ABSTRACT
Background: This study aimed to evaluate the efficacy and safety of preoperative PD-1 inhibitor tislelizumab combined with CRT in unresectable esophageal squamous cell carcinoma (ESCC). Method(s): This is a single-arm, phase II trial, planned to enroll 30 patients. Eligibility criteria include histologically confirmed unresectable thoracic ESCC stage at cT4bNxM0 (AJCC 8th). Radiotherapy was delivered to a total dose of 50Gy/25f, concurrently with cisplatin (25 mg/m2) and nab-paclitaxel (100 mg) (QW, at least 3 cycles). Then followed by two cycles of tislelizumab (200 mg, Q3W), cisplatin (75 mg/m2, Q3W) and nab-paclitaxel (150 mg/m2, Q3W). If curative resection was considered, esophagectomy was performed within 4 weeks. Patients with pathologically residual disease would receive tislelizumab (200 mg, Q3W) for 1 year. The primary endpoint is 1-year progression free survival rate. Result(s): From December 2021 to July 2022, 21 patients were enrolled. 16 patients completed chemoradiotherapy and received immunochemotherapy. Reasons for 5 patients who did not receive immunochemotherapy included patient refusal (n=1), progressive disease (n=1), esophageal fistula (n=2), waiting for immunochemotherapy (n=1). Finally, 13 patients proceeded to surgery, with R0 resection rate of 100%. Reasons for not undergoing surgery after immunochemotherapy were esophageal mediastinal fistula (n=1), surgery delay due to COVID-19 epidemic (n=2). The pCR and MPR rate was 61.5% (8/13) and 76.9% (10/13). G1, G2, G3 immune related pneumonia occurred in 3 patients, respectively. >=G3 AEs occurred in 9 (42.9%) patients. Postoperative complications included anastomotic fistula (2/13), pleural effusion (2/13), pneumonia (1/13), myocardial damage (1/13), delayed wound healing (1/13). Conclusion(s): Chemoradiotherapy followed by immunochemotherapy might be a useful conversional treatment option for unresectable ESCC. Esophagectomy after this could be safe with acceptable complications for unresectable locally advanced ESCC. Clinical trial identification: ChiCTR2100054327. Legal entity responsible for the study: The authors. Funding(s): Has not received any funding. Disclosure: All authors have declared no conflicts of interest. Copyright © 2022 European Society for Medical Oncology
ABSTRACT
The topic of source identification has attracted wide attention from researchers. In practice, the source identification method aims to locate the sources of rumors, computer viruses, and epidemics, such as COVID-19. However, there are two main problems with existing propagation source detection methods. First, most source detection methods are based on infinite networks, not in line with reality. Second, sources are often randomly selected in simulations, but different sources often cause significantly different detection results in real-world applications. To this end, we study how does the source location impact the effectiveness of source detection in finite networks. This paper first proposes a diameter-based node division method to classify the nodes based on their structural location. We further offer different evaluation indicators to measure the effectiveness of source detection methods. Then, we conduct systematic experiments on three synthetic networks and two real-world networks. Our experiments demonstrate that the location of the source directly effects detection effectiveness in finite networks for all source detection methods. Specifically, sources closer to the network boundary will lead to worse detection performance. It means that attackers can choose sources close to the network boundary to reduce the probability of detection to achieve a larger spreading scale. © 2022 IEEE.
ABSTRACT
Although the engineering education has been widely developed in China, the training for engineers cannot completely satisfy the requirements from the society. The output-based education (OBE) designs education process according to the results of education so that students can have a certain level of ability when they graduate. However, influenced by the COVID-19 pandemic, plenty of offline practical activities have changed to online ones. Thus, the digital image processing course is taken as an example, and the online class video is taken as the test data for students to do practical experiments. Moreover, an algorithm extracting harr-like features to detect face and recognize expression is applied. The eyes are further extracted from the detected face images to calculate the ratio of width and height, and then the students’ studying state can be determined. The experimental results demonstrate that the algorithm can help teachers understand the students’ state and improve the teaching efficiency. Moreover, the OBE online teaching mode can improve the students’ practical ability. © 2021, Springer Nature Switzerland AG.
ABSTRACT
The course of Principles of Communications involves a wide range of know ledge and is both theoretical and practical. It is difficult for beginners to closely link theory with practice. Therefore, it is necessary to consolidate theoretical teaching results through experiments. The traditional experimental teaching of Principles of Communications is to let students go to the laboratory and carry out experiments by programming on software radio hardware. However, during the COVID-19 pandemic period, students are unable to conduct experiments in the laboratory, so online experiments become the only feasible scheme. This paper introduces an online experiment scheme, compares the online scheme with lab experiments. The online experiment system has been used in one class of 28 students and the positive rate of the system from the students is 86%.