Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care.

Background: Subclinical depression (SD) is a mental health disorder characterized by minor depressive symptoms. Most SD patients are treated in the primary practice, but many respond poorly to treatment at the expense of provider resources. Stepped care approaches are appealing for tiering SD care to efficiently allocate scarce resources while jointly optimizing patient outcomes.

Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports.

Importance: Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). However, designing new NLP algorithms for each individual injury category is an unwieldy proposition.

Incidence of diabetes mellitus and factors associated with its development in HIV-positive patients over the age of 50.

Objective: We sought to determine the incidence and factors associated with development of diabetes mellitus (DM) in older HIV-infected individuals.

Research Design And Methods: We analyzed data from people living with HIV (PLWH) ≥50 years of age enrolled in a large urban HIV outpatient clinic in Vancouver, British Columbia. Patients were categorized as having DM if they had random blood sugar ≥11.

Regulation of flagellum number by FliA and FlgM and role in biofilm formation by Rhodobacter sphaeroides.

The FlgM secretion checkpoint plays a crucial role in coordinating bacterial flagellar assembly. Here we identify a new role for FlgM and FliA as part of a complex regulatory network which controls flagellum number and is essential for efficient swimming and biofilm formation in the monotrichous bacterium Rhodobacter sphaeroides.