The relationship between labial soft tissue changes and jumping spaces after immediate implant placement and restoration in the anterior maxilla: A prospective study

Oral implants have been increasingly used in the treatment of edentulous patients or those with dentition defects due to reliable treatment procedure and favorable long-term prognosis. We investigated the changes of labial soft tissue contours with different jumping spaces after immediate implant placement and restoration (IIPR) in the maxillary esthetic area and also provided a long-term stability measurement for the changing trend of soft tissue contour. All patients had been separated into three groups based on the jumping space: group A (horizontal defect dimension [HDD] 2 mm), group B (2 mm 3 mm) and the digital impressions were obtained in the first, third, and sixth month after the operation. The changes of gingival mucosa levels, the average thickness of soft tissue contour volume, and the linear change of submarginal level decreased gradually across the three groups, with the largest change of submarginal level being at 5mm. The size of the jumping space was moderately negatively correlated with the level and average thickness of gingival mucosa and the linear changes of 3 mm and 5 mm under gingival margin, while there was no significant correlation with pink esthetic score (PES) and the linear change of the 1 mm under the gingival margin. Generally, IIPR of upper anterior teeth can achieve esthetic satisfaction, and the level of soft tissue around the implant can be well preserved

RPS23RG1 modulates tau phosphorylation and axon outgrowth through regulating p35 proteasomal degradation

Tau蛋白病（Tauopathies）是由过度磷酸化的tau蛋白聚集形成神经纤维缠结为特征的一类神经退行性疾病，包括阿尔茨海默病（Alzheimer’s disease, AD）、进行性核上性麻痹（Progressive superanuclear palsy, PSP）、额颞叶痴呆（Frontotemporal dementia, FTD）等。随着全球社会结构的老龄化，tau蛋白病患者比率迅速增加，给个人和社会带来巨大的经济及精神负担。厦门大学神经科学研究所张云武教授团队最新发现RPS23RG1（RR1）的胞内羧基端区域能够与Cdk5激酶的激活蛋白p35的氨基端相互作用，介导p35的膜定位并影响其泛素化降解，从而调控在tau蛋白异常磷酸化过程中发挥重要作用的Cdk5激酶的活性。团队研究表明RPS23RG1通过其胞内羧基端与p35相互作用，介导p35膜结合和降解，从而抑制Cdk5活性，平衡tau磷酸化水平，促进轴突生长。此外，RPS23RG1的跨膜区与腺苷酸环化酶AC相互作用，抑制GSK3-β活性，同样控制tau过度磷酸化。提示RPS23RG1是改善tau过度磷酸化水平及治疗tau蛋白病的潜在靶点。 厦门大学医学院神经科学研究所博士后赵东栋为该研究第一作者，张云武教授为通讯作者。【Abstract】Tauopathies are a group of neurodegenerative diseases characterized by hyperphosphorylation of the microtubule-binding protein, tau, and typically feature axon impairment and synaptic dysfunction. Cyclin-dependent kinase5 (Cdk5) is a major tau kinase and its activity requires p35 or p25 regulatory subunits. P35 is subjected to rapid proteasomal degradation in its membrane-bound form and is cleaved by calpain under stress to a stable p25 form, leading to aberrant Cdk5 activation and tau hyperphosphorylation. The type Ib transmembrane protein RPS23RG1 has been implicated in Alzheimer’s disease (AD). However, physiological and pathological roles for RPS23RG1 in AD and other tauopathies are largely unclear. Herein, we observed retarded axon outgrowth, elevated p35 and p25 protein levels, and increased tau phosphorylation at major Cdk5 phosphorylation sites in Rps23rg1 knockout (KO) mice. Both downregulation of p35 and the Cdk5 inhibitor roscovitine attenuated tau hyperphosphorylation and axon outgrowth impairment in Rps23rg1 KO neurons. Interestingly, interactions between the RPS23RG1 carboxyl-terminus and p35 amino-terminus promoted p35 membrane distribution and proteasomal degradation. Moreover, P301L tau transgenic (Tg) mice showed increased tau hyperphosphorylation with reduced RPS23RG1 levels and impaired axon outgrowth. Overexpression of RPS23RG1 markedly attenuated tau hyperphosphorylation and axon outgrowth defects in P301L tau Tg neurons. Our results demonstrate the involvement of RPS23RG1 in tauopathy disorders, and implicate a role for RPS23RG1 in inhibiting tau hyperphosphorylation through homeostatic p35 degradation and suppression of Cdk5 activation. Reduced RPS23RG1 levels in tauopathy trigger aberrant Cdk5-p35 activation, consequent tau hyperphosphorylation, and axon outgrowth impairment, suggesting that RPS23RG1 may be a potential therapeutic target in tauopathy disorders.This work was supported by grants from National Key Research and Development Program of China (2016YFC1305903 and 2018YFC2000400 to Y-wZ), National Natural Science Foundation of China (81771377, U1705285, 91332112, and 81225008 to Y-wZ), Fundamental Research Funds for the Central Universities (20720180049 to Y-wZ), the Fujian Provincial Health Commission-Education Department Joint Tackling Plan (WKJ2016-2-18 to F-rL), and Postdoctoral Science Foundation of China (2020M671948 to DZ)

Kron reduction based on node ordering optimization for distribution network dispatching with flexible loads

Kron reduction is a general tool of network simplification for flow calculation. With a growing number of flexible loads appearing in distribution networks, traditional Kron reduction cannot be widely used in control and scheduling due to the elimination of controllable and variable load buses. Therefore, this paper proposes an improved Kron reduction based on node ordering optimization whose principles guarantee that all the boundary nodes are retained eventually after eliminating the first row and the first column in every step according to the order, thereby making it possible to take full advantage of their potential to meet different requirements in power system calculation and dispatching. The proposed method is verified via simulation models of IEEE 5-bus and 30-bus systems through illustrating the dynamic consistency of the output active power of the generator nodes and the power flow data of preserved nodes before and after reduction.Published versionThis work is funded by the National Natural Science Foundation of China (No. 61773137), the Natural Science Foundation of Shandong Province (Nos. ZR2019MF030 and ZR2018PEE018) and the China Postdoctoral Science Foundation (No. 2018M641830)

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

Abstract Background The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM. Aim Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free‐text medical record data and structured laboratory data to predict LM in postoperative CRC patients. Methods We used a robust dataset of 1463 patients and leveraged state‐of‐the‐art natural language processing (NLP) and machine learning techniques to construct a two‐layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two‐tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free‐text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score‐based nomogram using the top 13 valid predictors identified in our study. Results The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs. Conclusion This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision‐making