Bufalin Induces Mitochondria-Dependent Apoptosis in Pancreatic and Oral Cancer Cells by Downregulating hTERT Expression via Activation of the JNK/p38 Pathway

Bufalin, a digoxin-like active component of the traditional Chinese medicine Chan Su, exhibits potent antitumor activities in many human cancers. Bufalin induces mitochondria-dependent apoptosis in cancer cells, but the detailed molecular mechanisms are largely unknown. hTERT, the catalytic subunit of telomerase, protects against mitochondrial damage by binding to mitochondrial DNA and reducing mitochondrial ROS production. In the present study, we investigated the effects of bufalin on the cell viability, ROS production, DNA damage, and apoptosis of CAPAN-2 human pancreatic and CAL-27 human oral cancer cells. Bufalin reduced CAPAN-2 and CAL-27 cell viability with IC50 values of 159.2 nM and 122.6 nM, respectively. The reduced cell viability was accompanied by increased ROS production, DNA damage, and apoptosis and decreased expression of hTERT. hTERT silencing in CAPAN-2 and CAL-27 cells by siRNA resulted in increased caspase-9/-3 cleavage and DNA damage and decreased cell viability. Collectively, these data suggest that bufalin downregulates hTERT to induce mitochondria-dependent apoptosis in CAPAN-2 and CAL-27 cells. Moreover, bufalin increased the phosphorylation of JNK and p38-MAPK in CAPAN-2 and CAL-27 cells, and blocking the JNK/p38-MAPK pathway using the JNK inhibitor SP600125 or the p38-MAPK inhibitor SB203580 reversed bufalin-induced hTERT downregulation. Thus, the JNK/p38 pathway is involved in bufalin-induced hTERT downregulation and subsequent induction of apoptosis by the mitochondrial pathway

Weakly supervised label propagation algorithm classifies lung cancer imaging subtypes

Abstract Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph clustering methods are used to learn higher-level abstract features in CT imaging features. The unlabeled data is dynamically updated through the k-nearest label update strategy, and the unlabeled data is transformed into weak label data and continue to update the process of strong label data to optimize the clustering results and establish a classification model for predicting new subtypes of lung cancer imaging. Five imaging subtypes are confirmed on the lung cancer dataset containing CT, clinical and genetic information downloaded from the TCIA lung cancer database. The successful establishment of the new model has a significant accuracy rate for subtype classification (ACC = 0.9793), and the use of CT sequence images, gene expression, DNA methylation and gene mutation data from the cooperative hospital in Shanxi Province proves the biomedical value of this method. The proposed method also can comprehensively evaluate intratumoral heterogeneity based on the correlation between the final lung CT imaging features and specific molecular subtypes

Significantly enhanced high-temperature mechanical properties of Cu-Cr-Zn-Zr-Si alloy with stable second phases and grain boundaries

Improving high-temperature strength and resistance to high-temperature softening is an important method to promote the application of high-performance Cu-Cr-Zr alloy in fields such as resistance welding electrodes and high-speed railway contact wires. A Cu-1.0Cr-0.4Zn-0.1Zr-0.05Si alloy was designed and the combining effects of Zn and Si elements on the microstructure and high-temperature mechanical properties of the alloy were studied. The tensile strength of the alloy at room temperature was 556 MPa, and it was 349 MPa at 500℃ with a softening temperature of 620℃. The main strengthening phases of the alloy were submicron Cr3Si and nano-scaled Cr-rich precipitates. The Zn elements were uniformly solid-solved in the Cu matrix, and the addition of Zn and Si elements significantly retarded the phase transformation of the Cr-rich precipitates. Thermodynamics and kinetics analysis showed that Zn and Si elements promoted the dispersive precipitation of the nano-scaled FCC coherent Cr-rich precipitates by reducing the nucleation energy barrier, while the Si and Zr elements inhibited the coarsening of the Cr-rich precipitates by enriching at the phase boundaries, effectively impeding dislocation motion and grain boundary migration, which mainly contributed to good high-temperature strength and resistance to softening of the Cu-Cr-Zn-Zr-Si alloy

Integrating image and gene-data with a semi-supervised attention model for prediction of KRAS gene mutation status in non-small cell lung cancer.

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC

The association between sleep duration and physical performance in Chinese community-dwelling elderly.

BACKGROUND:Physical performance is an important healthy factor in elder people. Good living habits, which include sleep, can maintain physical strength and physical performance. The aim of the present study was to conduct a cross-sectional study to determine the association between total sleep duration and physical performance. METHODS:Our study population comprised residents of the township central hospital in the suburban of Tianjin, China. We measured muscle strength, walk speed and balance function by grip, 4-m walk test and timed up and go test (TUGT). We divided sleep duration into four groups 8-9h, >9h. RESULTS:A total 898 participants had completed data (392 men and 506 women, mean age 67.71 years). In man, adjusted sleep duration was associated with lower grip in > 9 h group, the mean value (95% CI) was 0.429 (0.409, 0.448), and longer TUGT time was also associated with long sleep duration, 10.46s (9.97 s, 10.95 s). In women, adjusted slower 4-m walk speed present an inverse U-shaped relation with sleep duration, by 0.93 m/s (0.86 m/s, 0.98 m/s), 0.97 m/s (0.96 m/s, 1.00 m/s), 0.97 m/s (0.95 m/s, 0.99 m/s) and 0.92 m/s (0.89 m/s, 0.96 m/s); longer TUGT time were associated with long sleep duration (> 9 h), by 11.23 s (10.70 s, 11.77 s). CONCLUSION:In Chinese community-dwelling elderly, lower muscle strength and lower balance function were associated with long sleep duration in men. Slower walk speed and lower balance function were associated with long sleep duration in women

Inhibition of MDM2 Re-Sensitizes Rapamycin Resistant Renal Cancer Cells via the Activation of p53

Background/Aims: Rapamycin is a potential anti-cancer agent, which modulates the activity of mTOR, a key regulator of cell growth and proliferation. However, several types of cancer cells are resistant to the anti-proliferative effects of rapamycin. In this study, we report a MDM2/p53-mediated rapamycin resistance in human renal cancer cells. Methods: Trypan blue exclusion tests were used to determine the cell viability. Changes in mRNA and protein expression were measured using real-time PCR and western blot, respectively. Xenograft models were established to evaluate the in vivo effects of rapamycin combined with a MDM2 inhibitor. Results: Rapamycin treatment suppresses the expression of MDM2 and exogenous overexpression of MDM2 in A498 cells contributes to rapamycin resistance. By establishing a rapamycin resistant cell line, we observed that MDM2 was significantly upregulated in rapamycin resistant cells than that in rapamycin sensitive cells. Importantly, the rapamycin resistant cells demonstrated attenuated accumulation of p53 in the nucleus in response to rapamycin treatment. Moreover, the inhibition of MDM2 by siMDM2 sensitizes A498 cells to rapamycin through the activation of p53. In both in vitro and in vivo models, the combination of rapamycin with the MDM2 inhibitor, MI-319, demonstrated a synergistic inhibitory effect on rapamycin resistant cells. Conclusion: Our study reports a novel mechanism for rapamycin resistance in human renal cancer and provides a new perspective for the development of anti-cancer drugs