Nonsteroidal anti-inflammatory drugs as a targeted therapy for bone marrow failure in Ghosal hematodiaphyseal dysplasia.

Advances in genomic diagnostics hold promise for improved care of rare hematologic diseases. Here, we describe a novel targeted therapeutic approach for Ghosal hematodiaphyseal dysplasia, an autosomal recessive disease characterized by severe normocytic anemia and bone abnormalities due to loss-of-function mutations in thromboxane A synthase 1 (TBXAS1). TBXAS1 metabolizes prostaglandin H2 (PGH2), a cyclooxygenase (COX) product of arachidonic acid, into thromboxane A2. Loss-of-function mutations in TBXAS result in an increase in PGH2 availability for other PG synthases. The current treatment for Ghosal hematodiaphyseal dysplasia syndrome consists of corticosteroids. We hypothesize that nonsteroidal anti-inflammatory drugs (NSAIDs), which inhibit COX-1 and COX-2, could ameliorate the effects of TBXAS1 loss and improve hematologic function by reducing prostaglandin formation. We treated 2 patients with Ghosal hematodiaphyseal dysplasia syndrome, an adult and a child, with standard doses of NSAIDs (aspirin or ibuprofen). Both patients had rapid improvements concerning hematologic parameters and inflammatory markers without adverse events. Mass spectrometry analysis demonstrated that urinary PG metabolites were increased along with proinflammatory lipoxygenase (LOX) products 5-hydroxyeicosatetraenoic acid and leukotriene E4. Our data show that NSAIDs at standard doses surprisingly reduced both COX and LOX products, leading to the resolution of cytopenia, and should be considered for first-line treatment for Ghosal hematodiaphyseal dysplasia syndrome.

"My mentor didn't speak to me for the first four weeks": Perceived Unfairness experienced by nursing students in clinical practice settings.

AIMS AND OBJECTIVES: To explore the perceived unfairness experienced by student nurses during their undergraduate clinical placements. BACKGROUND: It is important that student nurses feel supported by practice staff during their clinical placement education experiences. However, it has been reported that learners can feel ignored, unsupported and bullied by others in the clinical environment and this has a detrimental effect on their learning. It is important to understand the student nurse perspective and explore ways in which their feelings of belongingness might be enhanced in the clinical area. DESIGN: A descriptive narrative approach was used to explore the qualitative data generated by the survey and interviews. Limited closed-question survey data were acquired to explore a selection of quantified survey responses about placements and mentorship. METHODS: A survey was conducted with 1,425 student nurses from adult and mental health degree nursing pathways, across nine institutions in the North West of England, UK. Unstructured interviews were undertaken with 22 student nurses from across these nine institutions. The data generated from both methods (free-text survey responses and interview) were thematically analysed. RESULTS: There were times when student nurses felt that they had been treated unfairly by various members of the healthcare team during their clinical placements. Unfairness was related to being ignored and unsupported or being used as an "extra pair of hands" and having their supernumerary status ignored. Student nurses want to have feelings of belongingness in the clinical area and value enthusiasm for teaching from mentors. Certain positive mentor qualities were identified through the data in this study. These have been used to inform a tiered model of mentorship, to inform future thinking about student nurse education. CONCLUSION: Student nurses can feel like they are being treated unfairly in the clinical area in numerous ways. Identifying ways in which mentorship practice can be developed to adequately support education is important. This can lead to satisfaction and development on both sides of the student/educator relationship. RELEVANCE TO PRACTICE: Exploring student nurse perceptions of their learning is important when attempting to enhance educational practice in the clinical setting.

Students' perceptions of their learning experiences: A repeat regional survey of healthcare students.

BACKGROUND: Student experience is an international concern and recent research has focused on initiatives to improve students' learning experiences and ultimately reduce attrition levels. OBJECTIVE: To determine similarities and differences between students' perceptions of their learning experiences between 2011 and 2015 in relation to campus-based learning, placement-based learning and personal circumstances. DESIGN: A repeat online survey in 2011 and 2015; using a questionnaire developed from thematic analysis of narrative interviews with a subsample of the target population. SETTINGS: Nine universities in the North West of England. PARTICIPANTS: A total of 1080 students completed the survey in 2011 and 1983 students in 2015 from a target population of all students studying on commissioned pre-registration healthcare education programmes. METHODS: An online survey was made available to all undergraduate students studying on Health Education funded programmes within the region and survey respondents were invited to give demographic information and rate their agreement to statements on four-point Likert-type responses. RESULTS: Responses to a repeat survey of healthcare studying in the North West of England in 2015 were strikingly similar overall to those of an original 2011 survey. Although the students were positive overall about their experiences, a number were dissatisfied with some aspects of their experiences - particularly in relation to initial support on campus and whilst studying on placement. Four years on from the original survey, despite a considerable investment in improving students' experiences across the region, there appears to be little change in students' perceptions of their learning experiences CONCLUSION: In the short-term monitoring of student experience needs to be continued; and links to attrition (potential or actual) noted and acted upon. However, given that attrition from these courses has been a long-term problem and the complexity of its resolution a recurrent finding in the literature; new ways of framing and resolving the problem need to be considered.

Mll-AF4 Confers Enhanced Self-Renewal and Lymphoid Potential during a Restricted Window in Development.

MLL-AF4+ infant B cell acute lymphoblastic leukemia is characterized by an early onset and dismal survival. It initiates before birth, and very little is known about the early stages of the disease's development. Using a conditional Mll-AF4-expressing mouse model in which fusion expression is targeted to the earliest definitive hematopoietic cells generated in the mouse embryo, we demonstrate that Mll-AF4 imparts enhanced B lymphoid potential and increases repopulation and self-renewal capacity during a putative pre-leukemic state. This occurs between embryonic days 12 and 14 and manifests itself most strongly in the lymphoid-primed multipotent progenitor population, thus pointing to a window of opportunity and a potential cell of origin. However, this state alone is insufficient to generate disease, with the mice succumbing to B cell lymphomas only after a long latency. Future analysis of the molecular details of this pre-leukemic state will shed light on additional events required for progression to acute leukemia.

The utility of computed tomography in the management of fever and neutropenia in pediatric oncology.

BACKGROUND: Despite the frequent use and radiation exposure of computed tomography (CT) scans, there is little information on patterns of CT use and their utility in the management of pediatric patients with fever and neutropenia (FN). We examined the contribution of either the commonly employed pan-CT (multiple anatomical locations) or targeted CT (single location) scanning to identify possible infectious etiologies in this challenging clinical scenario. Procedure Pediatric patients with an underlying malignancy admitted for fever (temperature ≥ 38.3 °C) and an absolute neutrophil count <500 cells/µL from 2003-2009 were included. Risk factors associated with utilization, results, and effects on clinical management of CT scans were identified. Results Charts for 635 admissions for FN from 263 patients were reviewed. Overall, 139 (22%) admissions (93 individuals) had at least one scan. Of 188 scans, 103 (55%) were pan-scans. Changes in management were most strongly associated with the identification of evidence consistent with infection (OR = 12.64, 95% CI: 5.05-31.60, P < 0.001). Seventy-eight (41%) of all CT scans led to a change in clinical management, most commonly relating to use of antibiotic (N = 41, 53%) or antifungal/antiviral medications (N = 33, 42%). The odds of a change in clinical management did not differ for those receiving a pan-scan compared to those receiving a targeted scan (OR = 1.23; 95% CI, 0.61-2.46; P = 0.57). Conclusions When CT is clinically indicated, it is important for clinicians to strongly consider utilizing a targeted scan to reduce radiation exposure to patients as well as to decrease costs without compromising care.

A token centric part-of-speech tagger for biomedical text.

OBJECTIVE: Difficulties with part-of-speech (POS) tagging of biomedical text is accessing and annotating appropriate training corpora. These difficulties may result in POS taggers trained on corpora that differ from the tagger's target biomedical text (cross-domain tagging). In such cases where training and target corpora differ tagging accuracy decreases. This paper presents a POS tagger for cross-domain tagging called TcT. METHODS AND MATERIAL: TcT estimates a tag's likelihood for a given token by combining token collocation probabilities and the token's tag probabilities calculated using a Naive Bayes classifier. We compared TcT to three POS taggers used in the biomedical domain (mxpost, Brill and TnT). We trained each tagger on a non-biomedical corpus and evaluated it on biomedical corpora. RESULTS: TcT was more accurate in cross-domain tagging than mxpost, Brill and TnT (respective averages 83.9, 81.0, 79.5 and 78.8). CONCLUSION: Our analysis of tagger performance suggests that lexical differences between corpora have more effect on tagging accuracy than originally considered by previous research work. Biomedical POS tagging algorithms may be modified to improve their cross-domain tagging accuracy without requiring extra training or large training data sets. Future work should reexamine POS tagging methods for biomedical text. This differs from the work to date that has focused on retraining existing POS taggers.

Engineering natural language processing solutions for structured information from clinical text: extracting sentinel events from palliative care consult letters.

Despite a trend to formalize and codify medical information, natural language communications still play a prominent role in health care workflows, in particular when it comes to hand-overs between providers. Natural language processing (NLP) attempts to bridge the gap between informal, natural language information and coded, machine-interpretable data. This paper reports on a study that applies an advanced NLP method for the extraction of sentinel events in palliative care consult letters. Sentinel events are of interest to predict survival and trajectory for patients with acute palliative conditions. Our NLP method combines several novel characteristics, e.g., the consideration of topological knowledge structures sourced from an ontological terminology system (SNOMED CT). The method has been applied to the extraction of different types of sentinel events, including simple facts, temporal conditions, quantities, and degrees. A random selection of 215 anonymized consult letters was used for the study. The results of the NLP extraction were evaluated by comparison with coded sentinel event data captured independently by clinicians. The average accuracy of the automated extraction was 73.6%.

Building a biomedical tokenizer using the token lattice design pattern and the adapted Viterbi algorithm.

BACKGROUND: Tokenization is an important component of language processing yet there is no widely accepted tokenization method for English texts, including biomedical texts. Other than rule based techniques, tokenization in the biomedical domain has been regarded as a classification task. Biomedical classifier-based tokenizers either split or join textual objects through classification to form tokens. The idiosyncratic nature of each biomedical tokenizer's output complicates adoption and reuse. Furthermore, biomedical tokenizers generally lack guidance on how to apply an existing tokenizer to a new domain (subdomain). We identify and complete a novel tokenizer design pattern and suggest a systematic approach to tokenizer creation. We implement a tokenizer based on our design pattern that combines regular expressions and machine learning. Our machine learning approach differs from the previous split-join classification approaches. We evaluate our approach against three other tokenizers on the task of tokenizing biomedical text. RESULTS: Medpost and our adapted Viterbi tokenizer performed best with a 92.9% and 92.4% accuracy respectively. CONCLUSIONS: Our evaluation of our design pattern and guidelines supports our claim that the design pattern and guidelines are a viable approach to tokenizer construction (producing tokenizers matching leading custom-built tokenizers in a particular domain). Our evaluation also demonstrates that ambiguous tokenizations can be disambiguated through POS tagging. In doing so, POS tag sequences and training data have a significant impact on proper text tokenization.

Applying natural language processing toolkits to electronic health records - an experience report.

A natural language challenge devised by Informatics for Integrating Biology and the Bedside (i2b2) was to analyze free-text health data to construct a multi-class, multi-label classification system focused on obesity and its co-morbidities. This report presents a case study in which a natural language processing (NLP) toolkit, called NLTK, was used in the challenge. This report provides a brief review of NLP in the context of EHR applications, briefly surveys and contrasts some existing NLP toolkits, and reports on our experiences with the i2b2 case study. Our efforts uncovered issues including the lack of human annotated physician notes for use as NLP training data, differences between conventional free-text and medical notes, and potential hardware and software limitations affecting future projects.