EMG modulation in anticipation of a possible trip during walking in young and older adults.

This study investigated whether changes in lower limb muscle activity occurred in anticipation of a possible perturbation in 11 young (mean age 27 years) and 11 older (mean age 68 years) adults. Altered muscle activity could affect tripping responses and consequently the ecological validity of experimental results of studies on tripping. It was hypothesized that anticipatory muscle activity would be present immediately after a trip, and decrease after several subsequent unperturbed (forewarned) walking trials. Electromyograms of lower limb muscles were measured in 3 conditions: during normal walking, during forewarned walking immediately after a trip, and during forewarned walking several trials after a trip had occurred. Small but statistically significant differences in averaged muscle activity over a stride were found among conditions. Young adults showed slightly increased activity immediately after tripping (co-contraction) in hamstrings, quadriceps and tibialis anterior muscles. This increased activity diminished after several unperturbed trials, although it did not return to the baseline activity levels during normal walking. In older adults, an increased muscle activity among conditions was only discerned in tibialis anterior and soleus muscles. This suggested that older adults prefer to avoid contact with the obstacle over joint stiffening. Yet, for both age-groups, the increases in muscle activity were very small when compared to tripping responses reported in the literature. Therefore, anticipatory effects are not expected to jeopardize the validity of experiments in which subjects are perturbed more than once.     

What Drives the Occurrence of the Melioidosis Bacterium Burkholderia pseudomallei in Domestic Gardens?

Melioidosis is an often fatal infectious disease affecting humans and animals in tropical regions and is caused by the saprophytic environmental bacterium Burkholderia pseudomallei. Domestic gardens are not only a common source of exposure to soil and thus to B. pseudomallei, but they also have been found to contain more B. pseudomallei than other environments. In this study we addressed whether anthropogenic manipulations common to gardens such as irrigation or fertilizers change the occurrence of B. pseudomallei. We conducted a soil microcosm experiment with a range of fertilizers and soil types as well as a longitudinal interventional study over three years on an experimental fertilized field site in an area naturally positive for B. pseudomallei. Irrigation was the only consistent treatment to increase B. pseudomallei occurrence over time. The effects of fertilizers upon these bacteria depended on soil texture, physicochemical soil properties and biotic factors. Nitrates and urea increased B. pseudomallei load in sand while phosphates had a positive effect in clay. The high buffering and cation exchange capacities of organic material found in a commercial potting mix led to a marked increase in soil salinity with no survival of B. pseudomallei after four weeks in the potting mix sampled. Imported grasses were also associated with B. pseudomallei occurrence in a multivariate model. With increasing population density in endemic areas these findings inform the identification of areas in the anthropogenic environment with increased risk of exposure to B. pseudomallei.     

Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections

In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.