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Diffuse large B‐cell lymphoma (DLBCL) is up to 17‐fold more likely to occur, follows a more aggressive clinical course and frequently presents at advanced stages in HIV infected (+) individuals compared to HIV negative (−) individuals. However, the molecular pathology underpinning the clinical features of DLBCL in HIV(+) patients relative to the general population is poorly understood. We performed a retrospective study examining the transcriptional, genomic and protein expression differences between HIV(+) and HIV(−) germinal center B‐cell (GCB) DLBCL cases using digital gene expression analysis, array comparative genomic hybridization (CGH) and immunohistochemistry (IHC). Genes associated with cell cycle progression ( CCNA2 , CCNB1 , CDC25A , E2F1 ), DNA replication ( MCM2 , MCM4 , MCM7 ) and DNA damage repair, including eight Fanconi anemia genes ( FANCA , FANCD1/BRCA2 , FANCE , FANCG , FANCR/RAD51 , FANCS/BRCA1 , FANCT/UBE2T , FANCV/MAD2L2 ), were significantly increased in HIV(+) GCB‐DLBCL tumors compared to HIV(−) tumors. In contrast, genes associated with cell cycle inhibition ( CDKN1A , CDKN1B ) as well as apoptosis regulating BCL2 family members ( BCL2 , BAX , BIM , BMF , PUMA ) were significantly decreased in the HIV(+) cohort. BCL2 IHC confirmed this expression. Array CGH data revealed that HIV(+) GCB‐DLBCL tumors have fewer copy number variations than their HIV(−) counterparts, indicating enhanced genomic stability. Together, the results show that HIV(+) GCB‐DLBCL is a distinct molecular malignancy from HIV(−) GCB‐DLBCL; with an increased proliferative capacity, confirmed by Ki67 IHC staining, and enhanced genomic stability, the latter of which is likely related to the enhanced expression of DNA repair genes.

This article introduces the Consumer Ear Disease Risk Assessment (CEDRA) tool. CEDRA is a brief questionnaire designed to screen for targeted ear diseases. It offers an opportunity for consumers to self-screen for disease before seeking a hearing device and may be used by clinicians to help their patients decide the appropriate path to follow in hearing healthcare. Here we provide highlights of previously published validation in the context of a more thorough description of CEDRA’s development and implementation. CEDRA’s sensitivity and specificity, using a cut-off score of 4 or higher, was 90% and 72%, respectively, relative to neurotologist diagnoses in the initial training sample used to create the scoring algorithm (n = 246). On a smaller independent test sample (n = 61), CEDRA’s sensitivity and specificity were 76% and 80%, respectively. CEDRA has readability levels similar to many other patient-oriented questionnaires in hearing healthcare, and informal reports from pilot CEDRA-providers indicate that the majority of patients can complete it in less than 10 min. As the hearing healthcare landscape changes and provider intercession is no longer mandated, CEDRA provides a measure of safety without creating a barrier to access.

PurposeThis work aims to develop a new framework of image quality assessment using deep learning‐based model observer (DL‐MO) and to validate it in a low‐contrast lesion detection task that involves CT images with patient anatomical background.MethodsThe DL‐MO was developed using the transfer learning strategy to incorporate a pretrained deep convolutional neural network (CNN), a partial least square regression discriminant analysis (PLS‐DA) model and an internal noise component. The CNN was previously trained to achieve the state‐of‐the‐art classification accuracy over a natural image database. The earlier layers of the CNN were used as a deep feature extractor, with the assumption that similarity exists between the CNN and the human visual system. The PLSR model was used to further engineer the deep feature for the lesion detection task in CT images. The internal noise component was incorporated to model the inefficiency and variability of human observer (HO) performance, and to generate the ultimate DL‐MO test statistics. Seven abdominal CT exams were retrospectively collected from the same type of CT scanners. To compare DL‐MO with HO, 12 experimental conditions with varying lesion size, lesion contrast, radiation dose, and reconstruction types were generated, each condition with 154 trials. CT images of a real liver metastatic lesion were numerically modified to generate lesion models with four lesion sizes (5, 7, 9, and 11 mm) and three contrast levels (15, 20, and 25 HU). The lesions were inserted into patient liver images using a projection‐based method. A validated noise insertion tool was used to synthesize CT exams with 50% and 25% of routine radiation dose level. CT images were reconstructed using the weighted filtered back projection algorithm and an iterative reconstruction algorithm. Four medical physicists performed a two‐alternative forced choice (2AFC) detection task (with multislice scrolling viewing mode) on patient images across the 12 experimental conditions. DL‐MO was operated on the same datasets. Statistical analyses were performed to evaluate the correlation and agreement between DL‐MO and HO.ResultsA statistically significant positive correlation was observed between DL‐MO and HO for the 2AFC low‐contrast detection task that involves patient liver background. The corresponding Pearson product moment correlation coefficient was 0.986 [95% confidence interval (0.950, 0.996)]. Bland–Altman agreement analysis did not indicate statistically significant differences.ConclusionsThe proposed DL‐MO is highly correlated with HO in a low‐contrast detection task that involves realistic patient liver background. This study demonstrated the potential of the proposed DL‐MO to assess image quality directly based on patient images in realistic, clinically relevant CT tasks.

The duration of contact time of intratympanic steroids at the round window is a variable that can potentially affect the ultimate concentration within the cochlea. By placing Gelfoam saturated with dexamethasone directly over the round window, contact time is prolonged and potentially increases the dexamethasone concentration within the cochlea. This technique is simple, readily available with standard instruments and ingredients, and easily done in the office.

Objectives: Patients and caregivers can experience a range of physical, psychologic, and cognitive problems following critical care discharge. The use of peer support has been proposed as an innovative support mechanism. Design: We sought to identify technical, safety, and procedural aspects of existing operational models of peer support, among the Society of Critical Care Medicine Thrive Peer Support Collaborative. We also sought to categorize key distinctions between these models and elucidate barriers and facilitators to implementation. Subjects and Setting: Seventeen Thrive sites from the United States, United Kingdom, and Australia were represented by a range of healthcare professionals. Measurements and Main Results: Via an iterative process of in-person and email/conference calls, members of the Collaborative defined the key areas on which peer support models could be defined and compared, collected detailed self-reports from all sites, reviewed the information, and identified clusters of models. Barriers and challenges to implementation of peer support models were also documented. Within the Thrive Collaborative, six general models of peer support were identified: community based, psychologist-led outpatient, models-based within ICU follow-up clinics, online, groups based within ICU, and peer mentor models. The most common barriers to implementation were recruitment to groups, personnel input and training, sustainability and funding, risk management, and measuring success. Conclusions: A number of different models of peer support are currently being developed to help patients and families recover and grow in the postcritical care setting.