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Article ; Online: Inflammasome diversity: exploring novel frontiers in the innate immune response.

Yu, Gyeongju / Choi, Young Ki / Lee, SangJoon

2024 Volume 45, Issue 4, Page(s) 248–258

Abstract: Pathogens elicit complex mammalian immune responses by activating multiple sensors within inflammasomes, which recognize diverse pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). This simultaneous activation ...

Abstract	Pathogens elicit complex mammalian immune responses by activating multiple sensors within inflammasomes, which recognize diverse pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). This simultaneous activation induces the formation of protein complexes referred to as multiple inflammasomes, that orchestrate a spectrum of programmed cell death pathways, including pyroptosis, apoptosis, and necroptosis. This concept is crucial for comprehending the complexity of the innate immune system's response to diverse pathogens and its implications for various diseases. Novel contributions here include emphasizing simultaneous sensor activation by pathogens, proposing the existence of multiple inflammasome complexes, and advocating for further exploration of their structural basis. Understanding these mechanisms may offer insights into disease pathogenesis, paving the way for potential therapeutic interventions targeting inflammasome-mediated immune responses.
MeSH term(s)	Humans ; Animals ; Inflammasomes/metabolism ; Immunity, Innate ; Apoptosis ; Pyroptosis ; Mammals
Chemical Substances	Inflammasomes
Language	English
Publishing date	2024-03-21
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2036831-8
ISSN	1471-4981 ; 1471-4906
ISSN (online)	1471-4981
ISSN	1471-4906
DOI	10.1016/j.it.2024.02.004
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Article ; Online: Recent advances in ZBP1-derived PANoptosis against viral infections.

Oh, SuHyeon / Lee, SangJoon

Frontiers in immunology

2023 Volume 14, Page(s) 1148727

Abstract: Innate immunity is an important first line of defense against pathogens, including viruses. These pathogen- and damage-associated molecular patterns (PAMPs and DAMPs, respectively), resulting in the induction of inflammatory cell death, are detected by ... ...

Abstract	Innate immunity is an important first line of defense against pathogens, including viruses. These pathogen- and damage-associated molecular patterns (PAMPs and DAMPs, respectively), resulting in the induction of inflammatory cell death, are detected by specific innate immune sensors. Recently, Z-DNA binding protein 1 (ZBP1), also called the DNA-dependent activator of IFN regulatory factor (DAI) or DLM1, is reported to regulate inflammatory cell death as a central mediator during viral infection. ZBP1 is an interferon (IFN)-inducible gene that contains two Z-form nucleic acid-binding domains (Zα1 and Zα2) in the N-terminus and two receptor-interacting protein homotypic interaction motifs (RHIM1 and RHIM2) in the middle, which interact with other proteins with the RHIM domain. By sensing the entry of viral RNA, ZBP1 induces PANoptosis, which protects host cells against viral infections, such as influenza A virus (IAV) and herpes simplex virus (HSV1). However, some viruses, particularly coronaviruses (CoVs), induce PANoptosis to hyperactivate the immune system, leading to cytokine storm, organ failure, tissue damage, and even death. In this review, we discuss the molecular mechanism of ZBP1-derived PANoptosis and pro-inflammatory cytokines that influence the double-edged sword of results in the host cell. Understanding the ZBP1-derived PANoptosis mechanism may be critical for improving therapeutic strategies.
MeSH term(s)	Humans ; RNA-Binding Proteins/metabolism ; Virus Diseases ; Cell Death ; Cytokines/metabolism ; Immunity, Innate
Chemical Substances	RNA-Binding Proteins ; Cytokines
Language	English
Publishing date	2023-05-16
Publishing country	Switzerland
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2023.1148727
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Article ; Online: Machine learning descriptors in materials chemistry used in multiple experimentally validated studies: Oliynyk elemental property dataset.

Lee, Sangjoon / Chen, Clio / Garcia, Griheydi / Oliynyk, Anton

Data in brief

2024 Volume 53, Page(s) 110178

Abstract: Materials informatics employs data-driven approaches for analysis and discovery of materials. Features also referred to as descriptors are essential in generating reliable and accurate machine-learning models. While general data can be obtained through ... ...

Abstract	Materials informatics employs data-driven approaches for analysis and discovery of materials. Features also referred to as descriptors are essential in generating reliable and accurate machine-learning models. While general data can be obtained through public and commercial sources, features must be tailored to specific applications. Common featurizers suitable for generic chemical problems may not be effective in features-property mapping in solid-state materials with ML models. Here, we have assembled the Oliynyk property list for compositional feature generation, which performs well on limited datasets (50 to 1000 training data points) in the solid-state materials domain. The dataset contains 98 elemental features for atomic numbers from 1 to 92, including thermodynamic properties, electronic structure data, size, electronegativity, and bulk properties such as melting point, density, and conductivity. The dataset has been utilized peer-reviewed publications in predicting material hardness, classification, discovery of novel Heusler compounds, band gap prediction, and determining the site preference of atoms using machine learning models including support vector machines, random forests for classification, and support vector regression for regression problems. We have compiled the dataset by parsing data from publicly available databases and literature and further supplementing it by interpolating values with Gaussian process regression.
Language	English
Publishing date	2024-02-09
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2786545-9
ISSN	2352-3409 ; 2352-3409
ISSN (online)	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2024.110178
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Article ; Online: Improving Medical Speech-to-Text Accuracy using Vision-Language Pre-training Models.

Huh, Jaeyoung / Park, Sangjoon / Lee, Jeong Eun / Ye, Jong Chul

IEEE journal of biomedical and health informatics

2024 Volume 28, Issue 3, Page(s) 1692–1703

Abstract: Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows ... ...

Abstract	Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows by transcribing spoken words into text. In the medical field, STT has the potential to significantly reduce the workload of clinicians who rely on typists to transcribe their voice recordings. However, developing an STT model for the medical domain is challenging due to the lack of sufficient speech and text datasets. To address this issue, we propose a medical-domain text correction method that modifies the output text of a general STT system using the Vision Language Pre-training (VLP) method. VLP combines textual and visual information to correct text based on image knowledge. Our extensive experiments demonstrate that the proposed method offers quantitatively and clinically significant improvements in STT performance in the medical field. We further show that multi-modal understanding of image and text information outperforms single-modal understanding using only text information.
MeSH term(s)	Humans ; Speech ; Language ; Voice
Language	English
Publishing date	2024-03-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2023.3345897
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Article ; Online: Reply to Letter to the Editor: "Ultrasound-guided genitofemoral nerve block for femoral arterial access gain and closure: a randomized controlled trial".

Lee, Hyoung Nam / Cho, Youngjong / Park, Sung-Joon / Lee, Sangjoon / Heo, Nam Hun

European radiology

2023 Volume 34, Issue 2, Page(s) 1135–1136

Language	English
Publishing date	2023-10-28
Publishing country	Germany
Document type	Randomized Controlled Trial ; Letter
ZDB-ID	1085366-2
ISSN	1432-1084 ; 0938-7994 ; 1613-3749
ISSN (online)	1432-1084
ISSN	0938-7994 ; 1613-3749
DOI	10.1007/s00330-023-10375-y
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Article ; Online: Comprehensive Immunohistochemical Analysis of Mesonephric Marker Expression in Low-grade Endometrial Endometrioid Carcinoma.

Lee, Yurimi / Choi, Sangjoon / Kim, Hyun-Soo

International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists

2023 Volume 43, Issue 3, Page(s) 221–232

Abstract: Immunohistochemical markers shown to be useful in identifying/confirming mesonephric/mesonephric-like differentiation (MLD markers) include thyroid transcription factor (TTF1), GATA-binding protein 3 (GATA3), and cluster of differentiation 10 (CD10). ... ...

Abstract	Immunohistochemical markers shown to be useful in identifying/confirming mesonephric/mesonephric-like differentiation (MLD markers) include thyroid transcription factor (TTF1), GATA-binding protein 3 (GATA3), and cluster of differentiation 10 (CD10). Only a few studies have examined the expression levels of MLD markers in endometrial endometrioid carcinomas (EECs). This study aimed to analyze the frequency and pattern of MLD marker expression in low-grade EECs. We performed immunostaining for the detection of TTF1, GATA3, and CD10 expression in 50 low-grade EEC tissue samples and evaluated their staining proportion and intensity. Nine tumors (18.0%) expressed at least one MLD marker in varying proportions and intensities, and 2 of these tumors were positive for 2 MLD markers (TTF1/GATA3 and GATA3/CD10, respectively). Three (6.0%) tumors showed moderate-to-strong nuclear TTF1 immunoreactivity in ≤5% of the tumor cells. Five tumors (10.0%) had at least moderate nuclear GATA3 staining, and three of them displayed a staining proportion of ≥15%. Three tumors (6.0%) were focal (mean proportion, 15%) but strongly positive for CD10. Our findings indicate that a subset of EEC can express one or more MLD markers with varying staining proportions and intensities. Given that a diagnosis of uterine mesonephric-like adenocarcinoma should be established based on a combination of characteristic histologic features, unique immunophenotypes, and confirmed molecular findings, pathologists should not exclude EEC based only on the presence of focal immunoreactivity for MLD markers. Awareness of the atypical expression patterns of MLD markers in EEC helps pathologists avoid misdiagnosing EEC as a uterine mesonephric-like adenocarcinoma.
MeSH term(s)	Female ; Humans ; Carcinoma, Endometrioid/pathology ; Mesonephros/pathology ; Uterus/pathology ; Adenocarcinoma/pathology ; Biomarkers, Tumor/metabolism ; Endometrial Neoplasms/metabolism
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2023-08-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	604859-6
ISSN	1538-7151 ; 0277-1691
ISSN (online)	1538-7151
ISSN	0277-1691
DOI	10.1097/PGP.0000000000000976
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Article ; Online: Injection Sclerotherapy for Chronic Superficial Venous Insufficiency with Involuntary Movement of Toes.

Lee, Sangjoon / Lee, Hyoung Nam / Cho, Youngjong / Park, Sung-Joon / Shin, Seung Ho

Cardiovascular and interventional radiology

2023 Volume 47, Issue 1, Page(s) 139–141

MeSH term(s)	Humans ; Sclerotherapy ; Venous Insufficiency/diagnostic imaging ; Venous Insufficiency/therapy ; Lower Extremity ; Toes ; Dyskinesias ; Varicose Veins ; Chronic Disease
Language	English
Publishing date	2023-11-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	603082-8
ISSN	1432-086X ; 0342-7196 ; 0174-1551
ISSN (online)	1432-086X
ISSN	0342-7196 ; 0174-1551
DOI	10.1007/s00270-023-03601-z
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Article ; Online: Double-Pigtail Drainage Catheter: A New Design for Efficient Pleural Drainage.

Cho, Youngjong / Lee, Hyoung Nam / Shin, Ji Hoon / Park, Sung-Joon / Lee, Sangjoon / Song, Jae-Seok

Medicina (Kaunas, Lithuania)

2023 Volume 59, Issue 6

Abstract: Background and ... ...

Abstract	Background and Objectives
MeSH term(s)	Humans ; Retrospective Studies ; Pleura ; Pleural Effusion/surgery ; Catheters ; Drainage/methods
Language	English
Publishing date	2023-06-05
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/medicina59061089
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Article: Extraskeletal Mesenchymal Chondrosarcoma of the Uterus.

Lee, Yurimi / Choi, Sangjoon / Kim, Hyun-Soo

Diagnostics (Basel, Switzerland)

2022 Volume 12, Issue 3

Abstract: Mesenchymal chondrosarcoma is an uncommon malignant mesenchymal tumor with an aggressive behavior. Diagnoses of mesenchymal chondrosarcoma are established based on histomorphological, immunohistochemical, and molecular findings. Only one case of ... ...

Abstract	Mesenchymal chondrosarcoma is an uncommon malignant mesenchymal tumor with an aggressive behavior. Diagnoses of mesenchymal chondrosarcoma are established based on histomorphological, immunohistochemical, and molecular findings. Only one case of extraskeletal mesenchymal chondrosarcoma (EMC) of the uterus has been reported. This article presents the second case of primary uterine EMC, occurring in a 33-year-old woman. We describe the histological and immunophenotypical features of EMC. Our observations will help pathologists and clinicians perform accurate histological diagnoses of uterine EMC and plan appropriate treatment strategies for this rare tumor.
Language	English
Publishing date	2022-03-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics12030643
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Article ; Online: A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform

Qiuying Chen / SangJoon Lee

Applied Sciences, Vol 11, Iss 9927, p

2021 Volume 9927

Abstract: Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. ... ...

Abstract	Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. Keep is a home workout application dedicated to providing one-stop exercise solutions such as fitness teaching, cycling, running, yoga, and fitness diet guidance. We used a data crawler to collect the total training set data of 7734 Keep users and compared four supervised learning algorithms: support vector machine, k-nearest neighbor, random forest, and logistic regression. The receiver operating curve analysis indicated that the overall discrimination verification power of random forest was better than that of the other three models. The random forest model was used to classify 850 test samples, and a correct rate of 88% was obtained. This approach can predict the continuous usage of users after installing the home workout application. We considered 18 variables on Keep that were expected to affect the determination of continuous participation. Keep certification is the most important variable that affected the results of this study. Keep certification refers to someone who has verified their identity information and can, therefore, obtain the Keep certification logo. The results show that the platform still needs to be improved in terms of real identity privacy information and other aspects.
Keywords	home workout ; platform ; machine learning ; prediction ; customer usage ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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