The activation of Cdk5 by p35 plays a pivotal role in a multitude of nervous system activities ranging from neuronal differentiation to degeneration. A fraction of Cdk5 and p35 localizes in the nucleus where Cdk5-p35 exerts its functions via protein phosphorylation, and p35 displays a dynamic localization between the cytoplasm and the nucleus. Here, we examined the nuclear import properties of p35. In nuclear import assays, p35 was actively transported into the nuclei of digitonin-permeabilized HeLa cells and cortical neurons by cytoplasmic carriermediated mechanisms. Importin-, importin-5, and importin-7 were identified to import p35 into the nuclei via a direct interaction with it. An N-terminal region of p35 was defined to interact with the above importins, serving as a nuclear localization signal. Finally, we show that the nuclear localization of p35 does not require the association of Cdk5. Furthermore, Cdk5 and importin-/5/7 are mutually exclusive in binding to p35. These results suggest that p35 employs pathways distinct from that used by Cdk5 for transport to the nucleus.
Objectives:
To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage IB–IIA cervical squamous cell carcinoma (CSCC).
Methods:
A total of 153 stage IB–IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort ( n = 102) and a validation cohort ( n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson’s correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model.
Results:
Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status ( p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95% CI, 0.782 – 0.924) and the validation group (AUC 0.870; 95% CI, 0.747 – 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone.
Conclusion:
The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes.
Advances in knowledge:
A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.
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