The development of a brief and objective method for evaluating moral sensitivity and reasoning in medical students

BACKGROUND: Most medical schools in Japan have incorporated mandatory courses on medical ethics. To this date, however, there is no established means of evaluating medical ethics education in Japan. This study looks 1) To develop a brief, objective method of evaluation for moral sensitivity and reasoning; 2) To conduct a test battery for the PIT and the DIT on medical students who are either currently in school or who have recently graduated (residents); 3) To investigate changes in moral sensitivity and reasoning between school years among medical students and residents. METHODS: Questionnaire survey: Two questionnaires were employed, the Problem Identification Test (PIT) for evaluation of moral sensitivity and a portion of the Defining Issues Test (DIT) for moral reasoning. Subjects consisted of 559 medical school students and 272 residents who recently graduated from the same medical school located in an urban area of Japan. RESULTS: PIT results showed an increase in moral sensitivity in 4(th )and 5(th )year students followed by a decrease in 6(th )year students and in residents. No change in moral development stage was observed. However, DIT results described a gradual rising shift in moral decision-making concerning euthanasia between school years. No valid correlation was observed between PIT and DIT questionnaires. CONCLUSION: This study's questionnaire survey, which incorporates both PIT and DIT, could be used as a brief and objective means of evaluating medical students' moral sensitivity and reasoning in Japan

Bacterial infections in Lilongwe, Malawi: aetiology and antibiotic resistance

<p>Abstract</p> <p>Background</p> <p>Life-threatening infections present major challenges for health systems in Malawi and the developing world because routine microbiologic culture and sensitivity testing are not performed due to lack of capacity. Use of empirical antimicrobial therapy without regular microbiologic surveillance is unable to provide adequate treatment in the face of emerging antimicrobial resistance. This study was conducted to determine antimicrobial susceptibility patterns in order to inform treatment choices and generate hospital-wide baseline data.</p> <p>Methods</p> <p>Culture and susceptibility testing was performed on various specimens from patients presenting with possible infectious diseases at Kamuzu Central Hospital, Lilongwe, Malawi.</p> <p>Results</p> <p>Between July 2006 and December 2007 3104 specimens from 2458 patients were evaluated, with 60.1% from the adult medical service. Common presentations were sepsis, meningitis, pneumonia and abscess. An etiologic agent was detected in 13% of patients. The most common organisms detected from blood cultures were <it>Staphylococcus aureus</it>, <it>Escherichia </it><it>coli</it>, Salmonella species and <it>Streptococcus pneumoniae</it>, whereas <it>Streptococcus pneumoniae </it>and <it>Cryptococcus neoformans </it>were most frequently detected from cerebrospinal fluid. <it>Haemophilus influenzae </it>was rarely isolated. Resistance to commonly used antibiotics was observed in up to 80% of the isolates while antibiotics that were not commonly in use maintained susceptibility.</p> <p>Conclusions</p> <p>There is widespread resistance to almost all of the antibiotics that are empirically used in Malawi. Antibiotics that have not been widely introduced in Malawi show better laboratory performance. Choices for empirical therapy in Malawi should be revised accordingly. A microbiologic surveillance system should be established and prudent use of antimicrobials promoted to improve patient care.</p

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation