Improving cefazolin administration for surgical prophylaxis in reported penicillin allergy: A retrospective study of a health system intervention.

Background: Cefazolin is the most common first-line antibiotic to prevent surgical-site infections. Patients with penicillin allergy labels often receive alternative antibiotics, which is associated with increased rates of surgical-site infections, multi-drug-resistant infections, and cost.

Objective: We sought to determine whether a hospital-wide guideline recommending first-line surgical prophylaxis in patients with penicillin allergy labels can increase the use of cefazolin without compromising safety.

Electrophysiological insights into Alzheimer's disease: A review of human and animal studies.

This review highlights the crucial role of neuroelectrophysiology in illuminating the mechanisms underlying Alzheimer's disease (AD) pathogenesis and progression, emphasizing its potential to inform the development of effective treatments. Electrophysiological techniques provide unparalleled precision in exploring the intricate networks affected by AD, offering insights into the synaptic dysfunction, network alterations, and oscillatory abnormalities that characterize the disease. We discuss a range of electrophysiological methods, from non-invasive clinical techniques like electroencephalography and magnetoencephalography to invasive recordings in animal models.

Complexity demands more flexibility and the prefrontal cortex has an answer.

Cognitive flexibility allows us to adapt our behavior to keep up with a changing environment. In this issue of Neuron, Mugan and colleagues manipulate the complexity of an environment to demonstrate how the medial prefrontal cortex controls a cognitive flexibility circuit featuring the dorsolateral striatum and hippocampus.

A Prostate Imaging-Reporting and Data System version 2.1-based predictive model for clinically significant prostate cancer diagnosis.

Objectives: To develop and validate a Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 (v2.1)-based predictive model for diagnosis of clinically significant prostate cancer (csPCa), integrating clinical and multiparametric magnetic resonance imaging (mpMRI) data, and compare its performance with existing models.