105 research outputs found
Recommendations for the assessment of non‐extremity venous thromboembolism outcomes: communication from the SSC of the ISTH
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110812/1/jth12809.pd
Recommendations for future research in relation to pediatric pulmonary embolism: communication from the SSC of the ISTH
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142464/1/jth13902_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142464/2/jth13902.pd
Prevalent, Dynamic, and Conserved R-Loop Structures Associate with Specific Epigenomic Signatures in Mammals.
R-loops are three-stranded nucleic acid structures formed upon annealing of an RNA strand to one strand of duplex DNA. We profiled R-loops using a high-resolution, strand-specific methodology in human and mouse cell types. R-loops are prevalent, collectively occupying up to 5% of mammalian genomes. R-loop formation occurs over conserved genic hotspots such as promoter and terminator regions of poly(A)-dependent genes. In most cases, R-loops occur co-transcriptionally and undergo dynamic turnover. Detailed epigenomic profiling revealed that R-loops associate with specific chromatin signatures. At promoters, R-loops associate with a hyper-accessible state characteristic of unmethylated CpG island promoters. By contrast, terminal R-loops associate with an enhancer- and insulator-like state and define a broad class of transcription terminators. Together, this suggests that the retention of nascent RNA transcripts at their site of expression represents an abundant, dynamic, and programmed component of the mammalian chromatin that affects chromatin patterning and the control of gene expression
Improving dermatology classifiers across populations using images generated by large diffusion models
Dermatological classification algorithms developed without sufficiently
diverse training data may generalize poorly across populations. While
intentional data collection and annotation offer the best means for improving
representation, new computational approaches for generating training data may
also aid in mitigating the effects of sampling bias. In this paper, we show
that DALLE 2, a large-scale text-to-image diffusion model, can produce
photorealistic images of skin disease across skin types. Using the Fitzpatrick
17k dataset as a benchmark, we demonstrate that augmenting training data with
DALLE 2-generated synthetic images improves classification of skin
disease overall and especially for underrepresented groups.Comment: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Researc
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Large, labeled datasets have driven deep learning methods to achieve
expert-level performance on a variety of medical imaging tasks. We present
CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240
patients. We design a labeler to automatically detect the presence of 14
observations in radiology reports, capturing uncertainties inherent in
radiograph interpretation. We investigate different approaches to using the
uncertainty labels for training convolutional neural networks that output the
probability of these observations given the available frontal and lateral
radiographs. On a validation set of 200 chest radiographic studies which were
manually annotated by 3 board-certified radiologists, we find that different
uncertainty approaches are useful for different pathologies. We then evaluate
our best model on a test set composed of 500 chest radiographic studies
annotated by a consensus of 5 board-certified radiologists, and compare the
performance of our model to that of 3 additional radiologists in the detection
of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the
model ROC and PR curves lie above all 3 radiologist operating points. We
release the dataset to the public as a standard benchmark to evaluate
performance of chest radiograph interpretation models.
The dataset is freely available at
https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201
ETV6 germline mutations cause HDAC3/NCOR2 mislocalization and upregulation of interferon response genes
ETV6 is an ETS family transcription factor that plays a key role in hematopoiesis and megakaryocyte development. Our group and others have identified germline mutations in ETV6 resulting in autosomal dominant thrombocytopenia and predisposition to malignancy; however, molecular mechanisms defining the role of ETV6 in megakaryocyte development have not been well established. Using a combination of molecular, biochemical, and sequencing approaches in patient-derived PBMCs, we demonstrate abnormal cytoplasmic localization of ETV6 and the HDAC3/NCOR2 repressor complex that led to overexpression of HDAC3-regulated interferon response genes. This transcriptional dysregulation was also reflected in patient-derived platelet transcripts and drove aberrant proplatelet formation in megakaryocytes. Our results suggest that aberrant transcription may predispose patients with ETV6 mutations to bone marrow inflammation, dysplasia, and megakaryocyte dysfunction
Objective assessment of stored blood quality by deep learning
Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans’ assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis
Non-infected penile prosthesis cultures during revision surgery; comparison between antibiotic coated and non - coated devices
Germline mutations in ETV6 are associated with thrombocytopenia, red cell macrocytosis and predisposition to lymphoblastic leukemia
Some familial platelet disorders are associated with predisposition to leukemia, myelodysplastic syndrome (MDS) or dyserythropoietic anemia. We identified a family with autosomal dominant thrombocytopenia, high erythrocyte mean corpuscular volume (MCV) and two occurrences of B cell-precursor acute lymphoblastic leukemia (ALL). Whole-exome sequencing identified a heterozygous single-nucleotide change in ETV6 (ets variant 6), c.641C>T, encoding a p.Pro214Leu substitution in the central domain, segregating with thrombocytopenia and elevated MCV. A screen of 23 families with similar phenotypes identified 2 with ETV6 mutations. One family also had a mutation encoding p.Pro214Leu and one individual with ALL. The other family had a c.1252A>G transition producing a p.Arg418Gly substitution in the DNA-binding domain, with alternative splicing and exon skipping. Functional characterization of these mutations showed aberrant cellular localization of mutant and endogenous ETV6, decreased transcriptional repression and altered megakaryocyte maturation. Our findings underscore a key role for ETV6 in platelet formation and leukemia predisposition
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