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  • Wearable Digital Thermometer Improves Fever Detection

    Abstract Full Text
    JAMA. 2017; 318(6):510-510. doi: 10.1001/jama.2017.10248
  • Does This Child Have Pneumonia? The Rational Clinical Examination Systematic Review

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    JAMA. 2017; 318(5):462-471. doi: 10.1001/jama.2017.9039

    This systematic review analyzes 23 cohort studies to assess the accuracy of individual symptoms and physical examination findings for the diagnosis of radiographic pneumonia in children.

  • Diagnosis of Bacterial Infection Using a 2-Transcript Host RNA Signature in Febrile Infants 60 Days or Younger

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    JAMA. 2017; 317(15):1577-1578. doi: 10.1001/jama.2017.1365

    This study compares the diagnostic accuracy of a 2-transcript vs 66-transcript host RNA disease risk score for distinguishing bacterial from viral infection in febrile infants ≤60 days old presenting to the emergency department.

  • Association of RNA Biosignatures With Bacterial Infections in Febrile Infants Aged 60 Days or Younger

    Abstract Full Text
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    JAMA. 2016; 316(8):846-857. doi: 10.1001/jama.2016.9207

    This study describes the development and diagnostic accuracy of a host response RNA signature for distinguishing bacterial from nonbacterial infection in infants aged 60 days or younger.

  • JAMA August 23, 2016

    Figure 2: Classification of Patients Into Diagnostic Groups

    Febrile children with infections were recruited to the Immunopathology of Respiratory, Inflammatory and Infectious Disease Study and were classified into diagnostic groups as described in methods. To convert C-reactive protein (CRP) values to nmol/L, multiply by 0.9524.
  • Genetics and the Evaluation of the Febrile Child

    Abstract Full Text
    JAMA. 2016; 316(8):824-825. doi: 10.1001/jama.2016.11137
  • JAMA August 23, 2016

    Figure 2: RNA Biosignatures of Young Febrile Infants With and Without Bacterial Infections

    For panels A and C, the heat map representing RNA biosignatures are identified by class comparisons by nonparametric test (Mann-Whitney test, P < .01), with the Benjamini-Hochberg multiple test correction and 1.25-fold change. Transcripts are represented in rows and infants are represented in columns. Color bars indicate the normalized gene expression level (red, overexpressed; blue, underexpressed) relative to the healthy control infants (yellow, median gene expression of the healthy controls). The scale indicates the relative-fold differences in gene expression levels. For panels B and D, the dendrogram branches of the gene tree structure are colored according to condition (blue, healthy controls; red, bacterial infection; green, without bacterial infection). Unsupervised hierarchical clustering (euclidean distance, average linkage) of the transcripts applied to a separate test set of infants. For these analyses, the computer algorithm groups the samples of patients and healthy controls according to the similarities in gene expression patterns; this is performed in a blinded fashion because the samples are not preassigned to the infection or control group.
  • JAMA August 23, 2016

    Figure 3: Transcriptional Modular Analysis of Young Febrile Infants With and Without Bacterial Infections

    Modular maps were derived independently for the training and test sets. Modules (groups of coordinately expressed genes with similar biological function) are shown as the percentage of genes in that particular module that are expressed significantly differently from healthy controls. They are represented as dots on a grid. Module functional annotation is indicated by the color-code legend key. The first row on the grid includes modules that were identified in the first round of selection, when the modules were first described (M1; subnetwork constituted of genes coclustering; the letter M is used to name the modules). Modules that were identified in subsequent rounds of selection make up the next rows (M2, M3, M4, M5, M6). In this display only modules from the first 6 rounds of selection are shown because they are considered the most biologically relevant and have better functional characterization.A and B, the average modular transcriptional profile for patients with bacterial infections vs healthy controls is shown. C and D, the average modular transcriptional profile for patients without bacterial infections vs healthy controls.
  • JAMA August 23, 2016

    Figure 5: Discrimination of Febrile Infants With and Without Bacterial Infections by Classifier Genes

    The rectangles located on the top of the heat maps represent the patient’s classification according to standard bacterial cultures in dark colors (red for patients with bacterial infections and green for patients without bacterial infections) and below according to the κ–nearest neighbors (KNN) algorithm in light colors (white represents the patients not classified by the KNN algorithm). The arrows located at the bottom of the heat maps indicate the transcriptional profiles of 3 patients (2 in panel C and 1 panel D) with positive blood cultures for viridans streptococcus. The profiles of these 3 patients appear visually different from those of most other patients with bacteremia.A, Application of the KNN algorithm to the training set composed of febrile infants with and without bacterial infections identified classifier genes (listed in eTable 3 in the Supplement) that best discriminated the 2 groups. B. The accuracy of the classifier genes was confirmed in an independent test set of patients with and without bacterial infections. The heat maps represent the expression levels of the classifier genes in the training and test sets. Overexpressed transcripts are shown in red and underexpressed transcripts in blue. Genes are ordered in the heat map from top to bottom according to their ability to discriminate between the groups. A similar approach was followed to identify classifier genes to discriminate infants with bacteremia and those without bacterial infections. C, The KNN algorithm identified the classifier genes (listed in eTable 4 in the Supplement) in the training set composed of infants with bacteremia and those without bacterial infections that best discriminated the 2 groups. D, The accuracy of the classifier genes was confirmed in an independent test set of patients with bacteremia and without bacterial infections. The heat maps represent the expression levels of the classifier genes in the training and test sets.
  • Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children

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    JAMA. 2016; 316(8):835-845. doi: 10.1001/jama.2016.11236

    This study describes the development and diagnostic accuracy of a host-response RNA signature for distinguishing bacterial from viral infection in febrile children younger than 5 years.

  • Changing the “Working While Sick” Culture: Promoting Fitness for Duty in Health Care

    Abstract Full Text
    JAMA. 2016; 315(6):603-604. doi: 10.1001/jama.2016.0094

    This commentary discusses a mixed-methods analysis published in JAMA Pediatrics that investigated why attending physicians and advanced practice clinicians work while sick.

  • JAMA March 24, 2015

    Figure 1: Transient Fever and Low-Level Viremia Postvaccination With VSVΔG-ZEBOV

    The VSVΔG-ZEBOV vaccine was made up of a replicating, attenuated, recombinant vesicular stomatitis virus (serotype Indiana) whose surface glycoprotein gene was replaced by the Zaire Ebola virus glycoprotein gene. Prevaccination blood samples were not available. The needlestick occurred 43 hours before vaccination. A, The dashed horizontal line indicates 38°C. B, The number of polymerase chain reaction cycles required to amplify and detect the target RNA is known as the threshold cycle; thus, a lower threshold cycle value indicates that a higher concentration of target template is present. The dashed horizontal line indicates the limit of detection. Further details of real-time reverse transcription–polymerase chain reaction appear in the eMethods section in the Supplement.
  • Emergency Postexposure Vaccination With Vesicular Stomatitis Virus–Vectored Ebola Vaccine After Needlestick

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    JAMA. 2015; 313(12):1249-1255. doi: 10.1001/jama.2015.1995

    This report summarizes the experience of 1 patient with needlestick exposure to the Ebola virus and postexposure vaccination with VSVΔG-ZEBOV.

  • Rapidly Progressing Leg Ulcer With Fever in a Woman With Chronic Diarrhea

    Abstract Full Text
    JAMA. 2014; 312(20):2158-2160. doi: 10.1001/jama.2014.13007
  • Fever in Critical Neurologic Illness

    Abstract Full Text
    JAMA. 2014; 312(14):1456-1457. doi: 10.1001/jama.2014.12492
  • World Leaders Push to Prepare for Global Threats

    Abstract Full Text
    JAMA. 2014; 311(12):1189-1190. doi: 10.1001/jama.2014.2272
  • The Hospital versus the Home in the Care of the Sick; An Evolution

    Abstract Full Text
    JAMA. 2014; 311(10):1073-1073. doi: 10.1001/jama.2013.279383
  • JAMA February 27, 2013

    Figure: Herbal Medicine Examined

    A Chinese herbal remedy derived from a type of hydrangea and used for fever associated with malaria inhibits protein synthesis in the malaria parasite, a new study suggests.
  • ABOUT OURSELVES

    Abstract Full Text
    JAMA. 2012; 307(17):1782-1782. doi: 10.1001/jama.2012.503
  • Hyperthermia to Fight Cancer

    Abstract Full Text
    JAMA. 2011; 306(1):30-30. doi: 10.1001/jama.2011.912