Can children with sickle cell disease undergo open splenectomy without preoperative transfusion despite severe anemia? A report of three cases

Preoperative red cells transfusion to correct anaemia and to reduce the proportion of sickle red cells is part of standard preparation of children with sickle cell anaemia (HbSS) for major procedures including open abdominal surgeries. We report three children with sickle cell anaemia presenting with chronic massive splenomegaly and hypersplenism. The children were initially denied surgery because of extremely low haemoglobin levels and the inefficacy of transfusion. Subsequently, they underwent successful open abdominal splenectomy without any red cells transfusion. These observations are important to paediatricians and surgeons in settings where HbSS is common. They highlight the fact that surgery should not be withheld from children with sickle cell anaemia and massive splenomegaly purely on the basis of difficulty in correcting anaemia before the procedure.Key words: Sickle cell disease,surgery, splenectomy, transfusion

Aiming at the Global Elimination of Viral Hepatitis: Challenges along the Care Continuum

A recent international workshop, organised by the authors, analysed the obstacles facing the ambitious goal of eliminating viral hepatitis globally. We identified several policy areas critical to reaching elimination targets. These include: providing hepatitis B birth-dose vaccination to all infants within 24 hours of birth; preventing the transmission of blood-borne viruses through the expansion of national haemovigilance schemes; implementing the lessons learnt from the HIV epidemic regarding safe medical practices to eliminate iatrogenic infection; adopting point-of-care testing to improve coverage of diagnosis; and providing free or affordable hepatitis C treatment to all. We introduce Egypt as a case study for rapid testing and treatment scale-up: this country offers valuable insights to policy makers internationally, not only regarding how hepatitis C interventions can be expeditiously scaled-up, but also as a guide for how to tackle the problems encountered with such ambitious testing and treatment programmes

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks.

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed

Roles for Treg expansion and HMGB1 signaling through the TLR1-2-6 axis in determining the magnitude of the antigen-specific immune response to MVA85A

© 2013 Matsumiya et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedA better understanding of the relationships between vaccine, immunogenicity and protection from disease would greatly facilitate vaccine development. Modified vaccinia virus Ankara expressing antigen 85A (MVA85A) is a novel tuberculosis vaccine candidate designed to enhance responses induced by BCG. Antigen-specific interferon-γ (IFN-γ) production is greatly enhanced by MVA85A, however the variability between healthy individuals is extensive. In this study we have sought to characterize the early changes in gene expression in humans following vaccination with MVA85A and relate these to long-term immunogenicity. Two days post-vaccination, MVA85A induces a strong interferon and inflammatory response. Separating volunteers into high and low responders on the basis of T cell responses to 85A peptides measured during the trial, an expansion of circulating CD4+ CD25+ Foxp3+ cells is seen in low but not high responders. Additionally, high levels of Toll-like Receptor (TLR) 1 on day of vaccination are associated with an increased response to antigen 85A. In a classification model, combined expression levels of TLR1, TICAM2 and CD14 on day of vaccination and CTLA4 and IL2Rα two days post-vaccination can classify high and low responders with over 80% accuracy. Furthermore, administering MVA85A in mice with anti-TLR2 antibodies may abrogate high responses, and neutralising antibodies to TLRs 1, 2 or 6 or HMGB1 decrease CXCL2 production during in vitro stimulation with MVA85A. HMGB1 is released into the supernatant following atimulation with MVA85A and we propose this signal may be the trigger activating the TLR pathway. This study suggests an important role for an endogenous ligand in innate sensing of MVA and demonstrates the importance of pattern recognition receptors and regulatory T cell responses in determining the magnitude of the antigen specific immune response to vaccination with MVA85A in humans.This work was funded by the Wellcome Trust. MM has a Wellcome Trust PhD studentship and HM is a Wellcome Trust Senior Fello

Relationships among neurocognition, symptoms and functioning in patients with schizophrenia: a path-analytic approach for associations at baseline and following 24 weeks of antipsychotic drug therapy

<p>Abstract</p> <p>Background</p> <p>Neurocognitive impairment and psychiatric symptoms have been associated with deficits in psychosocial and occupational functioning in patients with schizophrenia. This post-hoc analysis evaluates the relationships among cognition, psychopathology, and psychosocial functioning in patients with schizophrenia at baseline and following sustained treatment with antipsychotic drugs.</p> <p>Methods</p> <p>Data were obtained from a clinical trial assessing the cognitive effects of selected antipsychotic drugs in patients with schizophrenia. Patients were randomly assigned to 24 weeks of treatment with olanzapine (n = 159), risperidone (n = 158), or haloperidol (n = 97). Psychosocial functioning was assessed with the Heinrichs-Carpenter Quality of Life Scale [QLS], cognition with a standard battery of neurocognitive tests; and psychiatric symptoms with the Positive and Negative Syndrome Scale [PANSS]. A path-analytic approach was used to evaluate the effects of changes in cognitive functioning on subdomains of quality of life, and to determine whether such effects were direct or mediated via changes in psychiatric symptoms.</p> <p>Results</p> <p>At baseline, processing speed affected functioning mainly indirectly via negative symptoms. Positive symptoms also affected functioning at baseline although independent of cognition. At 24 weeks, changes in processing speed affected changes in functioning both directly and indirectly via PANSS negative subscale scores. Positive symptoms no longer contributed to the path-analytic models. Although a consistent relationship was observed between processing speed and the 3 functional domains, variation existed as to whether the paths were direct and/or indirect. Working memory and verbal memory did not significantly contribute to any of the path-analytic models studied.</p> <p>Conclusion</p> <p>Processing speed demonstrated direct and indirect effects via negative symptoms on three domains of functioning as measured by the QLS at baseline and following 24 weeks of antipsychotic treatment.</p

Business process modelling to improve incident management process

Business process management (BPM) is an approach focused on the continuous improvement of business processes, providing for this a collection of best practices. These best practices enable the redesign of business processes to meet the desired performance. IT service management (ITSM) defines the management of IT operations as a service. There are several ITSM frameworks available, consisting in best practices that propose standardizing these pro- cesses for the respective operations. By adopting these frameworks, organisations can align IT with their business objectives. Therefore, the objective of this research is to understand how BPM can be used to improve of ITSM processes. An exploratory case study in a multinational company based in Lisbon, Portugal, is conducted for the improvement of the time performance of an inci- dent management process. Data were gained through documentation, archival records, interviews and focus groups with a team involved in IT support service. So far, the as-is process was elicited, and respective incongruences clarified. During the next months the authors intend to identify the main problems and simulate the appropriate BPM heuristics to understand the impact in the busi- ness organisation.info:eu-repo/semantics/acceptedVersio

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks