Automating detection of diagnostic error of infectious diseases using machine learning.

Diagnostic error, a cause of substantial morbidity and mortality, is largely discovered and evaluated through self-report and manual review, which is costly and not suitable to real-time intervention. Opportunities exist to leverage electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalized across diseases. We propose a novel automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality.

Successful long-term therapy of mucormycosis with isavuconazole.

We present a case of a 55-year-old poorly controlled diabetic who presented to the hospital with facial pain, ophthalmoplegia, vision changes, and diabetic ketoacidosis and was diagnosed with rhinocerebral mucormycosis due to . He was started on liposomal amphotericin B and micafungin and went for nasal endoscopy and debridement, but the infection had progressed through the base of the skull and he received the maximum tolerated debridement. Posaconazole was added and discontinued due to elevated liver chemistry tests and was replaced with oral isavuconazole.

Clade-specific variation in susceptibility of to broad-spectrum ultraviolet C light (UV-C).

Background: Candida auris is an emerging and often multidrug-resistant fungal pathogen with an exceptional ability to persist on hospital surfaces. These surfaces can act as a potential source of transmission. Therefore, effective disinfection strategies are urgently needed.