Almost all available information about the impact of radiation on the uterus comes from studies of radiation exposure during childhood or adolescence.An uncomplicated pregnancy is possible, even with doses as high as 54 Gy. Therefore, tumour treatment doses alone cannot at present be used to accurately predict uterine damage.
 
  Much of the data cannot be readily extrapolated to adult women who have had uterine radiation and the publications concerning adult women treated with AP RT are largely limited to case reports.
 
  This analysis offers clinical guidance and assists with patient counselling. It is important to include patients who have undergone AP RT or TBI in prospective studies to provide further evidence regarding uterine function, pregnancy outcomes and correlation of imaging with clinical outcomes.
 
  This study received no funding and there are no conflicts of interest.
 
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  Is expectant management (EM) of tubal ectopic pregnancy (EP) an effective and safe treatment strategy when compared to alternative interventions?
 
  There is insufficient evidence to conclude EM yields a difference in the resolution of tubal EP, the avoidance of surgery or time to resolution of tubal EP when compared to intramuscular methotrexate in stable patients with β-hCG <1500 IU/l.
 
  The utilisation of medical and surgical management for EP is well established. EM aims to allow spontaneous resolution of the EP without intervention.
 
  We performed a systematic review and meta-analysis, searching Ovid MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, OpenGrey.eu, Google Scholar, cross-referencing citations and trial registries to 15 December 2019. There were no limitations placed on language or publication date. Search terms included tubal EP and EM as well as variations of these terms.
 
  We considered studies that included patients with tubal EP, EM as a comparator, and that were randomised controllChildren's Research Foundation, outside the submitted work. M.A. As a medical research institute, NICM Health Research Institute receives research grants and donations from foundations, universities, government agencies and industry. Sponsors and donors provide untied and tied funding for work to advance the vision and mission of the Institute. This systematic review was not specifically supported by donor or sponsor funding to NICM. M.A. reports a partnership grant with Metagenetics outside the submitted work. G.C. reports grants from Australian Women and Children's Research Foundation, personal fees from Roche and GE Healthcare, outside the submitted work. The remaining authors report no conflicts of interest.
 
  CRD42020142736.
 CRD42020142736.Consumer, industrial, and commercial product usage is a source of exposure to potentially hazardous chemicals. In addition, cleaning agents, personal care products, coatings, and other volatile chemical products (VCPs), evaporate and react in the atmosphere producing secondary pollutants. Here, we show high air emissions from VCP usage (≥ 14 kg person-1 yr-1, at least 1.7× higher than current operational estimates) are supported by multiple estimation methods and constraints imposed by ambient levels of ozone, hydroxyl radical (OH) reactivity, and the organic component of fine particulate matter (PM2.5) in Pasadena, California. A near-field model, which estimates human chemical exposure during or in the vicinity of product use, indicates these high air emissions are consistent with organic product usage up to ~75 kg person-1 yr-1, and inhalation of consumer products could be a non-negligible exposure pathway. After constraining the PM2.5 yield to 5% by mass, VCPs produce ~41% of the photochemical organic PM2.5 (1.1 ± 0.3 μg m-3) and ~17% of maximum daily 8-hr average ozone (9 ± 2 ppb) in summer Los Angeles. Therefore, both toxicity and ambient criteria pollutant formation should be considered when organic substituents are developed for VCPs in pursuit of safer and sustainable products and cleaner air.As smartphones and consumer wearable devices become more ubiquitous, there is a growing opportunity to capture rich mobile sensor data continuously, passively, and in real-world settings with minimal burden.  https://www.selleckchem.com/products/baxdrostat.html  In the context of cancer, changes in these passively sensed digital biomarkers may reflect meaningful variation in functional status, symptom burden, quality of life, and risk for adverse clinical outcomes. These data could enable real-time remote monitoring of patients between clinical encounters and more proactive, comprehensive, and personalized care. Over the past few years, small studies across a variety of cancer populations support the feasibility and potential clinical value of mobile sensors in oncology. Barriers to implementing mobile sensing in clinical oncology care include the challenges of managing and making sense of continuous sensor data, patient engagement issues, difficulty integrating sensor data into existing electronic health systems and clinical workflows, and ethical and privacy concerns. Multidisciplinary collaboration is needed to develop mobile sensing frameworks that overcome these barriers and that can be implemented at large-scale for remote monitoring of deteriorating health during or after cancer treatment or for promotion and tailoring of lifestyle or symptom management interventions. Leveraging digital technology has the potential to enrich scientific understanding of how cancer and its treatment affect patient lives, to use this understanding to offer more timely and personalized support to patients, and to improve clinical oncology outcomes.Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. One important reason is that the amount of postoperative data is too massive and too high-dimensional to be effectively processed by the human operator. We therefore sought to develop a deep-learning-based algorithm that is able to predict postoperative AKI prior to the onset of symptoms and complications. Based on 96 routinely collected parameters we built a recurrent neural network (RNN) for real-time prediction of AKI after cardiothoracic surgery. From the data of 15,564 admissions we constructed a balanced training set (2224 admissions) for the development of the RNN. The model was then evaluated on an independent test set (350 admissions) and yielded an area under curve (AUC) (95% confidence interval) of 0.893 (0.862-0.924). We compared the performance of our model against that of experienced clinicians. The RNN significantly outperformed clinicians (AUC = 0.