Neurocognitive outcomes in children experiencing seizures during treatment for acute lymphoblastic leukemia

There is a growing literature for cognitive late effects among childhood cancer survivors, yet little empirical information is known regarding specific neurocognitive outcomes of children who experience seizures while treated for acute lymphoblastic leukemia. This study examined prevalence of on-protocol seizures, seizure risk factors, and neurocognitive change in children with therapy-related seizures in comparison to the normative sample and a matched cohort of children without on-protocol seizures. Participants included children enrolled on the St. Jude frontline leukemia treatment protocol, Total Therapy 15 (TOTXV) - the first systematic investigation of intensified chemotherapy agents plus optimal intrathecal therapy without irradiation. Out of 498 children, 19 experienced therapy-related seizures. To increase the statistical power of comparisons, the 19 children were matched on relevant variables to two children without on-protocol seizures. Neuropsychological assessment and magnetic resonance imaging each occurred across three treatment time-points. Results revealed a 3.82% two-year incidence of seizures during TOTXV with over 50 percent of seizures during induction and consolidation phases. No demographic or clinical factors were predictive of seizures; although, a trend for standard/high treatment intensity was observed. When the neuropsychological performance of the seizure group was compared to normative scores, patterns of differences emerged and maintained across time-points for domains of attention, working memory, and processing speed significantly elevated for the seizure group. Similar patterns also emerged across time-points between the seizure group and the non-seizure cohort. At therapy completion, the seizure group performed significantly worse for attention and working memory tasks than the cohort, and these deficits persisted two years later with the addition of processing speed deficits and significantly worse intellectual functioning. Imaging findings indicated that children with therapy-related seizures experienced more significant early neurotoxicity (i.e., leukoencephalopathy) than non-seizure cohorts. Based on these preliminary findings, it appears that children who experience treatment-related seizures are at greater neurocognitive risk when compared to counterparts who do not. Findings point to a relationship between on-therapy seizures, leukoencephalopathy, and deficits in neuropsychological performance, specifically attention, working memory, and processing speed skills, which may lead to overall declines in intellectual functioning. Further research is needed to identify changes in neurocognitive status that indicate risk for long-term CNS effect in the hope of providing greater comprehension on how to earlier treat and prevent cognitive late effects

Executive Functioning in Pediatric Youth: A Meta-Analysis

Executive functioning (EF) represents a set of cognitive skills that are important for daily functioning. EF can be influenced by a number of biopsychosocial factors, many of which are present in the pediatric population (i.e., youth with at least one medical condition). It is important to understand EF in this population as it affects aspects of their physical health (e.g., treatment adherence). Previous meta-analyses have been conducted to examine EF in the pediatric population, and they have generally found deficits in EF compared to healthy peers. However, these previous meta-analyses have only focused on specific medical conditions (e.g., pediatric youth with cancer). To the author’s knowledge, there has never been a meta-analysis of EF in the pediatric population more broadly. The current study serves to begin the process of closing this gap in the literature. Publications on EF in pediatric youth with a medical condition (i.e., cancer/tumor, epilepsy/seizure, or diabetes) were collected and used in a meta-analysis. Findings suggest pediatric youth have lower EF compared to healthy peers as a whole, though differences between the illness groups were noted. The epilepsy/seizure literature report the largest EF deficits across the various EF skills, and the diabetes group only showed small (though clinically and statistically significant) deficits in the domain of planning/organization. These findings provide early evidence for the benefit of considering cross-illness factors when working with pediatric youth, and suggest this area warrants further study

Alterations in brain structure related to breast cancer and its treatment: Chemotherapy and other considerations

Cognitive effects of cancer and its treatment have been a topic of increasing investigation over the past ∼30 years. Recent studies have focused on better understanding the neural correlates of these effects, with an emphasis on post-chemotherapy effects in breast cancer patients. Structural MRI studies have utilized both automated and manual approaches to quantify gray and white matter characteristics (e.g., regional volume and density) in breast cancer patients treated with chemotherapy relative to patients who did not receive chemotherapy and/or healthy controls. While most work to date has been retrospective, a small number of baseline (pre-systemic therapy) and prospective longitudinal studies have been conducted. Data have consistently shown lower gray and white matter volume and density in patients treated with chemotherapy, particularly in frontal and temporal brain regions. Host factors and/or the cancer disease process and other therapies (e.g., antiestrogen treatment) also seem likely to contribute to the observed differences, though the relative contributions of these effects have not yet been investigated in detail. These structural abnormalities have been shown to relate to subjective and objective cognitive functioning, as well as to biological factors that may help to elucidate the underlying mechanism(s). This review examines the currently available published observations and discusses the major themes and promising directions for future studies

New Advances in Stem Cell Transplantation

This book documents the increased number of stem cell-related research, clinical applications, and views for the future. The book covers a wide range of issues in cell-based therapy and regenerative medicine, and includes clinical and preclinical chapters from the respected authors involved with stem cell studies and research from around the world. It complements and extends the basics of stem cell physiology, hematopoietic stem cells, issues related to clinical problems, tissue typing, cryopreservation, dendritic cells, mesenchymal cells, neuroscience, endovascular cells and other tissues. In addition, tissue engineering that employs novel methods with stem cells is explored. Clearly, the continued use of biomedical engineering will depend heavily on stem cells, and this book is well positioned to provide comprehensive coverage of these developments

A Proposed Machine Learning Based Collective Disease Model to Enable Predictive Diagnostics in Necrotising Enterocolitis

© 2018 IEEE. Despite 60 years of research into necrotising enterocolitis (NEC), our understanding of the disease has not improved enough to achieve better outcomes. Even though NEC has remained the leading cause of death and poor outcomes in preterm infants, there remain vital questions on how to define, differentiate and detect the condition. Numerous international groups have recently highlighted NEC as a research priority and called for broader engagement of the scientific community to move the field forward. The three foremost barriers at present are lack of suitable definition(s), lack of clean datasets and consequently a lack of scope to gain sufficient insights from data. This research paper proposes a new direction of travel to advance neonatal gastro-intestinal monitoring and strengthen our efforts to gain better insights from global databases. An integrated machine learning based model is recommended to produce a comprehensive disease model to manage the complexity of this multi-variate disease. This intelligent disease model would be used in the daily neonatal settings to help aggregate data to support clinical decision making, better capture the complexity of each patient to enrich global datasets to create bigger and better data. This paper reviews current machine learning and CAD technologies in neonatology and suggests an innovative approach for an NEC disease model

Idea Analysis for the Development of Clinical Trial Strategies

Idea Analysis was investigated to determine its ability to organize scientific information and explain the results of specialists\u27 deliberations in designing new clinical trials. Ideas have long been recognized as the engine of creativity. By focusing on the capture of ideas from the scientific literature, idea analysis procedures enable the arrangement of the information into forms consistent with those developed by subject specialists. The most obvious example is the concept structure. Ideas containing a common frequently occurring term/phrase can be depicted as a primary node in the concept network. Related terms will appear as elements associated with that node. Ideas containing couplets of primary nodal terms/phrases can be used to link nodes, thus, completing the paths in the network. Using this methodology, information specialists can build and maintain knowledge structures for use by students, subject specialists and interested others. In contrast with expert systems, idea analysis does not attempt to duplicate thought processes performed by experts in a subject. Instead, it focuses on the management of ideas and the arrangement of those ideas using organizational models. The application of these techniques to the scientific literature dealing with brain tumors and to clinical trial protocols developed by subject specialists is illustrative. This study showed that, in the brain tumor literature and clinical trial protocols, the idea analysis approach was effective in accomplishing the two tasks required: 1. Organization of complex material into succinct and understandable descriptions--tabular and graphic; 2. provision of explanations of expert-derived research strategies and/or plans. This methodology enhanced identification, extraction, computerization and incorporation of ideas into knowledge structures in an efficient and effective manner

Ontology-based methods for disease similarity estimation and drug repositioning

Title from PDF of title page, viewed on October 2, 2012Dissertation advisor: Deendayal DinakarpandianVitaIncludes bibliographic references (p. 174-181)Thesis (Ph.D.)--School of Computing and Engineering and Dept. of Mathematics and Statistics. University of Missouri--Kansas City, 2012Human genome sequencing and new biological data generation techniques have provided an opportunity to uncover mechanisms in human disease. Using gene-disease data, recent research has increasingly shown that many seemingly dissimilar diseases have similar/common molecular mechanisms. Understanding similarity between diseases aids in early disease diagnosis and development of new drugs. The growing collection of gene-function and gene-disease data has instituted a need for formal knowledge representation in order to extract information. Ontologies have been successfully applied to represent such knowledge, and data mining techniques have been applied on them to extract information. Informatics methods can be used with ontologies to find similarity between diseases which can yield insight into how they are caused. This can lead to therapies which can actually cure diseases rather than merely treating symptoms. Estimating disease similarity solely on the basis of shared genes can be misleading as variable combinations of genes may be associated with similar diseases, especially for complex diseases. This deficiency can be potentially overcome by looking for common or similar biological processes rather than only explicit gene matches between diseases. The use of semantic similarity between biological processes to estimate disease similarity could enhance the identification and characterization of disease similarity besides indentifying novel biological processes involved in the diseases. Also, if diseases have similar molecular mechanisms, then drugs that are currently being used could potentially be used against diseases beyond their original indication. This can greatly benefit patients with diseases that do not have adequate therapies especially people with rare diseases. This can also drastically reduce healthcare costs as development of new drugs is far more expensive than re-using existing ones. In this research we present functions to measure similarity between terms in an ontology, and between entities annotated with terms drawn from the ontology, based on both co-occurrence and information content. The new similarity measure is shown to outperform existing methods using biological pathways. The similarity measure is then used to estimate similarity among diseases using the biological processes involved in them and is evaluated using a manually curated and external datasets with known disease similarities. Further, we use ontologies to encode diseases, drugs and biological processes and demonstrate a method that uses a network-based algorithm to combine biological data about diseases with drug information to find new uses for existing drugs. The effectiveness of the method is demonstrated by comparing the predicted new disease-drug pairs with existing drug-related clinical trials.Introduction and motivation -- Ontologies in biomedical domain -- Methods to compute ontological similarity -- Proposed approach for ontological term similarity -- Augmentation of vocabulary and annotation in ontologies -- Estimation of disease similarity -- Use of ontologies for drug repositioning -- Future directions-perspective from pharmaceutical industry -- Appendix 1. Table for the ontological similarity scores -- Appendix 2. Test set of 200 records for evaluating mapping of disease text to Disease Ontology -- Appendix 3. Curated set of disease similarities used as the benchmark set -- Appendix 4. F-scores for different combinations of Score-Pvalues and GO-Process-Pvalues for PSB estimates of disease similarity -- Appendix 5. Test set formed from opinions of medical residents http://rxinformatics.umn.edu/SemanticRelatednessResources.html -- Appendix 6. Drug repositioning candidate

Medical-Data-Models.org:A collection of freely available forms (September 2016)

MDM-Portal (Medical Data-Models) is a meta-data repository for creating, analysing, sharing and reusing medical forms, developed by the Institute of Medical Informatics, University of Muenster in Germany. Electronic forms for documentation of patient data are an integral part within the workflow of physicians. A huge amount of data is collected either through routine documentation forms (EHRs) for electronic health records or as case report forms (CRFs) for clinical trials. This raises major scientific challenges for health care, since different health information systems are not necessarily compatible with each other and thus information exchange of structured data is hampered. Software vendors provide a variety of individual documentation forms according to their standard contracts, which function as isolated applications. Furthermore, free availability of those forms is rarely the case. Currently less than 5 % of medical forms are freely accessible. Based on this lack of transparency harmonization of data models in health care is extremely cumbersome, thus work and know-how of completed clinical trials and routine documentation in hospitals are hard to be re-used. The MDM-Portal serves as an infrastructure for academic (non-commercial) medical research to contribute a solution to this problem. It already contains more than 4,000 system-independent forms (CDISC ODM Format, www.cdisc.org, Operational Data Model) with more than 380,000 dataelements. This enables researchers to view, discuss, download and export forms in most common technical formats such as PDF, CSV, Excel, SQL, SPSS, R, etc. A growing user community will lead to a growing database of medical forms. In this matter, we would like to encourage all medical researchers to register and add forms and discuss existing forms