Synthesis, in Vitro Evaluation, and Radiolabelling of Fluorinated Puromycin Analogues: Potential Candidates for PET Imaging of Protein Synthesis

There is currently no ideal radiotracer for imaging protein synthesis rate (PSR) by positron emission tomography (PET). Existing fluorine-18 labelled amino acid-based radiotracers predominantly visualize amino acid transporter processes, and in many cases they are not incorporated into nascent proteins at all. Others are radiolabelled with the short half-life positron emitter carbon-11 which is rather impractical for many PET centers. Based on the puromycin (6) structural manifold, a series of 10 novel derivatives of 6 was prepared via Williamson ether synthesis from a common intermediate. A bioluminescence assay was employed to study their inhibitory action on protein synthesis which identified fluoroethyl analogue (7b) as a lead compound. The fluorine-18 analogue was prepared via nucleophilic substitution of the corresponding tosylate precursor in modest radiochemical yield 2±0.6% and excellent radiochemical purity (>99%) and showed complete stability over 3 h at ambient temperature.H.M.B. acknowledges the Royal Society of Chemistry Research Fund for partial funding of this project and the NIHR Clinical Research Network (East Midlands) for funding her post. We are grateful to the U.K. Medical Research Council (MRC) for funding (Grant G9219778). C.T. was supported by the MRC/University of Nottingham Doctoral Training Program. S.M.S. acknowledges the EPSRC Mass Spectrometry Facility for funding her attendance at the Mass Spectrometry Summer School 2016. Dr. W. Chan (University of Nottingham) is acknowledged for access to synthetic chemistry facilities. The EPSRC Mass Spectrometry Facility at the University of Swansea is acknowledged for performing HRMS analyses

Rapid and Sensitive Detection of Yersinia pestis Using Amplification of Plague Diagnostic Bacteriophages Monitored by Real-Time PCR

BACKGROUND: Yersinia pestis, the agent of plague, has caused many millions of human deaths and still poses a serious threat to global public health. Timely and reliable detection of such a dangerous pathogen is of critical importance. Lysis by specific bacteriophages remains an essential method of Y. pestis detection and plague diagnostics. METHODOLOGY/PRINCIPAL FINDINGS: The objective of this work was to develop an alternative to conventional phage lysis tests--a rapid and highly sensitive method of indirect detection of live Y. pestis cells based on quantitative real-time PCR (qPCR) monitoring of amplification of reporter Y. pestis-specific bacteriophages. Plague diagnostic phages phiA1122 and L-413C were shown to be highly effective diagnostic tools for the detection and identification of Y. pestis by using qPCR with primers specific for phage DNA. The template DNA extraction step that usually precedes qPCR was omitted. phiA1122-specific qPCR enabled the detection of an initial bacterial concentration of 10(3) CFU/ml (equivalent to as few as one Y. pestis cell per 1-microl sample) in four hours. L-413C-mediated detection of Y. pestis was less sensitive (up to 100 bacteria per sample) but more specific, and thus we propose parallel qPCR for the two phages as a rapid and reliable method of Y. pestis identification. Importantly, phiA1122 propagated in simulated clinical blood specimens containing EDTA and its titer rise was detected by both a standard plating test and qPCR. CONCLUSIONS/SIGNIFICANCE: Thus, we developed a novel assay for detection and identification of Y. pestis using amplification of specific phages monitored by qPCR. The method is simple, rapid, highly sensitive, and specific and allows the detection of only live bacteria

EARLY DETECTION OF LUNGS CANCER USING MACHINE LEARNING ALGORITHMS

Medical healthcare systems store a large amount of clinical data about patients related to their biographies and disease information. Doctors use clinical data for the early detection of diseases that helps with proper patients’ treatments to save their lives. These clinical systems are helpful in detecting cancer diseases at early stages to save people's lives. Lung cancer is the third largely spreading disease in human beings all over the globe, which may lead so many people to death because of inaccurate detection of their disease at the initial stages. Therefore, this study will help doctors and radiologists in the detection of lung cancerous and non-cancerous patients at early stages with a random forest algorithm to save patients’ lives. In this research work, a new and novel model based on random forest algorithm was employed to detect lung cancer from the Wisconsin data set. Lung cancer was detected at early stages, and it was decided whether targeted patient was cancerous or non-cancerous. This experimental outcome showed that the proposed methodology achieved an accuracy rate that was batter compared to previous studies for early detection of lung cancer.</jats:p