• Journal of Oral Medicine and Pain
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• v.32 no.2
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• pp.187-200
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• 2007

This study attempted to contribute to the clinical application of implant operation by making a quantitative nerve examination using a neurometer for the evaluation of sensory disturbances that could be incurred after the implantation in the dental clinics, and it intended to establish an objective guideline in the evaluation of sensory nerve after the operation of implant. An inspection was performed with the frequencies of 2000Hz, 250 Hz and 5 Hz before and after the operations of tooth implant using $Neurometer^{(R)}$ CPT/C (Neurotron, Inc. Baltimore, Maryland, USA) for 44 patients who had performed an implant operation among the patients coming to Daejeon Sun Dental Hospital in 2006 and 30 people for control group. The measuring sites were maxillary nerve ending and mandibular nerve ending of trigeminal nerve according to the implant operating regions. The current perception threshold (CPT) by each nerve fiber was specifically responded under the electric stimulation of 2000 Hz in case of $A{\beta}$ fiber and of 250 Hz in case of $A{\delta}$ fiber and of 5Hz in case of C fiber. The CPT test could be performed to assess the damages of peripheral nerve in the trigeminal nerve area and it stimulated selective nerve fibers by generating the electricity of specific frequency in the peripheral nerve area. The nerve fibers with varied thickness were responsive selectively to the electric stimulation with different frequencies; accordingly, they applied the electric stimulation with different frequencies and the reaction threshold of $A{\beta},\;A{\delta}$ and C fibers selectively responsive to each electric current could be individually evaluated. In the assessment through the CPT, the increase and decrease of the CPT could be measured so that sensory disturbances such as hyperaesthesia or hypoaesthesia could be diagnosed. This study could obtain the following results after the assessment of the CPT before and after the implant operation. 1. In the assessment before and after the implant operation, the CPT in the frequencies of 2000 Hz, 250 Hz, 5 Hz for maxillary branch increased on the whole after the operation and the CPT for mandibular branch in the $A{\beta}$-fiber(2000 Hz) and C-fiber(5 Hz )after the operation increased statistically significantly. 2. For the groups of patients with medically compromised or its subsequent medicinal prescription, there were no significant differences before and after the implant operation and for the control groups, significantly high CPT was shown after the implant operation in the left $A{\beta}$-fiber(2000 Hz) and C-fiber(5 Hz). 3. In the comparison of the measured value of the CPT before the operation between the control group and the implant operation group, the latter group had a significantly high measured value of the CPT in the right $A{\beta}$-fiber(2000 Hz) and C-fiber(5 Hz) and there were significant differences in $A{\beta}$-fiber(2000 Hz) in the CPT assessment after the implant operation for the control group. 4. Male participants had higher CPT than female counterparts; however, there were no statistic significances. In the CPT evaluation before and after implant operation, there were no statistical differences in the male group while the right C-fiber(5 Hz) and left $A{\beta}$-fiber(2000Hz) were significantly high in the female group. 5. In the comparison between the group who complain sensory disturbance and the other group, the CPT increased on the whole in the former group, but there were no statistical significances. In the groups, whom there was an increase in VAS, the CPT after the implant operation in the right C-fiber(5 Hz) increased significantly; meanwhile, in case that the VAS mark was '0' before and after the operation, the CPT after the operation in the left $A{\beta}$-fiber(2000 Hz) increased significantly. This study suggested that the CPT measurements using $Neurometer^{(R)}$ CPT/C, provide useful information of objective and quantitative sensory disturbances for tooth implantation.

• Journal of Korean Society of Forest Science
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• v.83 no.2
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• pp.175-190
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• 1994

In order to get fundamental information for prediction of landslide hazard, both forest and site factors affecting slope stability were investigated in many areas of active landslides. Twelve descriptors were identified and quantified to develop the prediction model by multivariate statistical analysis. The main results obtained could be summarized as follows : The main factors influencing a large scale of landslide were shown in order of precipitation, age group of forest trees, altitude, soil texture, slope gradient, position of slope, vegetation, stream order, vertical slope, bed rock, soil depth and aspect. According to partial correlation coefficient, it was shown in order of age group of forest trees, precipitation, soil texture, bed rock, slope gradient, position of slope, altitude, vertical slope, stream order, vegetation, soil depth and aspect. The main factors influencing a landslide occurrence were shown in order of age group of forest trees, altitude, soil texture, slope gradient, precipitation, vertical slope, stream order, bed rock and soil depth. Two prediction models were developed by magnitude and frequency of landslide. Particularly, a prediction method by magnitude of landslide was changed the score for the convenience of use. If the total store of the various factors mark over 9.1636, it is evaluated as a very dangerous area. The mean score of landslide and non-landslide group was 0.1977 and -0.1977, and variance was 0.1100 and 0.1250, respectively. The boundary value between the two groups related to slope stability was -0.02, and its predicted rate of discrimination was 73%. In the score range of the degree of landslide hazard based on the boundary value of discrimination, class A was 0.3132 over, class B was 0.3132 to -0.1050, class C was -0.1050 to -0.4196, class D was -0.4195 below. The rank of landslide hazard could be divided into classes A, B, C and D by the boundary value. In the number of slope, class A was 68, class B was 115, class C was 65, and class D was 52. The rate of landslide occurrence in class A and class B was shown at the hige prediction of 83%. Therefore, dangerous areas selected by the prediction method of landslide could be mapped for land-use planning and criterion of disaster district. And also, it could be applied to an administration index for disaster prevention.