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This review concisely presents the chronology of events that shaped the development of echocardiography. The concept of 'seeing' structures using 'sound' dates back to the 1920s, when ultrasound produced by piezoelectric crystals was used to detect flaws in metals. In the early 1950s, Hertz and Edler described the use of ultrasound for assessing mitral-valve disease. Subsequently, Harvey Feigenbaum in the 1960s standardized the clinical use of M-mode echocardiography for quantitative assessment of left-ventricular dimensions. The advent of 2-dimensional echocardiography (1970s), pulsed Doppler (1970s), and color Doppler (1980s) introduced new methods for routine assessment of cardiac anatomy and hemodynamics at bedside. Flexible scopes and superior transducers further paved the way to the application of transesophageal echocardiography. Tissue Doppler and contrast echocardiography recently have emerged as important tools for evaluation of regional myocardial function and blood flow. Miniaturization and the ability to pack thousands of crystals in an electronic array have transformed the application of 3-dimensional echocardiography into a bedside tomographic tool. At the current pace of development, echocardiography will be able to provide complete assessment of the heart in terms of its anatomy, coronary flow, and physiology. Training people and making it available at every bedside may be the only remaining challenges. [ABSTRACT FROM AUTHOR]

Distinguishing the pathologic hypertrophy of hypertrophic cardiomyopathy (HC) from the physiologic hypertrophy of professional football players (PFP) can be challenging when septal wall thickness falls within a "gray zone" between 12 and 16 mm. It was hypothesized that 2-dimensional and speckle-tracking strain (ε) echocardiography could differentiate the hearts of PFPs from those of patients with HC with similar wall thicknesses. Sixty-six subjects, including 28 professional American football players and 21 patients with HC, with septal wall thicknesses of 12 to 16 mm, along with 17 normal controls, were studied using 2-dimensional echocardiography. Echocardiographic parameters, including modified relative wall thickness (RWT; septal wall thickness + posterior wall thickness/left ventricular end-diastolic diameter) and early diastolic annular tissue velocity (e'), were measured. Two-dimensional ε was analyzed by speckle tracking to measure endocardial and epicardial longitudinal ε and circumferential ε and radial cardiac ε. Septal wall thickness was higher in patients with HC than in PFPs (14.7 ± 1.1 vs 12.9 ± 0.9 mm, respectively, p <0.001), while posterior wall thickness showed no difference. RWT was larger in patients with HC than in PFPs (0.68 ± 0.10 vs 0.48 ± 0.06, p <0.001). Longitudinal endocardial ε and radial cardiac ε were significantly higher in PFPs than in patients with HC, while circumferential endocardial ε was no different. RWT was the parameter that most accurately differentiated PFPs from patients with HC. An RWT cut point of 0.6 differentiated PFPs from patients with HC, with an area under the curve of 0.97. In conclusion, a 2-dimensional echocardiographic measure of RWT (septal wall + posterior wall thickness/left ventricular end-diastolic dimension) accurately differentiated PFPs' hearts from those of patients with HC when septal wall thickness was in the gray zone of 12 to 16 mm. Two-dimensional strain analysis identifies variations in myocardial deformation between PFPs and patients with HC with gray-zone hypertrophy. [ABSTRACT FROM AUTHOR]

Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases., (© 2024. The Author(s).)

Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes-emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging., Competing Interests: Declaration of interests DD has received software royalties from and holds a patent with Cedars–Sinai Medical Center. RHD owns shares in Myocardium AI. PPS has served on the Advisory Board of RCE Technologies and HeartSciences and holds stock options; is an Associate Editor for the American College of Cardiology and is a guest editor for the American Society of Echocardiography; received grants or contracts from RCE Technologies, HeartSciences, Butterfly, and MindMics; and hold patents with Mayo Clinic (US8328724B2), HeartSciences (US11445918B2), and Rutgers Health (62/864,771; US202163152686P; WO2022182603A1; US202163211829P; WO2022266288A1; and US202163212228P). NY declares grants or contracts from MindMics, RCE Technologies, HeartSciences, and Abiomed; receives consulting fees from Turnkey Learning and Turnkey Insights; receives payment or honoraria and support for attending meetings or travel from West Virginia University (WVU) and National Science Foundation; is an advisory board member and chair of the student experience committee for Turnkey Learning and Turnkey Insights; is an advisor or board member for Research Spark Hub & Magnetic 3D; is an adjunct professor or faculty member at Carnegie Mellon University; is an editorial board member of American Society of Echocardiography; is a special government employee of the Center for Devices and Radiological Health at the US Food and Drug Association; and holds patents with Rutgers (US202163152686P; WO2022182603A1; US202163211829P; WO2022266288A1; US202163212228P; and WO2022266291A1) and with WVU (invention numbers 2021-20 and 2021-047). ND declares no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Large language models (LLMs) are a unique form of machine learning that facilitates inputs of unstructured text/numerical information for meaningful interpretation and prediction. Recently, LLMs have become commercialized, allowing the average person to access these incredibly powerful tools. Early adopters focused on LLM use in performing logical tasks, including-but not limited to-generating titles, identifying key words, summarizing text, initial editing of scientific work, improving statistical protocols, and performing statistical analysis. More recently, LLMs have been expanded to clinical practice and academia to perform higher cognitive and creative tasks. LLMs provide personalized assistance in learning, facilitate the management of electronic medical records, and offer valuable insights into clinical decision making in cardiology. They enhance patient education by explaining intricate medical conditions in lay terms, have a vast library of knowledge to help clinicians expedite administrative tasks, provide useful feedback regarding content of scientific writing, and assist in the peer-review process. Despite their impressive capabilities, LLMs are not without limitations. They are susceptible to generating incorrect or plagiarized content, face challenges in handling tasks without detailed prompts, and lack originality. These limitations underscore the importance of human oversight in using LLMs in medical science and clinical practice. As LLMs continue to evolve, addressing these challenges will be crucial in maximizing their potential benefits while mitigating risks. This review explores the functions, opportunities, and constraints of LLMs, with a focus on their impact on cardiology, illustrating both the transformative power and the boundaries of current technology in medicine., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Current risk stratification tools for acute myocardial infarction (AMI) have limitations, particularly in predicting mortality. This study utilizes cardiac ultrasound radiomics (i.e., ultrasomics) to risk stratify AMI patients when predicting all-cause mortality., Results: The study included 197 patients: (a) retrospective internal cohort (n = 155) of non-ST-elevation myocardial infarction (n = 63) and ST-elevation myocardial infarction (n = 92) patients, and (b) external cohort from the multicenter Door-To-Unload in ST-segment-elevation myocardial infarction [DTU-STEMI] Pilot Trial (n = 42). Echocardiography images of apical 2, 3, and 4-chamber were processed through an automated deep-learning pipeline to extract ultrasomic features. Unsupervised machine learning (topological data analysis) generated AMI clusters followed by a supervised classifier to generate individual predicted probabilities. Validation included assessing the incremental value of predicted probabilities over the Global Registry of Acute Coronary Events (GRACE) risk score 2.0 to predict 1-year all-cause mortality in the internal cohort and infarct size in the external cohort. Three phenogroups were identified: Cluster A (high-risk), Cluster B (intermediate-risk), and Cluster C (low-risk). Cluster A patients had decreased LV ejection fraction (P < 0.01) and global longitudinal strain (P = 0.03) and increased mortality at 1-year (log rank P = 0.05). Ultrasomics features alone (C-Index: 0.74 vs. 0.70, P = 0.04) and combined with global longitudinal strain (C-Index: 0.81 vs. 0.70, P < 0.01) increased prediction of mortality beyond the GRACE 2.0 score. In the DTU-STEMI clinical trial, Cluster A was associated with larger infarct size (> 10% LV mass, P < 0.01), compared to remaining clusters., Conclusions: Ultrasomics-based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification., (© 2024. The Author(s).)

Background: The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict., Objectives: The authors investigated whether a previously validated echocardiography-based deep learning (DL) model assessing diastolic dysfunction (DD) could identify the latent risk associated with the development and progression of AS., Methods: The authors evaluated 898 participants with AV sclerosis from the ARIC (Atherosclerosis Risk In Communities) cohort study and associated the DL-predicted probability of DD with 2 endpoints: 1) the new diagnosis of AS; and 2) the composite of subsequent mortality or AV interventions. Validation was performed in 2 additional cohorts: 1) in 50 patients with mild-to-moderate AS undergoing cardiac magnetic resonance (CMR) imaging and serial echocardiographic assessments; and 2) in 18 patients with AV sclerosis undergoing 18 F-sodium fluoride (NaF) and 18 F-fluorodeoxyglucose positron emission tomography (PET) combined with computed tomography (CT) to assess valvular inflammation and calcification., Results: In the ARIC cohort, a higher DL-predicted probability of DD was associated with the development of AS (adjusted HR: 3.482 [95% CI: 2.061-5.884]; P < 0.001) and subsequent mortality or AV interventions (adjusted HR: 7.033 [95% CI: 3.036-16.290]; P < 0.001). The multivariable Cox model (incorporating the DL-predicted probability of DD) derived from the ARIC cohort efficiently predicted the progression of AS (C-index: 0.798 [95% CI: 0.648-0.948]) in the CMR cohort. Moreover, the predictions of this multivariable Cox model correlated positively with valvular 18 F-NaF mean standardized uptake values in the PET/CT cohort (r = 0.62; P = 0.008)., Conclusions: Assessment of DD using DL can stratify the latent risk associated with the progression of early-stage AS., Competing Interests: Funding Support and Author Disclosures The work presented in this paper was supported in part by funds from the National Science Foundation (award number: 1920920). Dr Tokodi was supported by the New National Excellence Program (ÚNKP-23-4-II-SE-39) of the Ministry of Culture and Innovation in Hungary from the National Research, Development, and Innovation Fund. Dr Tokodi has received consulting fees from CardioSight outside the submitted work. Dr Hahn has received speaker fees from Abbott Structural, Baylis Medical, and Edwards Lifesciences; has institutional educational and consulting contracts for which she receives no direct compensation, with Abbott Structural, Boston Scientific, Edwards Lifesciences, and Medtronic; and is the chief scientific officer for the Echocardiography Core Laboratory at the Cardiovascular Research Foundation for multiple industry-sponsored trials for which she receives no direct industry compensation. Dr Dweck is supported by the British Heart Foundation (FS/14/78/31020); has received the Sir Jules Thorn Award for Biomedical Research 2015 (15/JTA); and has received speaker fees from Pfizer, Radcliffe Cardiology, Bristol Myers Squibb, Edwards, and Novartis and consulting fees from Novartis, Jupiter Bioventures, Beren, and Silence Therapeutics. Dr Pibarot has received funding from Edwards Lifesciences, Medtronic, Pi-Cardia, and Cardiac Success for echocardiography core laboratory analyses and research studies in transcatheter valve therapies, for which he received no personal compensation; and has received lecture fees from Edwards Lifesciences and Medtronic. Dr Yanamala serves as an advisor for Turnkey Techstart. Dr Sengupta serves as an advisor for RCE Technologies and HeartSciences. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims: Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF)., Methods and Results: A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4054) and Stage C/D (n = 1542) subgroups was assessed. The DeepNN-predicted high-risk compared with the low-risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank P < 0.0001 for all). In multi-variable analyses, the high-risk phenogroup remained an independent predictor of HF and death in both Stages A/B {adjusted hazard ratio [95% confidence interval (CI)] 6.52 [4.20-10.13] and 2.21 [1.68-2.91], both P < 0.0001} and Stage C/D [6.51 (4.06-10.44) and 1.03 (1.00-1.06), both P < 0.0001], respectively. In addition, DeepNN showed incremental value over the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) guidelines [net re-classification index, 0.5 (CI 0.4-0.6), P < 0.001; C-statistic improvement, DeepNN (0.76) vs. ASE/EACVI (0.70), P < 0.001] overall and maintained across stage groups., Conclusion: Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients., Competing Interests: Conflict of interest: P.P.S. is a consultant for RCE Technologies, Echo IQ. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Single coronary artery, giant coronary artery aneurysm, and coronary cameral fistula are rare congenital anomalies, and can cause a range of presentations. To our knowledge, this is the first reported case of all 3 entities occurring simultaneously in 1 patient, with largely unknown implications. Multimodal imaging was essential in prompt diagnosis and management., Competing Interests: The authors have reported that they have no relationships relevant to the contents of this paper to disclose.

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the "pattern" of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states., (© 2024. The Author(s).)

Theileria orientalis, the causal agent of oriental theileriosis, is known to cause mild disease in cattle and buffalo across the world. Recently, different genotypes of T. orientalis have emerged as pathogenic, causing high reported morbidity in cattle. This study focuses on investigating three suspected outbreaks of oriental theileriosis that resulted in fatalities among crossbred and indigenous bulls in Karnataka, India. Examination of blood smears revealed the presence of T. orientalis piroplasms within erythrocytes. The genetic characterization of T. orientalis was conducted by targeting specific markers, including the mpsp gene, p23 gene, and ribosomal DNA markers (18S rRNA gene, ITS-1, and ITS-2). Analysis based on the 18S rRNA gene unveiled the presence of both Type A and Type E genotypes of T. orientalis in the outbreaks. The mpsp gene-based analysis identified genotype 7 of T. orientalis in crossbred cows, whereas genotype 1 (Chitose B) was found to be present in indigenous bulls. Haplotype network analysis based on the mpsp gene revealed the presence of 39 distinct haplotypes within the 12 defined genotypes of T. orientalis with a high haplotype diversity of 0.9545 ± 0.017. Hematological and biochemical analysis revealed a decrease in calcium, hemoglobin levels, red blood cell counts, and phosphorus. This study constitutes the initial documentation of a clinical outbreak of oriental theileriosis in indigenous bulls with genotype 1 (Chitose 1B). Substantial epidemiological investigations are imperative to gain a comprehensive understanding of the geographical distribution of distinct genotypes and the diverse clinical manifestations of the disease across various hosts., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations., Competing Interests: Declaration of interests PPS is supported by a grant from the National Science Foundation (grant 1920920), is a consultant to RCE Technologies, and has equity options with RCE Technologies and Ultromics. S-PL is supported by grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, South Korea (grant number HI22C0154), and from the National Research Foundation of Korea, Ministry of Science and ICT, South Korea (grant number RS-2023-00208947). JKO is supported by a research grant on aortic stenosis from REDNVIA, holds royalties with Anumana, and has received consulting fees from Medtronic. JK is on the advisory board (unpaid) for Novostia and is a board member of the Heart Valve Society. AIPMS has research support from the Gravitation programme Materials-driven Regeneration (grant number 024.003.013) from the Dutch Research Council., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Primary mitral regurgitation (MR) is a heterogeneous clinical disease requiring integration of echocardiographic parameters using guideline-driven recommendations to identify severe disease., Objectives: The purpose of this preliminary study was to explore novel data-driven approaches to delineate phenotypes of MR severity that benefit from surgery., Methods: The authors used unsupervised and supervised machine learning and explainable artificial intelligence (AI) to integrate 24 echocardiographic parameters in 400 primary MR subjects from France (n = 243; development cohort) and Canada (n = 157; validation cohort) followed up during a median time of 3.2 years (IQR: 1.3-5.3 years) and 6.8 (IQR: 4.0-8.5 years), respectively. The authors compared the phenogroups' incremental prognostic value over conventional MR profiles and for the primary endpoint of all-cause mortality incorporating time-to-mitral valve repair/replacement surgery as a covariate for survival analysis (time-dependent exposure)., Results: High-severity (HS) phenogroups from the French cohort (HS: n = 117; low-severity [LS]: n = 126) and the Canadian cohort (HS: n = 87; LS: n = 70) showed improved event-free survival in surgical HS subjects over nonsurgical subjects (P = 0.047 and P = 0.020, respectively). A similar benefit of surgery was not seen in the LS phenogroup in both cohorts (P = 0.70 and P = 0.50, respectively). Phenogrouping showed incremental prognostic value in conventionally severe or moderate-severe MR subjects (Harrell C statistic improvement; P = 0.480; and categorical net reclassification improvement; P = 0.002). Explainable AI specified how each echocardiographic parameter contributed to phenogroup distribution., Conclusions: Novel data-driven phenogrouping and explainable AI aided in improved integration of echocardiographic data to identify patients with primary MR and improved event-free survival after mitral valve repair/replacement surgery., Competing Interests: Funding Support and Author Disclosures This work was supported by funds from the National Science Foundation (#1920920) and National Institute of General Medical Sciences of the National Institutes of Health (#5U54GM104942-04) and by a research grant (FDN-143225) from the Canadian Institutes of Health Research (CIHR), Ottawa, Ontario, Canada. Mr Bernard is supported by a doctoral scholarship from CIHR. Dr Pibarot holds the Canada Research Chair in Valvular Heart Diseases from CIHR, Ottawa, Ontario, Canada. Dr Pibarot has received funding from Edwards Lifesciences, Medtronic, and Phoenix Cardiac Devices for echocardiography core laboratory analyses with no direct personal compensation. Dr Sengupta is a consultant for Kencor Health, RCE Technologies, and Ultromics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023. Published by Elsevier Inc.)

Background: Trypanosoma evansi is a protozoan parasite that can infect a wide range of animals and is widespread around the world. In this study, we analyzed four fatal cases of T. evansi infection using clinical, parasitological, and molecular approaches. We also explored the genetic diversity, demographic history, and population-genetic structure of T. evansi using available Rode Trypanozoon antigenic type (RoTat) 1.2 gene sequences., Methods and Results: Clinical findings of infected animals revealed high fever, anemia, weakness, and anorexia. The animals were treated with diminazene aceturate, which was moderately effective, and hematobiochemical parameters showed changes in hemoglobin and glucose levels. The molecular and genetic diversity of T. evansi was analyzed using the RoTat 1.2 VSG gene. Phylogenetic and haplotype analysis revealed two distinct clusters of T. evansi circulating in India. The genetic diversity indices, neutrality tests, gene flow, and genetic differentiation outcomes confirmed the genetic diversity of the T. evansi population, with a lack of uniformity. The identification of two distinct clusters, exhibiting differential demographic histories and evolutionary forces, implies that the clusters may have undergone independent evolutionary trajectories or experienced different environmental pressures., Conclusion: The present findings underlined the need of an early and precise diagnosis in order to treat and control T. evansi infections, and the RoTat 1.2 VSG gene is an important genetic marker for understanding the genetic diversity and evolutionary history of T. evansi. This knowledge can be used to create tailored strategies to control and manage the infection in an endemic region., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation., Competing Interests: Funding Support and Author Disclosures The content of this manuscript is solely the responsibility of the authors and does not necessarily reflect the official views of the National Heart, Lung, and Blood Institute, National Institutes of Health, or the United States Department of Health and Human Services. The National Heart, Lung, and Blood Institute (NHLBI) Workshop on Artificial Intelligence in Cardiovascular Imaging: Translating Science to Patient Care, held on June 27 and 28, 2022, was supported by the Division of Cardiovascular Sciences, NHLBI. Dr Antani has been supported by the Intramural Research Program of the National Library of Medicine and National Institutes of Health (NIH). Dr Arnaout has been supported by the NIH, the Department of Defense, and the Gordon and Betty Moore Foundation. Dr Dey has received software royalties from Cedars Sinai; and funding support from NIH/NHLBI grants (1R01HL148787-01A1 and 1R01HL151266). Dr Leiner has served on the Advisory Board for Cart-Tech B.V. and AI4Med; has been a clinical advisor for Quantib B.V.; has been a consultant for Guerbet; and has received funding support from the Netherlands Heart Foundation. Dr Sengupta has served on the Advisory Boards of Echo IQ and RCE Technologies; and has received funding support from NSF Award: 2125872 and NRT-HDR: Bridges in Digital Health. Dr Shah has received consulting fees from AstraZeneca, Amgen, Aria CV, Axon Therapies, Bayer, Boehringer-Ingelheim, Boston Scientific, Bristol Myers Squib, Cytokinetics, Edwards Lifesciences, Eidos, Gordian, Intellia, Ionis, Merck, Novartis, Novo Nordisk, Pfizer, Prothena, Regeneron, Rivus, Sardocor, Shifamed, Tenax, Tenaya, and United Therapeutics; and has received funding support from the NIH (U54 HL160273, R01 HL140731, R01 HL149423), Corvia, and Pfizer. Dr Slomka has received software royalties from Cedars Sinai; and has received funding support from the NIH/NHLBI grant 1R35HL161195-01. Dr Williams has been a speaker at lectures sponsored by Canon Medical Systems and the Siemens Healthineers; and has received funding support from the British Heart Foundation (FS/ICRF/20/26002). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Left ventricular vortex formation optimizes the effective transport of blood volume while minimizing energy loss (EL). Vector flow mapping (VFM)-derived EL patterns have not been described in children, especially in those less than 1 yr of age. A prospective cohort of 66 (0 days-22 yr, 14 patients ≤ 2 mo) cardiovascularly normal children was used to determine left ventricular (LV) vortex number, size (mm 2 ), strength (m 2 /s), and energy loss (mW/m/m 2 ) in systole and diastole and compared across age groups. One early diastolic (ED) vortex at the anterior mitral leaflet and one late diastolic (LD) vortex at the LV outflow tract (LVOT) were seen in all newborns ≤ 2 mo. At >2 mo, two ED vortices and one LD vortex were seen, with 95% of subjects > 2 yr demonstrating this vortex pattern. Peak and average diastolic EL acutely increased in the same 2 mo-2-yr period and then decreased within the adolescent and young adult age groups. Overall, these findings suggest that the growing heart undergoes a transition to adult vortex flow patterns over the first 2 yr of life with a corresponding acute increase in diastolic EL. These findings offer an initial insight into the dynamic changes of LV flow patterns in pediatric patients and can serve to expand our understanding of cardiac efficiency and physiology in children. NEW & NOTEWORTHY This research article demonstrates, for the first time, echocardiographic evidence of a transition in left ventricular vortex patterns from the newborn to the adult period, with an associated change in cardiac efficiency, marked by increased energy loss, during infancy.

Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.

Bovine tropical fasciolosis, caused by Fasciola gigantica, is a major parasitic disease in tropical countries responsible for significant production losses in animal husbandry practices. The disease is transmitted by the Radix sp. snails. In the early developmental stage of the parasite, the juveniles and immature flukes cause considerable damage to the liver parenchyma of the bovine host while migrating through the liver. The cathepsin (cat) B5 is a cysteine protease that is present in the excretory-secretory product of the fluke both in immature and adult stages. The early detection of fasciolosis is very critical in effective disease management. In this study, the cathepsin B5 gene from newly excysted juveniles were cloned, sequenced and analyzed. The phylogenetic analysis revealed existence of two distinct clades. The clade I includes cat B 1 to B3 whereas clade II consist of cat B4 to B7. Further, the present study was aimed to develop an enzyme linked immuno sorbent assay (ELISA) using recombinant cat B5 antigen. The developed enzyme immuno assay showed 95.3 % sensitivity and 92.4 % specificity with a cut-off of 60 % percent positive. It revealed weighted Kappa value as 0.768 (95 % CI 0.648-0.889) when compared with ELISA using native cathepsin protein. Hence, the developed assay can be exploited as a potent tool in the diagnosis and sero-surveillance of bovine tropical fasciolosis., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Aims: Clinical differentiation of acute myocardial infarction (MI) from unstable angina and other presentations mimicking acute coronary syndromes (ACS) is critical for implementing time-sensitive interventions and optimizing outcomes. However, the diagnostic steps are dependent on blood draws and laboratory turnaround times. We tested the clinical feasibility of a wrist-worn transdermal infrared spectrophotometric sensor (transdermal-ISS) in clinical practice and assessed the performance of a machine learning algorithm for identifying elevated high-sensitivity cardiac troponin-I (hs-cTnI) levels in patients hospitalized with ACS., Methods and Results: We enrolled 238 patients hospitalized with ACS at five sites. The final diagnosis of MI (with or without ST elevation) and unstable angina was adjudicated using electrocardiography (ECG), cardiac troponin (cTn) test, echocardiography (regional wall motion abnormality), or coronary angiography. A transdermal-ISS-derived deep learning model was trained (three sites) and externally validated with hs-cTnI (one site) and echocardiography and angiography (two sites), respectively. The transdermal-ISS model predicted elevated hs-cTnI levels with areas under the receiver operator characteristics of 0.90 [95% confidence interval (CI), 0.84-0.94; sensitivity, 0.86; and specificity, 0.82] and 0.92 (95% CI, 0.80-0.98; sensitivity, 0.94; and specificity, 0.64), for internal and external validation cohorts, respectively. In addition, the model predictions were associated with regional wall motion abnormalities [odds ratio (OR), 3.37; CI, 1.02-11.15; P = 0.046] and significant coronary stenosis (OR, 4.69; CI, 1.27-17.26; P = 0.019)., Conclusion: A wrist-worn transdermal-ISS is clinically feasible for rapid, bloodless prediction of elevated hs-cTnI levels in real-world settings. It may have a role in establishing a point-of-care biomarker diagnosis of MI and impact triaging patients with suspected ACS., Competing Interests: Conflict of interests: P.S. is an advisor to RCE Technologies and Echo IQ and holds option equity with these companies. S.B. holds option equity with RCE Technologies. N.Y is an advisor to Turnkey Learning (P) Ltd. J.T. and A.B. are employees of RCE Technologies. All the other authors have nothing to disclose., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Technetium-99 pyrophosphate scintigraphy (99mTc-PYP) provides qualitative and semiquantitative diagnosis of ATTR cardiac amyloidosis (ATTR-CA) using the Perugini scoring system and heart/contralateral heart ratio (H/CL) on planar imaging. Standardized uptake values (SUV) with quantitative single photon emission computed tomography (xSPECT/CT) can offer superior diagnostic accuracy and quantification through precise myocardial contouring that enhances assessment of ATTR-CA burden. We examined the correlation of xSPECT/CT SUVs with Perugini score and H/CL ratio. We also assessed SUV correlation with cardiac magnetic resonance (CMR), echocardiographic, and baseline clinical characteristics. Retrospective review of 78 patients with suspected ATTR-CA that underwent 99mTc-PYP scintigraphy with xSPECT/CT. Patients were grouped off Perugini score (Grade 0-1 and Grade 2-3), H/CL ratio (≥ 1.5 and < 1.5). Two cohorts were also created: myocardium SUV max > 1.88 and ≤ 1.88 at 1-hour based off an AUC curve with 1.88 showing the greatest sensitivity and specificity. Cardiac SUV retention index was calculated as [SUV max myocardium/SUV max vertebrae] × SUV max paraspinal muscle. Primary outcome was myocardium SUV max at 1-hour correlation with Perugini grades, H/CL ratio, CMR, and echocardiographic data. Higher Perugini Grades corresponded with higher myocardium SUV max values, especially when comparing Perugini Grade 3 to Grade 2 and 1 (3.03 ± 2.1 vs 0.59 ± 0.97 and 0.09 ± 0.2, P < 0.001). Additionally, patients with H/CL ≥ 1.5 had significantly higher myocardium SUV max compared to patients with H/CL ≤ 1.5 (2.92 ± 2.18 vs 0.35 ± 0.60, P < 0.01). Myocardium SUV max at 1-hour strongly correlated with ECV (r = 0.91, P = 0.001), pre-contrast T1 map values (r = 0.66, P = 0.037), and left ventricle mass index (r = 0.80, P = 0.002) on CMR. SUVs derived from 99mTc-PYP scintigraphy with xSPECT/CT provides a discriminatory and quantitative method to diagnose and assess ATTR-CA burden. These findings strongly correlate with CMR., (© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.)

Background: Machine learning (ML) models of risk prediction with coronary artery calcium (CAC) and CAC characteristics exhibit high performance, but are not inherently interpretable., Objectives: To determine the direction and magnitude of impact of CAC characteristics on 10-year all-cause mortality (ACM) with explainable ML., Methods: We analyzed asymptomatic subjects in the CAC consortium. We trained ML models on 80% and tested on 20% of the data with XGBoost, using clinical characteristics ​+ ​CAC (ML 1) and additional CAC characteristics of CAC density and number of calcified vessels (ML 2). We applied SHAP, an explainable ML tool, to explore the relationship of CAC and CAC characteristics with 10-year all-cause and CV mortality., Results: 2376 deaths occurred among 63,215 patients [68% male, median age 54 (IQR 47-61), CAC 3 (IQR 0-94.3)]. ML2 was similar to ML1 to predict all-cause mortality (Area Under the Curve (AUC) 0.819 vs 0.821, p ​= ​0.23), but superior for CV mortality (0.847 vs 0.845, p ​= ​0.03). Low CAC density increased mortality impact, particularly ≤0.75. Very low CAC density ≤0.75 was present in only 4.3% of the patients with measurable density, and 75% occurred in CAC1-100. The number of diseased vessels did not increase mortality overall when simultaneously accounting for CAC and CAC density., Conclusion: CAC density contributes to mortality risk primarily when it is very low ≤0.75, which is primarily observed in CAC 1-100. CAC and CAC density are more important for mortality prediction than the number of diseased vessels, and improve prediction of CV but not all-cause mortality. Explainable ML techniques are useful to describe granular relationships in otherwise opaque prediction models., Competing Interests: Declaration of competing interest Dr. Lin received a research grant from GE. All other authors have no disclosures., (Copyright © 2022 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Background: General anesthesia has traditionally been used in transcatheter aortic valve replacement; however, there has been increasing interest and momentum in alternative anesthetic techniques., Aims: To perform a descriptive study of anesthetic management options in transcatheter aortic valve replacements in the United States, comparing trends in use of monitored anesthesia care versus general anesthesia., Settings and Design: Data evaluated from the American Society of Anesthesiologists' (ASA) Anesthesia Quality Institute's National Anesthesia Clinical Outcomes Registry., Materials and Methods: Multivariable logistic regression was used to identify predictors associated with use of monitored anesthesia care compared to general anesthesia., Results: The use of monitored anesthesia care has increased from 1.8% of cases in 2013 to 25.2% in 2017 (p = 0.0001). Patients were more likely ages 80+ (66% vs. 61%; p = 0.0001), male (54% vs. 52%; p = 0.0001), ASA physical status > III (86% vs. 80%; p = 0.0001), cared for in the Northeast (38% vs. 22%; p = 0.0001), and residents in zip codes with higher median income ($63,382 vs. $55,311; p = 0.0001). Multivariable analysis revealed each one-year increase in age, every 50 procedures performed annually at a practice, and being male were associated with 3% (p = 0.0001), 33% (p = 0.012), and 16% (p = 0.026) increased odds of monitored anesthesia care, respectively. Centers in the Northeast were more likely to use monitored anesthesia care (all p < 0.005). Patients who underwent approaches other than percutaneous femoral arterial were less likely to receive monitored anesthesia care (adjusted odds ratios all < 0.51; all p = 0.0001)., Conclusion: Anesthetic type for transcatheter aortic valve replacements in the United States varies with age, sex, geography, volume of cases performed at a center, and procedural approach.

Background: Changes in cardiac size, myocardial mass, cardiomyocyte appearance, and, ultimately, the function of the entire organ are interrelated features of cardiac remodeling that profoundly affect patient outcomes., Objectives: This study proposes that the application of radiomics for extracting cardiac ultrasonic textural features (ultrasomics) can aid rapid, automated assessment of left ventricular (LV) structure and function without requiring manual measurements., Methods: This study developed machine-learning models using cardiac ultrasound images from 1,915 subjects in 3 clinical cohorts: 1) an expert-annotated cardiac point-of-care-ultrasound (POCUS) registry (n = 943, 80% training/testing and 20% internal validation); 2) a prospective POCUS cohort for external validation (n = 275); and 3) a prospective external validation on high-end ultrasound systems (n = 484). In a type 2 diabetes murine model, echocardiography of wild-type (n = 10) and Leptr -/- (n = 8) mice were assessed longitudinally at 3 and 25 weeks, and ultrasomics features were correlated with histopathological features of hypertrophy., Results: The ultrasomics model predicted LV remodeling in the POCUS and high-end ultrasound external validation studies (area under the curve: 0.78 [95% CI: 0.68-0.88] and 0.79 [95% CI: 0.73-0.86], respectively). Similarly, the ultrasomics model predicted LV remodeling was significantly associated with major adverse cardiovascular events in both cohorts (P < 0.0001 and P = 0.0008, respectively). Moreover, on multivariate analysis, the ultrasomics probability score was an independent echocardiographic predictor of major adverse cardiovascular events in the high-end ultrasound cohort (HR: 8.53; 95% CI: 4.75-32.1; P = 0.0003). In the murine model, cardiomyocyte hypertrophy positively correlated with 2 ultrasomics biomarkers (R 2  = 0.57 and 0.52, Q < 0.05)., Conclusions: Cardiac ultrasomics-based biomarkers may aid development of machine-learning models that provide an expert-level assessment of LV structure and function., Competing Interests: Funding Support and Author Disclosures This work was supported by National Science Foundation grant 1920920 (to Drs Sengupta and Adjeroh), Community Foundation for the Ohio Valley Whipkey Trust (to Dr Hollander), and the American Heart Association grant 17PRE33660333/QAH/2017 (to Dr Hathaway). Dr Hathaway has served as the Chief Science Officer for Aspirations LLC. Dr Sengupta has served as a consultant to Echo IQ and RCE Technologies. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)