRNA) which sponges miR-1226-3p, therefore upregulates the expression of mucin1 (MUC1-C). CONCLUSIONS: Our study demonstrated that MiR210HG sponges miR-1226-3p to facilitate invasive breast cancer cell invasion and metastasis by regulating mucin-1c and EMT pathway, revealing the oncogenic role of MiR210HG in IBC cells.

[Multi-state model in the evaluation of outcome on mild cognitive impairment to Alzheimer's disease].

OBJECTIVE: The aim of this study was to introduce the multi-state Markov model for the prediction of mild cognitive impairment (MCI) to Alzheimer's disease (AD) and to find out the related factors for AD prevention and early intervention among the elderly. METHODS: MCI, moderate to severe cognitive impairment, and AD were defined as state 1, 2 and 3, respectively. A three-state homogeneous model with discrete states and discrete times from data on six follow-up visits was constructed to explore factors for various progressive stages from MCI to AD. Transition probability and survival curve were made after the model fit assessment. RESULTS: At the level of 0.05, data from the multivariate analysis showed that gender (HR=1.23, 95%CI: 1.12-1.38), age (HR=1.37, 95%CI: 1.07-1.72), hypertension (HR=1.54, 95%CI: 1.31-2.19) were statistically significant for the transition from state 1 to state 2, while age (HR=0.78, 95%CI: 0.69-0.98), education level (HR=1.35, 95%CI: 1.09-1.86) and reading (HR=1.20, 95%CI: 1.01-1.41) were statistically significant for transition from state 2 to state 1, and gender (HR=1.59, 95%CI: 1.33-1.89), age (HR=1.33, 95%CI: 1.02-1.64), hypertension (HR=1.22, 95%CI: 1.11-1.43), diabetes (HR=1.52, 95%CI: 1.12-2.00), ApoEe4 (HR=1.44, 95%CI: 1.09-1.68) were statistically significant for transition from state 2 to state 3. Based on the fitted model, the three-year transition probabilities during each state at average covariate level were estimated. CONCLUSION: To delay the disease progression of MCI, phase by phase prevention measures could be adopted based on the main factors of each stage. Multi-state Markov model could imitate the natural history of disease and showed great advantage in dynamically evaluating the development of chronic diseases with multi-states and multi-factors.

[Multi-state Markov model in expressing the outcome of mild cognitive impairment among community-based elderly residents].

OBJECTIVE: To introduce the Multi-state Markov model in studying the outcome prediction of mild cognitive impairment (MCI). METHODS: Based on the intelligence quotient (IQ) changes that reflecting the trends in cognitive function in the patients under follow-up program, we constructed a four states model and used Multi-state Markov model to analyze the patients. RESULTS: Among 600 MCI patients, there were 174 (29.0%) males and 426 (71.0%) females, with age range of 65-90 years-old (average 69.7 ± 6.6). For univariate analysis, gender, age, education level, marital status, smoking, household income, cerebral hemorrhage, hypertension, high cholesterol, diabetes, LDL-C, SBP and DBP were found to be associated with cognitive function. For multivariate analysis, female, older age, cerebral hemorrhage and higher SBP were shown to be the risk factors for transition from the state of cognitive stability to the state of severe deterioration, and their coefficients were 0.762, 0.366, 0.885, and 0.069, respectively. Age (0.038) could influence the transition from the state of cognitive stability to slight deterioration. Higher education level was shown to be the protective factor for these transitions (-0.219 and -0.297). Transition intensity from the state of cognitive stability to the state of slight and severe deterioration was 1.2 times that of transition to the state of improving. Transition intensity from state of slight deterioration to cognitive stability was 11.4 times that of transition to severe deterioration. CONCLUSION: Multi-state Markov model was an effective tool in dealing with longitudinal data.