Pre-vascularisation of the acellular bladder matrix using the omentum in a porcine model prior for bladder reconstruction-a step towards clinical application?

Aim: Research studies with porcine acellular bladder matrix (PABM) showed integration of only small sized stamps in recipient bladders, however for clinical use in bladder augmentation significantly larger patches are needed. We hypothesised pre-vascularisation with omentum may be a step towards clinical translation.

Method: Eight domestic pigs were operated three times 8-10 weeks apart: 1-Implantation; PABM with recorded dimensions were sutured around a tissue expanding device, wrapped in omentum and sutured to the anterior abdominal wall.

A Comparison of Treatment Practices for Newborn Seizure Management Across Level II and III Neonatal Intensive Care Units in the United States.

Background: Neonatal seizures (NS) represent an important clinical manifestation among critically ill infants and are often the first sign of underlying brain injury. Early recognition and treatment are essential to reduce morbidity and mortality. The present study investigated the NS management and treatment approaches employed by level II/III neonatal intensive care units (NICUs) across the United States to identify areas of consensus and variability.

Living With Spasticity During the COVID-19 Pandemic: A Qualitative Study of Patient, Carer and Physician Experiences.

Background: Approximately 4.4 million people in England (8% of the total population) are living with a long-term neurological condition. Within this group of vulnerable individuals, there will be individuals living with severe spasticity that requires regular outpatient treatment with botulinum toxin injection.

A Comparison of Automatically Extracted Quantitative EEG Features for Seizure Risk Stratification in Neonatal Encephalopathy.

Purpose: Seizures occur in up to 40% of neonates with neonatal encephalopathy. Earlier identification of seizures leads to more successful seizure treatment, but is often delayed because of limited availability of continuous EEG monitoring. Clinical variables poorly stratify seizure risk, and EEG use to stratify seizure risk has previously been limited by need for manual review and artifact exclusion.

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation.

Introduction: Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening.