Kamp Le (marchstar12)

These results suggested inland river discharges influenced the occurrence and abundance of azo dye degrading genes in the nearshore environments. Furthermore, the azoR genes had a significant negative relationship with total organic carbon, Hg, and Cr (p less then 0.05). This study provides critical insights into the biodegradation potential of indigenous microbial communities in nearshore environments and the influence of environmental factors on microbial structure, composition, and function which is essential for the development of technologies for bioremediation in azo dye contaminated sites.The association between elevated plasma vitamin B12 (B12) level and solid cancers has been documented by two national registries. However, their design did not allow for the adjustment for other conditions associated with elevated B12. The objectives of this study were to confirm this association after the adjustment for all causes of elevated B12, and to study the variations according to the increasing B12 level, the type of cancers, and the presence of metastases. We compared 785 patients with B12 ≥ 1000 ng/L with 785 controls matched for sex and age with B12 less then 1000 ng/L. Analyses were adjusted for the causes of elevated B12 myeloid blood malignancies, acute or chronic liver diseases, chronic kidney failure, autoimmune or inflammatory diseases, and excessive B12 supplementation. A B12 ≥ 1000 ng/L was associated with the presence of solid cancer without metastases (OR 1.96 [95%CI 1.18 to 3.25]) and with metastases (OR 4.21 [95%CI 2.67 to 6.64]) after adjustment for all elevated B12-related causes. The strength of the association rose with the increasing B12 level, in particular in cases of metastases. No association between liver cancers and elevated B12 level was found after adjustment for chronic liver diseases. In conclusion, unexplained elevated B12 levels should be examined as a possible marker of solid cancer.Background The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query" (precision = 0.48). Conclusion To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.BACKGROUND Experience in real clinical practice with ceftazidime-avibactam for the treatment of serious infections due to gram