Nemo-like kinase (NLK), an evolutionarily conserved serine/threonine kinase, is highly expressed in the brain, but its function in the adult brain remains not well understood. In this study, we identify NLK as an interactor of huntingtin protein (HTT). We report that NLK levels are significantly decreased in HD human brain and HD models. Importantly, overexpression of NLK in the striatum attenuates brain atrophy, preserves striatal DARPP32 levels and reduces mutant HTT (mHTT) aggregation in HD mice. In contrast, genetic reduction of NLK exacerbates brain atrophy and loss of DARPP32 in HD mice. Moreover, we demonstrate that NLK lowers mHTT levels in a kinase activity-dependent manner, while having no significant effect on normal HTT protein levels in mouse striatal cells, human cells and HD mouse models. The NLK-mediated lowering of mHTT is associated with enhanced phosphorylation of mHTT. Phosphorylation defective mutation of serine at amino acid 120 (S120) abolishes the mHTT-lowering effect of NLK, suggesting that S120 phosphorylation is an important step in the NLK-mediated lowering of mHTT. A further mechanistic study suggests that NLK promotes mHTT ubiquitination and degradation via the proteasome pathway. Taken together, our results indicate a protective role of NLK in HD and reveal a new molecular target to reduce mHTT levels.
This study aims to develop and validate an artificial intelligence model based on deep learning to predict early hematoma enlargement (HE) in patients with intracerebral hemorrhage. A total of 1,899 noncontrast computed tomography (NCCT) images of cerebral hemorrhage patients were retrospectively analyzed to establish a predicting model and 1,117 to validate the model. And a total of 118 patients with intracerebral hemorrhage were selected based on inclusion and exclusion criteria so as to validate the value of the model for clinical prediction. The baseline noncontrast computed tomography images within 6 h of intracerebral hemorrhage onset and the second noncontrast computed tomography performed at 24 ± 3 h from the onset were used to evaluate the prediction of intracerebral hemorrhage growth. In validation dataset 1, the AUC was 0.778 (95% CI, 0.768–0.786), the sensitivity was 0.818 (95% CI, 0.790–0.843), and the specificity was 0.601 (95% CI, 0.565–0.632). In validation dataset 2, the AUC was 0.780 (95% CI, 0.761–0.798), the sensitivity was 0.732 (95% CI, 0.682–0.788), and the specificity was 0.709 (95% CI, 0.658–0.759). The sensitivity of intracerebral hemorrhage hematoma expansion as predicted by an artificial intelligence imaging system was 89.3%, with a specificity of 77.8%, a positive predictive value of 55.6%, a negative predictive value of 95.9%, and a Yoden index of 0.671, which were much higher than those based on the manually labeled noncontrast computed tomography signs. Compared with the existing prediction methods through computed tomographic angiography (CTA) image features and noncontrast computed tomography image features analysis, the artificial intelligence model has higher specificity and sensitivity in the prediction of early hematoma enlargement in patients with intracerebral hemorrhage.
The effective conjugation of ortho and ortho-alt-para-arylene ethynylenes, with appropriately positioned pyridine and pyrazine heterocycles, increases upon binding to Ag(I) and Pd(II) cations. Significant bathochromic shifts in the electronic spectra, witnessed upon introduction of these metal bridges, are consistent with enhanced electron delocalization in the unsaturated backbone. Control studies suggest that this electronic behavior is attributable exclusively (in the case of Ag(I)) or partially (in the case of Pd(II)) to conformational restrictions of the conjugated backbones.
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