Back ground: Skin grafting is the most common form
of reconstructive surgery, and regeneration of
sensations in skin grafts is a complex process
influenced by many factors such as , the thickness of
the graft, the depth of the grafted bed, meshing of the
graft, the condition of the bed and the surrounding
area. So many studies performed on this subject, some
of them clinically based on subjective type of sensation
tests, and others histological to detect the presence of
nerve fibers in the grafted skin
Objectives: To detect return of sensations to split
thickness skin grafts by clinical methods.
Methods: From Oct. 1995 to Oct. 2010, a clinical
prospective study performed in Al wasity Hospital for
reconstructive surgery, Hilla teaching General
Hospital, and Al kindy teaching General Hospital on
recovery of sensations in human split thickness skin
grafts on 200 patients, 400 grafts. There were 120
male, 80 female patients, there ages ranged from20 -61
years with mean of 28 years. The regeneration of
sensation of pain, touch, cold, and warmth, was studied
with the usual clinical methods. We studied; different
graft thicknesses, depth of graft beds, meshing of the
grafts, grafts on early and late wound excisions
Results: in our study the regeneration of sensations
occurred in the following order; pain, touch, cold ,
warmth, and has been found to extend over a period of
16days to 3 months, and sensations improve with time
but never recover completely even after several years.
Conclusion: The recovery of sensation of grafted skin
is a complex process that is influenced by many
factors; some of them are related to the graft, to the
recipient bed, to the patient as a whole, and occurs if
the graft is applied on a sufficiently innervated bed.
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Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based dat
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