Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.

BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability. OBJECTIVE: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF. METHODS: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF). RESULTS: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001). CONCLUSIONS: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.

Pharmacokinetic Analysis of Levodropropizine and Its Potential Therapeutic Advantages Considering Eosinophil Levels and Clinical Indications.

Levodropropizine is a non-narcotic, non-centrally acting antitussive that inhibits the cough reflex triggered by neuropeptides. Despite the active clinical application of levodropropizine, the exploration of its inter-individual pharmacokinetic diversity and of factors that can interpret it is lacking. The purpose of this study was to explore effective covariates associated with variation in the pharmacokinetics of levodropropizine within the population and to perform an interpretation of covariate correlations from a therapeutic perspective. The results of a levodropropizine clinical trial conducted on 40 healthy Korean men were used in this pharmacokinetic analysis, and the calculated pharmacokinetic and physiochemical parameters were screened for effective correlations between factors through heatmap and linear regression analysis. Along with basic compartmental modeling, a correlation analysis was performed between the model-estimated parameter values and the discovered effective candidate covariates for levodropropizine, and the degree of toxicity and safety during the clinical trial of levodropropizine was quantitatively monitored, targeting the hepatotoxicity screening panel. As a result, eosinophil level and body surface area (BSA) were explored as significant (p-value < 0.05) physiochemical parameters associated with the pharmacokinetic diversity of levodropropizine. Specifically, it was confirmed that as eosinophil level and BSA increased, levodropropizine plasma exposure increased and decreased, respectively. Interestingly, changes in an individual's plasma exposure to levodropropizine depending on eosinophil levels could be interpreted as a therapeutic advantage based on pharmacokinetic benefits linked to the clinical indications for levodropropizine. This study presents effective candidate covariates that can explain the inter-individual pharmacokinetic variability of levodropropizine and provides a useful perspective on the first-line choice of levodropropizine in the treatment of inflammatory respiratory diseases.

Smartphone AI vs. Medical Experts: A Comparative Study in Prehospital STEMI Diagnosis.

PURPOSE: Prehospital telecardiology facilitates early ST-elevation myocardial infarction (STEMI) detection, yet its widespread implementation remains challenging. Extracting digital STEMI biomarkers from printed electrocardiograms (ECGs) using phone cameras could offer an affordable and scalable solution. This study assessed the feasibility of this approach with real-world prehospital ECGs. MATERIALS AND METHODS: Patients suspected of having STEMI by emergency medical technicians (EMTs) were identified from a policy research dataset. A deep learning-based ECG analyzer (QCG™ analyzer) extracted a STEMI biomarker (qSTEMI) from prehospital ECGs. The biomarker was compared to a group of human experts, including five emergency medical service directors (board-certified emergency physicians) and three interventional cardiologists based on their consensus score (number of participants answering "yes" for STEMI). Non-inferiority of the biomarker was tested using a 0.100 margin of difference in sensitivity and specificity. RESULTS: Among 53 analyzed patients (24 STEMI, 45.3%), the area under the receiver operating characteristic curve of qSTEMI and consensus score were 0.815 (0.691-0.938) and 0.736 (0.594-0.879), respectively (p=0.081). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of qSTEMI were 0.750 (0.583-0.917), 0.862 (0.690-0.966), 0.826 (0.679-0.955), and 0.813 (0.714-0.929), respectively. For the consensus score, sensitivity, specificity, PPV, and NPV were 0.708 (0.500-0.875), 0.793 (0.655-0.966), 0.750 (0.600-0.941), and 0.760 (0.655-0.880), respectively. The 95% confidence interval of sensitivity and specificity differences between qSTEMI and consensus score were 0.042 (-0.099-0.182) and 0.103 (-0.043-0.250), respectively, confirming qSTEMI's non-inferiority. CONCLUSION: The digital STEMI biomarker, derived from printed prehospital ECGs, demonstrated non-inferiority to expert consensus, indicating a promising approach for enhancing prehospital telecardiology.

Exploring the therapeutic potential of endolysin CD27L_EAD against Clostridioides difficile infection.

OBJECTIVES: Clostridioides difficile has emerged as a major cause of life-threatening diarrheal disease. Conventional antibiotics used in current standards of care exacerbate the emergence of antibiotic-resistant strains and pose a risk of recurrent C. difficile infection (CDI). Thus, there is an urgent need for alternative therapeutics that selectively eliminate C. difficile without disturbing the commensal microbiota. This study aimed to explore the potential of endolysins as an alternative therapeutic agent to antibiotics. Endolysin is a bacteriophage-derived peptidoglycan hydrolase that aids in the release of phage progeny during the final stage of infection. METHODS: In order to exploit endolysin as a therapeutic agent against CDI, the bactericidal activity of 23 putative endolysins was compared and ΦCD27 endolysin CD27L was selected and modified to CD27L_EAD by cleaving the cell-wall binding domain of CD27L. RESULTS: CD27L_EAD exhibited greater bacteriolytic activity than CD27L and its activity was stable over a wide range of salt concentrations and pH conditions. CD27L_EAD was added to a co-culture of human gut microbiota with C. difficile and the bacterial community structure was analyzed. CD27L_EAD did not impair the richness and diversity of the bacterial population but remarkably attenuated the abundance of C. difficile. Furthermore, the co-administration of vancomycin exerted synergistic bactericidal activity against C. difficile. ß-diversity analysis revealed that CD27L_EAD did not significantly disturb the composition of the microbial community, whereas the abundance of some species belonging to the family Lachnospiraceae decreased after CD27L_EAD treatment. CONCLUSIONS: This study provides insights into endolysin as a prospective therapeutic agent for the treatment of CDI without damaging the normal gut microbiota.

Eco-friendly natural mineral biotite as a cesium adsorbent: Utilizing low-concentration acid and hydrogen peroxide.

Biotite, a phyllosilicate mineral, possesses significant potential for cesium (Cs) adsorption owing to its negative surface charge, specific surface area (SSA), and frayed edge sites (FES). Notably, FES are known to play an important role in the adsorption of Cs. The objectives of this study were to investigate the Cs adsorption capacity and behavior of artificially weathered biotite and identify mineralogical characteristics for the development of an eco-friendly geologically-based Cs adsorbent. Through various analyses, it was confirmed that the FES of biotite was mainly formed by mineral structural distortion during artificial weathering. The Cs adsorption capacity is improved by approximately 39% (from 20.53 to 28.63 mg g-1) when FES are formed in biotite through artificial weathering using a low-concentration acidic solution mixed with hydrogen peroxide (H2O2). Especially, the Cs selectivity in Cs-containing seawater, including high concentrations of cations and organic matter, was significantly enhanced from 203.2 to 1707.6 mL g-1, an increase in removal efficiency from 49.5 to 89.2%. These results indicate that FES of artificially weathered biotite play an essential role in Cs adsorption. Therefore, this simple and economical weathering method, which uses a low-concentration acidic solution mixed with H2O2, can be applied to natural minerals for use as Cs adsorbents.

Performance of ECG-Derived Digital Biomarker for Screening Coronary Occlusion in Resuscitated Out-of-Hospital Cardiac Arrest Patients: A Comparative Study between Artificial Intelligence and a Group of Experts.

Acute coronary syndrome is a significant part of cardiac etiology contributing to out-of-hospital cardiac arrest (OHCA), and immediate coronary angiography has been proposed to improve survival. This study evaluated the effectiveness of an AI algorithm in diagnosing near-total or total occlusion of coronary arteries in OHCA patients who regained spontaneous circulation. Conducted from 1 July 2019 to 30 June 2022 at a tertiary university hospital emergency department, it involved 82 OHCA patients, with 58 qualifying after exclusions. The AI used was the Quantitative ECG (QCG™) system, which provides a STEMI diagnostic score ranging from 0 to 100. The QCG score's diagnostic performance was compared to assessments by two emergency physicians and three cardiologists. Among the patients, coronary occlusion was identified in 24. The QCG score showed a significant difference between occlusion and non-occlusion groups, with the former scoring higher. The QCG biomarker had an area under the curve (AUC) of 0.770, outperforming the expert group's AUC of 0.676. It demonstrated 70.8% sensitivity and 79.4% specificity. These findings suggest that the AI-based ECG biomarker could predict coronary occlusion in resuscitated OHCA patients, and it was non-inferior to the consensus of the expert group.

Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms.

BACKGROUND: Artificial intelligence (AI)-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS). OBJECTIVE: The purpose of this study was to assess the efficacy of an AI model in identifying AF based on SR ECGs in patients with ESUS. METHODS: A transformer-based vision AI model was developed using 737,815 SR ECGs from patients with and without AF to detect current paroxysmal AF or predict the future development of AF within a 2-year period. Probability of AF was calculated from baseline SR ECGs using this algorithm. Its diagnostic performance was further tested in a cohort of 352 ESUS patients from 4 tertiary hospitals, all of whom were monitored using an insertable cardiac monitor (ICM) for AF surveillance. RESULTS: Over 25.1-month follow-up, AF episodes lasting ≥1 hour were identified in 58 patients (14.4%) using ICMs. In the receiver operating curve (ROC) analysis, the area under the curve for the AI algorithm to identify AF ≥1 hour was 0.806, which improved to 0.880 after integrating the clinical parameters into the model. The AI algorithm exhibited greater accuracy in identifying longer AF episodes (ROC for AF ≥12 hours: 0.837, for AF ≥24 hours: 0.879) and a temporal trend indicating that the AI-based AF risk score increased as the ECG recording approached the AF onset (P for trend <.0001). CONCLUSIONS: Our AI model demonstrated excellent diagnostic performance in predicting AF in patients with ESUS, potentially enhancing patient prognosis through timely intervention and secondary prevention of ischemic stroke in ESUS cohorts.

Panoramic view of artificial fruit ripening agents sensing technologies and the exigency of developing smart, rapid, and portable detection devices: A review.

Recently, the availability of point-of-care sensor systems has led to the rapid development of smart and portable devices for the detection of hazardous analytes. The rapid flow of artificially ripened fruits into the market is associated with an elevated risk to human life, agriculture, and the ecosystem due to the use of artificial fruit ripening agents (AFRAs). Accordingly, there is a need for the development of "Point-of-care Sensors" to detect AFRAs due to several advantages, such as simple operation, promising detection mechanism, higher selectivity and sensitivity, compact, and portable. Traditional detection approaches are time-consuming and inappropriate for on-the-spot analyses. Presented comprehensive review aimed to reveal how such technology has systematically evolved over time (through conventional, advanced, and portable smart techniques) detection detect AFRA, till date. Moreover, focuses and highlights a framework of initiatives undertaken for technological advancements in the development of smart the portable detection techniques (kits) for the onsite detection of AFRAs in fruits with in-depth discussion over sensing mechanism and analytical performance of the sensing technology. Notably, colorimetric detection methods have the greatest potential for real-time monitoring of AFRA and its residues because they are easy to assemble, have a high level of selectivity and sensitivity, and can be read by the human eye independently. This study sought to differentiate between traditional credible strategies by presenting new prospects, perceptions, and challenges related to portable devices. This review provides systematic framework of advances in portable field recognition strategies for the on-spot AFRA detection in fruits and critical information for development of new paper-based portable sensors for fruit diagnostic sectors.

Artificial intelligence-enhanced electrocardiography analysis as a promising tool for predicting obstructive coronary artery disease in patients with stable angina.

Aims: The clinical feasibility of artificial intelligence (AI)-based electrocardiography (ECG) analysis for predicting obstructive coronary artery disease (CAD) has not been sufficiently validated in patients with stable angina, especially in large sample sizes. Methods and results: A deep learning framework for the quantitative ECG (QCG) analysis was trained and internally tested to derive the risk scores (0-100) for obstructive CAD (QCGObstCAD) and extensive CAD (QCGExtCAD) using 50 756 ECG images from 21 866 patients who underwent coronary artery evaluation for chest pain (invasive coronary or computed tomography angiography). External validation was performed in 4517 patients with stable angina who underwent coronary imaging to identify obstructive CAD. The QCGObstCAD and QCGExtCAD scores were significantly increased in the presence of obstructive and extensive CAD (all P < 0.001) and with increasing degrees of stenosis and disease burden, respectively (all P trend < 0.001). In the internal and external tests, QCGObstCAD exhibited a good predictive ability for obstructive CAD [area under the curve (AUC), 0.781 and 0.731, respectively] and severe obstructive CAD (AUC, 0.780 and 0.786, respectively), and QCGExtCAD exhibited a good predictive ability for extensive CAD (AUC, 0.689 and 0.784). In the external test, the QCGObstCAD and QCGExtCAD scores demonstrated independent and incremental predictive values for obstructive and extensive CAD, respectively, over that with conventional clinical risk factors. The QCG scores demonstrated significant associations with lesion characteristics, such as the fractional flow reserve, coronary calcification score, and total plaque volume. Conclusion: The AI-based QCG analysis for predicting obstructive CAD in patients with stable angina, including those with severe stenosis and multivessel disease, is feasible.

Comparative specialization of intrinsic cardiac neurons in humans, mice, and pigs.

Intrinsic cardiac neurons (ICNs) play a crucial role in the proper functioning of the heart; yet a paucity of data pertaining to human ICNs exists. We took a multidisciplinary approach to complete a detailed cellular comparison of the structure and function of ICNs from mice, pigs, and humans. Immunohistochemistry of whole and sectioned ganglia, transmission electron microscopy, intracellular microelectrode recording and dye filling for quantitative morphometry were used to define the neurophysiology, histochemistry, and ultrastructure of these cells across species. The densely packed, smaller ICNs of mouse lacked dendrites, formed axosomatic connections, and had high synaptic efficacy constituting an obligatory synapse. At Pig ICNs, a convergence of subthreshold cholinergic inputs onto extensive dendritic arbors supported greater summation and integration of synaptic input. Human ICNs were tonically firing, with synaptic stimulation evoking large suprathreshold excitatory postsynaptic potentials like mouse, and subthreshold potentials like pig. Ultrastructural examination of synaptic terminals revealed conserved architecture, yet small clear vesicles (SCVs) were larger in pigs and humans. The presence and localization of ganglionic neuropeptides was distinct, with abundant VIP observed in human but not pig or mouse ganglia, and little SP or CGRP in pig ganglia. Action potential waveforms were similar, but human ICNs had larger after-hyperpolarizations. Intrinsic excitability differed; 93% of human cells were tonic, all pig neurons were phasic, and both phasic and tonic phenotypes were observed in mouse. In combination, this publicly accessible, multimodal atlas of ICNs from mice, pigs, and humans identifies similarities and differences in the evolution of ICNs.