Modelling the transmission of healthcare associated infections: a systematic review.

BACKGROUND: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. METHODS: MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. RESULTS: In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data. CONCLUSIONS: Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models

Expression and distribution of Na, K-ATPase isoforms in the human uterus

Na, K-ATPase activity relies on the composition of its catalytic α, β, and FXYD constituents, all of which are expressed as multiple isoforms (4α, 4β, and 7 FXYD). We used reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry to study Na, K-ATPase expression in uterine samples from nonlaboring elective and laboring emergency caesarean sections (CSs). Transcripts of α1 to 3, β1 to 3, and FXYD1 isoforms were detected in all samples, but FXYD2 was only present in hysterectomy samples. Abundant immunoreactivity of α1 and moderate α2 was localized in myometrial smooth muscle and secretory glands of all groups. Smooth muscle and gland epithelia showed diffuse cytoplasmic α3 immunoreactivity. β isoforms were detected in all groups but β3 showed much denser immunoreactivity in myometrial samples taken from women in labor. In pregnancy, there was a switch in isoform expression, resulting in increased β3 and decreased FXYD2 at the protein and messenger RNA (mRNA) levels. Na, K-ATPase isoform alterations may modulate uterine contractility during labor