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Article: Managing oral biofilms to avoid enamel demineralization during fixed orthodontic treatment.

An, Jung-Sub / Lim, Bum-Soon / Ahn, Sug-Joon

2023 Volume 53, Issue 6, Page(s) 345–357

Abstract: Enamel demineralization represents the most prevalent complication arising from fixed orthodontic treatment. Its main etiology is the development of cariogenic biofilms formed around orthodontic appliances. Ordinarily, oral biofilms exist in a dynamic ... ...

Abstract	Enamel demineralization represents the most prevalent complication arising from fixed orthodontic treatment. Its main etiology is the development of cariogenic biofilms formed around orthodontic appliances. Ordinarily, oral biofilms exist in a dynamic equilibrium with the host's defense mechanisms. However, the equilibrium can be disrupted by environmental changes, such as the introduction of a fixed orthodontic appliance, resulting in a shift in the biofilm's microbial composition from non-pathogenic to pathogenic. This alteration leads to an increased prevalence of cariogenic bacteria, notably mutans streptococci, within the biofilm. This article examines the relationships between oral biofilms and orthodontic appliances, with a particular focus on strategies for effectively managing oral biofilms to mitigate enamel demineralization around orthodontic appliances.
Language	English
Publishing date	2023-09-15
Publishing country	Korea (South)
Document type	Journal Article ; Review
ZDB-ID	2888152-7
ISSN	2234-7518 ; 2234-7518 ; 1225-5610
ISSN (online)	2234-7518
ISSN	2234-7518 ; 1225-5610
DOI	10.4041/kjod23.184
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Article ; Online: Restoration of PM2.5-induced spermatogonia GC-1 cellular damage by parthenolide via suppression of autophagy and inflammation: An in vitro study.

Gu, Hyo Jin / Ahn, Jin Seop / Ahn, Gi Jeong / Shin, Seung Hee / Ryu, Buom-Yong

Toxicology

2023 Volume 499, Page(s) 153651

Abstract: ... to investigate the harmful effects of PM2.5 on mouse GC-1 spermatogonia cells (GC-1 spg cells) and to verify ... the ameliorative effects of parthenolide (PTL) treatment on damaged GC-1 spg cells. We observed a significant dose ...

Abstract	Particulate matter (PM) generated by environmental and air pollution is known to have detrimental effects on human health. Among these, PM2.5 particles (diameter < 2.5 µm) can breach the alveolar-capillary barrier and disseminate to other organs, posing significant health risks. Numerous studies have shown that PMs can harm various organs, including the reproductive system. Therefore, this study aimed to investigate the harmful effects of PM2.5 on mouse GC-1 spermatogonia cells (GC-1 spg cells) and to verify the ameliorative effects of parthenolide (PTL) treatment on damaged GC-1 spg cells. We observed a significant dose-dependent reduction in cell proliferation after PM2.5 concentration of 2.5 μg/cm
MeSH term(s)	Humans ; Male ; Mice ; Animals ; Signal Transduction ; NF-kappa B/metabolism ; Spermatogonia/metabolism ; Particulate Matter/toxicity ; Autophagy ; Inflammation/chemically induced ; Autophagy-Related Proteins
Chemical Substances	parthenolide (2RDB26I5ZB) ; GC 1 compound ; NF-kappa B ; Particulate Matter ; Autophagy-Related Proteins
Language	English
Publishing date	2023-10-17
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	184557-3
ISSN	1879-3185 ; 0300-483X
ISSN (online)	1879-3185
ISSN	0300-483X
DOI	10.1016/j.tox.2023.153651
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Article ; Online: Explainable Model Using Shapley Additive Explanations Approach on Wound Infection after Wide Soft Tissue Sarcoma Resection: "Big Data" Analysis Based on Health Insurance Review and Assessment Service Hub.

Choi, Ji-Hye / Choi, Yumin / Lee, Kwang-Sig / Ahn, Ki-Hoon / Jang, Woo Young

Medicina (Kaunas, Lithuania)

2024 Volume 60, Issue 2

Abstract: Background and ... ...

Abstract	Background and Objectives
MeSH term(s)	Humans ; Male ; Postoperative Complications/etiology ; Risk Factors ; Insurance, Health ; Wound Infection ; Sarcoma/surgery ; Sarcoma/complications ; Soft Tissue Neoplasms/complications ; Soft Tissue Neoplasms/pathology ; Soft Tissue Neoplasms/surgery ; Retrospective Studies
Language	English
Publishing date	2024-02-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2188113-3
ISSN	1648-9144 ; 1010-660X
ISSN (online)	1648-9144
ISSN	1010-660X
DOI	10.3390/medicina60020327
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Zs.A 6270: Show issues

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Article ; Online: Author Correction: Machine learning analysis for the association between breast feeding and metabolic syndrome in women.

Lee, Jue Seong / Choi, Eun-Saem / Lee, Hwasun / Son, Serhim / Lee, Kwang-Sig / Ahn, Ki Hoon

Scientific reports

2024 Volume 14, Issue 1, Page(s) 7146

Language	English
Publishing date	2024-03-26
Publishing country	England
Document type	Published Erratum
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-57571-4
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Article ; Online: Machine learning analysis for the association between breast feeding and metabolic syndrome in women.

Lee, Jue Seong / Choi, Eun-Saem / Lee, Hwasun / Son, Serhim / Lee, Kwang-Sig / Ahn, Ki Hoon

Scientific reports

2024 Volume 14, Issue 1, Page(s) 4138

Abstract: This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, ... ...

Abstract	This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010-2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age.
MeSH term(s)	Humans ; Female ; Breast Feeding ; Metabolic Syndrome/epidemiology ; Cardiovascular Diseases ; Cross-Sectional Studies ; Bayes Theorem ; Machine Learning ; Hypertension
Language	English
Publishing date	2024-02-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-53137-6
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Article: Artificial intelligence in obstetrics.

Ahn, Ki Hoon / Lee, Kwang-Sig

Obstetrics & gynecology science

2021 Volume 65, Issue 2, Page(s) 113–124

Abstract: This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods ...

Abstract	This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.
Language	English
Publishing date	2021-12-15
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2814367-X
ISSN	2287-8580 ; 2287-8572
ISSN (online)	2287-8580
ISSN	2287-8572
DOI	10.5468/ogs.21234
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Article ; Online: Single bacteria identification with second-harmonic generation in MoS

Kim, Young Chul / Jun, Seung Won / Ahn, Yeong Hwan

Biosensors & bioelectronics

2023 Volume 241, Page(s) 115675

Abstract: ... harmonic generation (SHG) in monolayer MoS ...

Abstract	Transition-metal dichalcogenides exhibit extraordinary optical nonlinearities, making them promising candidates for advanced photonic applications. Here, we present the microbial control over second-harmonic generation (SHG) in monolayer MoS
Language	English
Publishing date	2023-09-12
Publishing country	England
Document type	Journal Article
ZDB-ID	1011023-9
ISSN	1873-4235 ; 0956-5663
ISSN (online)	1873-4235
ISSN	0956-5663
DOI	10.1016/j.bios.2023.115675
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Zs.A 2275: Show issues

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Article: Differences in dentoskeletal and soft tissue changes due to rapid maxillary expansion using a tooth-borne expander between adolescents and adults: A retrospective observational study.

An, Jung-Sub / Seo, Bo-Yeon / Ahn, Sug-Joon

Korean journal of orthodontics

2022 Volume 52, Issue 2, Page(s) 131–141

Abstract: Objective: The purpose of this study was to compare the differences in dentoskeletal and soft tissue changes following conventional tooth-borne rapid maxillary expansion (RME) between adolescents and adults.: Methods: Dentoskeletal and soft tissue ... ...

Abstract	Objective: The purpose of this study was to compare the differences in dentoskeletal and soft tissue changes following conventional tooth-borne rapid maxillary expansion (RME) between adolescents and adults. Methods: Dentoskeletal and soft tissue variables of 17 adolescents and 17 adults were analyzed on posteroanterior and lateral cephalograms and frontal photographs at pretreatment (T1) and after conventional RME using tooth-borne expanders (T2). Changes in variables within each group between T1 and T2 were analyzed using Wilcoxon signed-rank test. Mann-Whitney Results: Despite similar amounts of expansion at the crown level in both groups, the adult group underwent less skeletal expansion with less intermolar root expansion after RME than the adolescent group. The skeletal vertical dimension increased significantly in both groups without significant intergroup difference. The anteroposterior position of the maxilla was maintained in both groups, while a greater backward displacement of the mandible was evident in the adult group than that in the adolescent group after RME. The soft tissue alar width increased in both groups without a significant intergroup difference. In the adolescent group, pretreatment age was not significantly correlated with transverse dentoskeletal changes. Conclusions: Conventional RME may induce similar soft tissue changes but different dentoskeletal changes between adolescents and adults.
Language	English
Publishing date	2022-03-23
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2888152-7
ISSN	2234-7518 ; 2234-7518 ; 1225-5610
ISSN (online)	2234-7518
ISSN	2234-7518 ; 1225-5610
DOI	10.4041/kjod.2022.52.2.131
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Article: Application of Artificial Intelligence in Early Diagnosis of Spontaneous Preterm Labor and Birth.

Lee, Kwang-Sig / Ahn, Ki Hoon

Diagnostics (Basel, Switzerland)

2020 Volume 10, Issue 9

Abstract: This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth ("preterm birth" hereafter). The summary of review suggests that different machine ... ...

Abstract	This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth ("preterm birth" hereafter). The summary of review suggests that different machine learning approaches would be optimal for different types of data regarding the prediction of preterm birth: the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohysterogram data; the recurrent neural network for text data; and the convolutional neural network for image data. The ranges of performance measures were 0.79-0.94 for accuracy, 0.22-0.97 for sensitivity, 0.86-1.00 for specificity, and 0.54-0.83 for the area under the receiver operating characteristic curve. The following maternal variables were reported to be major determinants of preterm birth: delivery and pregestational body mass index, age, parity, predelivery systolic and diastolic blood pressure, twins, below high school graduation, infant sex, prior preterm birth, progesterone medication history, upper gastrointestinal tract symptom, gastroesophageal reflux disease, Helicobacter pylori, urban region, calcium channel blocker medication history, gestational diabetes mellitus, prior cone biopsy, cervical length, myomas and adenomyosis, insurance, marriage, religion, systemic lupus erythematosus, hydroxychloroquine sulfate, and increased cerebrospinal fluid and reduced cortical folding due to impaired brain growth.
Language	English
Publishing date	2020-09-22
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics10090733
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Article ; Online: Machine learning analysis with population data for the associations of preterm birth with temporomandibular disorder and gastrointestinal diseases.

Lee, Kwang-Sig / Song, In-Seok / Kim, Eun Sun / Kim, Jisu / Jung, Sohee / Nam, Sunwoo / Ahn, Ki Hoon

PloS one

2024 Volume 19, Issue 1, Page(s) e0296329

Abstract: This study employs machine learning analysis with population data for the associations of preterm birth (PTB) with temporomandibular disorder (TMD) and gastrointestinal diseases. The source of the population-based retrospective cohort was Korea National ... ...

Abstract	This study employs machine learning analysis with population data for the associations of preterm birth (PTB) with temporomandibular disorder (TMD) and gastrointestinal diseases. The source of the population-based retrospective cohort was Korea National Health Insurance claims for 489,893 primiparous women with delivery at the age of 25-40 in 2017. The dependent variable was PTB in 2017. Twenty-one predictors were included, i.e., demographic, socioeconomic, disease and medication information during 2002-2016. Random forest variable importance was derived for finding important predictors of PTB and evaluating its associations with the predictors including TMD and gastroesophageal reflux disease (GERD). Shapley Additive Explanation (SHAP) values were calculated to analyze the directions of these associations. The random forest with oversampling registered a much higher area under the receiver-operating-characteristic curve compared to logistic regression with oversampling, i.e., 79.3% vs. 53.1%. According to random forest variable importance values and rankings, PTB has strong associations with low socioeconomic status, GERD, age, infertility, irritable bowel syndrome, diabetes, TMD, salivary gland disease, hypertension, tricyclic antidepressant and benzodiazepine. In terms of max SHAP values, these associations were positive, e.g., low socioeconomic status (0.29), age (0.21), GERD (0.27) and TMD (0.23). The inclusion of low socioeconomic status, age, GERD or TMD into the random forest will increase the probability of PTB by 0.29, 0.21, 0.27 or 0.23. A cutting-edge approach of explainable artificial intelligence highlights the strong associations of preterm birth with temporomandibular disorder, gastrointestinal diseases and antidepressant medication. Close surveillance is needed for pregnant women regarding these multiple risks at the same time.
MeSH term(s)	Humans ; Pregnancy ; Female ; Infant, Newborn ; Premature Birth/epidemiology ; Retrospective Studies ; Artificial Intelligence ; Temporomandibular Joint Disorders/epidemiology ; Gastroesophageal Reflux/complications ; Gastroesophageal Reflux/drug therapy ; Gastroesophageal Reflux/epidemiology ; Machine Learning
Language	English
Publishing date	2024-01-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0296329
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