The molecular characterization of immune subsets is important for designing effective strategies to understand and treat diseases. We characterized 29 immune cell types within the peripheral blood ...mononuclear cell (PBMC) fraction of healthy donors using RNA-seq (RNA sequencing) and flow cytometry. Our dataset was used, first, to identify sets of genes that are specific, are co-expressed, and have housekeeping roles across the 29 cell types. Then, we examined differences in mRNA heterogeneity and mRNA abundance revealing cell type specificity. Last, we performed absolute deconvolution on a suitable set of immune cell types using transcriptomics signatures normalized by mRNA abundance. Absolute deconvolution is ready to use for PBMC transcriptomic data using our Shiny app (https://github.com/giannimonaco/ABIS). We benchmarked different deconvolution and normalization methods and validated the resources in independent cohorts. Our work has research, clinical, and diagnostic value by making it possible to effectively associate observations in bulk transcriptomics data to specific immune subsets.
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•Characterization of 29 human immune cell type by RNA-seq and flow cytometry•Modules of specific, co-expressed, and housekeeping genes are defined•The mRNA heterogeneity and abundance are cell type specific•The proposed normalization approach enables absolute deconvolution
Monaco et al. generate an RNA-seq dataset on 29 immune cell types and identify modules of cell type-specific, co-expressed, and housekeeping genes. The mRNA heterogeneity and abundance of the different cell types were examined. Absolute deconvolution of PBMCs was obtained by taking into account mRNA abundance when normalizing the signature matrix.
Abstract
The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics ...Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports >500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Designed to empower researchers with access to Omics data and bioinformatic analyses, VEuPathDB projects integrate >1700 pre-analysed datasets (and associated metadata) with advanced search capabilities, visualizations, and analysis tools in a graphic interface. Diverse data types are analysed with standardized workflows including an in-house OrthoMCL algorithm for predicting orthology. Comparisons are easily made across datasets, data types and organisms in this unique data mining platform. A new site-wide search facilitates access for both experienced and novice users. Upgraded infrastructure and workflows support numerous updates to the web interface, tools, searches and strategies, and Galaxy workspace where users can privately analyse their own data. Forthcoming upgrades include cloud-ready application architecture, expanded support for the Galaxy workspace, tools for interrogating host-pathogen interactions, and improved interactions with affiliated databases (ClinEpiDB, MicrobiomeDB) and other scientific resources, and increased interoperability with the Bacterial & Viral BRC.
In this study, we used insurance claims for over one-third of the entire US population to create a subset of 128,989 families (481,657 unique individuals). We then used these data to (i) estimate the ...heritability and familial environmental patterns of 149 diseases and (ii) infer the genetic and environmental correlations for disease pairs from a set of 29 complex diseases. The majority (52 of 65) of our study's heritability estimates matched earlier reports, and 84 of our estimates appear to have been obtained for the first time. We used correlation matrices to compute environmental and genetic disease classifications and corresponding reliability measures. Among unexpected observations, we found that migraine, typically classified as a disease of the central nervous system, appeared to be most genetically similar to irritable bowel syndrome and most environmentally similar to cystitis and urethritis, all of which are inflammatory diseases.
At a workshop coordinated by the WHO Collaborating Centre for Oral Cancer and Precancer in the UK issues related to terminology, definitions and classification of oral precancer were discussed by an ...expert group. The consensus views of the Working Group are presented here. The term, ‘potentially malignant disorders’, was recommended to refer to precancer as it conveys that not all disorders described under this term may transform into cancer. Critically evaluating all definitions proposed so far for oral leukoplakia, the Working Group agreed that the term leukoplakia should be used to recognize ‘white plaques of questionable risk having excluded (other) known diseases or disorders that carry no increased risk for cancer’. An outline was proposed for diagnosing oral leukoplakia that will prevent other oral white disorders being misclassified as leukoplakia. The Working Group discussed the caveats involved in the current use of terminology and classification of oral potentially malignant disorders, deficiencies of these complex systems, and how they have evolved over the past several decades. The terminology presented in this report reflects our best understanding of multi‐step carcinogenesis in the oral mucosa, and aspires to engender consistency in use.
Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN ...tasks against adversarial attacks, as high-stake decision-making will be made based on the diagnosis. Several previous studies have considered simple adversarial attacks. However, the vulnerability of DNNs to more realistic and higher risk attacks, such as universal adversarial perturbation (UAP), which is a single perturbation that can induce DNN failure in most classification tasks has not been evaluated yet.
We focus on three representative DNN-based medical image classification tasks (i.e., skin cancer, referable diabetic retinopathy, and pneumonia classifications) and investigate their vulnerability to the seven model architectures of UAPs.
We demonstrate that DNNs are vulnerable to both nontargeted UAPs, which cause a task failure resulting in an input being assigned an incorrect class, and to targeted UAPs, which cause the DNN to classify an input into a specific class. The almost imperceptible UAPs achieved > 80% success rates for nontargeted and targeted attacks. The vulnerability to UAPs depended very little on the model architecture. Moreover, we discovered that adversarial retraining, which is known to be an effective method for adversarial defenses, increased DNNs' robustness against UAPs in only very few cases.
Unlike previous assumptions, the results indicate that DNN-based clinical diagnosis is easier to deceive because of adversarial attacks. Adversaries can cause failed diagnoses at lower costs (e.g., without consideration of data distribution); moreover, they can affect the diagnosis. The effects of adversarial defenses may not be limited. Our findings emphasize that more careful consideration is required in developing DNNs for medical imaging and their practical applications.
Abstract Background To present the rationale for the new Obsessive–Compulsive and Related Disorders (OCRD) grouping in the Mental and Behavioural Disorders chapter of the Eleventh Revision of the ...World Health Organization’s International Classification of Diseases and Related Health Problems (ICD-11), including the conceptualization and essential features of disorders in this grouping. Methods Review of the recommendations of the ICD-11 Working Group on the Classification for OCRD. These sought to maximize clinical utility, global applicability, and scientific validity. Results The rationale for the grouping is based on common clinical features of included disorders including repetitive unwanted thoughts and associated behaviours, and is supported by emerging evidence from imaging, neurochemical, and genetic studies. The proposed grouping includes obsessive–compulsive disorder, body dysmorphic disorder, hypochondriasis, olfactory reference disorder, and hoarding disorder. Body-focused repetitive behaviour disorders, including trichotillomania and excoriation disorder are also included. Tourette disorder, a neurological disorder in ICD-11, and personality disorder with anankastic features, a personality disorder in ICD-11, are recommended for cross-referencing. Limitations Alternative nosological conceptualizations have been described in the literature and have some merit and empirical basis. Further work is needed to determine whether the proposed ICD-11 OCRD grouping and diagnostic guidelines are mostly likely to achieve the goals of maximizing clinical utility and global applicability. Conclusion It is anticipated that creation of an OCRD grouping will contribute to accurate identification and appropriate treatment of affected patients as well as research efforts aimed at improving our understanding of the prevalence, assessment, and management of its constituent disorders.
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and ...histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
Abstract
Introduction:
Current AASM classification of hypopnea as obstructive (H-OB) is based on identification of flattening of inspiratory airflow, chest-abdominal paradox, or snoring. If none are ...present a hypopnea is classified as central (H-CEN). We hypothesized that surface chest wall EMG (CW-EMG, right 8th intercostal space), as a reflection of inspiratory effort, would be useful for hypopnea classification and AASM criteria validation.
Methods:
25 Consecutive adult positive airway pressure (PAP) titration studies with at least 10 hypopneas (including 3 putative central hypopneas) and an adequate CW-EMG signal were analyzed. The EMG signal was processed to remove ECG artifact, rectified and integrated. The integrated EMG signal (EF) was used to reflect effort. Five randomly chosen hypopneas from each patient were analyzed. An observer blind to CW-EMG and EF signals classified the hypopneas as OB or CEN based on AASM criteria. Inspiratory deflections in PAP flow (F) and EF were scaled based on pre-event breathing and a resistance (RES = EF/F) was calculated (pre-event breath RES = 1). An average RES for breaths in the first and second half of the hypopneas was calculated (odd number of breaths, middle breath included in both halves). The same observer classified hypopneas based ONLY on the smoothed flow (eliminating flattening), EF signal, and RES values. The two classifications (AASM and EF) were compared.
Results:
Events by AASM criteria: 68 H-OB and 32 H-CEN. The RES 1st half event (mean ± SD) was OB: 3.6 ± 3.4 versus CEN: 1.24 ± 0.7, P < 0.001 and 2nd half event was OB: 9.2 ± 8.0 versus CEN: 1.35 ± 0.7, P < 0.001. The RES ratio (RES 2nd /RES 1st half hypopnea) was OB: 3.3 ± 3.3 versus CEN 1.15 ± 0.3, P< 0.001. Agreement AASM/EF classifications: Kappa= 0.76, % agreement 89%.
Conclusion:
OB hypopneas had a greater resistance in both halves of the event than CEN hypopneas and the second half a larger relative RES (2nd half/1st half). There was good agreement between classification based on EF and AASM criteria. CW-EMG may be useful to classify hypopneas as obstructive or central.
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