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Clopidogrel is the mainstay for antiplatelet treatment after percutaneous coronary intervention (PCI). The relationship of platelet reactivity and genetic polymorphism with clinical outcomes with newer-generation drug-eluting stents is unclear. We analysed 4,587 patients for the most powerful single-nucleotide polymorphisms (CYP2C19, CYP2C9, ABCB1, PON1, and P2Y12) related to on-treatment platelet reactivity (OPR). The optimal cut-off value of high OPR for major adverse thrombotic events was 266. CYP2C19 was significantly associated with high OPR and the number of CYP2C19*R (*2 or *3) alleles was proportional to the increased risk of high OPR. Death, myocardial infarction (MI), stroke, stent thrombosis, and bleeding events were assessed during a 1-year follow-up period. Primary endpoints were death and non-fatal MI. The cumulative 1-year incidence of death and stent thrombosis was significantly higher in patients with CYP2C19*2/*2, CYP2C19*2/*3, and CYP2C19*3/*3 (Group 3) than in patients with CYP2C19*1/*1 (Group 1). Multivariate Cox proportional hazard model showed that cardiac death risk was significantly higher in Group 3 than in Group 1 (hazard ratio 2.69, 95% confidence interval 1.154–6.263, p = 0.022). No association was reported between bleeding and OPR. Thus, CYP2C19 may exert a significant impact on the prognosis of PCI patients even in the era of newer-generation drug-eluting stents.

Adipose-derived stem cells (ADSCs) have the potential to differentiate into various cell lineages and they are easily obtainable from patients, which makes them a promising candidate for cell therapy. However, a drawback is their limited life span during in vitro culture. Therefore, hTERT-immortalized CD34+ and CD34- mouse ADSC lines (mADSCshTERT) tagged with GFP were established. We evaluated the proliferation capacity, multi-differentiation potential, and secretory profiles of CD34+ and CD34- mADSCshTERT in vitro, as well as their effects on cardiac function and systemic inflammation following transplantation into a rat model of acute myocardial infarction (AMI) to assess whether these cells could be used as a novel cell source for regeneration therapy in the cardiovascular field. CD34+ and CD34- mADSCshTERT demonstrated phenotypic characteristics and multi-differentiation potentials similar to those of primary mADSCs. CD34+ mADSCshTERT exhibited a higher proliferation ability compared to CD34- mADSCshTERT, whereas CD34- mADSCshTERT showed a higher osteogenic differentiation potential compared to CD34+ mADSCshTERT. Primary mADSCs, CD34+, and CD34- mADSCshTERT primarily secreted EGF, TGF-β1, IGF-1, IGF-2, MCP-1, and HGFR. CD34+ mADSCshTERT had higher secretion of VEGF and SDF-1 compared to CD34- mADSCshTERT. IL-6 secretion was severely reduced in both CD34+ and CD34- mADSCshTERT compared to primary mADSCs. Transplantation of CD34+ and CD34- mADSCshTERT significantly improved the left ventricular ejection fraction and reduced infarct size compared to AMI-induced rats after 28 days. At 28 days after transplantation, engraftment of CD34+ and CD34- mADSCshTERT was confirmed by positive Y chromosome staining, and differentiation of CD34+ and CD34- mADSCshTERT into endothelial cells was found in the infarcted myocardium. Significant decreases were observed in circulating IL-6 levels in CD34+ and CD34- mADSCshTERT groups compared to the AMI-induced control group. Transplantation of CD34- mADSCshTERT significantly reduced circulating MCP-1 levels compared to the AMI control and CD34+ mADSCshTERT groups. GFP-tagged CD34+ and CD34- mADSCshTERT are valuable resources for cell differentiation studies in vitro as well as for regeneration therapy in vivo.

Although dual antiplatelet therapy is essential for patients who undergo percutaneous coronary interventions, the risk of bleeding remains an unsolved problem, and there is limited information on the potential relationship between genetic variants and major bleeding. We analyzed the correlations between four major single nucleotide polymorphisms (CYP2C19, ABCB1, PON1, and P2Y12 G52T polymorphisms) and clinical outcomes in 4489 patients from a prospective multicenter registry. The primary endpoint was major bleeding, defined as a Bleeding Academic Research Consortium ≥ 3 bleeding event. The allelic frequencies of ABCB1, PON1, and both individual and combined CYP2C19 variants did not differ significantly between patient groups with and without major bleeding. However, the allelic frequency of the P2Y12 variant differed significantly between the two groups. Focusing on the P2Y12 G52T variant, patients in the TT group had a significantly higher rate of major bleeding (6.4%; adjusted hazard ratio [HR] 2.51; 95% confidence interval [CI] 1.08–5.84; p = 0.033) than patients in the other groups (GG [2.9%] or GT [1.9%]). Therefore, the TT variant of the P2Y12 G52T polymorphism may be an independent predictor of major bleeding. Trial registration : NCT02707445 ( https://clinicaltrials.gov/ct2/show/NCT02707445?term=02707445&draw=2&rank=1 ).

Understanding signals in the microenvironment that regulate endothelial cell behavior are important in tissue engineering. Although many studies have examined the cellular effects of nanotopography, no study has investigated the functional regulation of human endothelial cells grown on nano-sized gradient hole substrate. We examined the cellular response of human umbilical vein endothelial cells (HUVECs) by using a gradient nanohole substrate (GHS) with three different types of nanohole patterns (HP): which diameters were described in HP1, 120–200 nm; HP2, 200–280 nm; HP3, 280–360 nm. In results, HP2 GHS increased the attachment and proliferation of HUVECs. Also, gene expression of focal adhesion markers in HUVECs was significantly increased on HP2 GHS. In vitro tube formation assay showed the enhancement of tubular network formation of HUVECs after priming on GHS compared to Flat. Furthermore, leukocyte adhesion was also reduced in the HUVECs in a hole-diameter dependent manner. To summarize, optimal proliferations with reduced leukocyte adhesion of HUVECs were achieved by gradient nanohole substrate with 200–280 nm-sized holes.

With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score.

Existing bacterial culture test results for infectious diseases are written in unrefined text, resulting in many problems, including typographical errors and stop words. Effective spelling correction processes are needed to ensure the accuracy and reliability of data for the study of infectious diseases, including medical terminology extraction. If a dictionary is established, spelling algorithms using edit distance are efficient. However, in the absence of a dictionary, traditional spelling correction algorithms that utilize only edit distances have limitations. In this research, we proposed a similarity-based spelling correction algorithm using pretrained word embedding with the BioWordVec technique. This method uses a character-level N-grams-based distributed representation through unsupervised learning rather than the existing rule-based method. In other words, we propose a framework that detects and corrects typographical errors when a dictionary is not in place. For detected typographical errors not mapped to Systematized Nomenclature of Medicine (SNOMED) clinical terms, a correction candidate group with high similarity considering the edit distance was generated using pretrained word embedding from the clinical database. From the embedding matrix in which the vocabulary is arranged in descending order according to frequency, a grid search was used to search for candidate groups of similar words. Thereafter, the correction candidate words were ranked in consideration of the frequency of the words, and the typographical errors were finally corrected according to the ranking. Bacterial identification words were extracted from 27,544 bacterial culture and antimicrobial susceptibility reports, and 16 types of spelling errors and 914 misspelled words were found. The similarity-based spelling correction algorithm using BioWordVec proposed in this research corrected 12 types of typographical errors and showed very high performance in correcting 97.48% (based on F1 score) of all spelling errors. This tool corrected spelling errors effectively in the absence of a dictionary based on bacterial identification words in bacterial culture and antimicrobial susceptibility reports. This method will help build a high-quality refined database of vast text data for electronic health records.

Pathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword extraction for pathology reports is necessary to summarize the informative text and reduce intensive time consumption. In this study, we employed a deep learning model for the natural language process to extract keywords from pathology reports and presented the supervised keyword extraction algorithm. We considered three types of pathological keywords, namely specimen, procedure, and pathology types. We compared the performance of the present algorithm with the conventional keyword extraction methods on the 3115 pathology reports that were manually labeled by professional pathologists. Additionally, we applied the present algorithm to 36,014 unlabeled pathology reports and analysed the extracted keywords with biomedical vocabulary sets. The results demonstrated the suitability of our model for practical application in extracting important data from pathology reports.

Patients with diabetes mellitus (DM) undergoing percutaneous coronary intervention face higher risks of restenosis and adverse cardiovascular outcomes compared to those without DM. This study compared the real-world safety and effectiveness of the Cre8/Cre8 EVO stents in patients with and without diabetes. We performed an investigator-initiated, prospective, single-arm observational trial at 28 sites in South Korea. The primary endpoint was a composite of cardiac death, target vessel-related myocardial infarction (MI), and any clinically driven repeat revascularization at 12 months. All-cause mortality was a key secondary endpoint. The adjusted outcomes of DM and non-DM groups were compared using 1:1 propensity score (PS) matching. A total of 2,043 patients (66.0 ± 11.5 years of age; 76.2% male) were analyzed. Diabetic patients (n = 773; HbA1c 7.3 ± 1.4%) were more likely to be older, female, and have hypertension, dyslipidemia, or chronic kidney disease. Among these, 20.2% (156 patients) were using insulin. There were 54 cases of primary endpoint, 22 (cumulative incidence, 3.4%) in the DM group and 32 (3.0%) in the non-DM group (p = 0.61). The DM group exhibited a higher all-cause mortality rate compared to the non-DM group (2.1% vs. 1.3%; p = 0.19). The adjusted risk of 1-year primary endpoint was similar between the DM and non-DM groups (hazard ratio, 1.20; 95% confidence interval, 0.63-2.30), with comparable safety profiles. In this real-world study, the DM group treated with amphilimus-eluting stents demonstrated sufficient safety and effectiveness at 12 months, with a similar occurrence of cardiovascular events compared to the non-DM group.

Objective To develop bilingual Korean-English medical language models through domain- and language-adaptive pre-training and evaluate their performance in clinical text analysis tasks, specifically semantic similarity and multi-label classification. Methods A bilingual corpus comprising Korean (medical textbooks and online health articles) and English (medical textbooks, health-related articles, and MIMIC-IV EHRs) clinical texts were constructed. Three BERT-based foundation models (Korea Medical [KM-BERT], English Biomedical [BioBERT], and multilingual general domain [M-BERT]) underwent additional pre-training using a newly created bilingual WordPiece vocabulary (45,000 tokens). Model performance was assessed intrinsically on the medical semantic textual similarity (MedSTS) benchmark and extrinsically through multi-label classification of chest computed tomography (CT) reports from tertiary hospitals. Macro F1 scores and Pearson’s correlation coefficients were used as primary evaluation metrics. Results After bilingual pre-training, the Korean semantic similarity performance of bi-BioBERT improved significantly from a Pearson correlation coefficient ranging 0.190–0.871. In the multi-label classification of chest CT reports, all bilingual models outperformed their respective foundation models; bi-KM-BERT achieved the highest Macro F1 score in both internal (0.9460 vs. 0.8902 for KM-BERT) and external validation (0.9288 vs. 0.8495 for KM-BERT). However, bi-KM-BERT and bi-M-BERT experienced semantic performance declines in Korean tasks, indicating catastrophic forgetting, and gradient-based token-importance heatmaps confirmed that the bilingual models captured critical cross-lingual medical contexts more effectively. Conclusion The findings underscore that careful bilingual vocabulary curation and targeted domain-adaptive pre-training enhance natural language processing (NLP) performance in multilingual clinical environments, even with modest training resources. Continual-learning strategies should be explored to mitigate minor forgetting effects. Domain- and language-adaptive pre-training of bilingual medical corpora improves NLP model performance in multilingual clinical settings, thereby providing a scalable strategy for enhancing clinical text analysis capabilities in resource-limited bilingual contexts.

Little is known about the mechanisms underlying the effects of Cyclosporin A (CsA) on the fate of stem cells, including cardiomyogenic differentiation. Therefore, we investigated the effects and the molecular mechanisms behind the actions of CsA on cell lineage determination of P19 cells. CsA induced cardiomyocyte-specific differentiation of P19 cells, with the highest efficiency at a concentration of 0.32 μM during embryoid body (EB) formation via activation of the Wnt signaling pathway molecules, Wnt3a, Wnt5a, and Wnt8a, and the cardiac mesoderm markers, Mixl1, Mesp1, and Mesp2. Interestingly, cotreatment of P19 cells with CsA plus dimethyl sulfoxide (DMSO) during EB formation significantly increases cardiac differentiation. In contrast, mRNA expression levels of hematopoietic and endothelial lineage markers, including Flk1 and Er71, were severely reduced in CsA-treated P19 cells. Furthermore, expression of Flk1 protein and the percentage of Flk1+ cells were severely reduced in 0.32 μM CsA-treated P19 cells compared to control cells. CsA significantly modulated mRNA expression levels of the cell cycle molecules, p53 and Cyclins D1, D2, and E2 in P19 cells during EB formation. Moreover, CsA significantly increased cell death and reduced cell number in P19 cells during EB formation. These results demonstrate that CsA induces cardiac differentiation but inhibits hemato-endothelial differentiation via activation of the Wnt signaling pathway, followed by modulation of cell lineage-determining genes in P19 cells during EB formation.