Cyclin-dependent kinase 2 (CDK2) complex is significantly over-activated in many cancers. While it makes CDK2 an attractive target for cancer therapy, most inhibitors against CDK2 are ATP competitors that are either nonspecific or highly toxic, and typically fail clinical trials. One alternative approach is to develop non-ATP competitive inhibitors; they disrupt interactions between CDK2 and either its partners or substrates, resulting in specific inhibition of CDK2 activities. In this report, we identify two potential druggable pockets located in the protein-protein interaction interface (PPI) between CDK2 and Cyclin A. To target the potential druggable pockets, we perform a LIVS in silico screening of a library containing 1925 FDA approved drugs. Using this approach, homoharringtonine (HHT) shows high affinity to the PPI and strongly disrupts the interaction between CDK2 and cyclins. Further, we demonstrate that HHT induces autophagic degradation of the CDK2 protein via tripartite motif 21 (Trim21) in cancer cells, which is confirmed in a leukemia mouse model and in human primary leukemia cells. These results thus identify an autophagic degradation mechanism of CDK2 protein and provide a potential avenue towards treating CDK2-dependent cancers.
Neddylation, a posttranslational protein modification, refers to the specific conjugation of NEDD8 to substrates, which is of great significance to various biological processes. Besides members of the cullin protein family, other key proteins can act as a substrate for neddylation modification, which remarkably influences neurodevelopment and neurodegenerative diseases. Normal levels of protein neddylation contribute to nerve growth, synapse strength, neurotransmission, and synaptic plasticity, whereas overactivation of protein neddylation pathways lead to apoptosis, autophagy of neurons, and tumorigenesis. Furthermore, impaired neddylation causes neurodegenerative diseases. These facts suggest that neddylation may be a target for treatment of these diseases. This review focuses on the current understanding of neddylation function in neurodevelopment as well as neurodegenerative diseases. Meanwhile, the recent view that different level of neddylation pathway may contribute to the opposing disease progression, such as neoplasms and Alzheimer's disease, is discussed. The review also discusses neddylation inhibitors, which are currently being tested in clinical trials. However, potential drawbacks of these drugs are noted, which may benefit the development of new pharmaceutical strategies in the treatment of nervous system diseases.
Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and methods: We obtained data on 314534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the Renal Disease Treatment Information System. IDH was defined as a systolic blood pressure drop≥20 mmHg, a mean arterial pressure drop≥10 mmHg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation (SHAP) was used to explain the contribution of each variable to the best predictive model. Results: A total of 3906 patients and 314534 dialysis sessions were included, of which 142237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval[CI], 0.811-0.813), 0.748 (95% CI, 0.747-0.749), and 0.743 (95% CI, 0.742-0.744), respectively. Conclusion: Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.
In order to realize the automatic cutting of arch shed pillars, an automatic cuttage device for an arch shed pillar with force feedback was designed in this study. First, the wind resistance of the arch shed was simulated and analyzed using ANSYS, and the cuttage depth of the arch shed pillar was determined. According to the environment for the cuttage operation of the arch shed pillar and the agronomic requirements, such as the arch shed span, arch shed height, and cuttage depth, the function, structure, and basic design parameters of the arch shed automatic cuttage device were determined. Then, to reduce the damage rate of the pillar and achieve equal-depth cuttage, a force feedback system for the actuator of the cuttage device was constructed to estimate the cuttage resistance and depth in real time. To reduce the impact of the starting and stopping of each motor in the actuator, trajectory planning of the execution end in the pillar transfer stage was performed in the Cartesian coordinate system. The motion law of portal trajectory based on the Láme curve was analyzed, and MATLAB simulations were used to solve the relevant motion parameters. In addition, the modality of key components of the cuttage device was simulated and analyzed by using the SOLIDWORKS simulation plug-in. Finally, the experimental prototype was constructed according to the simulation results. The simulation and field cuttage experiments showed that the cuttage device produced equal-depth cuttage for the arch shed pillar, where the depth of the arch shed pillar was 10 cm, the average cuttage time of a single pillar was 6.2 s, and the error of the cuttage depth was ±0.5 cm in wet soil. The operation of the device was stable, as evidenced by the smooth and mutation-free operation trajectory and speed curve of the execution end. The results of the modal experiment suggest that resonance would not occur during the operation for resonance frequencies between 303 Hz and 565 Hz. This arch shed pillar automatic cuttage device has an optimal operation performance and meets the agronomic requirements of arch shed pillar cuttage.
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