VNTRs, due to the low cost and high mutation rates, make them particularly useful to detect genetic differences in recent outbreaks, and while SNPs have a lower mutation rate per locus than VNTRs, they deliver more stable and reliable genetic relationships between isolates. Both methods are used to construct phylogenies for genetic analysis, however, SNPs are more suitable for studies on phylogenies contraction. However, it can be difficult for these methods to accurately simulate CSTs everts. Estimates of CST based on phylogenies made using the VNTR marker can be biased towards detecting CST events across a wide range of the parameters. SNPs tend to be less biased and variable in estimates of CST when estimations of CST rates are low and a low number of SNPs is used. In general, CST rate estimates using these methods are most reliable in systems with more mutations, more markers, and high genetic differences between introduced strains. CST is very complex and models need to account for a lot of parameters to accurately represent the phenomena. Models that oversimplify reality can result in biased data. Multiple parameters such as number of mutations accumulated since introduction, stochasticity, the genetic difference of strains introduced, and the sampling effort can make unbiased estimates of CST difficult even with whole-genome sequences, especially if sampling is limited, mutation rates are low, or if pathogens were recently introduced. More information on the factors that influence CST rates is needed for the contraction of more appropriate models to study these events.
Treatment of lung cancer or other causes of hypertrophic osteoarthropathy results in regression of symptoms for some patients. Etymology
The eponymous Bamberger–Marie syndrome is named for Austrian internist Eugen von Bamberger and French neurologist Pierre Marie. See also
Periosteal reaction
Hypertrophic osteopathy

References


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