Background and Objective: Atherosclerosis (AS), is characterized by the subintima lipid accumulation and chronic inflammation inside the arterial wall, causing much mortality and morbidity worldwide.Activating transcription factor 3 (ATF3) is a member of ATF/cAMP-responsive element-binding (CREB) family of transcription factors, which acts as a master regulator of adaptive response. Recent studies have indicated the implicated role of ATF3 in atherogenesis and AS progression due to its impact on metabolic disorder, vascular injury, plaque formation, and stability. In this review, we summarize the current advances in the mechanism of ATF3 activation and the contribution of ATF3 in AS, highlighting vascular intrinsic and extrinsic mechanisms of how ATF3 influences the pathology of AS. Methods:The relevant literature (from origin to March 2022) was retrieved through PubMed research to explore the regulatory mechanism of ATF3 and the specific role of ATF3 in AS. Only English publications were reviewed in this paper.Key Content and Findings: ATF3 acts as a key regulator of AS progression, which not only directly affects atherosclerotic lesions by regulating vascular homeostasis, but also gets involved in AS through systemic glucolipid metabolism and inflammatory response. The two different promoters, transcript variants, and post-translational modification in distinct cell types partly contribute to the regulatory diversity of ATF3 in AS.Conclusions: ATF3 is a crucial transcription regulatory factor during atherogenesis and AS progression.Gaining a better understanding of how ATF3 affects vascular, metabolic, and immune homeostasis would advance the progress of ATF3-targeted therapy in AS.

Nowadays most cancer patients are treated by radiotherapy. The treatment duration of patients will grow longer over time because of the half-life decaying effect of the radioactive source, which can be regarded as a continuous non-linear deteriorating effect. How to sequence the treatment of the patients before the radioactivity is reduced to the lowest available intensity is an important and complex problem. Meanwhile, the treatment sequence of cancer patients should not only be based on the waiting time but also on the severity of their illness. Therefore, the dual factors which reflect the severity of patients' illness as well as waiting time should be considered. The dual factors are denoted as the treatment value of patients in this paper and we determine patients in the waiting list to be selected, assigned and sorted for treatment, so as to maximize the overall treatment value of all patients. We also consider the setup time for each time of treatment, which cannot be ignored in reality. The original problem model is difficult to solve directly, so we reformulate the original problem to a set covering problem and it is solved by a column generation approach we develop. The master problem of selecting plans for treatment blocks and subproblems of generating plans are solved by GUROBI and dynamic programming, respectively. Numerical experiments are conducted to demonstrate the efficiency of the proposed column generation approach.

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