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Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event.sup.1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD).sup.2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma.sup.5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment.sup.6-9 to mitigate subsequent psychopathology in high-risk populations.sup.10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care. A machine-learning algorithm using electronic medical records and self-reported measures of stress at admission to the emergency department due to trauma can predict the risk and long-term trajectories of post-traumatic stress disorder in two independent cohorts., Author(s): Katharina Schultebraucks [sup.1] [sup.2] [sup.3] , Arieh Y. Shalev [sup.1] , Vasiliki Michopoulos [sup.4] [sup.5] , Corita R. Grudzen [sup.6] , Soo-Min Shin [sup.6] , Jennifer S. Stevens [sup.4] [...]

The biosand filter (BSF) is widely applied in developing counties as an appropriate technology-based product for supplying "safe" water. Biosand filters exhibit relatively high purifying efficiency because of the schmutzdecke (biofilm) embedded in them. However, schmutzdecke should be cleaned or discarded on a regular basis to maintain the purifying efficiency of the BSF. Due to its role in BSFs, the purifying function of schmutzdecke, rather than its potential risk when not properly discarded, has so far been the primary focus of research. This study aims to provide a risk assessment of schmutzdecke in an attempt to draw attention to a wholly new angle of schmutzdecke usage. We conducted 16S rRNA gene sequencing and phylogenetic analysis to identify opportunistic pathogens in schmutzdecke developed using water from the Hyung-San River. The results reveal that the schmutzdecke derived from this water source contains diverse and relatively high portions of opportunistic pathogen strains; 55% of all isolates collected from schmutzdecke were identified as opportunistic pathogens. Moreover, the diversity of microorganisms is increased in the schmutzdecke compared to its water source in terms of diversity of genus, phylum and opportunistic pathogen strain. As a whole, our study indicates a potential risk associated with schmutzdecke and the necessity of a solid guideline for the after-treatment of discarded schmutzdecke.