Diagnostic improvements of calcium-removal image reconstruction algorithm using photon-counting detector CT for calcified coronary lesions.

OBJECTIVE: To investigate the diagnostic performance of a calcium-removal image reconstruction algorithm with photon-counting detector-computed tomography (PCD-CT), a technology that hides only the calcified plaque from the spectral data in coronary calcified lesions. METHODS: This retrospective study included 17 patients who underwent PCD-coronary CT angiography (CCTA) with at least one significant coronary stenosis (≥50 %) with calcified plaque by CCTA and invasive coronary angiography (ICA) performed within 60 days of CCTA. A total of 162 segments with calcified plaque were evaluated for subjective image quality using a 4-point scale. Their calcium-removal images were reconstructed from conventional images, and both images were compared with ICA images as the reference standard. The contrast-to noise ratios for both images were calculated. RESULTS: Conventional and calcium-removal images had a subjective image quality of 2.7 ± 0.5 and 3.2 ± 0.9, respectively (p < 0.001). The percentage of segments with a non-diagnostic image quality was 32.7 % for conventional images and 28.3 % for calcium-removal images (p < 0.001). The segment-based diagnostic accuracy revealed an area under the receiver operating characteristic curve of 0.87 for calcium-removal images and 0.79 for conventional images (p = 0.006). Regarding accuracy, the specificity and positive predictive value of calcium-removal images were significantly improved compared with those of conventional images (80.5 % vs. 69.5 %, p = 0.002 and 64.1 % vs. 52.0 %, p < 0.001, respectively). The objective image quality of the mean contrast-to-noise ratio did not differ between the images (13.9 ± 3.6 vs 13.3 ± 3.4, p = 0.356) CONCLUSIONS: Calcium-removal images with PCD-CT can potentially be used to evaluate diagnostic performance for calcified coronary artery lesions.

Induced Pluripotent Stem Cell-Derived Cardiomyocytes Therapy for Ischemic Heart Disease in Animal Model: A Meta-Analysis.

Ischemic heart disease (IHD) poses a significant challenge in cardiovascular health, with current treatments showing limited success. Induced pluripotent derived-cardiomyocyte (iPSC-CM) therapy within regenerative medicine offers potential for IHD patients, although its clinical impacts remain uncertain. This study utilizes meta-analysis to assess iPSC-CM outcomes in terms of efficacy and safety in IHD animal model studies. A meta-analysis encompassing PUBMED, ScienceDirect, Web of Science, and the Cochrane Library databases, from inception until October 2023, investigated iPSC therapy effects on cardiac function and safety outcomes. Among 51 eligible studies involving 1012 animals, despite substantial heterogeneity, the iPSC-CM transplantation improved left ventricular ejection fraction (LVEF) by 8.23% (95% CI, 7.15 to 9.32%; p < 0.001) compared to control groups. Additionally, cell-based treatment reduced the left ventricle fibrosis area and showed a tendency to reduce left ventricular end-systolic volume (LVESV) and end-diastolic volume (LVEDV). No significant differences emerged in mortality and arrhythmia risk between iPSC-CM treatment and control groups. In conclusion, this meta-analysis indicates iPSC-CM therapy's promise as a safe and beneficial intervention for enhancing heart function in IHD. However, due to observed heterogeneity, the efficacy of this treatment must be further explored through large randomized controlled trials based on rigorous research design.

Clinical characteristics of electrical storm in patients with early repolarization syndrome.

BACKGROUND: Early repolarization syndrome (ERS) is an idiopathic ventricular fibrillation (VF) associated with inferolateral J waves. While electrical storm (ES) in ERS is not rare, their characteristics and risk factors are not fully understood. OBJECTIVE: This study aimed to clarify the significance of ES in ERS. METHODS: We evaluated 44 patients with ERS who experienced VF/sudden cardiac death or arrhythmic syncope. We assessed clinical characteristics to identify the risk factors for ES. RESULTS: In total, 13 patients (30%) experienced ES (ES group). Of these, 11 patients (85%) experienced ES during the acute phase of initial VF episodes and 2 patients (2%) experienced ES during follow-up. VF associated with ES occurred during therapeutic hypothermia in 6 of 13 patients (46%). The J-wave voltage during therapeutic hypothermia was higher in the ES group than that in the patients without ES. Isoproterenol was used in 5 patients (38%), which decreased J-wave voltage and relieved ES. Among the clinical markers, shorter QT and QTp intervals (the interval from QRS onset to the peak of T wave), pilsicainide-induced ST elevation, and high scores on the Shanghai Score System were associated with ES. Although pilsicainide induced ST elevation in 6 of 34 patients (18%), spontaneous Brugada electrocardiographic patterns did not appear to be associated with VF. Therapeutic hypothermia was also a risk factor for acute phase ES. CONCLUSION: Patients with ERS in the ES group frequently had short QT and QTp intervals, pilsicainide-induced ST elevations, and high Shanghai Score System scores. Therapeutic hypothermia was also associated with acute phase ES.

"Artificial Intelligence-Enabled Assessment of Right Ventricular to Pulmonary Artery Coupling in Patients Undergoing Transcatheter Tricuspid Valve Intervention".

BACKGROUND: Right ventricular to pulmonary artery (RV-PA) coupling has been established as a prognostic marker in patients with severe tricuspid regurgitation (TR) undergoing transcatheter tricuspid valve interventions (TTVI). RV-PA coupling assesses right ventricular systolic function related to pulmonary artery pressure levels, which are ideally measured by right heart catheterization. This study aims to improve the RV-PA coupling concept by relating tricuspid annular plane systolic excursion (TAPSE) to mean pulmonary artery pressure (mPAP) levels. Moreover, instead of right heart catheterization, this study sought to employ an extreme gradient boosting (XGB) algorithm to predict mPAP levels based on standard echocardiographic parameters. METHODS AND RESULTS: This multicenter study included 737 patients undergoing TTVI for severe TR; among them, 55 patients from one institution served for external validation. Complete echocardiography and right heart catheterization data were available from all patients. The XGB algorithm trained on 10 echocardiographic parameters could reliably predict mPAP levels as evaluated on right heart catheterization data from external validation (Pearson correlation coefficient R: 0.68; p-value: 1.3x10-8). Moreover, predicted mPAP (mPAPpredicted) levels were superior to echocardiographic systolic pulmonary artery pressure (sPAPechocardiography) levels in predicting 2-year mortality after TTVI (area under the curve [AUC]: 0.607 vs. 0.520; p-value: 1.9x10-6). Furthermore, TAPSE/mPAPpredicted was superior to TAPSE/sPAPechocardiography in predicting 2-year mortality after TTVI (AUC: 0.633 vs. 0.586; p-value: 0.008). Finally, patients with preserved RV-PA coupling (defined as TAPSE/mPAPpredicted > 0.617 mm/mmHg) showed significantly higher 2-year survival rates after TTVI than patients with reduced RV-PA coupling (81.5% vs. 58.8%, p-value: < 0.001). Moreover, independent association between TAPSE/mPAPpredicted levels and 2-year mortality after TTVI was confirmed by multivariate regression analysis (p-value: 6.3x10-4). CONCLUSIONS: Artificial intelligence-enabled RV-PA coupling assessment can refine risk stratification prior to TTVI without necessitating invasive right heart catheterization. A comparison with conservatively treated patients is mandatory to quantify the benefit of TTVI in accordance with RV-PA coupling.

Perspectives of hospitalized heart failure patients: preferred and perceived participation roles in treatment decisions.

Shared decision-making (SDM) is a pivotal process in seeking optimal individual treatment and incorporating clinical evidence and patients' autonomous preferences. However, patients' actual attitudes toward participation in decision-making for state-of-the-art heart failure (HF) treatment remain unclear. We conducted a questionnaire-based survey distributed by nurses and physicians specializing in HF care to assess patients' preferred and perceived participation roles in treatment decision-making during the index hospitalization, rated on five scales (from extremely passive to purely autonomous attitudes). Simultaneously, we investigated the important factors underlying treatment decision-making from the perspective of hospitalized HF patients. Of the 202 patients who were approached by our multidisciplinary HF team between 2017 and 2020, 166 (82.2%) completed the survey. Logistic regression analyses were conducted to identify the clinical determinants of patients who reported that they left all decisions to physicians (i.e., extremely passive attitude). Of the 166 participants (male 67.5%, median age 73 years), 32.5% preferred an extremely passive attitude, while 61.4% reported that they actually chose an extremely passive attitude. A sole determinant of choosing an extremely passive decision-making role was lower educational status (odds ratio: 2.11, 95% confidence interval 1.11-4.00). The most important factor underlying the decision-making was "Physician recommendation" (89.2%). Notably, less than 50% considered "In alignment with my values and preferences" as an important factor underlying treatment decision-making. The majority of HF patients reported that they chose an extremely passive approach, and patients prioritized physician recommendation over their own values and preferences.

Machine learning in cardiology: Clinical application and basic research.

Machine learning is a subfield of artificial intelligence. The quality and versatility of machine learning have been rapidly improving and playing a critical role in many aspects of social life. This trend is also observed in the medical field. Generally, there are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Each type of learning is adequately selected for the purpose and type of data. In the field of medicine, various types of information are collected and used, and research using machine learning is becoming increasingly relevant. Many clinical studies are conducted using electronic health and medical records, including in the cardiovascular area. Machine learning has also been applied in basic research. Machine learning has been widely used for several types of data analysis, such as clustering of microarray analysis and RNA sequence analysis. Machine learning is essential for genome and multi-omics analyses. This review summarizes the recent advancements in the use of machine learning in clinical applications and basic cardiovascular research.

Genetic Testing Enables the Diagnosis of Familial Hypercholesterolemia Underdiagnosed by Clinical Criteria: Analysis of Japanese Early-Onset Coronary Artery Disease Patients.

Definitive diagnosis of familial hypercholesterolemia (FH) is paramount for the risk management of patients and their relatives. The present study aimed to investigate the frequency of gene variants contributing to low-density lipoprotein cholesterol (LDL-C) metabolism and their clinical relevance in patients with early-onset coronary artery disease (EOCAD). Among 63 consecutive patients with EOCAD (men <55 years or women <65 years) who underwent percutaneous coronary intervention (PCI) from 2013 to 2019 at Keio University Hospital, 52 consented to participate in this retrospective study. Targeted sequencing of LDLR, PCSK9, APOB, and LDLRAP1 was performed. Of the 52 patients enrolled (42 men; mean age: 50 ± 6 years), one (LDLR, c.1221_1222delCGinsT) harbored a pathogenic mutation, and one (APOB, c.10591A>G) harbored variants of uncertain significance. Both the patients harboring the variants were male, showing no history of diabetes mellitus or chronic kidney disease, no family history of EOCAD, and no physical findings of FH (i.e., tendon xanthomas or Achilles tendon thickening). Patients harboring the LDLR variant had three-vessel disease, were on a statin prescription at baseline, and had stable LDL-C levels; however, the case showed a poor response to the intensification of medication after PCI. Approximately 3.8% of patients with EOCAD harbored variants of gene related to LDL-C metabolism; there were no notable indicators in the patients' background or clinical course to diagnose FH. Given the difficulty in diagnosing FH based on clinical manifestations and family history, genetic testing could enable the identification of hidden risk factors and provide early warnings to their relatives.

A genetic and developmental biological approach for a family with complex congenital heart diseases-evidence of digenic inheritance.

Objective: Congenital heart disease (CHD) is caused by cardiovascular developmental defects and has a global prevalence of ∼1%. The etiology of CHD is multifactorial and remains generally unknown, despite advances in analytical techniques based on next-generation sequencing (NGS). The aim of our study was to elucidate the multi-genetic origin and pathogenesis of an intriguing familial case with complex CHD. Methods: We performed an original trio-based gene panel analysis using NGS of the family, including two siblings with CHD of single ventricular phenotype, and their unaffected parents. The pathogenicity of the detected rare variants was investigated in silico, and the functional effects of the variants were confirmed in vitro using luciferase assays. The combinatorial effect of gene alterations of the putative responsible genes was tested in vivo using genetically engineered mutant mice. Results: NGS-based gene panel analyses revealed two heterozygous rare variants in NODAL and in TBX20 common to the siblings and to just one of parents. Both variants were suspected pathogenic in silico, and decreased transcriptional activities of downstream signaling pathways were observed in vitro. The analyses of Nodal and Tbx20 double mutant mice demonstrated that Nodal+/-Tbx20-/- embryos showed more severe defects than Nodal+/+Tbx20-/- embryos during early heart development. The expression of Pitx2, a known downstream target of Nodal, was downregulated in Tbx20-/- mutants. Conclusions: Two rare variants on NODAL and TBX20 genes detected in this family were considered to be loss-of-function mutations. Our results suggest that NODAL and TBX20 may be complementary for the cardiac development, and a combinatorial loss-of-function of NODAL and TBX20 could be implicated in digenic inherence as the etiology of complex CHD associated with single ventricle defects in this family.

Epiphenomenon or Prognostically Relevant Interventional Target? A Novel Proportionality Framework for Severe Tricuspid Regurgitation.

Background Tricuspid regurgitation (TR) frequently develops in patients with long-standing pulmonary hypertension, and both pathologies are associated with increased morbidity and mortality. This study aimed to improve prognostic assessment in patients with severe TR undergoing transcatheter tricuspid valve intervention (TTVI) by relating the extent of TR to pulmonary artery pressures. Methods and Results In this multicenter study, we included 533 patients undergoing TTVI for moderate-to-severe or severe TR. The proportionality framework was based on the ratio of tricuspid valve effective regurgitant orifice area to mean pulmonary artery pressure. An optimal threshold for tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio was derived on 353 patients with regard to 2-year all-cause mortality and externally validated on 180 patients. Patients with a tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio ≤1.25 mm2/mm Hg (defining proportionate TR) featured significantly lower 2-year survival rates after TTVI than patients with disproportionate TR (56.6% versus 69.6%; P=0.005). In contrast with patients with disproportionate TR (n=398), patients with proportionate TR (n=135) showed more pronounced mPAP levels (37.9±9.06 mm Hg versus 27.9±8.17 mm Hg; P<2.2×10-16) and more severely impaired right ventricular function (tricuspid annular plane systolic excursion: 16.0±4.11 versus 17.0±4.64 mm; P=0.012). Moreover, tricuspid valve effective regurgitant orifice area was smaller in patients with proportionate TR when compared with disproportionate TR (0.350±0.105 cm2 versus 0.770±0.432 cm2; P<2.2×10-16). Importantly, proportionate TR remained a significant predictor for 2-year mortality after adjusting for demographic and clinical variables (hazard ratio, 1.7; P=0.006). Conclusions The proposed proportionality framework promises to improve future risk stratification and clinical decision-making by identifying patients who benefit the most from TTVI (disproportionate TR). As a next step, randomized controlled studies with a conservative treatment arm are needed to quantify the net benefit of TTVI in patients with proportionate TR.

Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair.

AIMS: Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach. METHODS AND RESULTS: Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 ± 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 ± 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 ± 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 ± 0.360 cm2) and highest systolic pulmonary artery pressures (68.4 ± 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 ± 10.4%) and enlarged LVESD (53.2 ± 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 ± 113 mL; right atrial area: 46.0 ± 8.83 cm2) although LVEF was only slightly reduced (51.5 ± 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed. CONCLUSION: ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.

Confidence in self-care after heart failure hospitalization.

BACKGROUND: Understanding patient perspectives of self-care is critical for improving multidisciplinary education programs and adherence to such programs. However, perspectives of self-care for patients with heart failure (HF) as well as the association between patient perspectives and patient-physician communication remain unclear. METHODS: Confidence levels regarding self-care behaviors (eight lifestyle behaviors and four consulting behaviors) and self-monitoring were assessed using a self-administered questionnaire survey, which was directly distributed by dedicated physicians and nurses to consecutive patients hospitalized with HF in a tertiary-level hospital. Patient-physician communication was evaluated according to the quality of physician-provided information regarding "treatment and treatment choices" and "prognosis" using the Prognosis and Treatment Perception Questionnaire. Out of 202 patients, 187 (92.6 %) agreed to participate, and 176 completed the survey [valid response rate, 87.1 %; male, 67.0 %; median age, 73 (63-81) years]. Multivariate logistic regression analyses were conducted to predict low confidence in self-care (score in the lowest quartile). RESULTS: High confidence (confident or completely confident >75 % of patients) was observed for all self-care behavior categories except low-salt diet (63.1 %), regular exercise (63.1 %), and flu vaccination (65.9 %). Lower confidence in self-care behavior was associated with low quality of patient-physician communication. With regard to self-monitoring, 62.5 % of patients were not confident in distinguishing worsening symptoms of HF from other diseases; non-confidence was also associated with low quality of patient-physician communication. CONCLUSIONS: Hospitalized patients with HF had low confidence regarding regular exercise, salt restriction, and flu vaccination. The results also suggest patient-physician communication affects patient confidence.

Upregulation of neuropeptide Y in cardiac sympathetic nerves induces stress (Takotsubo) cardiomyopathy.

Substantial emotional or physical stress may lead to an imbalance in the brain, resulting in stress cardiomyopathy (SC) and transient left ventricular (LV) apical ballooning. Even though these conditions are severe, their precise underlying mechanisms remain unclear. Appropriate animal models are needed to elucidate the precise mechanisms. In this study, we established a new animal model of epilepsy-induced SC. The SC model showed an increased expression of the acute phase reaction protein, c-Fos, in the paraventricular hypothalamic nucleus (PVN), which is the sympathetic nerve center of the brain. Furthermore, we observed a significant upregulation of neuropeptide Y (NPY) expression in the left stellate ganglion (SG) and cardiac sympathetic nerves. NPY showed neither positive nor negative inotropic and chronotropic effects. On the contrary, NPY could interrupt ß-adrenergic signaling in cardiomyocytes when exposure to NPY precedes exposure to noradrenaline. Moreover, its elimination in the left SG via siRNA treatment tended to reduce the incidence of SC. Thus, our results indicated that upstream sympathetic activation induced significant upregulation of NPY in the left SG and cardiac sympathetic nerves, resulting in cardiac dysfunctions like SC.

Social Isolation and Implementation of Advanced Care Planning Among Hospitalized Patients With Heart Failure.

Background The implementation of advance care planning (ACP) in heart failure management is insufficient. Social isolation (SI) could be a barrier to ACP initiation, albeit the relationship between SI and patients' preference for ACP or end-of-life care remains unknown. Methods and Results We conducted a questionnaire survey, including assessments of SI using the 6-item Lubben Social Network Scale as well as patients' perspectives on ACP and end-of-life care. Of the 160 patients approached by our multidisciplinary heart failure team during admission, 120 patients (75.0%) completed the survey (median age, 73.0 years; men, 74.2%). A Cox proportional hazard model was constructed to elucidate the short-term (180-day) prognostic impact of SI. Overall, 28.3% of participants were at high risk for SI (6-item Lubben Social Network Scale score <12). High-risk patients had more negative attitudes toward ACP than those without (61.8% versus 80.2%; P=0.035). The actual performance of ACP conversation in patients with and without high risk were 20.6% and 30.2%, respectively. Regarding preference in end-of-life care, "Saying what one wants to tell loved ones" (73.5% versus 90.6%; P=0.016) and "Spending enough time with family" (58.8% versus 77.9%; P=0.035) were less important in high-risk patients. High risk for SI was associated with higher 180-day risk-adjusted all-cause mortality (hazard ratio, 7.89 [95% CI, 1.53-40.75]). Conclusions In hospitalized patients with heart failure, high risk for SI was frequently observed. High-risk patients were associated with a negative attitude toward ACP, despite higher mortality. Further research is required to establish an ideal approach to provide ACP in socially vulnerable patients.

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.

The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it. First, primary cultured cardiomyocytes from neonatal rats were stimulated by both angiotensin II and endothelin-1, and cellular images were captured using a phase-contrast microscope. Subsequently, we used a deep learning model for instance segmentation and established a system to automatically and unbiasedly evaluate the cardiomyocyte size and perimeter. Using this system, we screened 100 FDA-approved drugs library and identified 12 drugs that inhibited cardiomyocyte hypertrophy. We focused on ezetimibe, a cholesterol absorption inhibitor, that inhibited cardiomyocyte hypertrophy in a dose-dependent manner in vitro. Additionally, ezetimibe improved the cardiac dysfunction induced by pressure overload in mice. These results suggest that the deep learning-based system is useful for the evaluation of cardiomyocyte hypertrophy and drug screening, leading to the development of new treatments for heart failure.

Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement.

OBJECTIVE: A novel artificial intelligence-based phenotyping approach to stratify patients with severe aortic stenosis (AS) prior to transcatheter aortic valve replacement (TAVR) has been proposed, based on echocardiographic and haemodynamic data. This study aimed to analyse the recovery of extra-aortic valve cardiac damage in accordance with this novel stratification system following TAVR. METHODS: The proposed phenotyping approach was previously established employing data from 366 patients with severe AS from a bicentric registry. For this consecutive study, echocardiographic follow-up data, obtained on day 147±75.1 after TAVR, were available from 247 patients (67.5%). RESULTS: Correction of severe AS by TAVR significantly reduced the proportion of patients suffering from concurrent severe mitral regurgitation (from 9.29% to 3.64%, p value: 0.0015). Moreover, pulmonary artery pressures were ameliorated (estimated systolic pulmonary artery pressure: from 47.2±15.8 to 43.3±15.1 mm Hg, p value: 0.0079). However, right heart dysfunction as well as the proportion of patients with severe tricuspid regurgitation remained unchanged. Clusters with persistent right heart dysfunction ultimately displayed 2-year survival rates of 69.2% (95% CI 56.6% to 84.7%) and 74.6% (95% CI 65.9% to 84.4%), which were significantly lower compared with clusters with little or no persistent cardiopulmonary impairment (88.3% (95% CI 83.3% to 93.5%) and 85.5% (95% CI 77.1% to 94.8%)). CONCLUSIONS: This phenotyping approach preprocedurally identifies patients with severe AS, who will not recover from extra-aortic valve cardiac damage following TAVR and whose survival is therefore significantly reduced. Importantly, not the degree of pulmonary hypertension at initial presentation, but the irreversibility of right heart dysfunction determines prognosis.

The complement C3-complement factor D-C3a receptor signalling axis regulates cardiac remodelling in right ventricular failure.

Failure of the right ventricle plays a critical role in any type of heart failure. However, the mechanism remains unclear, and there is no specific therapy. Here, we show that the right ventricle predominantly expresses alternative complement pathway-related genes, including Cfd and C3aR1. Complement 3 (C3)-knockout attenuates right ventricular dysfunction and fibrosis in a mouse model of right ventricular failure. C3a is produced from C3 by the C3 convertase complex, which includes the essential component complement factor D (Cfd). Cfd-knockout mice also show attenuation of right ventricular failure. Moreover, the plasma concentration of CFD correlates with the severity of right ventricular failure in patients with chronic right ventricular failure. A C3a receptor (C3aR) antagonist dramatically improves right ventricular dysfunction in mice. In summary, we demonstrate the crucial role of the C3-Cfd-C3aR axis in right ventricular failure and highlight potential therapeutic targets for right ventricular failure.

Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.

Induced pluripotent stem cells (iPSCs) are terminally differentiated somatic cells that differentiate into various cell types. iPSCs are expected to be used for disease modeling and for developing novel treatments because differentiated cells from iPSCs can recapitulate the cellular pathology of patients with genetic mutations. However, a barrier to using iPSCs for comprehensive drug screening is the difficulty of evaluating their pathophysiology. Recently, the accuracy of image analysis has dramatically improved with the development of artificial intelligence (AI) technology. In the field of cell biology, it has become possible to estimate cell types and states by examining cellular morphology obtained from simple microscopic images. AI can evaluate disease-specific phenotypes of iPS-derived cells from label-free microscopic images; thus, AI can be utilized for disease-specific drug screening using iPSCs. In addition to image analysis, various AI-based methods can be applied to drug development, including phenotype prediction by analyzing genomic data and virtual screening by analyzing structural formulas and protein-protein interactions of compounds. In the future, combining AI methods may rapidly accelerate drug discovery using iPSCs. In this review, we explain the details of AI technology and the application of AI for iPSC-based drug screening.

Recent Technological Innovations to Promote Cardiovascular Research.

Cardiovascular disease (CVD) is a major global health concern. Several therapeutic strategies for CVDs are available, such as medicine, cardiac assist devices, and heart transplantation. However, they are insufficient for the treatment of severe CVD. To develop novel, innovative treatment approaches for CVDs, it is imperative to understand the underlying pathophysiology and to undertake basic research on this facet. The generation of induced pluripotent stem (iPS) cells has opened avenues for developing new strategies for disease analysis and drug development. This technology has made it possible to obtain pluripotent stem cells from patients with genetic disorders, model the disease in a dish, and use such cells for future regeneration therapy. Meanwhile, artificial intelligence (AI), which is widely used for big data analysis in basic research, has potential in various applications in medicine. New tools such as iPS cells and AI can provide much needed novel insights into CVDs. This review focuses on the recent progress in cardiovascular research using these new technologies.

Solving the Pulmonary Hypertension Paradox in Patients With Severe Tricuspid Regurgitation by Employing Artificial Intelligence.

OBJECTIVES: This study aimed to improve echocardiographic assessment of pulmonary hypertension (PH) in patients presenting with severe tricuspid regurgitation (TR). BACKGROUND: Echocardiographic assessment of PH in patients with severe TR carries several pitfalls for underestimation, hence concealing the true severity of PH in very sick patients in particular, and ultimately obscuring the impact of PH on survival after transcatheter tricuspid valve intervention (TTVI). METHODS: All patients in this study underwent TTVI for severe TR between 2016 and 2020. To predict the mean pulmonary artery pressure (mPAP) solely based on echocardiographic parameters, we trained an extreme gradient boosting (XGB) algorithm. The derivation cohort was constituted by 116 out of 162 patients with both echocardiography and right heart catheterization data, preprocedurally obtained, from a bicentric registry. Moreover, 142 patients from an independent institution served for external validation. RESULTS: Systolic pulmonary artery pressure was consistently underestimated by echocardiography in comparison to right heart catheterization (40.3 ± 15.9 mm Hg vs 44.1 ± 12.9 mm Hg; P = 0.0066), and the assessment was most discrepant among patients with severe defects of the tricuspid valve and impaired right ventricular systolic function. Using 9 echocardiographic parameters as input variables, an XGB algorithm could reliably predict mPAP levels (R = 0.96, P < 2.2 × 10-16). Moreover, patients with elevations in predicted mPAP levels ≥29.9 mm Hg showed significantly reduced 2-year survival after TTVI (58.3% [95% CI: 41.7%-81.6%] vs 78.8% [95% CI: 68.7%-90.5%]; P = 0.026). Importantly, the poor prognosis associated with elevation in predicted mPAP levels was externally confirmed (HR for 2-year mortality: 2.9 [95% CI: 1.5-5.7]; P = 0.002). CONCLUSIONS: PH in patients with severe TR can be reliably assessed based on echocardiographic parameters in conjunction with an XGB algorithm, and elevations in predicted mPAP levels translate into increased mortality after TTVI.