Our study explored the tumor-suppressive effect of curcumin on cervical cancer cells. Cervical cancer is one of the most common cancers among women worldwide. Acquired resistance to chemotherapeutics and toxicity of such drugs has undermined the effectiveness of cervical cancer treatments. Therefore, the identification of novel chemotherapeutics is key to improving the survival of patients with cervical cancer. Curcumin has been shown to have various bioactivities, including antioxidant and antitumor effects; however, its effect on cervical cancer remains elusive. Here, we used the SiHa human cervical cancer cell line to test various concentrations of curcumin on the proliferation and apoptosis of cervical cancer cells. The involvement of autophagy and reactive oxygen species (ROS) in these effects were also tested by using specific autophagy inhibitors and ROS scavengers. Our results showed that curcumin induced ROS accumulation, apoptosis, autophagy, cell cycle arrest, and cellular senescence accompanied by upregulation of p53 and p21 proteins in SiHa cells.

The persistent infection of high-risk human papillomavirus (HPV) is one of the most common causes of cervical cancer worldwide, and HPV type 58 is the third most common HPV type in eastern Asia. The E7 oncoprotein is constitutively expressed in HPV58-associated cervical cancer cells and plays a key role during tumorigenesis. To study the biological function of HPV58 E7 and to characterize E7 protein-host cell interactions, we cloned the human HPV58 E7 gene and produced specific E7 antibodies. The HPV58 E7 gene was cloned into a prokaryotic expression vector, pGEX-4T2. The recombinant plasmid pGEX-4T2-(HPV58-E7) was transformed into Escherichia coli DH5α and expressed as a fusion protein containing a GST tag. After purification and removal of the GST affinity tag, the E7 protein was used as an antigen for the production of antiserum in rabbits. The specificity of the purified HPV58 E7 antibody was detected by western blotting, immunofluorescence and immunohistochemistry analysis. These methods demonstrated that the polyclonal antibody could specifically recognize the endogenous and the recombinant HPV58 E7 proteins. Immunohistochemistry analysis indicated that the E7 protein was localized in the nucleus of cervical cancer cells.

Objective. To investigate the value of preoperative prediction of breast cancer axillary lymph node metastasis based on intratumoral and peritumoral dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) radiomics nomogram. Material and Methods. In this study, a radiomics model was developed based on a training cohort involving 250 patients with breast cancer (BC) who had undergone axillary lymph node (ALN) dissection between June 2019 and January 2021. The intratumoral and peritumoral radiomics features were extracted from the second postcontrast images of DCE-MRI. Based on filtered radiomics features, the radiomics signature was built by using the least absolute shrinkage and selection operator method. The Support Vector Machines (SVM) learning algorithm was used to construct intratumoral, periatumoral, and intratumoral combined periatumoral models for predicting axillary lymph node metastasis (ALNM) in BC. Nomogram performance was determined by its discrimination, calibration, and clinical value. Multivariable logistic regression was adopted to establish a radiomics nomogram. Results. The intratumoral combined peritumoral radiomics signature, which was composed of fifteen ALN status-related features, showed the best predictive performance and was associated with ALNM in both the training and validation cohorts ( P < 0.001 ). The prediction efficiency of the intratumoral combined peritumoral radiomics model was higher than that of the intratumoral radiomics model and the peritumoral radiomics model. The AUCs of the training and verification cohorts were 0.867 and 0.785, respectively. The radiomics nomogram, which incorporated the radiomics signature, MR-reported ALN status, and MR-reported maximum diameter of the lesion, showed good calibration and discrimination in the training (AUC = 0.872) and validation cohorts (AUC = 0.863). Conclusion. The intratumoral combined peritumoral radiomics model derived from DCE-MRI showed great predictive value for ALNM and may help to improve clinical decision-making for BC.

Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV.