Citations

Context.— Distinguishing between renal oncocytic tumors, such as renal oncocytoma (RO), and a subset of tumors with overlapping characteristics, including the recently identified low-grade oncocytic tumor (LOT), can present a diagnostic challenge for pathologists owing to shared histopathologic features. Objective.— To develop an automatic computational classifier for stratifying whole slide images of biopsy and resection specimens into 2 distinct groups: RO and LOT. Design.— A total of 269 whole slide images from 125 cases across 6 institutions were collected. A weakly supervised attention-based multiple-instance–learning deep learning (DL) model was trained and initially evaluated through 5-fold cross validation with case-level stratification, followed by validation using an independent holdout data set. Quantitative performance evaluation was based on accuracy and the area under the receiver operating characteristic curve (AUC). Results.— The developed model data set yielded generalizable performance, with a 5-fold average testing accuracy of 84% (AUC = 0.78), and a closely aligning accuracy of 83% (AUC = 0.92) on the independent holdout data set. Conclusions.— The proposed artificial intelligence approach contributes toward a comprehensive solution for addressing commonly encountered renal oncocytic neoplasms, encompassing well-established entities like RO along with the challenging “gray zone” LOT, thereby proving applicable in clinical practice.

Introduction Diarrhoea is a frequent concern in the intensive care unit (ICU) and is associated with prolonged mechanical ventilation, increased length of ICU stay, skin breakdown and renal dysfunction. However, its prevalence, aetiology and prognosis in the critically ill have been poorly studied. The primary objectives of this study are to determine the incidence, risk factors and consequences of diarrhoea in critically ill adults. The secondary objectives are to estimate the incidence of Clostridium difficile- associated diarrhoea (CDAD) in ICU patients and to validate the Bristol Stool Chart and Bliss Stool Classification System characterising bowel movements in the ICU. Our primary outcome is the incidence of diarrhoea . Our secondary outcomes include: CDAD, ICU and hospital mortality and ICU and hospital length of stay. Methods and analysis This international prospective cohort study will enrol patients over 10 weeks in 12 ICUs in Canada, the USA, Poland and Saudi Arabia. We will include all patients 18 years of age and older who are admitted to the ICU for at least 24 hours and follow them daily until ICU discharge. Our primary outcome is the incidence of diarrhoea based on the WHO definition, during the ICU stay. Our secondary outcomes include: CDAD, ICU and hospital mortality and ICU and hospital length of stay. We will use logistic regression to identify factors associated with diarrhoea (as defined using WHO criteria) and the kappa statistic to measure agreement on diarrhoea rates between the WHO definition and the Bristol Stool Chart and Bliss Stool Classification System. Ethics and dissemination The protocol has been approved by the research ethics board of all participating centres. The diarrhoea interventions, consequences and epidemiology in the intensive care unit (DICE-ICU) study will generate evidence about diarrhoea and its frequency, predisposing factors and consequences, to inform critical care practice and future research. Lay summary Diarrhoea is a frequent clinical problem for hospitalised patients including those who are critically ill in the ICU. Diarrhoea can cause complications such as skin damage, dehydration and kidney problems. It is not clear how common diarrhoea is in the ICU, the factors that cause it or the best way for clinicians to assess it. The DICE-ICU study is an international prospective observational study to examine the frequency, risk factors and outcomes of diarrhoea during critical illness.

OBJECTIVE Fasting hyperinsulinemia in the presence of normoglycemia usually indicates insulin resistance and is characteristic of populations at high risk for developing NIDDM. Hyperinsulinemia predicts the development of impaired glucose tolerance and NIDDM in Pima Indians, a population with a high incidence of NIDDM. Insulin concentrations in population-based samples of children who have different risks of developing NIDDM later in life have not been reported previously. RESEARCH DESIGN AND METHODS We compared fasting insulin concentrations in two populations of nondiabetic children, 6–19 yr of age: Pima Indians from southern Arizona and Caucasians from Minnesota. RESULTS Insulin concentration varied with age, sex, glucose concentration, and relative weight. Mean fasting insulin concentration was 140.3 pM in Pima Indian males, 94.4 pM in Caucasian males, 171.5 pM in Pima Indian females, and 107.1 pM in Caucasian females. For each sex, the mean fasting insulin concentration, controlled for age, glucose, and relative weight, was significantly higher in the Pima Indians than in the Caucasians (P < 0.001). CONCLUSIONS From a young age, Pima Indian children have higher fasting insulin concentrations than Caucasian children. As hyperinsulinemia predicts subsequent NIDDM, these data suggest that the susceptibility to NIDDM is manifest at a young age as fasting hyperinsulinemia.