Journal of Korean medical science
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J. Korean Med. Sci. · Nov 2023
Observational StudyComparative Effectiveness of COVID-19 Bivalent Versus Monovalent mRNA Vaccines in the Early Stage of Bivalent Vaccination in Korea: October 2022 to January 2023.
This retrospective observational matched-cohort study of 2,151,216 individuals from the Korean coronavirus disease 2019 (COVID-19) vaccine effectiveness cohort aimed to evaluate the comparative effectiveness of the COVID-19 bivalent versus monovalent vaccines in providing additional protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, critical infection, and death in Korea. ⋯ The bivalent booster dose provided additional protection against SARS-CoV-2 infections, critical infections, and deaths during the omicron variant phase of the COVID-19 pandemic.
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J. Korean Med. Sci. · Nov 2023
Case ReportsLymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer.
In pancreatic cancer surgery, anatomical understanding of lymph node metastases is required. Distinguishing lymph nodes in computed tomography or magnetic resonance imaging is challenging for novice doctors and medical students because of their small size and similar color to surrounding tissues. This study aimed to enhance our understanding of the clinical anatomy of lymph node stations relevant to pancreatic cancer using newly sectioned images of a cadaver with true color and high resolution and their three-dimensional (3D) models. ⋯ The lymph node stations relevant to pancreatic cancer can be anatomically understood by using the sectioned images and 3D models which contain true color and high resolution.
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J. Korean Med. Sci. · Nov 2023
Characteristics of Retracted Publications From Kazakhstan: An Analysis Using the Retraction Watch Database.
Retraction is a correction process for the scientific literature that acts as a barrier to the dissemination of articles that have serious faults or misleading data. The purpose of this study was to investigate the characteristics of retracted papers from Kazakhstan. ⋯ The vast majority of the publications were research articles and conference papers. Russia was the leading collaborative country. The most prominent retraction reasons were fake-biased peer review, plagiarism, and duplication. Efforts to raise researchers' understanding of the grounds for retraction and ethical research techniques are required in Kazakhstan.
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J. Korean Med. Sci. · Nov 2023
Impact of Positron Emission Tomography Viability Imaging: Guided Revascularizations on Clinical Outcomes in Patients With Myocardial Scar on Single-Photon Emission Computed Tomography Scans.
Positron emission tomography (PET) viability scan is used to determine whether patients with a myocardial scar on single-photon emission computed tomography (SPECT) may need revascularization. However, the clinical utility of revascularization decision-making guided by PET viability imaging has not been proven yet. The purpose of this study was to investigate the impact of PET to determine revascularization on clinical outcomes. ⋯ Revascularization improved left ventricular systolic function and survival of patients with a myocardial scar on SPECT scans, irrespective of myocardial viability on PET scans.
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J. Korean Med. Sci. · Nov 2023
ReviewPolygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). ⋯ Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.