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Background: The coronavirus disease 2019 (COVID-19) has become a worldwide pandemic since mid-December 2019, which greatly challenge public medical systems. With limited medical resources, it is a natural strategy, while adopted, to access the severity of patients then determine the treatment priority. However, our work observes the fact that the condition of many mild outpatients quickly worsens in a short time, i.e. deteriorate into severe/critical cases. Hence, it has been crucial to early identify those cases and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression. Methods: A total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients at admission were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at severe/critical stage of the two groups were compared with Chi-square test, Fisher’s exact test, and t test. Both traditional logistic regression and deep learning-based methods were used to build the prediction models. The area under ROC curve (AUC) was used to evaluate the models. Findings: The deep learning-based method significantly outperformed logistic regression (AUC 0·954 vs. 0·893). The deep learning-based method achieved a prediction AUC of 0·938 by combining the clinical data and the CT data, significantly outperforming its counterpart trained with clinical data only by 0.141. By further considering the temporal information of the CT sequence, our model achieved the best AUC of 0·954. Interpretation: The proposed model can be effectively used for finding out the mild patients who are easy to deteriorate into severe/critical cases so that such patients get timely treatments while alleviating the limitations of medical resources. Funding Statement:This work was supported by National Key R&D Program of China (No. 2018YFB1004600), HUST COVID-19 Rapid Response Call (No. 2020kfyXGYJ093, No. 2020kfyXGYJ094), National Key R&D Program of China (No.2017YFC1309100), National Science Fund for Distinguished Young Scholars (No.81925023), National Natural Science Foundation of China (No.61703049, No. 81771912, No. 81401390) Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The authors stated this was an IRB-approved study.

Microbial dysbiosis has an increasingly appreciated impact on carcinogenesis and cervicovaginal microbiome plays a critical role in microenvironmental inflammation. Here we investigated the involvement of the female genital tract microbiome in a gynaecological cancer. We transferred Peptostreptococcus species into the uteri of BALB/c nude mice and observed indoleacrylic acid (IAA) production. Intratumoral injection of conditioned medium from bacterial cultures into tumour-xenografted mice for 2 months promoted tumour growth. IAA upregulated IL-10 expression by M2 macrophages, which increased IFN-γ expression by CD8+ T cells. IFN-γ induced indoleamine-2,3-dioxygenase 1 (IDO1) expression in endometrial cancer (EC) cell line. Co-culture of IDO1-expressing EC cells with peripheral blood mononuclear cells upregulated the proportion of regulatory T cells and decreased the M1/M2 ratio. Importantly, P. anaerobius was more abundant amongst the uterine microbiota of clinical EC patient samples than control samples. IAA, IDO1, and the kynurenine/tryptophan ratio were all higher in EC tissue than in healthy tissue, and the M1/M2 ratio was lower. Thus, we conclude that the overabundance of uterine commensal Peptostreptococcus species in EC induces IDO1 expression by producing IAA. Our study sheds light on the link between IDO1 induction and uterine Peptostreptococcus dysbiosis.

The prevalence of electronic devices has propelled the tremendous growth of the semiconductor industry in the past half century. The manufacturing of semiconductor wafers is a long and delicate process, where defect points may occur and cause the failure of the entire circuit. As more advanced devices introduce tiny and harder-to-detect defects, improvements in wafer defect inspection technologies are highly desired by the industry. One promising direction is to optimize the positioning of inspection regions for the e-beam inspection tool, so as to cover all suspect defect points on a target area of a wafer while maximizing the throughput of the scan. To this end, we formulate this defect inspection optimization problem (DIOP) as a mixed integer linear programming (MILP) model, and enhance the formulation using variable reduction. To address the computational challenge of solving large-scale DIOP instances, we further propose three approximation methods with theoretical bounds, namely, a constant-factor approximation algorithm that provides heuristic solutions quickly, a polynomial-time approximation scheme (PTAS) that has a strong performance guarantee, and an MILP-based hybrid method that combines the PTAS framework and MILP formulation and is suitable for parallel computing by design. Numerical experiments on synthetic and industry instances show the efficiency and effectiveness of the proposed methods. Particularly, for large-scale instances, the hybrid algorithm is capable of generating near-optimal solutions with theoretical guarantees within practically acceptable computational time.

Resistance to DDP has become the leading cause of mortality in NSCLC. The microRNA dysregulation plays a vital role in tumor progression. In our study, we found that miR-495 was significantly downregulated in lung cancer tissue specimens. Our objective was to elucidate the functions, direct target genes, and molecular mechanisms of miR-495 in lung cancer. We found that miR-495 downregulated the expression of its substrate UBE2C by directly binds to the 3′-UTR of UBE2C. UBE2C is a proto-oncogene that is activated in lung cancer; however, its role in drug resistance is unclear. We found here that UBE2C expression was higher in cisplatin resistant NSCLC cells; this was associated with induced cisplatin resistant NSCLC cells proliferation, invasion and cisplatin resistant. EMT was contributed to cisplatin resistance. We also found that UBE2C knockdown downregulated the expression of the vimentin. In contrast, E-cadherin was upregulated. Importantly we found that miR-495 and UBE2C were associated with cisplatin resistance. We performed to evaluate their effects on cell proliferation and cisplatin resistant. We also performed EMT, cell migration and invasion assays in cisplatin resistant NSCLC cells overexpressing miR-495 and under expressing UBE2C. Furthermore, Bioinformatics assays coupled with WB and luciferase assays revealed that UBE2C directly binds to the 5′-UTR of the drug resistance genes, ABCG2 and ERCC1. We also found that miR-495 reduced the expression of ABCG2 and ERCC1 via regulation of UBE2C. Collectively, our results indicated that miR495-UBE2C-ABCG2/ERCC1 axis reverses DDP resistance via downregulation of anti-drug genes and reducing EMT in cisplatin resistant NSCLC cells. Funding Statement: This work was supported by the Science and Technology Development Foundation of Yantai (2015ZH082), Natural Science Foundation of Shandong Province (ZR2018QH004, ZR2016HB55, ZR2017PH067 and ZR2017PH028), and Research Foundation of Binzhou Medical University (BY2015KYQD29 and BY2015KJ14). Competing Interests: The authors declare no competing financial interests. Ethics Approval Statement: The experimental protocol was approved by the Research Ethics Committee of Binzhou Medical University, China (No. 2017-016-01 for human lung cancer specimen and No. 2017-009-09 for mouse experiments in vivo) and the written informed consent was obtained from all subjects. Informed consent was obtained from all individual participants included in the study. All patients were staged based on the criteria of the 7th Edition of the AJCC Cancer Staging Manual: Stomach (2010).