An osseous lesion in a 10-year-old boy with Hodgkin's lymphoma: a case report

<p>Abstract</p> <p>Introduction</p> <p>Osseous involvement of Hodgkin's lymphoma is uncommon. When osteolytic lesions are seen on imaging it is important to evaluate potential other causes.</p> <p>Case presentation</p> <p>We report the case of a 10-year-old Caucasian boy who presented to our facility with a bony lesion of the right clavicle and enlarged cervical lymph nodes. A simultaneous biopsy of the lymph node and of the osteolytic process of his right proximal clavicle was performed and revealed two different kinds of lesions: a mixed cellularity Hodgkin's lymphoma and an osteochondroma.</p> <p>Conclusions</p> <p>Since the latter is a common benign bone tumor, which should not interfere with the staging of the lymphoma, we emphasize the importance of ensuring that all efforts are made to acquire a diagnostic biopsy of all atypical lesions.</p

Unsupervised Analysis of Classical Biomedical Markers: Robustness and Medical Relevance of Patient Clustering Using Bioinformatics Tools

Motivation: It has been proposed that clustering clinical markers, such as blood test results, can be used to stratify patients. However, the robustness of clusters formed with this approach to data pre-processing and clustering algorithm choices has not been evaluated, nor has clustering reproducibility. Here, we made use of the NHANES survey to compare clusters generated with various combinations of pre-processing and clustering algorithms, and tested their reproducibility in two separate samples. Method: Values of 44 biomarkers and 19 health/life style traits were extracted from the National Health and Nutrition Examination Survey (NHANES). The 1999–2002 survey was used for training, while data from the 2003–2006 survey was tested as a validation set. Twelve combinations of pre-processing and clustering algorithms were applied to the training set. The quality of the resulting clusters was evaluated both by considering their properties and by comparative enrichment analysis. Cluster assignments were projected to the validation set (using an artificial neural network) and enrichment in health/life style traits in the resulting clusters was compared to the clusters generated from the original training set. Results: The clusters obtained with different pre-processing and clustering combinations differed both in terms of cluster quality measures and in terms of reproducibility of enrichment with health/life style properties. Z-score normalization, for example, dramatically improved cluster quality and enrichments, as compared to unprocessed data, regardless of the clustering algorithm used. Clustering diabetes patients revealed a group of patients enriched with retinopathies. This coul

A meta-review of literature reviews assessing the capacity of patients with severe mental disorders to make decisions about their healthcare.

Background: Determining the mental capacity of psychiatric patients for making healthcare related decisions is crucial in clinical practice. This meta-review of review articles comprehensively examines the current evidence on the capacity of patients with a mental illness to make medical care decisions. Methods: Systematic review of review articles following PRISMA recommendations. PubMed, Scopus, CINAHL and PsycInfo were electronically searched up to 31 January 2020. Free text searches and medical subject headings were combined to identify literature reviews and meta-analyses published in English, and summarising studies on the capacity of patients with serious mental illnesses to make healthcare and treatment related decisions, conducted in any clinical setting and with a quantitative synthesis of results. Publications were selected as per inclusion and exclusion criteria. The AMSTAR II tool was used to assess the quality of reviews. Results: Eleven publications were reviewed. Variability on methods across studies makes it difficult to precisely estimate the prevalence of decision-making capacity in patients with mental disorders. Nonetheless, up to three-quarters of psychiatric patients, including individuals with serious illnesses such as schizophrenia or bipolar disorder may have capacity to make medical decisions in the context of their illness. Most evidence comes from studies conducted in the hospital setting; much less information exists on the healthcare decision making capacity of mental disorder patients while in the community. Stable psychiatric and non-psychiatric patients may have a similar capacity to make healthcare related decisions. Patients with a mental illness have capacity to judge risk-reward situations and to adequately decide about the important treatment outcomes. Different symptoms may impair different domains of the decisional capacity of psychotic patients. Decisional capacity impairments in psychotic patients are temporal, identifiable, and responsive to interventions directed towards simplifying information, encouraging training and shared decision making. The publications complied satisfactorily with the AMSTAR II critical domains. Conclusions: Whilst impairments in decision-making capacity may exist, most patients with a severe mental disorder, such as schizophrenia or bipolar disorder are able to make rational decisions about their healthcare. Best practice strategies should incorporate interventions to help mentally ill patients grow into the voluntary and safe use of medications

Discourse structure and language technology

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.Peer Reviewe

Comparative enrichment analysis of biomarker-based patient clusters: A quantitative analysis.

<p>The number of validated terms found to be enriched in clusters generated with the different pre-processing procedures and clustering algorithms tested in this study. Validated enrichments and validated clusters are defined by the recurrence of statistically significant enrichment in the training and validation datasets. The clusters that were generated from the test dataset using a particular pre-processing clustering combination were subjected to enrichment analysis with 19 health/lifestyle labels (i.e. searching for statistically significant over-representation of patients with the trait in each cluster). An artificial neural network, trained with the cluster assignment of each individual in the training dataset, was used to classify individuals from the validation dataset using the same clinical biomarkers subjected to the same pre-processing algorithm as was the test dataset. The resulting clustering of the validation set was also subjected to enrichment analysis with the same terms as was the training set. An enrichment was deemed to be a validated enrichment if the same label was enriched in the test and validation datasets. A validated cluster was defined as a cluster sharing at least one enriched term between the test and validation sets (i.e. the number of clusters enriched in the training set). The enrichment factor for each pipeline is the average enrichment factor of the three most significant enrichments. <i>K-mns</i> = <i>K-means</i>; <i>NoramTranf</i> = transformation to normal; <i>Z-score</i> = Z-score normalization; <i>AgeAdj</i> = age adjustment followed by Z-score normalization.</p

Enrichment analysis of biomarker-based patient clusters: A qualitative view.

<p>The NHANES code and description of validated terms found in clusters generated by pre-processing with the Z-score normalization method and clustering algorithm with three algorithms (CLICK, K-means and SOM) (top) or using the CLICK clustering algorithm with four pre-processing procedures (bottom). Raw = no transformation; Norm = transformation to normal; Z-score normalization or Z-score normalization with age-adjustment). All the marked terms were enriched significantly (hyper geometric test, P value<0.05) in both the training and validation sets.</p

Methodology overview.

<p>A test-validation approach was used to test the impact of methodological choices on the clustering of individuals according to their classical blood biomedical marker values. The data from the NHANES 1999–2002 surveys was used as a training set, while the 2003–2006 dataset was used for validation. Various combinations of pre-processing and clustering algorithms were used to define clusters from the training set (black). For pre-processing (top row), transformation to normal of otherwise non-normal variables, Z-score normalized and Z-score normalized-with age adjustment using linear regression, were considered (top block). Each resulting dataset was clustered with three different clustering algorithms (second row): CLICK <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029578#pone.0029578-Sharan1" target="_blank">[18]</a>, K-means <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029578#pone.0029578-Hartigan1" target="_blank">[21]</a> and self-organizing maps <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029578#pone.0029578-Tamayo1" target="_blank">[22]</a>. The resulting clusters were used for enrichment analysis with health/lifestyle traits and for training an artificial neural network (third row). The artificial neural network was subsequently used to assign individuals from the validation set to clusters (third row), using the same pre-processing procedure as used to generate the training set clusters (bottom row). The resulting validation set clusters were also tested for enrichment with the same health/life-style traits as the training set. Enrichments found in both sets were compared.</p

The correlation between selected blood markers and age.

<p>Linear regression was calculated for all biomarkers, and least square regression lines (red) were fitted for each marker. r – Pearson correlation coefficient, P – p value, CI- confidence interval of the p-value. (A) Raw data from the training set; (B) training set data after age adjustment. (C) diabetic males, raw data from the training set.</p

Selected clusters from the NHANES training set.

<p>(A) The mean and standard deviation of biomarker values are shown for three selected clusters generated with 4152 males 20 years of age or older from the NHANES training set, using the Z-score normalized/CLICK pre-processing/clustering combination. For each cluster, the total number of individuals (top, right) and selected health/lifestyle traits that are significantly enriched in that cluster (top, left) are provided. For each enriched term, the enrichment factor (i.e. the frequency of the term in a cluster divided by its frequency in the entire dataset) is also provided. (B) Comparison of the original values of selected biomarkers in clusters 1, 3 and 14. The values for Hb are enlarged in the top middle section of the figure.</p