• Asian Pacific Journal of Cancer Prevention
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• v.16 no.11
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• pp.4745-4749
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• 2015

Background: The aim of this study was to assess detection of circulating tumor cells (CTC) using anti-CEA pre and post surgery in Chilean patients with colo-rectal cancer. Materials and Methods: The presence of CTCs was evaluated in 80 colorectal cancer patients pre and post surgery using standard immunocytochemistry and the results were compared with findings for standard clinico-pathological parameters. Results: In patients presurgery CEA (+) CTCs were frequently found, with no relation to tumor size or nodal status. After surgery, the presence of CTCs was associated with such clinico-pathological parameters. The frequency of CTC detection in node positive patients did not change after surgery. In patients with metastasis there was also no change in the frequency of CTC detection, and clusters of 3 or more CTCs were evident. Conclusions: Secondary CTCs are associated with clinico-pathological parameters only after surgical removal of the primary tumor, and might be important in identifying patients at high risk of relapse. Primary CTCs detected before surgical removal are frequently found, are not associated with the clinico-pathological parameters and might have a role in cancer screening. These findings suggest the need for studies with a larger population of patients.

• Speech Sciences
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• v.5 no.1
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• pp.151-165
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• 1999

This paper describes a series of researches to diagnose vocal diseases using the statistical method and the acoustic signal analysis method. Speech materials are collected at the hospital. Using the pathological database, the basic parameters for the diagnosis are obtained. Based on the statistical characteristics of the parameters, valid parameters are chosen and those are used to diagnose the pathological speech signal. Cepstrum is used to extract parameters which represents characteristics of pathological speech. 3 layered neural network is used to train and classify pathological speech into normal, benign and malignant case.

• Proceedings of the Korean Society of Toxicology Conference
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• 2005.05a
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• pp.197-197
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• 2005

• Speech Sciences
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• v.4 no.2
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• pp.17-24
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• 1998

In this paper a method to diagnose pathological voices using wavelet transform is sug gested. Pathological voices are collected from hospital and analyzed by the suggested method. Normal voices are collected separately and analyzed. Then the results are compared to find the differences in their characteristics. Three level wavelet transform is used. Normalized energy ratios between the levels and normalized peak-to-peak values are used as parameters. As a result, it was possible to distinguish between normal and pathological voices.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.249-252
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• 2001

The most basic means of communication among humans is a voice. Without speaking of voice technologies, we found it is important and convenient to use a voice in everyday life. But. in consideration to speech recognition systems, we can't always desire a normal voice input as input signal to the system. Generally speaking. a pathological voice as against a normal which is a voice with a problem in the larynx. could be also special case of input voice. Of course, but the distortion of a speech signal by environmental effects i.e., noise or transmission channel was a raised problem. we will take up a pathological voices with laryngeal disease which is essential distortion factor in voice. Also, we are to find out the difference of acoustic parameters distribution between normal and pathological voice by a statistical method in our research.

• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.99-108
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• 2011

The perturbation parameters like jitter, shimmer, and signal-to-noise ratio (SNR) are largely estimated in the particular segment from the subjective or whole portion of the given pathological voice signal although there are many possible regions to be able to analyze the voice signals. In this paper, the pathological voice signals were classified as type 1, 2, 3, or 4 according to narrow band spectrogram and the value differences of the perturbation parameters extracted in the subjective and entire portion tended to be getting bigger as from type 1 to type 4 signals. Therefore, sample selection method based on moving window to analyze type 2 and 3 signals as well as type 1 signals is proposed. Although type 3 signals cannot be analyzed using the perturbation analysis, the type 3 signals by selecting out the samples in which error count is less than 10 through moving window were analyzed. At present, there is no method to be able to analyze the type 4 signals. Future research will endeavor to determine the best way to evaluate such voices.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.6 no.1
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• pp.17-23
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• 2003

Purpose: The aim of this study was to determine clinical significance of dual-probe esophageal pH monitoring and to compare four pH monitoring parameters between proximal and distal esophagus in pathological gastroesophageal reflux disease with recurrent respiratory symptoms. Methods: Among the thirty-four patients who were performed 24 hr pH monitoring, seventeen patients with pathological distal reflux were classified into two groups: Group I (n:12) had recurrent respiratory symptoms and Group II (n:5) hadn't recurrent respiratory symptoms. The ambulatory dual-probe esophageal pH monitoring was performed for 18~24 hr. A pathologic GER was defined when reflux index (percent of the investigation time a pH<4) exceeded the 95th percentile of normal value. Results: Among the sixteen patients with recurrent respiratory symptoms, twelve patients (75%) have pathological distal reflux. Whereas among the eighteen patients without recurrent respiratory symptom, five patients (28%) have pathological distal reflux. In the Group I, the significant differences between proximal and distal esophageal pH recordings persisted for all parameters, but didn't persist in group II except for longest episode. Comparing esophageal pH four parameters between group I and group II at the proximal esophageal site, all parameters didn't show statistically significant differences. Conclusion: Regardless of respiratory symptoms, patients with pathological distal reflux didn't show statistically significant differences in the all parameters at the proximal esophageal site. Therefore we may reconsider usefulness of dual probe pH meter in patients with recurrent respiratory symptoms.

• Speech Sciences
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• v.11 no.1
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• pp.21-29
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• 2004

In this paper we examined the effect of normalization on discriminating the pathological voice into normal and abnormal classes using artificial neural network. Average values per each parameter were used to normalize each set of parameter values. Artificial neural networks were used as classifiers. And the effect of normalization was evaluated by comparing the discrimination results between original and normalized parameter sets.

• Biomedical Science Letters
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• v.7 no.4
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• pp.211-216
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• 2001

This studs was carried out to evaluate KBrO$_3$-induced acute toxicity by clinical pathological parameters in rats. Fourty rats were divided into 4 groups including normal group and three KBrO$_3$-treated groups with doses of 200, 300, and 400 mg/kg, p. o., respectively. Creatinine and BUN were increased remarkably by KBrO$_3$ at 400 mg/kg, respectively (p<0.05, p<0.01). Phosphorus content increased two times the control at 400 mg/kg (p<0.05). Osmolarity was increased, whereas $CO_2$ content showed decrease at 400 mg/kg, respectively (p<0.01, p<0.05). Histopathological findings also showed dose-dependent renal failure. On the other hand, AST was increased three times the control at 400 mg/kg (p<0.01). WBC was increased by KBrO$_3$ depending on the dosage. Platelet was decreased at 200 mg/kg, whereas it was increased at 400 mg/kg (p<0.05). The results above suggest that clinical pathological parameters could be used as indices for the evaluation of KBrO$_3$-induced acute toxic reponse occuring in not only kidney but other organs including liver, when the dosage is as high as 400 mg/kg.

• Speech Sciences
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• v.14 no.1
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• pp.151-162
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• 2007

This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chosen. Then the classification method based on the artificial neural network and Gaussian mixture method was employed to discriminate the data into normal and pathological speech. The GMM method attained 98.4% average correct classification rate with training data and 95.2% average correct classification rate with test data. The different mixture number (3 to 15) of GMM was used in order to obtain an optimal condition for classification. We also compared the average classification rate based on GMM, ANN and HMM. The proper number of mixtures on Gaussian model needs to be investigated in our future work.