Low-dose acetylsalicylic acid for cancer prevention considering risk factors: a retrospective cohort study

PURPOSE

Aspirin (acetylsalicylic acid) has been reported to protect against certain cancers. However, patient-related risk factors may moderate protective effects, including excess weight, smoking, risky alcohol use, and diabetes. We explore the cancer-risk relationship between aspirin intake and those four factors.

METHODS

Retrospective cohort study of cancers, aspirin intake, and four risk factors in persons aged ≥50 years. Participants received medication during 2007-2016, and cancers were diagnosed in 2012-2016. Adjusted hazard ratios (aHR) for 95% confidence intervals (95%CI) were calculated for aspirin intake and risk factors using Cox proportional hazard modeling.

RESULTS

Of 118,548 participants, 15,793 consumed aspirin, and 4003 had cancer. Results indicated a significant protective effect of aspirin against colorectal (aHR: 0.7; 95%CI: 0.6-0.8), pancreatic (aHR: 0.5; 95%CI: 0.2-0.9), prostate (aHR: 0.6; 95%CI: 0.5-0.7) cancers and lymphomas (aHR: 0.5; 95%CI: 0.2-0.9), and also, although not significantly, against esophageal (aHR: 0.5; 95%CI: 0.2-1.8), stomach (aHR: 0.7; 95%CI: 0.4-1.3), liver (aHR: 0.7; 95%CI: 0.3-1.5), breast (aHR: 0.8; 95%CI: 0.6-1.0), and lung and bronchial (aHR: 0.9; 95%CI: 0.7-1.2) cancers. Aspirin intake was not significantly protective against leukemia (aHR: 1.0; 95%CI: 0.7-1.4) or bladder cancer (aHR: 1.0; 95%CI: 0.8-1.3).

CONCLUSIONS

Our results suggest that aspirin intake is associated with a reduced incidence of colorectal, pancreatic, and prostate cancers and lymphomas.

Acetylsalicylic Acid Effect in Colorectal Cancer Taking into Account the Role of Tobacco, Alcohol and Excess Weight

Excess weight, smoking and risky drinking are preventable risk factors for colorectal cancer (CRC). However, several studies have reported a protective association between aspirin and the risk of CRC. This article looks deeper into the relationships between risk factors and aspirin use with the risk of developing CRC. We performed a retrospective cohort study of CRC risk factors and aspirin use in persons aged >50 years in Lleida province. The participants were inhabitants with some medication prescribed between 2007 and 2016 that were linked to the Population-Based Cancer Registry to detect CRC diagnosed between 2012 and 2016. Risk factors and aspirin use were studied using the adjusted HR (aHR) with 95% confidence intervals (CI) using a Cox proportional hazard model. We included 154,715 inhabitants of Lleida (Spain) aged >50 years. Of patients with CRC, 62% were male (HR = 1.8; 95% CI: 1.6-2.2), 39.5% were overweight (HR = 2.8; 95% CI: 2.3-3.4) and 47.3% were obese (HR = 3.0; 95% CI: 2.6-3.6). Cox regression showed an association between aspirin and CRC (aHR = 0.7; 95% CI: 0.6-0.8), confirming a protective effect against CRC and an association between the risk of CRC and excess weight (aHR = 1.4; 95% CI: 1.2-1.7), smoking (aHR = 1.4; 95% CI: 1.3-1.7) and risky drinking (aHR = 1.6; 95% CI: 1.2-2.0). Our results show that aspirin use decreased the risk of CRC and corroborate the relationship between overweight, smoking and risky drinking and the risk of CRC.

Indoor Scene Recognition via Object Detection and TF-IDF

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision and natural language processing (YOLO and TF-IDF, respectively). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

Multi-armed bandits for bid shading in first-price real-time bidding auctions

Online advertisements are bought through a mechanism called real-time bidding (RTB). In RTB, the ads are auctioned in real-time on every webpage load. The ad auctions can be of two types: second-price or first-price auctions. In second-price auctions, the bidder with the highest bid wins the auction, but they only pay the second-highest bid. This paper focuses on first-price auctions, where the buyer pays the amount that they bid. This research evaluates how multi-armed bandit strategies optimize the bid size in a commercial demand-side platform (DSP) that buys inventory through ad exchanges. First, we analyze seven multi-armed bandit algorithms on two different offline real datasets gathered from real second-price auctions. Then, we test and compare the performance of three algorithms in a production environment. Our results show that real data from second-price auctions can be used successfully to model first-price auctions. Moreover, we found that the trained multi-armed bandit algorithms reduce the bidding costs considerably compared to the baseline (naïve approach) on average 29%and optimize the whole budget by slightly reducing the win rate (on average 7.7%). Our findings, tested in a real scenario, show a clear and substantial economic benefit for ad buyers using DSPs.

Autonomous Industrial Management via Reinforcement Learning

Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper, we indicate the distinctions between automated and autonomous systems as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as digital twins, how to train reinforcement learning agents to learn specific tasks through generalisation. Once generalisation is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment that focuses on how the agents can adapt to different environments. We introduce an initial prototype of our ideas by solving a multi-armed bandit problem using two ε-greedy algorithms. Further, we discuss future applications in the industrial management realm and propose a modular architecture for improving the decision-making process via autonomous agents.