Search

Research on clinical data sets of Alzheimer’s disease can predict and develop early intervention treatment. Missing data is a common problem in medical research. Failure to deal with more missing data will reduce the efficiency of the test, resulting in information loss and result bias. To address these issues, this paper designs and implements the missing data interpolation method of mixed interpolation according to columns by combining the four methods of mean interpolation, regression interpolation, support vector machine (SVM) interpolation, and multiple interpolation. By comparing the effects of the mixed interpolation method with the above four interpolation methods and giving the comparison results, the experiment shows that the results of the mixed interpolation method under different data missing rates have better performance in terms of root mean square error (RMSE), mean absolute error (MSE), and error rate, which proves the effectiveness of the interpolation mechanism. The characteristics of different variables might lead to different interpolation strategy choices, and column-by-column mixed interpolation can dynamically select the best method according to the difference of features. To a certain extent, it selects the best method suitable for each feature and improves the interpolation effect of the data set as a whole, which is beneficial to the clinical study of Alzheimer’s disease. In addition, in the processing of missing data, a combination of deletion method and interpolation method is adopted with reference to expert knowledge. Compared with the direct interpolation method, the data set obtained by this method is more accurate.

OBJECTIVE Increased permeability and changes in gut microbiota contributed to the pathogenesis of Alzheimer's disease (AD). Zonulin is a key modulator that regulates intestinal barrier function. Peripheral platelet alterations have been involved in AD pathology. C-type lectin-like receptor 2 (CLEC-2) is a receptor on the platelet surface for activation. The purpose of this study was to determine zonulin and CLEC-2 levels in mild cognitive impairment (MCI) and AD, and investigate the relationship between zonulin and CLEC-2. METHODS In this study, CLEC-2 and zonulin levels were measured using ELISA assay in 110 AD patients, 110 MCI patients, and 110 non-demented control subjects. RESULTS Increased CLEC-2 and zonulin levels were observed in MCI and AD patients. Furthermore, AD patients had higher CLEC-2 and zonulin levels compared with MCI patients. In addition, CLEC-2 levels were positively correlated with zonulin levels, after adjusting confounding factors (r = .592, P

Background. The gut microbiota is involved in the occurrence and development of chronic liver diseases. Zonulin is considered a marker of intestinal permeability. The purpose of this study was to assess zonulin levels in patients with chronic hepatitis B (CHB), HBV-associated liver cirrhosis (LC), and HBV-associated hepatocellular carcinoma (HCC). Materials and Methods. The study population consisted of 90 HBV-associated HCC patients, 90 HBV-associated LC patients, 90 CHB patients, and 90 healthy subjects. Serum levels of zonulin and AFP were determined. The diagnostic accuracy of each marker was evaluated using receiver operating characteristic (ROC) curve analysis (AUC). Results. Serum zonulin levels were significantly higher in patients with HCC than in patients with LC or CHB or healthy subjects (p<0.001). Moreover, the zonulin levels were increased in the advanced stage of LC and HCC. ROC curve analysis revealed that serum zonulin could be used to differentiate CHB from cirrhosis. In addition, the combination of zonulin and AFP exhibited a significantly larger AUC compared with zonulin or AFP alone. Conclusions. Serum zonulin levels were significantly increased both in LC and in HCC and correlated with the advanced stage of LC and HCC. Moreover, the combination of zonulin and AFP confers significant benefit to diagnostic accuracy in differentiating LC from HCC.