Brucellosis is considered to be a zoonotic infection
with a predominant
incidence in most parts of Iran that may even simply involve diagnostic
laboratory personnel. In the present study, we apply matrix-assisted
laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF
MS) for rapid and reliable discrimination of Brucella abortus and Brucella melitensis, based on proteomic mass
patterns from chemically treated whole-cell analyses. Biomarkers of
the low molecular weight proteome in the MALDI-TOF MS spectra were
assigned to conserved ribosomal and structural protein families that
were found in genome assemblies of B. abortus and B. melitensis in the NCBI database. Significant protein
mass signals successfully mapped to ribosomal proteins and structural
proteins, such as integration host factor subunit alpha, cold-shock
proteins, HU family DNA-binding protein, ATP synthase subunit C, and
GNAT family N-acetyltransferase, with specific biomarker peaks that
have been identified for each virulent and vaccine strain. Web-accessible
bioinformatics algorithms, with a robust data analysis workflow, followed
by ribosomal and structural protein mapping, significantly enhanced
the reliable assignment of key proteins and accurate identification
of Brucella species. Furthermore, clinical samples
were analyzed to confirm the most dominant protein biomarker candidates
and their relevance for the identifications of B. melitensis and B. abortus. With proper optimization, we envision
that the presented MALDI-TOF MS proteomics analyses, coupled with
special usage of bioinformatics, could be used as a cost-efficient
strategy for the diagnostics of brucellosis and introduce a reliable
identification protocol for species of dangerous bacteria.
Background: Nurses play a key role in increasing the efficiency of healthcare systems. Given the 24-hour performance of hospitals and the small number of nurses in the field of treatment, it is quintessential to re-shift them in the hospital. This study set out to achieve coherence in nursing shift planning and justice in the order of shifts in hospital.
Methods: This applied and a developmental study was performed from 2019 to 2020. We used genetic algorithm to provide operational solutions and define flexible shifts and plan nurses' working hours in Yas Hospital, Tehran University of Medical Sciences Hospital, Tehran, Iran.
Results: Based on the selection of each nurse and determining the approved shifts of each ward, the possibility of appropriate planning was provided to determine the required shifts per month and to estimate the needs of each department.
Conclusion: Using genetic algorithm and nursing shift in office automation console provides useful tools for managers at all organizational levels, according to which a good balance between the hospital's need for nurse and nurses’ demands in different time periods.
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