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Abstract It would be clinically valuable if the efficacy of antiarrhythmic drugs could be simulated in advance. We developed a digital twin to predict amiodarone efficacy in high-risk atrial fibrillation (AF) patients post-ablation. Virtual left atrium models were created from computed tomography and electroanatomical maps to simulate AF and evaluate its response to varying amiodarone concentrations. As the amiodarone concentration increased in the virtual setting, action potential duration lengthened, peak upstroke velocities decreased, and virtual AF termination became more frequent. Patients were classified into effective (those with virtually terminated AF at therapeutic doses) and ineffective groups. The one-year clinical outcomes after AF ablation showed significantly better results in the effective group compared to the ineffective group, with AF recurrence rates of 20.8% vs. 45.1% (log-rank p = 0.031, adjusted hazard ratio, 0.37 [0.14-0.98]; p = 0.046). This study highlights the potential of a digital twin-guided approach in predicting amiodarone’s effectiveness and improving personalized AF management. Clinical Trial Registration Name: The Evaluation for Prognostic Factors After Catheter Ablation of Atrial Fibrillation: Cohort Study, Registration number: NCT02138695. The date of registration: 2014-05. URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02138695.

Abstract The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (

Abstract Background High‐power short‐duration (HPSD) ablation creates wide, shallow lesions using radiofrequency (RF) heating. It is uncertain if adjusting RF energy based on atrial wall thickness provides extra benefits. We studied the safety and effectiveness of tailored HPSD energy based on left atrial (LA) wall thickness (LAWT) for circumferential pulmonary vein isolation (CPVI) in patients with paroxysmal atrial fibrillation (PAF). Methods We enrolled 212 patients (68.4% male, mean age: 59.5 ± 11.0 years) and randomly assigned them to two groups: LAWT‐guided CPVI (WT, n = 108) and conventional CPVI (control, n = 104). Both groups used an open irrigated‐tip deflectable catheter to apply 50 W for 10 s to the posterior LA, while controls used 60 W for 15 s on other LA regions. RF delivery time in WT was titrated (15 s at LAWT > 2.1 mm, 13 s at 1.4–2.1 mm, and 11 s at .999). The total procedure time, cardioversion rate, and post‐procedural AAD prescription rates did not significantly differ between the groups. Conclusions The LAWT‐guided energy titration strategy did not result in improved procedural safety and efficacy compared to the conventional 50–60 W‐HPSD CPVI in patients with PAF.

Abstract Although pulmonary vein isolation (PVI) gaps and extrapulmonary vein triggers contribute to recurrence after atrial fibrillation (AF) ablation, their precise mechanisms remain unproven. Our study assessed the impact of PVI gaps on rhythm outcomes using a human AF digital twin. We included 50 patients (76.0% with persistent AF) who underwent catheter ablation with a realistic AF digital twin by integrating computed tomography and electroanatomical mapping. We evaluated the final rhythm status, including AF and atrial tachycardia (AT), across 600 AF episodes, considering factors including PVI level, PVI gap number, and pacing locations. Our findings revealed that antral PVI had a significantly lower ratio of AF at the final rhythm (28% vs. 56%, p = 0.002) than ostial PVI. Increasing PVI gap numbers correlated with an increased ratio of AF at the final rhythm (p

Abstract Antibody sensor to detect viruses has been widely used but has problems such as the difficulty of right direction control of the receptor site on solid substrate, and long time and high cost for design and production of antibodies to new emerging viruses. The virus detection sensor with a recombinant protein embedded liposome (R/Li) was newly developed to solve the above problems, in which R/Li was assembled on AuNPs (Au@R/Li) to increase the sensitivity using localized surface plasmon resonance (LSPR) method. Recombinant angiotensin-converting enzyme-2 (ACE2) was used as host receptors of SARS-CoV and SARS-CoV-2, and the direction of enzyme active site for virus attachment could be controlled by the integration with liposome. The recombinant protein embedded liposomes were assembled on AuNPs, and LSPR method was used for detection. With the sensor platform S1 protein of both viruses was detected with detection limit of 10 pg/ml and SARS-CoV-2 in clinical samples was detected with 10 ~ 35 Ct values. In the selectivity test, MERS-CoV did not show a signal due to no binding with Au@R/Li. The proposed sensor platform can be used as promising detection method with high sensitivity and selectivity for the early and simple diagnosis of new emerging viruses. Graphical Abstract

Abstract Adequate serotonin levels are pivotal to human well-being; thus, serotonin can be used as a biomarker because it regulates a wide range of physical and psychological functions. As an imbalance of serotonin is highly likely to initiate the pathogenesis of various disorders, monitoring serotonin levels in real time is in high demand for the early detection of disease. We fabricated a field-effect transistor (FET) biosensor based on aptamer-immobilized conducting polymer nanohybrids, which showed an instantaneous response toward serotonin in solution. The mechanism of serotonin detection was based on aptamer deformation after aptamer-ligand interaction and the consequential decrease in the charge carrier density of the FET template. Docking simulations with AutoDock/Vina and PyMOL were successfully used to investigate the binding site of serotonin in the loop structure of the aptamer. The fabricated FET template showed high sensitivity toward serotonin in the range of 10 fM to 100 nM, and the limit of detection (LOD) was exceptionally low at 10 fM. Moreover, the selectivity toward serotonin was confirmed by observing no signal after the injection of structural analogs, functional analogs and excess physiological biomolecules. The potential clinical application of this sensor was confirmed because it remained consistent when the buffer solution was exchanged for artificial serum or artificial cerebrospinal fluid (CSF). † S.G.L. and S.E.S. contributed equally to this work.

It would be clinically valuable if the efficacy of antiarrhythmic drugs could be simulated in advance. We developed a digital twin to predict amiodarone efficacy in high-risk atrial fibrillation (AF) patients post-ablation. Virtual left atrium models were created from computed tomography and electroanatomical maps to simulate AF and evaluate its response to varying amiodarone concentrations. As the amiodarone concentration increased in the virtual setting, action potential duration lengthened, peak upstroke velocities decreased, and virtual AF termination became more frequent. Patients were classified into effective (those with virtually terminated AF at therapeutic doses) and ineffective groups. The one-year clinical outcomes after AF ablation showed significantly better results in the effective group compared to the ineffective group, with AF recurrence rates of 20.8% vs. 45.1% (log-rank p = 0.031, adjusted hazard ratio, 0.37 [0.14-0.98]; p = 0.046). This study highlights the potential of a digital twin-guided approach in predicting amiodarone's effectiveness and improving personalized AF management. Clinical Trial Registration Name: The Evaluation for Prognostic Factors After Catheter Ablation of Atrial Fibrillation: Cohort Study, Registration number: NCT02138695. The date of registration: 2014-05. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02138695. [ABSTRACT FROM AUTHOR]

Acetonitrile plays an important role in atmospheric processes and serves as a key tracer of biomass burning, the major emission source of primary carbonaceous particles and trace gases in the global atmosphere. Recognizing its significance, the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) has designated acetonitrile as one of the target volatile organic compounds for global atmospheric monitoring, aiming for data quality better than 20 % accuracy and 15 % precision. Meeting the objectives of the WMO GAW program requires accurate and stable calibration standards with uncertainties of less than 5 %. In this study, we explored the feasibility of gravimetry for developing primary standard gas mixtures (PSMs) in three different types of aluminium cylinders, each with distinct internal surface treatments, at nmol mol-1 and µmol mol-1 levels with a relative expanded uncertainty of less than 5 %. We found that all three types of cylinders were inadequate, without further passivation, for developing acetonitrile PSMs below 10 nmol mol-1 due to significant adsorption losses (6 %–49 %) onto the cylinder's inner surface. To overcome this challenge, we prepared acetonitrile gas mixtures at 100 nmol mol-1 using a modified gravimetric method and at 10 µmol mol-1 using a conventional gravimetric method and then evaluated their long-term stability. Results showed that the effect of the adsorption loss at 100 nmol mol-1 and 10 µmol mol-1 was negated and negligible, respectively. Stability results show that we can disseminate acetonitrile calibration standards at both 100 nmol mol-1 and 10 µmol mol-1 with a relative expanded uncertainty of 3 % and 1 %, respectively (with an expiration period of 3 years), meeting the target uncertainty of the WMO GAW program. Further research is still needed to develop accurate and stable acetonitrile calibration standards below 10 nmol mol-1 that are closer to atmospheric levels. [ABSTRACT FROM AUTHOR]

Background: Dyslipidemia is an important risk factor for acute myocardial infarction. However, real-world data on its prevalence and lipid management trends for Korean patients with acute myocardial infarction are limited. This study aimed to determine the 10-year temporal trends in dyslipidemia prevalence and lipid management in this patient population. Methods and findings: The study used a merged database of two nationwide observational cohorts (2011–2020) that included 26,751 participants. The primary endpoints were the achievement rates of the (1) absolute low-density lipoprotein cholesterol (LDL-C) target of <70 mg/dL (<1.8 mmol/L), (2) relative LDL-C target reduction of >50% from the baseline, (3) absolute or relative LDL-C target (American target), and (4) both absolute and relative LDL-C targets (European target). The dyslipidemia prevalence increased from 11.1% to 17.1%, whereas the statin prescription rate increased from 92.9% to 97.0% from 2011 to 2020. The rate of high-intensity statin use increased from 12.80% in 2012 to 69.30% in 2020. The rate of ezetimibe use increased from 4.50% in 2016 to 22.50% in 2020. The high-intensity statin and ezetimibe prescription rates (0.20% to 9.30% from 2016 to 2020) increased gradually. The absolute and relative LDL-C target achievement rates increased from 41.4% and 20.8% in 2012 to 62.5% and 39.5% in 2019, respectively. The American (45.7% in 2012 to 68.6% in 2019) and European (16.5% in 2012 to 33.8% in 2019) target achievement rates also increased. Conclusions: The adoption of lipid management guidelines in clinical practice has improved. However, continued efforts are needed to reduce the risk of recurrent ischemic events. [ABSTRACT FROM AUTHOR]

This study aims to enhance breast cancer detection accuracy through an AI-driven ultrasound tool, Vis-BUS, developed by Barreleye Inc., Seoul, South Korea. Vis-BUS incorporates Lesion Detection AI (LD-AI) and Lesion Analysis AI (LA-AI), along with a Cancer Probability Score (CPS), to differentiate between benign and malignant breast lesions. A retrospective analysis was conducted on 258 breast ultrasound examinations to evaluate Vis-BUS's performance. The primary methods included the application of LD-AI and LA-AI to b-mode ultrasound images and the generation of CPS for each lesion. Diagnostic accuracy was assessed using metrics such as the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision-Recall curve (AUPRC). The study found that Vis-BUS achieved high diagnostic accuracy, with an AUROC of 0.964 and an AUPRC of 0.967, indicating its effectiveness in distinguishing between benign and malignant lesions. Logistic regression analysis identified that 'Fatty' lesion density had an extremely high odds ratio (OR) of 27.7781, suggesting potential convergence issues. The 'Unknown' density category had an OR of 0.3185, indicating a lower likelihood of correct classification. Medium and large lesion sizes were associated with lower likelihoods of correct classification, with ORs of 0.7891 and 0.8014, respectively. The presence of microcalcifications showed an OR of 1.360. Among Breast Imaging-Reporting and Data System categories, category C5 had a significantly higher OR of 10.173, reflecting a higher likelihood of correct classification. Vis-BUS significantly improves diagnostic precision and supports clinical decision-making in breast cancer screening. However, further refinement is needed in areas like lesion density characterization and calcification detection to optimize its performance. [ABSTRACT FROM AUTHOR]

Despite advancements in high-precision segmentation technology for computed tomographic angiography (CTA)-based cardiac wall segmentation, the accurate detection of the endocardial (Endo) and epicardial (Epi) boundaries remains a prerequisite for automated measurements of the cardiac wall thickness (WT). We proposed a novel algorithm for automated three-dimensional (3D) atrial WT (AWT) measurements, including an automatic Endo-Epi boundary detection. We detected the boundaries that were topologically indistinguishable due to an open geometry at the anatomical boundaries using the combined Convex hull and Poisson solver methods. The Laplace equation for the WT measurement was solved by a partial differential equation combining the two detected boundaries of the myocardial wall. We verified the robustness of our algorithm in mask images of the atrial wall that were separated from the CTA images of 20 patients and a phantom model. The accuracy of the automatically detected Endo-Epi boundaries was acceptable as compared to that manually extracted from the phantom model (Dice coefficient = 0.979). The 3D AWTs calculated by the novel automated method from the CTA images obtained from 20 patients with atrial fibrillation had

Abstract With the aging society, the prevalence of atrial fibrillation (AF) continues to increase. Nevertheless, there are still limitations in antiarrhythmic drugs (AAD) or catheter interventions for AF. If it is possible to predict the outcome of AF management according to various AADs or ablation lesion sets through computational modeling, it will be of great clinical help. AF computational modeling has been utilized for in-silico arrhythmia research and enabled high-density entire chamber mapping, reproducible condition control, virtual intervention, not possible clinically or experimentally, in-depth mechanistic research. With the recent development of computer science and technology, more sophisticated and faster computational modeling has become available for clinical application. In particular, it can be applied to determine the extra-PV target of persistent AF catheter ablation or to select the AAD with the best effect. AF computational modeling combined with artificial intelligence is expected to contribute to precision medicine for more diverse uses in the future. Therefore, in this review, we will deal with the history, development, and various applications of computation modeling.

BackgroundGreater epicardial adipose tissue (EAT) is related to higher recurrences after atrial fibrillation catheter ablation (AFCA). We investigated the effects of posterior wall box isolation (POBI) in conjunction with circumferential pulmonary vein isolation (CPVI) on rhythm outcomes according to varying EAT volumes among patients with persistent atrial fibrillation (PeAF).Materials and methodsWe included 1,187 patients with PeAF undergoing a de novo AFCA including those receiving CPVI alone (n = 687) and those receiving additional POBI (n = 500). The rhythm outcomes at 2 years post-AFCA were compared in subgroups stratified by the EAT volume using propensity overlap weighting.ResultsA reduced EAT volume was linearly associated with more favorable rhythm outcomes for additional POBI than for CPVI alone (P for interaction = 0.002). Among the patients with smaller EAT volumes (≤116.23 mL, the median value, n = 594), additional POBI was associated with a reduced AF recurrence risk as compared to CPVI only [weighted HR (hazard ratio) 0.74, 95% CI (confidence interval) 0.56–0.99]. In contrast, among the remaining 593 patients with greater EAT volumes (>116.23 mL), No difference was observed in the recurrence risk between the additional POBI and CPVI alone groups (weighted HR 1.13, 95% CI 0.84–1.52). Among 205 patients with repeat ablations, the POBI reconnection rate was more frequent in the large EAT group (77.4%) than in the small EAT group (56.7%, P = 0.034).ConclusionWhile PeAF patients with a smaller EAT volume averted AF recurrence by additional POBI after CPVI, no benefit of the POBI was observed in those with a greater EAT volume.

Herein, we developed a bio-functionalized solution-immersed silicon (SIS) sensor at the single-cell level to identify Erwinia amylovora (E. amylovora), a highly infectious bacterial pathogen responsible for fire blight, which is notorious for its rapid spread and destructive impact on apple and pear orchards. This method allows for ultra-sensitive measurements without pre-amplification or labeling compared to conventional methods. To detect a single cell of E. amylovora, we used Lipopolysaccharide Transporter E (LptE), which is involved in the assembly of lipopolysaccharide (LPS) at the surface of the outer membrane of E. amylovora, as a capture agent. We confirmed that LptE interacts with E. amylovora via LPS through in-house ELISA analysis, then used it to construct the sensor chip by immobilizing the capture molecule on the sensor surface modified with 3′-Aminopropyl triethoxysilane (APTES) and glutaraldehyde (GA). The LptE-based SIS sensor exhibited the sensitive and specific detection of the target bacterial cell in real time. The dose–response curve shows a linearity (R2 > 0.992) with wide dynamic ranges from 1 to 107 cells/mL for the target bacterial pathogen. The sensor showed the value change (dΨ) of approximately 0.008° for growing overlayer thickness induced from a single-cell E. amylovora, while no change in the control bacterial cell (Bacillus subtilis) was observed, or negligible change, if any. Furthermore, the bacterial sensor demonstrated a potential for the continuous detection of E. amylovora through simple surface regeneration, enabling its reusability. Taken together, our system has the potential to be applied in fields where early symptoms are not observed and where single-cell or ultra-sensitive detection is required, such as plant bacterial pathogen detection, foodborne pathogen monitoring and analysis, and pathogenic microbial diagnosis.

In this study, we developed a highly sensitive and specific bimolecular fluorescence complementation (BiFC)-based influenza A virus (IAV)-sensing system by combining a galactose/glucose-binding protein (GGBP) with an N-terminal large domain (YN1-172) and a C-terminal small domain (YC173-239) made up of enhanced yellow fluorescence protein (eYFP). The GGBP-based BiFC reporter exhibits the fluorescence reconstitution as a result of conformational changes in GGBP when lactose, which was derived from 6′-silalyllactose and used as a substrate for neuraminidase (NA), binds to GGBP in the presence of IAV. The system showed a linear dynamic range extending from 1 × 100 to 1 × 107 TCID50/mL, and it had a detection limit of 1.1 × 100 TCID50/mL for IAV (H1N1), demonstrating ultra-high sensitivity. Our system exhibited fluorescence intensity enhancements in the presence of IAV, while it displayed weak fluorescence signals when exposed to NA-deficient viruses, such as RSV A, RSV B, adenovirus and rhinovirus, thereby indicating selective responses for IAV detection. Overall, our system provides a simple, highly sensitive and specific IAV detection platform based on BiFC that is capable of detecting ligand-induced protein conformational changes, obviating the need for virus culture or RNA extraction processes.