No abstract

Urinary tract infections (UTIs) are associated with significant morbidity. We rely on clinical presentation, urinalysis, and urine culture to diagnose UTI. To differentiate between lower UTI and pyelonephritis, we depend on the clinical presentation. In the extremes of age and in immunocompromised individuals, clinical presentation is often atypical posing a challenge to diagnosis. In the elderly, the high prevalence of asymptomatic bacteriuria is another confounder. We conducted a search of publications to find novel biomarkers to diagnose UTI and to ascertain its severity. We searched PUBMED, MEDLINE and SCOPUS databases for studies pertaining to novel biomarkers and UTI. Two reviewers independently evaluated the methodology of the studies using the STARD (Standards for Reporting of Diagnostic Accuracy) criteria. We have identified procalcitonin as a biomarker to differentiate lower UTI from pyelonephritis in the pediatric age group. Elevated serum procalcitonin levels can result in early and aggressive treatment at the time of presentation. Interleukin 6 has also shown some promise in differentiating between lower UTI and pyelonephritis but needs further validation. Lastly, given the paucity of data in certain subgroups like diabetics, kidney transplant recipients, and individuals with spinal cord injury, further studies should be conducted in these populations to improve diagnostic criteria that will inform clinical management decisions.

Purpose The aim of the study was to determine the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in a university student population. Methods This was a cross-sectional survey study based on the World Health Organization population-based seroepidemiological investigational protocol for SARS-CoV-2 conducted between April 29, 2020, and May 8, 2020, examining SARS-CoV-2 antibody prevalence among 790 university students in Los Angeles, CA. Participants completed a questionnaire on potential risk factors before blood sampling. Samples were analyzed using the EUROIMMUN Anti-SARS-CoV-2 ELISA (IgG) for the qualitative detection of IgG class antibodies to SARS-CoV-2 in human serum or plasma. Results The estimated prevalence of SARS-CoV-2 antibody was 4.0% (3.0%, 5.1%). Factors associated with having a positive test included history of anosmia and/or loss of taste (95% CI: 1.4–9.6). A history of respiratory symptoms, with or without fever, was not associated with a positive antibody test. Conclusions Prevalence of SARS-CoV-2 antibodies in the undergraduate and graduate student university population was similar to community prevalence.

Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with $$\text {AUC} = 0.80$$ AUC = 0.80 for predicting ICU need and $$\text {AUC} = 0.82$$ AUC = 0.82 for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predictors with a reduced set of five features that retained the performance of the predictors trained on all features. These predictors, which rely only on quantitative data, are less prone to errors and subjectivity.

Background & aims-Many patients with pancreatic adenocarcinoma (PDAC) carry germline mutations associated with increased risk of cancer. It is not clear whether patients with intraductal