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A significant number of pregnancies are lost in the first trimester and 1-2% are ectopic pregnancies (EPs). Early pregnancy loss in general can cause significant morbidity with bleeding or infection, while EPs are the leading cause of maternal mortality in the first trimester. Symptoms of pregnancy loss and EP are very similar (including pain and bleeding); however, these symptoms are also common in live normally sited pregnancies (LNSP). To date, no biomarkers have been identified to differentiate LNSP from pregnancies that will not progress beyond early gestation (non-viable or EPs), defined together as combined adverse outcomes (CAO). In this study, we present a novel machine learning pipeline to create prediction models that identify a composite biomarker to differentiate LNSP from CAO in symptomatic women. This prospective cohort study included 370 participants. A single blood sample was prospectively collected from participants on first emergency presentation prior to final clinical diagnosis of pregnancy outcome: LNSP, miscarriage, pregnancy of unknown location (PUL) or tubal EP (tEP). Miscarriage, PUL and tEP were grouped together into a CAO group. Human chorionic gonadotrophin β (β-hCG) and progesterone concentrations were measured in plasma. Serum samples were subjected to untargeted metabolomic profiling. The cohort was randomly split into train and validation data sets, with the train data set subjected to variable selection. Nine metabolite signals were identified as key discriminators of LNSP versus CAO. Random forest models were constructed using stable metabolite signals alone, or in combination with plasma hormone concentrations and demographic data. When comparing LNSP with CAO, a model with stable metabolite signals only demonstrated a modest predictive accuracy (0.68), which was comparable to a model of β-hCG and progesterone (0.71). The best model for LNSP prediction comprised stable metabolite signals and hormone concentrations (accuracy = 0.79). In conclusion, serum metabolite levels and biochemical markers from a single blood sample possess modest predictive utility in differentiating LNSP from CAO pregnancies upon first presentation, which is improved by variable selection and combination using machine learning. A diagnostic test to confirm LNSP and thus exclude pregnancies affecting maternal morbidity and potentially life-threatening outcomes would be invaluable in emergency situations., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

This paper analyzes the actors in the climate change arena and their influence in directing Mexico toward policies that decrease greenhouse gas emissions, such as the carbon tax and climate change law. The network analysis of the agreement of these laws and public policies in Mexico is a lesson for any country that is in the process of designing and adopting environmental laws. The research is performed using a network analysis that is derived from interviews with various main actors and a discourse analysis of the media. Results show that actors do not coordinate their efforts-they meet frequently but in different inter-ministerial commissions-and do not enforce the same policies. The actors in the industry have formed strong coalitions against the carbon tax and the General Law on Climate Change, whereas international institutions have formed coalitions that support these policies and laws., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Objectives: To know the opinion of medical residents in hospitals in Catalonia about the need for and usefulness of the training they receive in the emergency department., Material and Methods: We sent an electronic questionnaire to all residents in Catalonia, through their cooperating supervisors. The questionnaire contained items to collect information on sociodemographic variables and attitudes toward emergency medicine. Items related to training covered the residents' assessment of the need for a rotation in the emergency department and the knowledge and skills acquired during the rotation (case history writing, relations with patients' relatives, teamwork, decision-making, identifying and managing critical patients, acquisition of diagnostic and therapeutic techniques). We compiled descriptive statistics and compared the results for residents from different specialties., Results: Questionnaires were sent to 1431 residents in 21 hospitals and other training facilities. Responses were received from 427 (29.8%). Mean (SD) scores expressed on a scale of 1 to 10 were high for both the need for training in emergency medicine (8.9 [1.7]) and knowledge acquired during the rotation (8.2 [1.9]). The residents reported that they had acquired more knowledge in the areas of decision-making and management of critical patients. Family medicine residents expressed greater interest in choosing the specialty of emergency medicine (33.7% vs 6.1% for other residents, P<.001), and their opinion of the need for training in emergency medicine was also higher than other residents' (9.2 [1.5] vs 8.7 [1.8], P=.006)., Conclusion: Medical residents in Catalonia believe that a rotation in the emergency department provides necessary and useful training. Family medicine residents are the ones who value emergency training most highly.