Forecasting severe respiratory disease hospitalizations using machine learning algorithms.

Background: Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload.

Quality improvement approach to reduce patient cycle time at a student-run free healthcare clinical network.

Background: The University of Florida (UF) Equal Access Clinic Network (EACN) is the largest student-run free healthcare clinic network in Florida. The UF EACN serves those who are underinsured or uninsured in Alachua County and its surrounding area. Nationally, average total clinic time per medical visit has been established to be 84 min.

Aloe vera leaf mucilage and lemon oil as potential penetration-enhancing agents to increase lornoxicam transdermal administration using nano vesicular gel.

The goal of the existing work was to create matrix transdermal patches with lornoxicam (LXM) gel using lemon oil (LO) and Aloe vera leaves mucilage (AVLM) as penetration enhancers to boost LXM transport crossways the skin and test its in vivo analgesic effects. Nine formulas were produced for this purpose using Design Expert® 11 in line with CCD design. The response factors, on the other hand, were Q (Y), Q (Y) and Q, or LXM permeation at days 1, 2 and 3.