Improved Diagnostic Sensitivity of Bowel Disease of Prematurity on Contrast-Enhanced Ultrasound

Bowel diseases of prematurity, including necrotizing enterocolitis, are dreaded ailments of neonates. Early diagnosis is difficult, with clinical and radiographic findings often inconclusive. We present a novel use of contrast-enhanced ultrasound in detection of pediatric bowel disease. Early identification of compromised blood flow or an at-risk bowel can be quantitatively detected and monitored. This ability has implications for guidance of emerging therapies, allowing targeting of inflammation. These findings represent an advancement in detection of bowel disease in neonates.     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

An overview of existing mesothelioma registries worldwide,and the need for a US Registry

The association between asbestos exposure, mainly in occupational settings, and malignant mesothelioma has been well established; this has prompted several countries to establish mesothelioma epidemiologic surveillance programs often at the request of national agencies. This review compares currently existing mesothelioma registries worldwide to develop a concept model for a US real-time case capture mesothelioma registry. Five countries were identified with a mesothelioma specific registry, including Italy, France, UK, Australia, and South Korea. All, except the UK, used interviews to collect exposure data. Linkage with the national death index was available or was in future plans for all registries. The registries have limited information on treatment, quality of life, and other patient-centered outcomes such as symptoms and pain management. To thoroughly collect exposure data, “real-time” enrollment is preferable; to maximize the capture of mesothelioma cases, optimal coverage, and a simplified consent process are needed.