Characteristics, Predictors, and Outcomes of Early mTOR Inhibitor Use After Heart Transplantation: Insights From the UNOS Database.

Background The clinical characteristics of mTOR (mammalian target of rapamycin) inhibitors use in heart transplant recipients and their outcomes have not been well described. Methods and Results We compared patients who received mTOR inhibitors within the first 2 years after heart transplantation to patients who did not by inquiring the United Network for Organ Sharing (UNOS) database between 2010 and 2018. The primary end point was all-cause mortality with retransplantation as a competing event. Rejection, malignancy, hospitalization for infection, and renal transplantation were secondary end points. There were 1619 (9%) and 15 686 (81%) mTOR inhibitors+ and mTOR inhibitors- patients, respectively. Body mass index, induction, cardiac allograft vasculopathy, calculated panel reactive antibody, and fewer days in 1A status were independently associated with mTOR inhibitors+ status. Over a follow-up of 10.4 years, there was no difference in all-cause mortality after adjusting for donor and recipient characteristics (adjusted subdistribution hazard ratio, 1.03 [0.90-1.19]; P=0.66). mTOR inhibitors+ were independently associated with increased risk for rejection (odds ratio [OR], 1.43 [1.11-1.83]; P=0.005) and basal skin cancer (OR, 1.35 [1.19-1.51]; P=0.012) but not for infection or renal transplantation. Conclusions mTOR inhibitors are used in <10% patients in the first 2 years after heart transplantation and are noninferior to contemporary immunosuppression regimens in terms of all-cause mortality, infection, malignancy, or renal transplantation. They are associated with risk for rejection.

From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development.

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.