A very low thymus function identifies patients with substantial increased risk for long-term mortality after kidney transplantation

Background: End-stage renal disease is associated with premature ageing of the T cell immune system but inter-individual variation is substantial. The hypothesis was tested that advanced immunological T cell ageing assessed by peripheral T cell differentiation increases the long-term mortality risk after renal transplantation. Results: Circulating T cells of 211 recipients of a kidney from a living donor were analyzed before and in the first year after transplantation. The number of CD31-positive naive T cells (as a marker for recent thymic emigrants) and the differentiation status of the memory T cells was assessed. Thirty recipients died during follow-up of at least 5 years. Absolute numbers of naive CD4+ (living:258 cells/μl vs. deceased:101 cells/μl, p < 0.001) and naive CD8+ T cells (living:97 cells/μl vs. deceased:37 cells/μl, p < 0.001) were significantly lower in the deceased group prior to transplantation. In a multivariate proportional hazard analysis the number of naive CD4+ T cells remained associated with all-cause mortality (HR 0.98, CI 0.98-0.99, p < 0.001). The low number of naive T cells in the deceased patient group was primarily caused by a decrease in recent thymic emigrants (i.e. less CD31+ naive T cells) indicating a lowered thymus function. In addition, the physiological age-related compensatory increase in CD31- naïve T cells was not observed. Within the first year after transp

Increased CD16 expression on NK cells is indicative of antibody-dependent cell-mediated cytotoxicity in chronic-active antibody-mediated rejection

Chronic-active antibody mediated rejection (c-aABMR) contributes significantly to late renal allograft failure. The antibodies directed against donor-derived antigens, e.g. anti-HLA antibodies, cause inflammation at the level of the microvascular endothelium. This is characterized by signs of local activation of the complement system and accumulation of immune cells within the capillaries. Non-invasive biomarkers of c-aABMR are currently not available but could be valuable for early detection. We therefore analyzed the activation profiles of circulating T and B cells, NK cells and monocytes in the peripheral blood of 25 kidney transplant recipients with c-aABMR and compared them to 25 matched recipients to evaluate whether they could serve as a potential biomarker. No significant differences were found in the total percentage and distribution of NK cells, B cells and T cells between the c-aABMRpos and c-aABMRneg cases. There was however a higher percentage of monocytes present in c-aABMRpos cases (p < .05). Additionally, differences were found in activation status of circulating monocytes, NK cells and γδ T cells, mainly concerning the activation marker CD16. Although statistically significant, these differences were not sufficient for use as a biomarker of c-aABMR

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Source-Relaxed Domain Adaptation for Image Segmentation

Domain adaptation (DA) has drawn high interests for its capacity to adapt a model trained on labeled source data to perform well on unlabeled or weakly labeled target data from a different domain. Most common DA techniques require the concurrent access to the input images of both the source and target domains. However, in practice, it is common that the source images are not available in the adaptation phase. This is a very frequent DA scenario in medical imaging, for instance, when the source and target images come from different clinical sites. We propose a novel formulation for adapting segmentation networks, which relaxes such a constraint. Our formulation is based on minimizing a label-free entropy loss defined over target-domain data, which we further guide with a domain invariant prior on the segmentation regions. Many priors can be used, derived from anatomical information. Here, a class-ratio prior is learned via an auxiliary network and integrated in the form of a Kullback-Leibler (KL) divergence in our overall loss function. We show the effectiveness of our prior-aware entropy minimization in adapting spine segmentation across different MRI modalities. Our method yields comparable results to several state-of-the-art adaptation techniques, even though is has access to less information, the source images being absent in the adaptation phase. Our straight-forward adaptation strategy only uses one network, contrary to popular adversarial techniques, which cannot perform without the presence of the source images. Our framework can be readily used with various priors and segmentation problems

Keith Stirling : An introduction to his life and examination of his music

This study introduces the life and examines the music of Australian jazz trumpeter Keith Stirling (1938-2003). The paper discusses the importance and position of Stirling in the jazz culture of Australian music, introducing key concepts that were influential not only to the development of Australian jazz but also in his life. Subsequently, a discussion of Stirling’s metaphoric tendencies provides an understanding of his philosophical perspectives toward improvisation as an art form. Thereafter, a discourse of the research methodology that was used and the resources that were collected throughout the study introduce a control group of transcriptions. These transcriptions provide an origin of phrases with which to discuss aspects of Stirling’s improvisational style. Instrumental approaches and harmonic concepts are then discussed and exemplified through the analysis of the transcribed phrases. Stirling’s instrumental techniques and harmonic concepts are examined by means of his own and student’s hand written notes and quotes from lesson recordings that took place in the early 1980s

End-stage renal disease causes skewing in the TCR Vβ-repertoire primarily within CD8+ T Cell subsets

A broad T cell receptor (TCR-) repertoire is required for an effective immune response. TCR-repertoire diversity declines with age. End-stage renal disease (ESRD) patients have a prematurely aged T cell system which is associated with defective T cell-mediated immunity. Recently, we showed that ESRD may significantly skew the TCR Vβ-repertoire. Here, we assessed the impact of ESRD on the TCR Vβ-repertoire within different T cell subsets using a multiparameter flow-cytometry-based assay, controlling for effects of aging and CMV latency. Percentages of 24 different TCR Vβ-families were tested in circulating naive and memory T cell subsets of 10 ESRD patients and 10 age- and CMV-serostatus-matched healthy individuals (HI). The Gini-index, a parameter used in economics to describe the distribution of income, was calculated to determine the extent of skewing at the subset level taking into account frequencies of all 24 TCR Vβ-families. In addition, using HI as reference population, the differential impact of ESRD was assessed on clonal expansion at the level of an individual TCR Vβ-family. CD8+, but not CD4+, T cell differentiation was

End stage renal disease patients have a skewed T cell receptor Vβ repertoire

BACKGROUND: End stage renal disease (ESRD) is associated with defective T-cell mediated immunity. A diverse T-cell receptor (TCR) Vβ repertoire is central to effective T-cell mediated immune responses to foreign antigens. In this study, the effect of ESRD on TCR Vβ repertoire was assessed. RESULTS: A higher proportion of ESRD patients (68.9 %) had a skewed TCR Vβ repertoire compared to age and cytomegalovirus (CMV) – IgG serostatus matched healthy individuals (31.4 %, P < 0.001). Age, CMV serostatus and ESRD were independently associated with an increase in shifting of the TCR Vβ repertoire. More differentiated CD8(+) T cells were observed in young ESRD patients with a shifted TCR Vβ repertoire. CD31-expressing naive T cells and relative telomere length of T cells were not significantly related to TCR Vβ skewing. CONCLUSIONS: ESRD significantly skewed the TCR Vβ repertoire particularly in the elderly population, which may contribute to the uremia-associated defect in T-cell mediated immunity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12979-015-0055-7) contains supplementary material, which is available to authorized users

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

Generative adversarial network-based semi-supervised learning for pathological speech classification

A challenge in applying machine learning algorithms to pathological speech classification is the labelled data shortage problem. Labelled data acquisition often requires significant human effort and time-consuming experimental design. Further, for medical applications, privacy and ethical issues must be addressed where patient data is collected. While labelled data are expensive and scarce, unlabelled data are typically inexpensive and plentiful. In this paper, we propose a semi-supervised learning approach that employs a generative adversarial network to incorporate both labelled and unlabelled data into training. We observe a promising accuracy gain with this approach compared to a baseline convolutional neural network trained only on labelled pathological speech data