In recent years, increasing attention has been given to the identification of the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Brain neuroimaging techniques have been widely used to support the classification or prediction of MCI. The present study combined magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose PET (FDG-PET), and 18F-florbetapir PET (florbetapir-PET) to discriminate MCI converters (MCI-c, individuals with MCI who convert to AD) from MCI non-converters (MCI-nc, individuals with MCI who have not converted to AD in the follow-up period) based on the partial least squares (PLS) method. Two types of PLS models (informed PLS and agnostic PLS) were built based on 64 MCI-c and 65 MCI-nc from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the three-modality informed PLS model achieved better classification accuracy of 81.40%, sensitivity of 79.69%, and specificity of 83.08% compared with the single-modality model, and the three-modality agnostic PLS model also achieved better classification compared with the two-modality model. Moreover, combining the three modalities with clinical test score (ADAS-cog), the agnostic PLS model (independent data: florbetapir-PET; dependent data: FDG-PET and MRI) achieved optimal accuracy of 86.05%, sensitivity of 81.25%, and specificity of 90.77%. In addition, the comparison of PLS, support vector machine (SVM), and random forest (RF) showed greater diagnostic power of PLS. These results suggested that our multimodal PLS model has the potential to discriminate MCI-c from the MCI-nc and may therefore be helpful in the early diagnosis of AD.

In this study, a highly sensitive upconversion fluorescence (FL) biosensor was developed for the detection of organophosphorus pesticides (OPs) based on an acetylcholinesterase (AChE) modulated FL "off−on−off" strategy. The luminescence of synthesized UCNPs could be quenched strongly by Cu 2+ due to an energy transfer effect. Upon addition of AChE and acetylthiocholine (ATCh), the enzymatic hydrolysate (thiocholine) could seize Cu 2+ from UCNPs-Cu 2+ mixture, resulting in the quenched FL triggered on. OPs could irreversibly impede the activity of AChE, which caused the formation of thiocholine to decrease, thus, reduced the recovery of FL. Under the optimum conditions, a linear detection range from 0.1 to 50 ng/mL was achieved for the representative OPs (diazinon) with LOD of 0.05 ng/mL. Furthermore, the ability of the biosensor to detect OPs was also confirmed in adulterated environmental and agricultural samples. In validation analysis, the proposed sensor showed satisfactory results (p > 0.05) with GC−MS.

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