Investigating the impact of electroconvulsive therapy on brain networks and sleep: an observational study protocol.

Introduction: Electroconvulsive therapy (ECT) is a highly effective treatment for refractory depression, but it may also cause cognitive side effects. Despite decades of use, the mechanisms by which ECT exerts both its antidepressant and cognitive effects are still poorly understood, with the latter substantially limiting referral and adherence to therapy. ECT induces changes in correlated neural activity-functional connectivity-across various brain networks, which may underlie both its clinical efficacy and associated cognitive side effects.

Revisiting tribromoethanol as a safe and effective murine anesthetic for veterinary clinical and biomedical research.

Mus musculus, the house mouse, is the most widely used mammalian model in biomedical research. Mice frequently undergo injectable anesthesia for numerous research procedures, with the most common anesthetic protocol being ketamine-xylazine (K/X). 2,2,2-Tribromoethanol (TBE), a non-regulated chemical, is also used, but has been linked to peritonitis.

The 2022 ADEA Climate Study in U.S. and Canadian Dental Schools and Allied Dental Programs: Methodological Considerations.

January 28, 2025: This article published in Early View in error. This article is under embargo and will republish in April.

The lived experience of women studying nursing online in regional, rural and remote areas: an integrative literature review.

Background: There is a critical shortage of registered nurses working in regional, rural and remote Australia. It is the people from these areas that are most likely to address this shortage. This research therefore sets out to explore the question "what is known about the experiences of regional, rural and remote Australian women undertaking a Bachelor of Nursing program delivered online?".

Scaling convolutional neural networks achieves expert level seizure detection in neonatal EEG.

Neonatal seizures require urgent treatment, but often go undetected without expert EEG monitoring. We have developed and validated a seizure detection model using retrospective EEG data from 332 neonates. A convolutional neural network was trained and tested on over 50,000 hours (n = 202) of annotated single-channel EEG containing 12,402 seizure events.