The poly(ADP-ribose) polymerases (PARPs) is an important group of enzymes in DNA repair pathways, especially the base excision repair (BER) for DNA single-strand breaks (SSBs) repair. Inhibition of PARP in DNA repair-defective tumors (like those with BRAC1/2 mutations) can lead to cell death and genomic instability, what is so called "synthetic lethality". Currently, PARP inhibitors combined with cytotoxic chemotherapeutic agents in the treatment of BRCA-1/2 deficient cancers are in the clinical development. In this review, we will be focused on the development of combination application of PARP inhibitors with other anticancer agents in clinical trials.

Objective:To investigate the difference of HRCT imaging features between COVID-19 and the ground-glass opacity(GGO) lesion of early-stage lung carcinoma, standardize the diagnosis and treatment process of ground-glass opacity(GGO) degeneration during the epidemic.Methods:A total of 34 patients with diagnosed COVID-19 who confirmed by positive results of the new coronavirus nucleic acid test were collected as observation group 40 patients with pathologically diagnosed early-stage lung carcinoma whose preoperative HRCT examination showed pure ground glass lesions and received surgical intervention were recruited from the Department of Thoracic Surgery (The First Affiliated Hospital of Xiamen University) from January 2018 to December 2019 as the control group. The HRCT imaging features of these two groups of patients were compared and statistically analyzed.Results:The HRCT imaging features of the new type of COVID-19 showed significant difference by characteristics of multiple lesions, lesion rapid variation within 3 days, reticular pattern, vacuolar sign and clear boundary compared to the GGO lesion of early-stage lung carcinoma( P<0.05). The chinical and imaging characteristic the sex, age, with pleural effusion or not and the lesion location showed no significant difference between these 2 groups ( P>0.05). Conclusion:Contrast with inert early lung carcinoma lesions, COVID-19 disease developed rapidly. Imaging dynamic examination can provide evidences to distinguish Novel Coronavirus Pneumonia and early-stage lung carcinoma.

Objective    To evaluate the effectiveness of the artificial intelligence-assisted diagnosis and treatment system in distinguishing benign and malignant lung nodules and the infiltration degree. Methods    Clinical data of 87 patients with pulmonary nodules admitted to the First Affiliated Hospital of Xiamen University from January 2019 to August 2020 were retrospectively analyzed, including 33 males aged 55.1±10.4 years, and 54 females aged 54.5±14.1 years. A total of 90 nodules were included, which were divided into a malignant tumor group (n=80) and a benign lesion group (n=10), and the malignant tumor group was subdivided into an invasive adenocarcinoma group (n=60) and a non-invasive adenocarcinoma group (n=20). The malignant probability and doubling time of each group were compared and its ability to predict the benign and malignant nodules and the invasion degree was analyzed. Results    Between the malignant tumor group and the benign lesion group, the malignant probability was significantly different, and the malignant probability could better distinguish malignant nodules and benign lesions (87.2%±9.1% vs. 28.8%±29.0%, P=0.000). The area under the curve (AUC) was 0.949. The maximum diameter of nodules in the benign lesion group was significantly longer than that in the malignant tumor group (1.270±0.481 cm vs. 0.990±0.361 cm, P=0.026); the doubling time of benign lesions was significantly longer than that of malignant nodules (1 083.600±258.180 d vs. 527.025±173.176 d, P=0.000), and the AUC was 0.975. The maximum diameter of the nodule in the invasive adenocarcinoma group was longer than that of the non-invasive adenocarcinoma group (1.350±0.355 cm vs. 0.863±0.271 cm, P=0.000), and there was no statistical difference in the probability of malignancy between the invasive adenocarcinoma group and the non-invasive adenocarcinoma group (89.7%±5.7% vs. 86.4%±9.9%, P=0.082). The AUC was 0.630. The doubling time of the invasive adenocarcinoma group was significantly shorter than that of the non-invasive adenocarcinoma group (392.200±138.050 d vs. 571.967±160.633 d, P=0.000), and the AUC was 0.829. Conclusion    The malignant probability and doubling time of lung nodules calculated by the artificial intelligence-assisted diagnosis and treatment system can be used in the assessment of the preoperative benign and malignant lung nodules and the infiltration degree.