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Article ; Online: Do we need Moodle in medical education? A review of its impact and utility

Seri Jeong / Hyunyong Hwang

Kosin Medical Journal, Vol 38, Iss 3, Pp 159-

2023 Volume 168

Abstract: Various learning management systems (LMSs) are available to facilitate the development, management, and distribution of digital resources for both face-to-face and online instruction. In recent decades, these methods have shown potential for greater ... ...

Abstract	Various learning management systems (LMSs) are available to facilitate the development, management, and distribution of digital resources for both face-to-face and online instruction. In recent decades, these methods have shown potential for greater efficiency compared to traditional "chalk and talk" approaches. Additionally, they have paved the way for the establishment of ubiquitous learning environments, marking a new era in education. In a trend accelerated by the coronavirus disease 2019 pandemic, LMSs have been increasingly adopted to overcome the restrictions inherent to in-person education. In medical education, LMSs such as Moodle, Canvas, Blackboard Learn, and others have been introduced and used to support teaching, learning, and assessment activities. Of these, Moodle stands out as the most popular choice for many medical schools and institutions, primarily due to its flexibility, functionality, and user-friendliness. The learning environment is gradually transforming from traditional in-person teaching to a hybrid educational approach, driven by the need to fulfill diverse educational demands. Numerous research studies have examined the usability of Moodle in medical education, demonstrating its effectiveness in addressing challenges related to adaptive personalized learning, collaborative learning, blended learning, and more. Consequently, Moodle has emerged as a valuable solution for medical educators seeking a versatile and robust platform to enhance their teaching methodologies. The present review focuses on the practical utilization of Moodle in medical education and the advantages it offers to this field.
Keywords	adaptive learning ; collaborative learning ; flexibility ; functionality ; learning management system ; Medicine (General) ; R5-920
Subject code	370
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Kosin University College of Medicine
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Performance comparison between Elecsys Anti-SARS-CoV-2 and Anti-SARS-CoV-2 S and Atellica IM SARS-CoV-2 Total and SARS-CoV-2 IgG assays

Seri Jeong / Yoo Rha Hong / Hyunyong Hwang

Kosin Medical Journal, Vol 37, Iss 2, Pp 154-

2022 Volume 162

Abstract: Background Although serological severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests from several manufacturers have been introduced in South Korea and some are commercially available, the performance of these test kits has not yet been ... ...

Abstract	Background Although serological severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests from several manufacturers have been introduced in South Korea and some are commercially available, the performance of these test kits has not yet been sufficiently validated. Therefore, we compared the performance of Elecsys Anti-SARS-CoV-2 (ACOV2) and Anti-SARS-CoV-2 S (ACOV2S) and Atellica IM SARS-CoV-2 Total (COV2T) and SARS-CoV-2 IgG (sCOVG) serological tests in this study. Methods A total of 186 patient samples were used. For each test, we analyzed the positive rate of serological antibody tests, precision, linearity, and agreement among the four assays. Results The positive rates of COV2T, sCOVG, and ACOV2S were high (81.7%–89.2%) in total, with those for ACOV2S being the was the highest, while those of ACOV2 were as low as 44.6%. This may be related to the high completion rate of vaccination in Korea. The repeatability and within-laboratory coefficients of variation were within the claimed allowable imprecision; however, further research is needed to establish an allowable imprecision at low concentrations. COV2T showed a linear fit, whereas sCOVG and ACOV2S were appropriately modeled with a nonlinear fit. Good agreement was found among COV2T, sCOVG, and ACOV2S; however, the agreement between ACOV2 and any one of the other methods was poor. Conclusions Considering the different antigens used in serological SARS-CoV-2 antibody assays, the performance of the tested assays is thought to show no significant difference for the qualitative detection of antibodies to SARS-CoV-2.
Keywords	covid-19 ; nucleocapsid proteins ; quantitative evaluations ; sars-cov-2 ; serological test ; Medicine (General) ; R5-920
Subject code	621
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	Kosin University College of Medicine
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: De Novo Assembly and Annotation of the Vaginal Metatranscriptome Associated with Bacterial Vaginosis

Won Kyong Cho / Yeonhwa Jo / Seri Jeong

International Journal of Molecular Sciences, Vol 23, Iss 1621, p

2022 Volume 1621

Abstract: The vaginal microbiome plays an important role in women’s health and disease. Here we reanalyzed 40 vaginal transcriptomes from a previous study of de novo assembly (metaT-Assembly) followed by functional annotation. We identified 286,293 contigs and ... ...

Abstract	The vaginal microbiome plays an important role in women’s health and disease. Here we reanalyzed 40 vaginal transcriptomes from a previous study of de novo assembly (metaT-Assembly) followed by functional annotation. We identified 286,293 contigs and further assigned them to 25 phyla, 209 genera, and 339 species. Lactobacillus iners and Lactobacillus crispatus dominated the microbiome of non-bacterial vaginosis (BV) samples, while a complex of microbiota was identified from BV-associated samples. The metaT-Assembly identified a higher number of bacterial species than the 16S rRNA amplicon and metaT-Kraken methods. However, metaT-Assembly and metaT-Kraken exhibited similar major bacterial composition at the species level. Binning of metatranscriptome data resulted in 176 bins from major known bacteria and several unidentified bacteria in the vagina. Functional analyses based on Clusters of Orthologous Genes (COGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways suggested that a higher number of transcripts were expressed by the microbiome complex in the BV-associated samples than in non-BV-associated samples. The KEGG pathway analysis with an individual bacterial genome identified specific functions of the identified bacterial genome. Taken together, we demonstrated that the metaT-Assembly approach is an efficient tool to understand the dynamic microbial communities and their functional roles associated with the human vagina.
Keywords	vagina ; transcriptome ; de novo assembly ; microbiome ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	572
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes

Nuri Lee / Seri Jeong / Min-Jeong Park / Wonkeun Song

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 8

Abstract: Abstract Myelodysplastic syndromes (MDS) are a group of hematologic neoplasms accompanied by dysplasia of the bone marrow hematopoietic cells with cytopenia. Detecting dysplasia is important in the diagnosis of MDS, but it takes considerable time and ... ...

Abstract	Abstract Myelodysplastic syndromes (MDS) are a group of hematologic neoplasms accompanied by dysplasia of the bone marrow hematopoietic cells with cytopenia. Detecting dysplasia is important in the diagnosis of MDS, but it takes considerable time and effort. Also, since the assessment of dysplasia is subjective and difficult to quantify, a more efficient tool is needed for quality control and standardization of bone marrow aspiration smear interpretation. In this study, we developed and evaluated an algorithm to automatically discriminate hematopoietic cell lineages and detect dysplastic cells in bone marrow aspiration smears using deep learning technology. Bone marrow aspiration images were acquired from 34 patients diagnosed with MDS and from 24 normal bone marrow slides. In total, 8065 cells were classified into eight categories: normal erythrocytes, normal granulocytes, normal megakaryocytes, dysplastic erythrocytes, dysplastic granulocytes, dysplastic megakaryocytes, blasts, and others. The algorithm demonstrated acceptable performance in classifying dysplastic cells, with an AUC of 0.945–0.996 and accuracy of 0.912–0.993. The algorithm developed in this study could be used as an auxiliary tool for diagnosing patients with MDS and is expected to contribute to shortening the time required for MDS bone marrow aspiration diagnosis and standardizing visual reading.
Keywords	Medicine ; R ; Science ; Q
Subject code	616
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparison of three serological chemiluminescence immunoassays for SARS-CoV-2, and clinical significance of antibody index with disease severity.

Nuri Lee / Seri Jeong / Min-Jeong Park / Wonkeun Song

PLoS ONE, Vol 16, Iss 6, p e

2021 Volume 0253889

Abstract: Background The clinical significance of the quantitative value of antibodies in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains mostly unidentified. We investigated the dynamics and clinical implications of the SARS-CoV-2 ... ...

Abstract	Background The clinical significance of the quantitative value of antibodies in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains mostly unidentified. We investigated the dynamics and clinical implications of the SARS-CoV-2 antibody over time using three automated chemiluminescence immunoassays targeting either nucleocapsids or spikes. Methods A total of 126 specimens were collected from 23 patients with confirmed and indeterminate COVID-19 identified by molecular tests. SARS-CoV-2 antibody index was measured using SARS-CoV2 IgG reagent from Alinity (Abbott) and Access (Beckman Coulter) and SARS-CoV2 Total (IgG + IgM) from Atellica (Siemens). Results Three immunoassays showed strong correlations with each other (range of Pearson' s correlation coefficient (r) = 0.700-0.854, P < 0.001). Eleven (8.7%) specimens showed inconsistencies. SARS-CoV-2 IgG showed a statistically significantly higher value in patients with severe disease than that in non-severe disease patients (P < 0.001) and was significantly associated with clinical markers of disease severity. Conclusion The quantitative value of the SARS-CoV-2 IgG antibody measured using automated immunoassays is a significant indicator of clinical severity in patients with COVID-19.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development and validation of a deep learning-based protein electrophoresis classification algorithm.

Nuri Lee / Seri Jeong / Kibum Jeon / Wonkeun Song / Min-Jeong Park

PLoS ONE, Vol 17, Iss 8, p e

2022 Volume 0273284

Abstract: Background Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based ... ...

Abstract	Background Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based PEP classification algorithm to supplement the labor-intensive PEP interpretation and enhance inter-observer reliability. Methods A total of 2,578 gel images and densitogram PEP images from January 2018 to July 2019 were split into training (80%), validation (10%), and test (10.0%) sets. The PEP images were assessed based on six major findings (acute-phase protein, monoclonal gammopathy, polyclonal gammopathy, hypoproteinemia, nephrotic syndrome, and normal). The images underwent processing, including color-to-grayscale and histogram equalization, and were input into neural networks. Results Using densitogram PEP images, the area under the receiver operating characteristic curve (AUROC) for each diagnosis ranged from 0.873 to 0.989, and the accuracy for classifying all the findings ranged from 85.2% to 96.9%. For gel images, the AUROC ranged from 0.763 to 0.965, and the accuracy ranged from 82.0% to 94.5%. Conclusions The deep learning algorithm demonstrated good performance in classifying PEP images. It is expected to be useful as an auxiliary tool for screening the results and helpful in environments where specialists are scarce.
Keywords	Medicine ; R ; Science ; Q
Subject code	571
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Quantitative Analysis of Anti-N and Anti-S Antibody Titers of SARS-CoV-2 Infection after the Third Dose of COVID-19 Vaccination

Nuri Lee / Seri Jeong / Su Kyung Lee / Eun-Jung Cho / Jungwon Hyun / Min-Jeong Park / Wonkeun Song / Hyun Soo Kim

Vaccines, Vol 10, Iss 7, p

2022 Volume 1143

Abstract: We quantitatively analyzed SARS-CoV-2 antibody levels in patients after two doses of the ChAdOx1 nCoV-19 vaccine and the third BNT162b2 booster. We obtained 255 serum samples from 149 healthcare workers 1 and 4 months after the third dose. Of the 149 ... ...

Abstract	We quantitatively analyzed SARS-CoV-2 antibody levels in patients after two doses of the ChAdOx1 nCoV-19 vaccine and the third BNT162b2 booster. We obtained 255 serum samples from 149 healthcare workers 1 and 4 months after the third dose. Of the 149 participants, 58 (38.9%) experienced COVID-19 infection during the 4-month study period, with infection occurring 7–62 days before the second blood draw. Total antibody titers against the anti-spike (anti-S) and anti-nucleocapsid (anti-N) proteins of SARS-CoV-2 were measured using Elecsys Anti-SARS-CoV-2 S and Elecsys Anti-SARS-CoV-2 assays (Roche), respectively. The median anti-S antibody titer in the non-infected groups at 4 months after the third dose was significantly decreased compared to that at 1 month after the third dose (from 17,777 to 3673 U/mL, p < 0.001). The infected group showed higher median anti-S antibody titers at 4 months (19,539 U/mL) than the non-infected group (3673 U/mL). The median anti-N antibody titer in the infected group at 4 months after the third dose was a 5.07 cut-off index (79.3% positivity). Anti-N antibody titers in the infected group were correlated with the number of days after SARS-CoV-2 infection. These data provide useful information for determining quarantine strategies and fourth vaccination requirements.
Keywords	SARS-CoV-2 ; BNT162 vaccine ; ChAdOx1 nCoV-19 ; COVID-19 booster shot ; Medicine ; R
Subject code	616
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Currently Applied Molecular Assays for Identifying ESR1 Mutations in Patients with Advanced Breast Cancer

Nuri Lee / Min-Jeong Park / Wonkeun Song / Kibum Jeon / Seri Jeong

International Journal of Molecular Sciences, Vol 21, Iss 8807, p

2020 Volume 8807

Abstract: Approximately 70% of breast cancers, the leading cause of cancer-related mortality worldwide, are positive for the estrogen receptor (ER). Treatment of patients with luminal subtypes is mainly based on endocrine therapy. However, ER positivity is reduced ...

Abstract	Approximately 70% of breast cancers, the leading cause of cancer-related mortality worldwide, are positive for the estrogen receptor (ER). Treatment of patients with luminal subtypes is mainly based on endocrine therapy. However, ER positivity is reduced and ESR1 mutations play an important role in resistance to endocrine therapy, leading to advanced breast cancer. Various methodologies for the detection of ESR1 mutations have been developed, and the most commonly used method is next-generation sequencing (NGS)-based assays (50.0%) followed by droplet digital PCR (ddPCR) (45.5%). Regarding the sample type, tissue (50.0%) was more frequently used than plasma (27.3%). However, plasma (46.2%) became the most used method in 2016–2019, in contrast to 2012–2015 (22.2%). In 2016–2019, ddPCR (61.5%), rather than NGS (30.8%), became a more popular method than it was in 2012–2015. The easy accessibility, non-invasiveness, and demonstrated usefulness with high sensitivity of ddPCR using plasma have changed the trends. When using these assays, there should be a comprehensive understanding of the principles, advantages, vulnerability, and precautions for interpretation. In the future, advanced NGS platforms and modified ddPCR will benefit patients by facilitating treatment decisions efficiently based on information regarding ESR1 mutations.
Keywords	estrogen receptor ; ESR1 ; breast cancer ; next-generation sequencing ; droplet digital polymerase chain reaction ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	616
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Evaluation of an automated connective tissue disease screening assay in Korean patients with systemic rheumatic diseases.

Seri Jeong / Heeyoung Yang / Hyunyong Hwang

PLoS ONE, Vol 12, Iss 3, p e

2017 Volume 0173597

Abstract: This study aimed to evaluate the diagnostic utilities of the automated connective tissues disease screening assay, CTD screen, in patients with systemic rheumatic diseases. A total of 1093 serum samples were assayed using CTD screen and indirect ... ...

Abstract	This study aimed to evaluate the diagnostic utilities of the automated connective tissues disease screening assay, CTD screen, in patients with systemic rheumatic diseases. A total of 1093 serum samples were assayed using CTD screen and indirect immunofluorescent (IIF) methods. Among them, 162 were diagnosed with systemic rheumatic disease, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and mixed connective tissue disease (MCT). The remaining 931 with non-systemic rheumatic disease were assigned to the control group. The median ratios of CTD screen tests were significantly higher in the systemic rheumatic disease group than in the control group. The positive likelihood ratios of the CTD screen were higher than those of IIF in patients with total rheumatic diseases (4.1 vs. 1.6), including SLE (24.3 vs. 10.7). The areas under the receiver operating characteristic curves (ROC-AUCs) of the CTD screen for discriminating total rheumatic diseases, RA, SLE, and MCT from controls were 0.68, 0.56, 0.92 and 0.80, respectively. The ROC-AUCs of the combinations with IIF were significantly higher in patients with total rheumatic diseases (0.72) and MCT (0.85) than in those of the CTD screen alone. Multivariate analysis indicated that both the CTD screen and IIF were independent variables for predicting systemic rheumatic disease. CTD screen alone and in combination with IIF were a valuable diagnostic tool for predicting systemic rheumatic diseases, particularly for SLE.
Keywords	Medicine ; R ; Science ; Q
Subject code	610 ; 630
Language	English
Publishing date	2017-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection

Yoonje Lee / Yu-Seop Kim / Da-in Lee / Seri Jeong / Gu-Hyun Kang / Yong Soo Jang / Wonhee Kim / Hyun Young Choi / Jae Guk Kim / Sang-hoon Choi

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 10

Abstract: Abstract Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction ...

Abstract	Abstract Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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