Predicting mild cognitive impairment progression to Alzheimer's disease based on machine learning analysis of cortical morphological features.

PURPOSE: To establish a model for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) using morphological features extracted from a joint analysis of voxel-based morphometry (VBM) and surface-based morphometry (SBM). METHODS: We analyzed data from 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, 32 of whom progressed to AD during a 4-year follow-up period and were classified as the progression group, while the remaining 89 were classified as the non-progression group. Patients were divided into a training set (n = 84) and a testing set (n = 37). Morphological features measured by VBM and SBM were extracted from the cortex of the training set and dimensionally reduced to construct morphological biomarkers using machine learning methods, which were combined with clinical data to build a multimodal combinatorial model. The model's performance was evaluated using receiver operating characteristic curves on the testing set. RESULTS: The Alzheimer's Disease Assessment Scale (ADAS) score, apolipoprotein E (APOE4), and morphological biomarkers were independent predictors of MCI progression to AD. The combinatorial model based on the independent predictors had an area under the curve (AUC) of 0.866 in the training set and 0.828 in the testing set, with sensitivities of 0.773 and 0.900 and specificities of 0.903 and 0.747, respectively. The number of MCI patients classified as high-risk for progression to AD was significantly different from those classified as low-risk in the training set, testing set, and entire dataset, according to the combinatorial model (P < 0.05). CONCLUSION: The combinatorial model based on cortical morphological features can identify high-risk MCI patients likely to progress to AD, potentially providing an effective tool for clinical screening.

Cas9 exo-endonuclease eliminates chromosomal translocations during genome editing.

The mechanism underlying unwanted structural variations induced by CRISPR-Cas9 remains poorly understood, and no effective strategy is available to inhibit the generation of these byproducts. Here we find that the generation of a high level of translocations is dependent on repeated cleavage at the Cas9-targeting sites. Therefore, we employ a strategy in which Cas9 is fused with optimized TREX2 to generate Cas9TX, a Cas9 exo-endonuclease, which prevents perfect DNA repair and thereby avoids repeated cleavage. In comparison with CRISPR-Cas9, CRISPR-Cas9TX greatly suppressed translocation levels and enhanced the editing efficiency of single-site editing. The number of large deletions associated with Cas9TX was also reduced to very low level. The application of CRISPR-Cas9TX for multiplex gene editing in chimeric antigen receptor T cells nearly eliminated deleterious chromosomal translocations. We report the mechanism underlying translocations induced by Cas9, and propose a general strategy for reducing chromosomal abnormalities induced by CRISPR-RNA-guided endonucleases.