1.Anti-inflammatory effect of sea buckthorn in an HCl-induced cystitis rat model
Hyun Suk YOON ; Juyeon YU ; Shinhoon KANG ; Hana YOON
Investigative and Clinical Urology 2025;66(1):67-73
Purpose:
Although the mechanism underlying interstitial cystitis/bladder pain syndrome (IC/BPS) remains unclear, oxidative stress is suggested to be implicated in IC/BPS development. Sea buckthorn (SB; Hippophae rhamnoides L.) contains several compounds with antioxidant properties. In addition, intravesical application of hydrochloric acid (HCl) in rats induces histological changes similar to those observed in humans with IC. Therefore, the aim of this study was to evaluate the anti-inflammatory effects of SB in an HCl-induced rat cystitis model.
Materials and Methods:
Twenty 8-week-old female Sprague–Dawley rats were instilled with HCl in their bladders to create an IC/BPS model. The model rats were divided into three groups and orally administrated distilled water (control, n=4), concentrated SB (n=8), or pentosan polysulfate (PPS, n=8) daily. Pathologic inflammation grade (H&E staining), number of mast cells per square millimeter (toluidine blue staining), fibrotic changes (Masson’s trichrome staining), and apoptosis (terminal deoxynucleotidyl transferase dUTP nick end labeling staining) of bladder tissue samples were compared among the groups.
Results:
Compared to the control group, the SB and PPS groups showed reduced edema (5.25±0.96 vs. 2.25±0.46 vs. 2.50±0.54, p=0.004, p=0.005, respectively), number of mast cells (12.5±3.6 vs. 6.8±1.9 vs. 6.6±1.8, p=0.010, p=0.002, respectively), ratio of fibrotic submucosal tissue (63.9%±7.0% vs. 43.6%±9.9% vs. 40.5%±5.2%, p<0.001, p<0.001, respectively), and ratio of apoptotic nucleus (40.7%±11.7% vs. 7.7%±6.5% vs. 5.1%±4.9%, p<0.001, p<0.001, respectively).
Conclusions
SB exhibited anti-inflammatory effects comparable to those of PPS in the HCl-induced chemical cystitis model.
2.Nutrition Intervention through Interdisciplinary Medical Treatment in Hospice Patients: From Admission to Death.
Hyelim KANG ; Yu Jin YANG ; Juyeon PARK ; Gyu Jin HEO ; Jeong Im HONG ; Hye Jin KIM
Clinical Nutrition Research 2018;7(2):146-152
The demand for hospice services as well as for ‘well-dying’ of terminal patients is increasing as patient financial burden is decreasing due to National Health Insurance coverage for hospice care. Hospice institutions utilize interdisciplinary teams comprising doctors, nurses, dietitians, and other health staffs to provide comprehensive patient management. This report examined the nutritional status of a hospice patient from admission to death as well as the nutrition management of this patient in the hospice ward through nutrition interventions performed by a dietitian in the interdisciplinary team. The patient in the present case was a 74-year-old man diagnosed with pancreatic head cancer who died after 26 days of hospice care following transfer from the general ward. During hospice care, the dietitian monitored the patient's nutritional status and performed 8 nutrition interventions, but his oral intake decreased as the patient's symptoms worsened. The average energy intake rates were 30% and 17% of required rates for oral and artificial nutrition, respectively. In line with a report suggesting that the main focus of nutrition in palliative care should be on improving the quality of life and reducing worry in patients, rather than aggressive nutritional management, there is a need for nutrition interventions that are personalized to individual patients by monitoring progress and offering continuous counseling from the time of admission. In addition, further studies such as comparative analysis of nutritional management in Korean hospice ward will be needed for better nutrition management for terminally ill patients.
Aged
;
Counseling
;
Energy Intake
;
Head and Neck Neoplasms
;
Hospice Care
;
Hospices*
;
Humans
;
National Health Programs
;
Nutritional Status
;
Nutritionists
;
Palliative Care
;
Patients' Rooms
;
Quality of Life
;
Terminally Ill
3.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
4.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
5.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
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