Background and Objectives: Cigarette smoking is a major risk factor for atherosclerosis.Nicotine, a crucial constituent of tobacco, contributes to atherosclerosis development and progression. However, evidence of the association between nicotine and neointima formation is limited. We aimed to evaluate whether nicotine enhances neointimal hyperplasia in the native epicardial coronary arteries of pigs after percutaneous coronary intervention (PCI) with drug-eluting stents (DES). Methods: After coronary angiography (CAG) and quantitative coronary angiography (QCA), we implanted 20 DES into 20 pigs allocated to 2 groups: no-nicotine (n=10) and nicotine (n=10) groups. Post-PCI CAG and QCA were performed immediately. Follow-up CAG, QCA, optical coherence tomography (OCT), and histopathological analyses were performed 2 months post-PCI. Results: Despite intergroup similarities in the baseline QCA findings, OCT analysis showed that the nicotine group had a smaller mean stent and lumen areas, a larger mean neointimal area, greater percent area stenosis, and higher peri-strut fibrin and inflammation scores than the no-nicotine group. In immunofluorescence analysis, the nicotine group displayed higher expression of CD68 and α-smooth muscle actin but lower CD31 expression than the no-nicotine group. Conclusions Nicotine inhibited re-endothelialization and promoted inflammation and NIH after PCI with DES in a porcine model.

Background and Objectives: Cigarette smoking is a major risk factor for atherosclerosis.Nicotine, a crucial constituent of tobacco, contributes to atherosclerosis development and progression. However, evidence of the association between nicotine and neointima formation is limited. We aimed to evaluate whether nicotine enhances neointimal hyperplasia in the native epicardial coronary arteries of pigs after percutaneous coronary intervention (PCI) with drug-eluting stents (DES). Methods: After coronary angiography (CAG) and quantitative coronary angiography (QCA), we implanted 20 DES into 20 pigs allocated to 2 groups: no-nicotine (n=10) and nicotine (n=10) groups. Post-PCI CAG and QCA were performed immediately. Follow-up CAG, QCA, optical coherence tomography (OCT), and histopathological analyses were performed 2 months post-PCI. Results: Despite intergroup similarities in the baseline QCA findings, OCT analysis showed that the nicotine group had a smaller mean stent and lumen areas, a larger mean neointimal area, greater percent area stenosis, and higher peri-strut fibrin and inflammation scores than the no-nicotine group. In immunofluorescence analysis, the nicotine group displayed higher expression of CD68 and α-smooth muscle actin but lower CD31 expression than the no-nicotine group. Conclusions Nicotine inhibited re-endothelialization and promoted inflammation and NIH after PCI with DES in a porcine model.

Background and Objectives: Cigarette smoking is a major risk factor for atherosclerosis.Nicotine, a crucial constituent of tobacco, contributes to atherosclerosis development and progression. However, evidence of the association between nicotine and neointima formation is limited. We aimed to evaluate whether nicotine enhances neointimal hyperplasia in the native epicardial coronary arteries of pigs after percutaneous coronary intervention (PCI) with drug-eluting stents (DES). Methods: After coronary angiography (CAG) and quantitative coronary angiography (QCA), we implanted 20 DES into 20 pigs allocated to 2 groups: no-nicotine (n=10) and nicotine (n=10) groups. Post-PCI CAG and QCA were performed immediately. Follow-up CAG, QCA, optical coherence tomography (OCT), and histopathological analyses were performed 2 months post-PCI. Results: Despite intergroup similarities in the baseline QCA findings, OCT analysis showed that the nicotine group had a smaller mean stent and lumen areas, a larger mean neointimal area, greater percent area stenosis, and higher peri-strut fibrin and inflammation scores than the no-nicotine group. In immunofluorescence analysis, the nicotine group displayed higher expression of CD68 and α-smooth muscle actin but lower CD31 expression than the no-nicotine group. Conclusions Nicotine inhibited re-endothelialization and promoted inflammation and NIH after PCI with DES in a porcine model.

Background and Objectives: Cigarette smoking is a major risk factor for atherosclerosis.Nicotine, a crucial constituent of tobacco, contributes to atherosclerosis development and progression. However, evidence of the association between nicotine and neointima formation is limited. We aimed to evaluate whether nicotine enhances neointimal hyperplasia in the native epicardial coronary arteries of pigs after percutaneous coronary intervention (PCI) with drug-eluting stents (DES). Methods: After coronary angiography (CAG) and quantitative coronary angiography (QCA), we implanted 20 DES into 20 pigs allocated to 2 groups: no-nicotine (n=10) and nicotine (n=10) groups. Post-PCI CAG and QCA were performed immediately. Follow-up CAG, QCA, optical coherence tomography (OCT), and histopathological analyses were performed 2 months post-PCI. Results: Despite intergroup similarities in the baseline QCA findings, OCT analysis showed that the nicotine group had a smaller mean stent and lumen areas, a larger mean neointimal area, greater percent area stenosis, and higher peri-strut fibrin and inflammation scores than the no-nicotine group. In immunofluorescence analysis, the nicotine group displayed higher expression of CD68 and α-smooth muscle actin but lower CD31 expression than the no-nicotine group. Conclusions Nicotine inhibited re-endothelialization and promoted inflammation and NIH after PCI with DES in a porcine model.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Prolonged skin inflammation is caused by disrupted skin barrier resulting in chronic inflammatory diseases such as atopic dermatitis. As a potent natural product with anti-inflammatory property, Aster glehni (A. glehni) is a traditional edible herb and has been used to treat diabetes or colitis-associated colon cancer. In present study, we figured out an additional effect of A. glehni ethanol extract (AGE) in pro-inflammatory cytokines-stimulated human keratinocytes. Mixture of tumor necrosis factor-alpha (TNF-α) and interferongamma (IFN-γ) was used to induce inflammatory responses in the HaCaT keratinocytes. AGE suppressed activation of ERK mitogen-activated protein kinase, nuclear factor (NF)-κB, and signal transducer and activator of transcription 1 and 3 (STAT1 and STAT3). The treatment of AGE inhibited mRNA expressions of proinflammatory cytokines in TNF-α and IFN-γ-stimulated HaCaT cells. Also, AGE induced up-regulated expressions of skin barrier molecules like filaggrin, loricrin, or ZO-1. We evaluated the effects of AGE on protein or mRNA expression levels using western blot or qRT-PCR, respectively. Taken together, these results suggest that the treatment of AGE exerts anti-inflammatory effect on keratinocytes through suppressing inflammatory signaling pathways and up-regulating skin molecules in HaCaT keratinocytes.

Background and Objectives: Real-world evidence on the relationship between delayed hospitalization and outcomes in myocardial infarction with nonobstructive coronary arteries (MINOCA) is lacking. Hence, we aimed to evaluate the clinical characteristics of patients with MINOCA and the 2-year mortality outcomes in this patient population according to the symptom-to-door time (SDT). Methods: Overall, 861 patients with MINOCA from 2 Korean nationwide observational registries (2011–2020) were included and categorized as early or late presenters. Late presentation was defined as SDT ≥12 hours in patients with ST-segment elevation myocardial infarction (STEMI) and SDT ≥24 hours in patients with non-STEMI. The primary outcome was 2-year all-cause mortality. Propensity score matching (PSM) and age-sex adjusted analysis were used to determine whether late presentation independently affected mortality.Multivariate logistic regression analysis was used to examine the independent factors correlated with late presentation. Results: In unadjusted data, late presenters had a notably higher risk of 2-year all-cause mortality than early presenters (hazard ratio [HR], 2.44; 95% confidence interval [CI], 1.47–4.08). This trend persisted in age-sex adjusted analysis (adjusted HR, 2.29; 95% CI, 1.36–3.84) and PSM-adjusted analysis (adjusted HR, 2.18; 95% CI, 1.05–4.53). The positive independent factors for late presentation included female sex, no emergency medical service use and high creatinine level, whereas the negative independent factor was a dyslipidemia. Conclusions Late presentation is associated with higher mortality in patients with MINOCA.Multidisciplinary efforts are needed to reduce pre-hospital delay, thereby improving the clinical outcomes in these patients.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Prolonged skin inflammation is caused by disrupted skin barrier resulting in chronic inflammatory diseases such as atopic dermatitis. As a potent natural product with anti-inflammatory property, Aster glehni (A. glehni) is a traditional edible herb and has been used to treat diabetes or colitis-associated colon cancer. In present study, we figured out an additional effect of A. glehni ethanol extract (AGE) in pro-inflammatory cytokines-stimulated human keratinocytes. Mixture of tumor necrosis factor-alpha (TNF-α) and interferongamma (IFN-γ) was used to induce inflammatory responses in the HaCaT keratinocytes. AGE suppressed activation of ERK mitogen-activated protein kinase, nuclear factor (NF)-κB, and signal transducer and activator of transcription 1 and 3 (STAT1 and STAT3). The treatment of AGE inhibited mRNA expressions of proinflammatory cytokines in TNF-α and IFN-γ-stimulated HaCaT cells. Also, AGE induced up-regulated expressions of skin barrier molecules like filaggrin, loricrin, or ZO-1. We evaluated the effects of AGE on protein or mRNA expression levels using western blot or qRT-PCR, respectively. Taken together, these results suggest that the treatment of AGE exerts anti-inflammatory effect on keratinocytes through suppressing inflammatory signaling pathways and up-regulating skin molecules in HaCaT keratinocytes.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.