The 10 kDa bacteriophage T4 antisigma protein AsiA binds the Escherichia coli RNA polymerase promoter specificity subunit, sigma 70, with high affinity and inhibits its transcription activity. AsiA binds to sigma 70 primarily through an interaction with sigma 70 conserved region 4.2, which has also been implicated in sequence-specific recognition of the -35 consensus promoter element. Here we show that AsiA forms a stable ternary complex with core RNA polymerase (RNAP) and sigma 70 and thus does not inhibit sigma 70 activity by preventing its binding to core RNAP. We investigated the effect of AsiA on open promoter complex formation and abortive initiation at two -10/-35 type promoters and two "extended -10" promoters. Our results indicate that the binding of AsiA to sigma 70 and the interaction of sigma 70 region 4.2 with the -35 consensus promoter element of -10/-35 promoters is mutually exclusive. In contrast, AsiA has much less effect on open promoter complex formation and abortive initiation from extended -10 promoters, which lack a -35 consensus element and do not require sigma 70 conserved region 4.2. From these results we conclude that T4 AsiA inhibits E. coli RNAP sigma 70 holoenzyme transcription at -10/-35 promoters by interfering with the required interaction between sigma 70 conserved region 4.2 and the -35 consensus promoter element.
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http://dx.doi.org/10.1006/jmbi.1998.1742 | DOI Listing |
Background And Aims: Neonatal sepsis is a major cause of neonatal mortality worldwide. It remains a detrimental bottleneck to the WHO goal of eradicating preventable deaths for children below 5 years of age by 2030. Though the risk factors for adverse clinical outcomes for neonatal sepsis have been widely studied there is no universal consensus.
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December 2024
National Reference Laboratory, Abu Dhabi, UAE.
Background: An increasing number of wearable medical devices are being used for personal monitoring and professional health care purposes. These mobile health devices collect a variety of biometric and health data but do not routinely connect to a patient's electronic health record (EHR) or electronic medical record (EMR) for access by a patient's health care team.
Methods: The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MHBLM) developed consensus recommendations for consideration when interfacing mobile health devices to an EHR/EMR.
EJIFCC
December 2024
Section of Chemical Pathology, Department of Pathology and Laboratory Medicine.
Introduction: The standardization of reporting in clinical laboratories, particularly regarding Serum Protein Electrophoresis (SPEP) and Urine Protein Electrophoresis (UPEP), is crucial for effective communication of findings to clinicians and optimal patient management. However, in countries like Pakistan with limited healthcare resources and a prevalent self-payment model, challenges arise in achieving standardized reporting practices. This manuscript addresses the need for standardized guidelines for protein electrophoresis reporting in Pakistan, aiming to enhance laboratory practices and patient care.
View Article and Find Full Text PDFRural Remote Health
January 2025
Indiana University School of Medicine, Indianapolis, Indiana, USA.
Introduction: Perceived social support is a psychological construct that is used to describe the 'perception of adequacy' of the support being provided by a person's social network. Higher perceived social support has been linked to multiple benefits across numerous studies over the past several decades and among multiple populations. The Multidimensional Scale of Perceived Social Support (MSPSS) is a 12-item scale to assess the construct of perceived social support.
View Article and Find Full Text PDFMedicine (Baltimore)
November 2024
Department of Radiology, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich, Baden, Switzerland.
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection.
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