Optical Coherence Tomography Angiography in Patients With the Boston Keratoprosthesis Type 1.

Purpose: To report on optical coherence tomography angiography (OCTA) in patients with a type 1 Boston keratoprosthesis (KPro) and determine its feasibility through assessment of imaging artifacts.

Methods: KPro and non-KPro subjects were matched for age, gender, and glaucoma diagnosis. OCTA images of the peripapillary optic nerve were obtained, reviewed by 2 readers masked to the diagnosis for artifacts and usability, and used for microvascular measurements.

Host-virus interactions during infection with a wild-type ILTV strain or a glycoprotein G deletion mutant ILTV vaccine strain in an system.

Previous studies have demonstrated the safety and efficacy of a live-attenuated glycoprotein G (gG) deletion mutant vaccine strain of ILTV (∆gG-ILTV). In the current study, transcriptional profiles of chicken tracheal organ cultures (TOCs), 24 h post inoculation with ∆gG-ILTV or the gG-expressing parent wild-type strain, CSW-1 ILTV were explored and compared with the mock-infected TOCs using RNA-seq analysis. Transcriptomes of the vaccine and wild-type ILTV were also compared with each other.

Advancing Delirium Treatment Trials in Older Adults: Recommendations for Future Trials From the Network for Investigation of Delirium: Unifying Scientists (NIDUS).

Objectives: To summarize the delirium treatment trial literature, identify the unique challenges in delirium treatment trials, and formulate recommendations to address each in older adults.

Design: A 39-member interprofessional and international expert working group of clinicians (physicians, nurses, and pharmacists) and nonclinicians (biostatisticians, epidemiologists, and trial methodologists) was convened. Four expert panels were assembled to explore key subtopics (pharmacological/nonpharmacologic treatment, methodological challenges, and novel research designs).

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

Background: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time-dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO.