1.Changes in schistosomiasis prevalence after 2 years of an integrated intervention in the Itilima district of Tanzania
Humphrey MAZIGO ; Jungim LEE ; Yoonho CHO ; Seungman CHA ; Yan JIN
Parasites, Hosts and Diseases 2025;63(1):75-86
Schistosomiasis remains one of the most prevalent neglected tropical diseases in Tanzania. World Vision Tanzania, in collaboration with the Ministry of Health through the National Neglected Tropical Diseases Control Programme, implemented school- and community-based mass drug administrations, community-led total sanitation, and community voice and action from 2020 to 2022. This study assessed changes in the prevalence of schistosomiasis in the Itilima district of northwestern Tanzania following the implementation of these integrated interventions. A total of 1,405 students from 22 schools participated in the baseline survey in August to September 2020, and 1,320 in September 2022. Additionally, 368 adults from 8 villages participated in the baseline survey, and 401 in the endline survey. The prevalence difference was calculated to assess changes before and after the integrated interventions. We also investigated risk factors for Schistosoma haematobium infection using endline data. The prevalence difference between 2020 and 2022 was -20.0% (95% confidence interval (CI)=-22.2%–-17.7%, p<0.001) for students and -19.6% (95% CI=-22.2%–-17.7%, p<0.001) for adults. Individuals without a latrine were more likely to have schistosomiasis (adjusted odds ratio=5.9, 95% CI=1.7–21.5, p=0.01) compared to those who had a latrine. The findings indicate substantial changes in schistosomiasis prevalence in the study area following the implementation of integrated interventions. To sustain these achievements in Itilima, a multi-sectorial approach is highly recommended to integrate additional measures for eliminating schistosomiasis as a public health problem.
2.Changes in schistosomiasis prevalence after 2 years of an integrated intervention in the Itilima district of Tanzania
Humphrey MAZIGO ; Jungim LEE ; Yoonho CHO ; Seungman CHA ; Yan JIN
Parasites, Hosts and Diseases 2025;63(1):75-86
Schistosomiasis remains one of the most prevalent neglected tropical diseases in Tanzania. World Vision Tanzania, in collaboration with the Ministry of Health through the National Neglected Tropical Diseases Control Programme, implemented school- and community-based mass drug administrations, community-led total sanitation, and community voice and action from 2020 to 2022. This study assessed changes in the prevalence of schistosomiasis in the Itilima district of northwestern Tanzania following the implementation of these integrated interventions. A total of 1,405 students from 22 schools participated in the baseline survey in August to September 2020, and 1,320 in September 2022. Additionally, 368 adults from 8 villages participated in the baseline survey, and 401 in the endline survey. The prevalence difference was calculated to assess changes before and after the integrated interventions. We also investigated risk factors for Schistosoma haematobium infection using endline data. The prevalence difference between 2020 and 2022 was -20.0% (95% confidence interval (CI)=-22.2%–-17.7%, p<0.001) for students and -19.6% (95% CI=-22.2%–-17.7%, p<0.001) for adults. Individuals without a latrine were more likely to have schistosomiasis (adjusted odds ratio=5.9, 95% CI=1.7–21.5, p=0.01) compared to those who had a latrine. The findings indicate substantial changes in schistosomiasis prevalence in the study area following the implementation of integrated interventions. To sustain these achievements in Itilima, a multi-sectorial approach is highly recommended to integrate additional measures for eliminating schistosomiasis as a public health problem.
3.Changes in schistosomiasis prevalence after 2 years of an integrated intervention in the Itilima district of Tanzania
Humphrey MAZIGO ; Jungim LEE ; Yoonho CHO ; Seungman CHA ; Yan JIN
Parasites, Hosts and Diseases 2025;63(1):75-86
Schistosomiasis remains one of the most prevalent neglected tropical diseases in Tanzania. World Vision Tanzania, in collaboration with the Ministry of Health through the National Neglected Tropical Diseases Control Programme, implemented school- and community-based mass drug administrations, community-led total sanitation, and community voice and action from 2020 to 2022. This study assessed changes in the prevalence of schistosomiasis in the Itilima district of northwestern Tanzania following the implementation of these integrated interventions. A total of 1,405 students from 22 schools participated in the baseline survey in August to September 2020, and 1,320 in September 2022. Additionally, 368 adults from 8 villages participated in the baseline survey, and 401 in the endline survey. The prevalence difference was calculated to assess changes before and after the integrated interventions. We also investigated risk factors for Schistosoma haematobium infection using endline data. The prevalence difference between 2020 and 2022 was -20.0% (95% confidence interval (CI)=-22.2%–-17.7%, p<0.001) for students and -19.6% (95% CI=-22.2%–-17.7%, p<0.001) for adults. Individuals without a latrine were more likely to have schistosomiasis (adjusted odds ratio=5.9, 95% CI=1.7–21.5, p=0.01) compared to those who had a latrine. The findings indicate substantial changes in schistosomiasis prevalence in the study area following the implementation of integrated interventions. To sustain these achievements in Itilima, a multi-sectorial approach is highly recommended to integrate additional measures for eliminating schistosomiasis as a public health problem.
4.Changes in schistosomiasis prevalence after 2 years of an integrated intervention in the Itilima district of Tanzania
Humphrey MAZIGO ; Jungim LEE ; Yoonho CHO ; Seungman CHA ; Yan JIN
Parasites, Hosts and Diseases 2025;63(1):75-86
Schistosomiasis remains one of the most prevalent neglected tropical diseases in Tanzania. World Vision Tanzania, in collaboration with the Ministry of Health through the National Neglected Tropical Diseases Control Programme, implemented school- and community-based mass drug administrations, community-led total sanitation, and community voice and action from 2020 to 2022. This study assessed changes in the prevalence of schistosomiasis in the Itilima district of northwestern Tanzania following the implementation of these integrated interventions. A total of 1,405 students from 22 schools participated in the baseline survey in August to September 2020, and 1,320 in September 2022. Additionally, 368 adults from 8 villages participated in the baseline survey, and 401 in the endline survey. The prevalence difference was calculated to assess changes before and after the integrated interventions. We also investigated risk factors for Schistosoma haematobium infection using endline data. The prevalence difference between 2020 and 2022 was -20.0% (95% confidence interval (CI)=-22.2%–-17.7%, p<0.001) for students and -19.6% (95% CI=-22.2%–-17.7%, p<0.001) for adults. Individuals without a latrine were more likely to have schistosomiasis (adjusted odds ratio=5.9, 95% CI=1.7–21.5, p=0.01) compared to those who had a latrine. The findings indicate substantial changes in schistosomiasis prevalence in the study area following the implementation of integrated interventions. To sustain these achievements in Itilima, a multi-sectorial approach is highly recommended to integrate additional measures for eliminating schistosomiasis as a public health problem.
5.Changes in schistosomiasis prevalence after 2 years of an integrated intervention in the Itilima district of Tanzania
Humphrey MAZIGO ; Jungim LEE ; Yoonho CHO ; Seungman CHA ; Yan JIN
Parasites, Hosts and Diseases 2025;63(1):75-86
Schistosomiasis remains one of the most prevalent neglected tropical diseases in Tanzania. World Vision Tanzania, in collaboration with the Ministry of Health through the National Neglected Tropical Diseases Control Programme, implemented school- and community-based mass drug administrations, community-led total sanitation, and community voice and action from 2020 to 2022. This study assessed changes in the prevalence of schistosomiasis in the Itilima district of northwestern Tanzania following the implementation of these integrated interventions. A total of 1,405 students from 22 schools participated in the baseline survey in August to September 2020, and 1,320 in September 2022. Additionally, 368 adults from 8 villages participated in the baseline survey, and 401 in the endline survey. The prevalence difference was calculated to assess changes before and after the integrated interventions. We also investigated risk factors for Schistosoma haematobium infection using endline data. The prevalence difference between 2020 and 2022 was -20.0% (95% confidence interval (CI)=-22.2%–-17.7%, p<0.001) for students and -19.6% (95% CI=-22.2%–-17.7%, p<0.001) for adults. Individuals without a latrine were more likely to have schistosomiasis (adjusted odds ratio=5.9, 95% CI=1.7–21.5, p=0.01) compared to those who had a latrine. The findings indicate substantial changes in schistosomiasis prevalence in the study area following the implementation of integrated interventions. To sustain these achievements in Itilima, a multi-sectorial approach is highly recommended to integrate additional measures for eliminating schistosomiasis as a public health problem.
6.Characterization of Glycosyl Inositol Phosphoceramides from Panax ginseng using Nanospray Tandem Mass Spectrometry
So-Hyun KIM ; Ye-Been LEE ; Yoonho JEONG ; Jae-Yeon CHO ; Hyung-Kyoon CHOI
Natural Product Sciences 2024;30(2):103-116
Korean ginseng (Panax ginseng C. A. Meyer) is one of the most popular medicinal herbs in the world. This plant is known to have many health benefits and contain a wide variety of bioactive components. However, the knowledge on its lipid compound is still far from being fully explored. Although glycosyl inositol phosphoceramides (GIPCs) are the main sphingolipids in plant tissues, GIPCs of P. ginseng are unknown. The present study employed nanoESI-MS n , which generated characteristic fragmentation pattern that were used for the structural identification of P. ginseng GIPCs. In addition to detecting a typical mass fragmentation pattern for GIPC in positive ion mode, novel fragmentation correlating with cleavage of the last carbohydrate and fatty acyl chain of the ceramide moiety was identified. In total, 42 GIPC species were detected in P. ginseng. The major P. ginseng GIPC structure was hexose (R 1 )-hexuronic acid-inositol phosphoceramide, with three types of R 1 (amine, N-acetylamine, or hydroxyl). The most intense peak was found at m/z 1136.3 ([M+H] + ion), corresponding to a GIPC (d18:0/h16:0, R 1 = OH). Only three GIPC subtypes showed significantly different levels in five- and six-year-old P. ginseng tap roots.
7.MRI Findings in Parkinson’s Disease: Radiologic Assessment of Nigrostriatal Degeneration
Yun Jung BAE ; Jong-Min KIM ; Byung Se CHOI ; Yoo Sung SONG ; Yoonho NAM ; Se Jin CHO ; Jae Hyoung KIM ; Sang Eun KIM
Journal of the Korean Radiological Society 2022;83(3):508-526
Parkinson’s disease (PD) is a movement disorder that develops due to degenerative loss of dopaminergic cells in the substantia nigra of the midbrain. Recent advances in MRI techniques have demonstrated various imaging findings that can reflect the underlying pathophysiological processes occurring in Parkinson’s disease. Many imaging studies have shown that such findings can assist in the diagnosis of Parkinson’s disease and its differentiation from atypical parkinsonism. In this review, we present MRI techniques that can be used in clinical assessment, such as nigrosome imaging and neuromelanin imaging, and we provide the detailed imaging features of Parkinson’s disease reflecting nigrostriatal degeneration.
8.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.
9.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.
10.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.