Purple urine bag syndrome (PUBS) is a complication of urinary tract infections (UTIs) where catheter bags and tubing turn purple. It is alarming for patients, families, and clinicians; however, it is in itself a benign phenomenon. PUBS is the result of UTIs with specific bacteria that produce sulphatases and phosphatases which lead tryptophan metabolism to produce indigo (blue) and indirubin (red) pigments, a mixture of which becomes purple. Risk factors include female gender, immobility, constipation, chronic catheterisation, and renal disease. Management involves reassurance, antibiotics, and regular changing of catheters, although there are debates regarding how aggressively to treat and no official guidelines. Prognosis is good, but PUBS is associated with high morbidity and mortality due to the backgrounds of patients. Here, we review the literature available on PUBS, present a summary of case studies from the last five years, and propose the Oxford Urine Chart as a tool to aid such diagnoses.
Shared decision‐making (SDM) is a collaborative process through which patients and clinicians work together to arrive at a mutually agreed‐upon treatment plan. The use of SDM has gathered momentum, with it being legally mandated in some areas; however, despite being a ubiquitously applicable intervention, its maturity in use varies across the specialties and requires an appreciation of the nuanced and different challenges they each present. It is therefore our aim in this paper to review the current and potential use of SDM across a wide variety of specialties in order to understand its value and the challenges in its implementation. The specialties we consider are Primary Care, Mental Health, Paediatrics, Palliative Care, Medicine, and Surgery.
SDM has been demonstrated to improve decision quality in many scenarios across all of these specialties. There are, however, many challenges to its successful implementation, including the need for high‐quality decision aids, cultural shift, and adequate training. SDM represents a paradigm shift towards more patient‐centred care but must be implemented with continued people centricity in order to realize its full potential.
To develop care of diabetes further a specialist nurse established contact with general practices in Sheffield Health District and identified difficulties in providing a service for diabetics. One hundred and thirty practices were visited, and full data were collected from 104. Each practice agreed to establish a register of diabetics, and information and support were subsequently provided to help in developing services. In collecting information from each practice the nurse covered specific points on staff, facilities, and organisation.Over two years the service offered in 60 practices considerably improved, allowing a minimum standard of diabetic care to be achieved. This allowed coordinated and effective referral of certain patients from hospital diabetic clinics and improved services to those not attending any clinics.
Objectives
Positron emission tomography (PET) imaging is a costly tracer-based imaging modality used to visualise abnormal metabolic activity for the management of malignancies. The objective of this study is to demonstrate that non-contrast CTs alone can be used to differentiate regions with different Fluorodeoxyglucose (FDG) uptake and simulate PET images to guide clinical management.
Methods
Paired FDG-PET and CT images (n = 298 patients) with diagnosed head and neck squamous cell carcinoma (HNSCC) were obtained from The cancer imaging archive. Random forest (RF) classification of CT-derived radiomic features was used to differentiate metabolically active (tumour) and inactive tissues (ex. thyroid tissue). Subsequently, a deep learning generative adversarial network (GAN) was trained for this CT to PET transformation task without tracer injection. The simulated PET images were evaluated for technical accuracy (PERCIST v.1 criteria) and their ability to predict clinical outcome [(1) locoregional recurrence, (2) distant metastasis and (3) patient survival].
Results
From 298 patients, 683 hot spots of elevated FDG uptake (elevated SUV, 6.03 ± 1.71) were identified. RF models of intensity-based CT-derived radiomic features were able to differentiate regions of negligible, low and elevated FDG uptake within and surrounding the tumour. Using the GAN-simulated PET image alone, we were able to predict clinical outcome to the same accuracy as that achieved using FDG-PET images.
Conclusion
This pipeline demonstrates a deep learning methodology to simulate PET images from CT images in HNSCC without the use of radioactive tracer. The same pipeline can be applied to other pathologies that require PET imaging.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.