Pharmacokinetic interactions between fexuprazan, a potassium-competitive acid blocker, and nonsteroidal anti-inflammatory drugs in healthy males.

Fexuprazan, a novel potassium-competitive acid blocker, is expected to be used for the prevention of nonsteroidal anti-inflammatory drugs (NSAIDs) induced ulcer. This study aimed to evaluate pharmacokinetic (PK) interactions between fexuprazan and NSAIDs in healthy subjects. A randomized, open-label, multicenter, six-sequence, one-way crossover study was conducted in healthy male subjects. Subjects randomly received one of the study drugs (fexuprazan 40 mg BID, celecoxib 200 mg BID, naproxen 500 mg BID, or meloxicam 15 mg QD) for 5 or 7 days in the first period followed by the combination of fexuprazan and one of NSAIDs for the same days and the perpetrator additionally administered for 1-2 days in the second period. Serial blood samples for PK analysis were collected until 48- or 72-h post-dose at steady state. PK parameters including maximum plasma concentration at steady state (Cmax,ss) and area under plasma concentration-time curve over dosing interval at steady state (AUCτ,ss) were compared between monotherapy and combination therapy. The PKs of NSAIDs were not significantly altered by fexuprazan. For fexuprazan, differences in PK parameters (22% in Cmax, 19% in AUCτ,ss) were observed when co-administered with naproxen, but not clinically significant. The geometric mean ratio (90% confidence interval) of combination therapy to monotherapy for Cmax,ss and AUCτ,ss was 1.22 (1.02-1.46) and 1.19 (1.00-1.43), respectively. There were no significant changes in the systemic exposure of fexuprazan by celecoxib and meloxicam. Fexuprazan and NSAIDs did not show clinically meaningful PK interactions.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. METHODS: AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). RESULTS: AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. CONCLUSION: AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Optimal Minimal Stent Area and Impact of Stent Underexpansion in Left Main Up-Front 2-Stent Strategy.

BACKGROUND: We previously reported the use of minimal stent area to predict angiographic in-stent restenosis after drug-eluting stent implantation for unprotected left main (LM) disease. We aimed to evaluate the optimal minimal stent area criteria for up-front LM 2-stenting based on long-term clinical outcomes. METHODS: We identified 292 consecutive patients with LM bifurcation stenosis who were treated using the crush technique. The final minimal stent area was measured in the ostial left anterior descending artery (LAD), ostial left circumflex artery (LCX), and distal LM. The primary outcome was 5-year major adverse cardiac events, including all-cause death, myocardial infarction, and target lesion revascularization. RESULTS: The minimal stent area cutoff values that best predicted the 5-year major adverse cardiac events were 11.8 mm2 for distal LM (area under the curve, 0.57; P=0.15), 8.3 mm2 for LAD ostium (area under the curve, 0.62; P=0.02), and 5.7 mm2 for LCX ostium (area under the curve, 0.64; P=0.01). Using these criteria, the risk of 5-year major adverse cardiac events was significantly associated with stent underexpansion in the LAD ostium (hazard ratio, 3.14; [95% CI, 1.23-8.06]; P=0.02) and LCX ostium (hazard ratio, 2.60 [95% CI, 1.11-6.07]; P=0.03) but not in the distal LM (hazard ratio, 0.81 [95% CI, 0.34-1.91]; P=0.63). Patients with stent underexpansion in both ostial LAD and LCX had a significantly higher rate of 5-year major adverse cardiac events than those with no or 1 underexpanded stent of either ostium (P<0.01). CONCLUSIONS: Stent underexpansion in the LAD and LCX ostium was significantly associated with long-term outcomes in patients who underwent up-front 2-stenting for LM bifurcation stenosis.

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography.

BACKGROUND: Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room. PURPOSE: This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA. METHODS: Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients. RESULTS: The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second. CONCLUSION: Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.

Automated multi-class classification for prediction of tympanic membrane changes with deep learning models.

BACKGROUNDS AND OBJECTIVE: Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks. STUDY DESIGN: This retrospective study investigated the impact of concurrent diseases in the tympanic membrane on diagnostic performance using multi-class classification. A customized architecture of EfficientNet-B4 was introduced to predict the primary class (otitis media with effusion (OME), chronic otitis media (COM), and 'None' without OME and COM) and secondary classes (attic cholesteatoma, myringitis, otomycosis, and ventilating tube). RESULTS: Deep-learning classifications accurately predicted the primary class with dice similarity coefficient (DSC) of 95.19%, while misidentification between COM and OME rarely occurred. Among the secondary classes, the diagnosis of attic cholesteatoma and myringitis achieved a DSC of 88.37% and 88.28%, respectively. Although concurrent diseases hampered the prediction performance, there was only a 0.44% probability of inaccurately predicting two or more secondary classes (29/6,630). The inference time per image was 2.594 ms on average. CONCLUSION: Deep-learning classification can be used to support clinical decision-making by accurately and reproducibly predicting tympanic membrane changes in real time, even in the presence of multiple concurrent diseases.

Reinforced PEI/PVdF Multicore-Shell Structure Composite Membranes by Phase Prediction on a Ternary Solution.

To construct a polyetherimide (PEI)-reinforced polyvinylidene fluoride (PVdF) composite membrane with multicore-shell structure, a ternary solution was prepared and electrospun by single-nozzle electrospinning. A theoretical prediction was made for the feasibility of complete distinction of two phases. The diameters of the membrane fibers and the PEI multi-core fibrils varied with the PEI ratio and the spinning time, respectively. The tensile strength and modulus were improved to 48 MPa and 1.5 GPa, respectively. The shrinkage of the membrane was only 6.6% at 180 °C, at which temperature the commercial PE separator melted down. The reinforcement in mechanical and thermal properties is associated with multiple PEI nanofibrils oriented along the fiber axis. Indeed, the unique morphology of self-assembled multicore-shell fibers plays an important role in their properties. All in all, PEI/PVdF membranes are appropriate for a lithium-ion battery application due to their high mechanical strength, excellent thermal stability, and controllable textural properties.

GAGE12 mediates human gastric carcinoma growth and metastasis.

The spontaneous metastasis from human gastric carcinoma (GC) remains poorly reproduced in animal models. Here, we established an experimental mouse model in which GC progressively developed in the orthotopic stomach wall and metastasized to multiple organs; the tumors colonized in the ovary exhibited typical characteristics of Krukenberg tumor. The expression of mesenchymal markers was low in primary tumors and high in those in intravasating and extravasating veins. However, the expression of epithelial markers did not differ, indicating that the acquisition of mesenchymal markers without a concordant loss of typical epithelial markers was associated with metastasis. We identified 35 differentially expressed genes (DEGs) in GC cells metastasized to ovary, among which overexpression of GAGE12 family genes, the top-ranked DEGs, were validated. In addition, knockdown of the GAGE12 gene family affected transcription of many of the aforementioned 35 DEGs and inhibited trans-well migration, tumor sphere formation in vitro and tumor growth in vivo. In accordance, GAGE12 overexpression augmented migration, tumor sphere formation and sustained in vivo tumor growth. Taken together, the GAGE12 gene family promotes GC growth and metastasis by modulating the expression of GC metastasis-related genes.

Esco2 is a novel corepressor that associates with various chromatin modifying enzymes.

Accurate chromosome segregation during cell division requires that sister chromatids are kept together by cohesin complex until anaphase, when the chromatids separate and distribute to the two daughter cells. Esco2 is an acetyltransferase that is required for the establishment of sister chromatid cohesion during S phase. Here, we report that Esco2 interacts with several component proteins of the CoREST complex, including a transcription corepressor CoREST, histone demethlyase LSD1, HDAC1, HDAC2, BRAF35, and PHF21A. Esco2 also interacts with various histone methyltransferases Suv39h1, SETDB1 and G9a. Esco2 complex purified from HeLa nuclear extract possesses histone H3 K9 methylation activity and functions as a transcription repressor. Esco2 fused to Gal4 DNA binding domain represses transcription by increasing methylation of histone H3 K9 in the promoter region. These results suggest a novel function of Esco2 in transcription repression through modulation of the chromatin structure.