bluuebunny/crossref_metadata_2025 huggingface dataset:
The Determinant of Excellent Horticulture Commodity in Ngawi Regency
Authors: Yanuan Putra Mohammad Edwinsyah , Mar’atus Sholihah Elis Rahmawati , Al Ayyubi Waridad Umais , Musi Ngani Alwirya , Wardana Aditya , Firmansyah Anwar , Dhandy Rahmat
Published: January 2025

Ngawi Regency based on statistical data has the potential for horticultural commodities that have the 3 highest rankings, namely red onion, cayenne pepper, and big chilies. Based on these data, further studies need to be carried out to provide recommendations to further increase the optimization of one of the horticultural commodities so that it can become one of the leading commodities for Ngawi Regency. Increasing food security and sustainability of the agricultural sector is a major focus in developing agribusiness in various countries. In this context, horticultural commodities play an important role, considering their strategic role in providing nutrition and improving the local economy. The selection of superior horticultural commodities, that can provide optimal results and meet market needs, is a crucial step to ensure sustainable growth in this sector. The SWOT method helps in evaluating internal and external factors that affect the potential of a horticultural commodity. The AHP method offers a quantitative approach to overcome the complexity in decision-making.

The combination of these two methods provides a comprehensive and data-based approach in determining superior horticultural commodities and strategies that can be carried out by Ngawi Regency to improve their quality and quantity. The SWOT method provides a framework for understanding the internal and external context, while AHP offers a tool for making more structured decisions based on comprehensive criteria evaluation.

Horticultural commodities from Ngawi Regency that are considered to be superior horticultural commodities are large chili commodities with an AHP value of 0.659, and the strategy that can be carried out to maintain the sustainability of this commodity is to increase production with an AHP value of 0.558. This can be a recommendation for Ngawi Regency to be able to increase attention to large chili commodities so that they can survive as superior horticultural commodities by maintaining and improving both in terms of quality and quantity.
Determination Of Processed Products From Pomelo Orange Outer Peel Using Analytic Hierarchy Process (AHP)
Authors: Makkulawu Andi Ridwan , Soemarno  , Santoso Imam , Mustaniroh Siti Asmaul
Published: January 2025

The increasing production of Pomelo oranges and the need to diversify their use to enhance economic value have prompted research into developing processed products from Pomelo’s outer peel. This study aims to rank potential processed products derived from the outer peel of Pomelo using the Analytic Hierarchy Process (AHP). Data were collected by distributing questionnaires to five experts, who conducted pairwise comparisons of the selected products. The AHP method was applied to rank these products based on their priority weights. The results show that candy ranked first with a priority weight of 0.35, followed by snack (0.25), aromatherapy (0.20), scrubs (0.10), and sauce (0.09). The consistency of expert evaluations was validated through the Consistency Ratio (CR), calculated at 0.059, indicating a high level of consistency. The findings highlight candy as having the highest potential for further development, with consistent results supporting its prioritisation for product development from Pomelo Peel.
Experimental investigation of nano-additive enhanced Azolla biodiesel blends for improved diesel engine performance and emission mitigation
Authors: D Senthilkumar , Murugesan Sivanesan , T Praveenkumar , A Naresh
Published: January 2025

This study investigates the performance and emission characteristics of a diesel engine fueled with Azolla microphylla biodiesel blends and the effects of aluminium oxide (Al2O3) and copper oxide (CuO) nanoparticles as fuel additives. Azolla microphylla, an aquatic fern, was used to produce biodiesel through a transesterification process. Diesel fuel was blended with Azolla biodiesel in ratios of 10:90 (B10), 20:80 (B20), and 30:70 (B30). Engine performance parameters, including brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke, were evaluated. The results showed that the B30 blend exhibited the highest BTE under maximum load conditions. To address the high viscosity and poor combustion performance of higher biodiesel blends, Al2O3 and CuO nanoparticles were added to the B30 blend at concentrations of 50 ppm and 100 ppm. The B30 blend with 100 ppm Al2O3 nanoparticles demonstrated the highest BTE and the lowest emissions of HC, CO, CO2, and smoke compared to the other test blends. The improved combustion and reduced emissions were attributed to the enhanced heat transfer and catalytic properties of the Al2O3 nanoparticles. These findings suggest that Azolla biodiesel, especially when combined with Al2O3 nanoparticles, can be a viable and sustainable alternative to conventional diesel fuel.
Advanced glaucoma detection through retinal fundus image segmentation and stacking classifier
Authors: Amar Abhishek , S Rajesh I , Bilakeri Shavantrevva
Published: January 2025

Identification of diseases affecting the optic nerve is paramount in ophthalmology. Traditional diagnostic methods often rely heavily on manual intervention, leading to potential errors and delays. Consequently, there’s a pressing need for automated systems capable of early disease detection to initiate timely treatment, thereby averting permanent vision impairment. Glaucoma, a chronic eye condition with the potential for irreversible blindness, stands as a prime example. Recent strides in deep learning have revolutionized the diagnosis of glaucoma through the analysis of retinal fundus images. These technologies enable the development of algorithms capable of identifying subtle signs indicative of glaucoma, often imperceptible to the human eye. By harnessing vast datasets, these algorithms can learn to distinguish between healthy and diseased retinas with remarkable accuracy, facilitating early intervention and treatment. The novelty of the work comes from the combination of existing machine learning techniques on tabular datasets as well as deep learning image segmentation to increase the performance compared to using any one technique alone. The proposed methodology for glaucoma diagnosis makes use of a combination of a deep learning model and several machine learning and achieves Receiver Operating Characteristics Area Under Curve (ROC-AUC) of 88% (0.88) on healthy, glaucomatous and suspicious images in the testing data of the publicly available PAPILA dataset. The mean absolute error for the horizontal cup to disc ratio of 0.093 was obtained and the mean absolute error for the vertical cup to disc ratio of 0.082 was obtained on the testing dataset.
When is a polarised abelian variety determined by its 𝑝-divisible group?
Authors: Ibukiyama Tomoyoshi , Karemaker Valentijn , Yu Chia-Fu
Published: January 2025

We study the Siegel modular variety

A

g

⊗

F

¯

p

\mathscr {A}_g\otimes \overline {\mathbb {F}}_p

of genus

g
g

and its supersingular locus 

S

g

\mathscr {S}_g

. As our main result we determine precisely when

S

g

\mathscr {S}_g

is irreducible, and we list all 

x
x

in 

A

g

⊗

F

¯

p

\mathscr {A}_g\otimes \overline {\mathbb {F}}_p

for which the corresponding central leaf

C

(
x
)

\mathscr {C}(x)

consists of one point, that is, for which 

x
x

corresponds to a polarised abelian variety which is uniquely determined by its associated polarised

p
p

-divisible group. The first problem translates to a class number one problem for quaternion Hermitian lattices. The second problem also translates to a class number one problem, whose solution involves mass formulae, automorphism groups, and a careful analysis of Ekedahl-Oort strata in genus

g
=
4

g=4

.
Equivalence of definitions of AF groupoid
Authors: Clark Lisa , an Huef Astrid , Lima Rafael , Sehnem Camila
Published: January 2025

We prove the equivalence of two definitions of AF groupoid in the literature: one by Renault and the other by Farsi, Kumjian, Pask and Sims. In both definitions, an AF groupoid is an increasing union of more basic groupoids, called elementary groupoids. Surprisingly, the two definitions of elementary groupoid are not equivalent; they coincide if and only if the local homeomorphism that characterises them is a covering map.
Free surface flows induced by re-entrant jet with surface tension effect in a channel
Authors: Laiadi Abdelkader
Published: January 2025

In this paper, we study the two-dimensional free surface cavity flows past a wedge in a channel. A re-entrant jet cavity termination model is introduced. Gravity is neglected, but surface tension is included in the dynamic boundary condition. The fluid is assumed to be inviscid and incompressible and the flow is assumed to be steady and irrotational. When the surface tension is negligible, we calculate an exact free streamline solution by applying conformal mapping techniques. We also compute solutions including the effects of surface tension by using the series truncation method. The results of calculations of the effect of the Weber number and the half angle of the wedge

β
\beta

on the free surface profiles are presented. Good agreement between the analytic free streamline solution and the numerical solution provides a check on our numerical scheme.
Reform Trajectories
Authors: Gentilini Ugo
Published: January 2025

The chapter examines the reform of cash transfers and wider relief systems. Many of such reforms rationalized “indiscriminate” transfers and reorganized them along tighter criteria and stricter provisions. The first section identifies episodes of reforms that changed the main provider in full or in part. Such a shift involves two cases: a change from ecclesiastical to state authorities across the European continent in the early 1500s; and a switch in provider in the opposing direction, that is, away from the state and towards civil society (e.g., the United States in the later 1800s). The second section shows a reform within the same provider: the new poor laws of 1834 England were a critical example of consolidation within the same (state) institutional provider, including a reconfiguration of assistance from local to central administrative units. The third section features a blend model, including a case both embedding a shift within the same provider (although from central to local authorities) and a wider role played by private sector actors. The chapter ends with a discussion on how reforms were carried out in practice and the empirical evidence on the reforms’ effects.
scHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss
Authors: Meng Hua , Qin Chuan , Long Zhiguo
Published: January 2025

Motivation

The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant. However, existing methods utilizing neural networks typically focus on minimizing reconstruction loss and maintaining similarity in embeddings of directly related cells, but fail to consider dissimilarity, thus lacking separability and limiting the performance of clustering.

Results

We propose a novel clustering algorithm, called scHNTL (scRNA-seq data clustering augmented by high-order neighbors and triplet loss). It first constructs an auxiliary similarity graph and uses a Graph Attentional Autoencoder to learn initial embeddings of cells. Then it identifies similar and dissimilar cells by exploring high-order structures of the similarity graph and exploits a triplet loss of contrastive learning, to improve the embeddings in preserving structural information by separating dissimilar pairs. Finally, this improvement for embedding and the target of clustering are fused in a self-optimizing clustering framework to obtain the clusters. Experimental evaluations on 16 real-world datasets demonstrate the superiority of scHNTL in clustering over the state-of-the-arts single-cell clustering algorithms.

Availability and implementation

Python implementation of scHNTL is available at Figshare (https://doi.org/10.6084/m9.figshare.27001090) and Github (https://github.com/SWJTU-ML/scHNTL-code).

Supplementary information

Supplementary data are available at Bioinformatics online.
Deep learning-enabled electrocardiogram assessment of valvular heart disease
Authors: Cinq-Mars A , Carbonati T , Fiorina L , Neylon A , Porquet P , Nguyen A , Waks J , Johnston A , Pollard T , Gow B , Mark R , Horng S , Greenbaum N , Singh J
Published: January 2025

Background

Valvular heart disease is a highly prevalent condition that contributes significantly to cardiovascular morbidity and mortality. Echocardiography is the gold-standard for valvular disease evaluation; however, diagnosis may be delayed due to limited resources or expertise. Deep learning analysis of electrocardiography (ECG) has demonstrated the ability to identify subtle structural and functional abnormalities within the cardiovascular system.

Purpose

The aim of this study was to evaluate the accuracy of a deep learning algorithm to identify valvular heart disease from a standard 12-lead ECG.

Methods

From 2008 to 2019, a total of 27,689 patients who were admitted to the Medical Center with an echocardiogram and ECG performed within 14 days of each other were included in the study. We trained a convolutional neural network to identify the presence of moderate or severe aortic stenosis (AS), aortic regurgitation (AR), mitral regurgitation (MR), and/or tricuspid regurgitation (TR) using a 12-lead ECG. Model performance was assessed using area under the receiver-operating characteristic (AUROC). Patients were randomly divided into development (n = 22,151; 80%) and independent validation sets (n = 5,538; 20[AN1] %). Data used for the study was collected from two open access databases, MIMIC-IV-ECG and MIMIC-IV-Note.

Results

Mean age of the cohort was 65.9 ± 16.6 years old and 51% were female. On the independent validation dataset, AS was present in 126 (2.3%) of patients, AR in 51 (1%), MR in 337 (6.1%), and TR in 450 (8.1 %) patients. During validation, the model accurately identified AS, AR, MR, and TR with AUROCs of 0.80 (95% CI 0.77-0.83), 0.74 (95% CI 0.69-0.80), 0.86 (95% CI 0.84-0.88), and 0.85 (95% CI 0.83-0.87), respectively (Table 1). Test characteristics were dependent on underlying prevalence and selected sensitivity levels. For a composite of any of AS, AR, MR, or TR, the model achieved a positive predictive value of 28% with 79.4% sensitivity.

Conclusion

A deep learning-enabled ECG demonstrates robust performance to identify patients living with valvular heart disease. This model may serve as a simple and promising tool for improving the early detection of valvular heart disease, showcasing the potential for an early development valvular disease screening program.
Deep learning-driven automated assessment of left ventricular diastolic function in echocardiography
Authors: Karuzas A , Miscikas L , Sablauskas K , Kazukauskiene I , Verikas D , Naskauskas K , Sakalyte G , Jarusevicius G , Plisiene J , Lesauskaite V
Published: January 2025

Introduction

Assessing left ventricular (LV) diastolic dysfunction is essential, because it can lead to heart failure with preserved ejection fraction (HFpEF), significantly impacting morbidity and mortality rates. Echocardiography is the primary imaging tool for detecting LV diastolic dysfunction, however its assessment is time consuming and requires experienced clinicians to interpret multiple echocardiographic parameters. Recent advancements in deep learning might offer the potential to reduce analysis time and standardize the evaluation of LV diastolic function.

Purpose

The aim of this study was to assess the potential of deep convolutional neural networks (CNNs) for the evaluation of LV diastolic dysfunction.

Methods

Prospective observational study enrolled 233 patients, a total of 3238 2D echocardiographic images were obtained. The study included patients with normal LV systolic function, who were suspected of HFpEF. The ensemble of four CNN’s was used for the evaluation of transmitral inflow waves (E and A peak velocities), mitral (septal and lateral) velocities (e’) and peak tricuspid regurgitation velocity (TR Vmax). Additional diastolic function parameters, such as the E/A and E/e’ ratios, were calculated to assess diastolic function classes based on the 2016 ASE/EACVI recommendations. Manual measurements were performed by an expert cardiologist. CNN’s results detecting diastolic dysfunction were compared to the evaluation of expert cardiologists.

Results

The analysis revealed high yields of CNN’s measurements across various parameters, ranging from 81.12% for the E/e' ratio to 89.27% for the A wave. CNNs perform well in measuring maximum velocities using different Doppler imaging modes. The root mean square error (RMSE) values indicated the degree of deviation between the automated system and the cardiologist measurements, with the lowest RMSE observed for the E/A ratio (0.4). Bias showed minor discrepancies for the E/A ratio (-0.05), with the largest bias observed in the A wave (5.95) (Table 1).

Conclusion

This study demonstrates that automated echocardiographic analysis shows high yield and good agreement with cardiologist measurements, though some parameters require further enhancement to ensure greater accuracy and consistency. The application of CNNs can potentially simplify the assessment process, reduce analysis time, and provide standardized evaluations.

Table 1. Results of statistical analysis
Effect of image quality on automated evaluation of the left-sided heart volumes
Authors: Kazukauskiene I , Sablauskas K , Ciampi Q , Cortigiani L , Wierzbowska-Drabik K , Kasprzak J D , Lowenstein J , Karuzas A , Prota C , Gaibazzi N , Lepone A , Marconi S , Kiziela A , Arbucci R , Picano E
Published: January 2025

Introduction

The accuracy of echocardiography interpretations is crucial. However, echocardiography has varying degrees of image quality, which can impact diagnostic decision-making. Automated chamber quantification of the left ventricle (LV) and left atrium (LA) has been shown to be feasible using machine learning (ML) systems. However, the effect of image quality on the performance of these ML systems has not been investigated.

Purpose

To assess the effect of different echocardiographic image quality on the performance of left-sided heart quantification using an ML system.

Methods

A multi-center study enrolled 240 patients who underwent stress echocardiography across five centers. The acquired rest phase images were anonymized for subsequent analysis. Expert cardiologists reviewed the studies, selecting apical four-chamber (4Ch) and two-chamber (2Ch) view images. The cardiologists identified end-systolic (ES) and end-diastolic (ED) frames. They traced the endocardial borders for the LA in ES and the LV in ED and ES. The same ED and ES frames were then processed by an ML algorithm trained on a separate set of images. Subsequently, an expert cardiologist, blinded to the previous measurements, assessed the image quality of LA and LV. The image quality for each chamber in the ES or ED frame was categorized into one of four grades: poor, suboptimal, fair, and optimal. Agreement analysis between human and AI measurements was performed within each image quality group. Yield was defined as the percentage of studies where the ML system predicted an echocardiographic parameter compared to a cardiologist.

Results

The yield of study parameters varied significantly with image quality. The lowest yield was 45%, observed for LV end-diastolic volume in the 2Ch view when image quality was poor. In contrast, the yield of 100% was seen for LV end-diastolic volumes in both the 4Ch and 2Ch views for optimal quality images. The correlation between automated and expert cardiologist measurements of left heart volumes varied depending on image quality. For poor or suboptimal images, the Pearson correlation coefficient ranged from 0.66 to 0.91 and improved to 0.85-0.92 in fair and optimal-quality images. Additionally, there was an inverse relation between agreement metrics (bias, limits of agreement, and coefficient of variation) and improvements in image quality. However, this tendency was less pronounced or nonexistent when comparing fair and optimal-quality images. (Table 1).

Conclusions

Image quality affects the yield of automated analysis and the agreement between cardiologists and automated measurements in echocardiography, highlighting the need for high-quality image acquisition in use with ML systems.

Table 1
Performance of human expert and AI measurements in conditions of progressive image degradation
Authors: Jevsikov J , Stowell C , Ng T , Unsworth B , Zolgharni M , Francis D P , Shun-Shin M J
Published: January 2025

Background

A major challenge in real-world echocardiography is the difficulty in obtaining high quality images in some patients or some clinical settings. Is AI only useful when image quality is good?

Purpose

To artificially degrade adequate-quality images, and compare the ability of human experts and AI, to make measurements correctly, as image degradation worsens.

Methods

PLAX dimension measurements were made on videos of 30 patients with a range LV dimensions (mean 138mm, SD 37mm). To set the gold standard, 9 experts measured each image, blinded to the measurements made by others.

For each original image, 5 degraded versions were then made, each progressively more degraded. The degradation was designed to be maximally confusing for this measurement, namely the addition of faint ghost images of random PLAX views of other patients. This process is automatable, reproducible, and not easy to undo by conventional image processing techniques.

The 30 cases, each in original form plus 5 degraded versions, totalled 180 images, They were presented in random order for labelling by a pool of experts. They were also analysed by the Unity UK Echocardiography AI collaboratives.

Results

An example degradation sequence is shown in Figure 1 (upper panel), with images cropped for this abstract.

Across all measurements, the expert and AI suffered progressively greater measurement error as the level of image degradation increased. On average, the AI error was N smaller than the expert error (p<0.05).

For example, for LV internal dimension, the progressive rise in human error was 2.2mm, 2.5mm, 3.1mm, 3.6mm, 5.3mm, 9.6 mm, p<0.001 for trend, Figure 1 lower panel grey bars.

Meanwhile for the AI, the corresponding errors were: 2.5mm, 2.5 mm, 2.7 mm, 3 mm, 3.9 mm, 8 mm. (p<0.001 for trend, Figure 1 lower panel grey bars).

The Minimum Heatmap Amplitude (MHA), an automatic index of AI confidence in its measurement, also declined progressively (p<0.001 for trend).

Conclusion

Both humans and AI alike make less accurate measurements as image quality degrades, although the deterioration in accuracy is more predictable for AI measurements. Importantly, deterioration in image quality (and therefore doubtfulness of the measurement) can be automatically quantified through the MHA to flag measurements needing special attention.

Figure 1: Progressive degradation in images (upper panel) and corresponding increase in measurement error by both human experts and AI (lower panel)
Cardiac shear wave elastography: assessing the performance of different algorithms for shear wave speed estimation
Authors: Araujo Andrade Sousa A , Orlowska M , Wouters L , Youssef A , Caenen A , Hooge J D , Voigt J U
Published: January 2025

Introduction

Cardiac Shear Wave Elastography utilizes ultrafast echocardiographic imaging to assess myocardial stiffness through the propagation velocities of shear waves (SWs). Despite advancements, variability in SW speed measurements remains a challenge. Current assessment lacks a gold standard method and still relies on subjective visual assessment with manual measurements leading to inherent variability.

Purpose

Manual and nine semi-automatic algorithms for measuring SW speed were systematically tested, aiming to reduce variability and enhance reproducibility of the estimations.

Methods

High-frame-rate echocardiography images (1055±159 Hz) were acquired in parasternal long axis view. Anatomical M-modes were drawn along the septal wall. To visualize the SW, M-modes were color coded for tissue acceleration. SWs then appear as green bands, and their slopes correspond to tissue propagation velocity.

SW slopes from healthy volunteers (n=67) and patients (n=53) with different cardiac conditions, covering a wide age range (19-89 years-old) were included to evaluate the performance of the methods in a clinical context. Measurements were performed manually and with 9 automatic methods: Time-frequency correlation (TFC), Regression window (RW), Radon Transform (RT), and 6 segmentation-based methods (e.g. maximum value). Automatic algorithms were initiated by indicating the position of SW by a mouse click. To assess reproducibility, this procedure was repeated 3 times for different sites along the SW. Failure of SW detection was counted to assess feasibility. Since the visualization of the SW’s and segmentation threshold depends on the selected color scale limits, 3 scales (0.5, 1, 2 m/s²) were tested (Figure 1). Finally, manual measurements served as reference, and agreement analysis was conducted.

Results

When accessing reproducibility across different starting points, only TFC exhibited variability (SD ±0.3 vs ±0.0 m/s for others). In terms of the color scale settings, RW and RT showed no variability (SD ±0.0 vs ± 0.3 m/s for manual), while Regression Weighted Maximum Value (RMW), emerged as the segmentation method with the least variability (SD ±0.1 m/s). RMW also showed highest feasibility [99%, (RW: 76% and RT: 90%)], correlation (r=0.77, r= 0.72, and r= 0.76 resp.), and agreement with a manual measurements (Figure 2).

Conclusion

Among the tested algorithms, Regression Weighted Maximum Value (RWM) demonstrated the highest feasibility, correlation and agreement with manual measurements. Our findings suggest this semi-automatic method provides an objective alternative to manual SW velocity estimations.
Operational feasibility and reproducibility of serial echocardiographic parameters of left ventricular remodelling and function for evaluation of drug efficacy in multicentre clinical trial settings
Authors: Surkova E , Vavere A , Ely Pizzato P , Keck K , Weightman J , Matheson M , Aurell M , Marchong M , Collen A , Lima J
Published: January 2025

Background

Operational feasibility and reproducibility of echocardiographic (Echo) parameters are essential for planning interventional trials with surrogate imaging endpoints. Existing data from large cohort studies performed at institutions with high expertise in Echo may not be representative of the multicentre clinical trial settings where substantial variability in local standards and equipment exists.

Purpose

To assess feasibility of standardized Echo data acquisition, measurability, and reproducibility of key Echo endpoints in a large multicentre clinical trial.

Methods

ENDEAVOR was a phase 2b/3 multicentre, international, placebo-controlled trial investigating the effects of mitiperstat on symptoms and function in participants with heart failure (HF) and left ventricular ejection fraction (LVEF) >40%. Echo was used for efficacy assessment at 3 time points (baseline, 16 and 24 weeks). Echo data were acquired according to the study protocol at sites with various equipment and expertise and analysed centrally by an academic core laboratory. Key Echo metrics were LVEF, LV global longitudinal strain (GLS), LV mass, E/E’ and left atrial volume (LAV). LVGLS was measured from three apical views (4, 2, and 3-chamber) by 2DSTRAIN TomTec software. All images were evaluated for image quality using the scale from 1 (Very poor) to 5 (Excellent) and measured by a single reader.

Results

711 participants at 142 sites in 18 countries were randomised in ENDEAVOR trial (mean age 72.4y, mean BMI 30.5kg/m2). A total of 2052 Echo exams were analysed: 735 at baseline, 661 at 16 and 656 at 24 weeks. Majority were of excellent to good (1282/62%) or adequate (732/36%) image quality. Doppler images and apical 4ch view were the most feasible to acquire, while apical 2ch view was the most challenging (Fig, A).

LVEF, LV mass, E/E’ and LAV were measurable in most studies (88-96%), however fewer studies were interpretable for LVGLS (61% at baseline to 70% at week 24). When matching echo exams at 2 or 3 visits were analysed, the number of interpretable matches dropped more significantly for LVGLS (to 47% for 2 and 41% for 3 time points), while the proportion of interpretable matches for standard echo endpoints remained acceptable (>83% and >75%, respectively).

A total of 82 scans were re-read for reproducibility. Most of the echo endpoints demonstrated good or excellent reproducibility, with LVGLS showing better reproducibility than LVEF (Fig, B).

Conclusions

We report feasibility of Echo data acquisition, measurability, and reproducibility of key parameters of LV remodelling and function in a large multicentre, international, placebo-controlled clinical trial in HF and EF>40%. These data will help to inform future trial design to drive drug development decision making.
The relationship between circadian blood pressure profile and left atrial mechanics, arterial stiffness and left ventricular performance in hypertensive patients
Authors: Olszanecka A , Stopa M , Jamros J , Jakubowski W , Sordon K , Dadanski E , Rajzer M
Published: January 2025

Background

The left atrium plays a crucial role in managing left ventricular filling and contractility through its functions as a reservoir, conduit, and boost pump. Consequently, the interaction between the left atrium(LA), left ventricle(LV), and the arterial system appears as a critical determinant of comprehensive cardiovascular performance. Non-dipping profile of blood pressure(BP) in 24-hr monitoring(ABPM) is associated with increased cardiovascular mortality and morbidity, including atrial fibrillation and stroke.

Objective

We aimed to investigate the association between LA mechanics, LV performance, arterial stiffness and circadian rhythm of BP in essential hypertension.

Design and Methods: Hypertensive patients without overt cardiovascular disease were recruited. In all subjects 24-hr ABPM and extended echocardiographic image acquisition protocol were performed (Vivid E95, GE). Carotid-femoral pulse wave velocity (PWV) was measured with the SphygmoCor device (Atcor Medical). All analyses, including LV global longitudinal strain(GLS) and LA reservoir strain(LAR), LA conduit strain(LACD), LA booster pump strain(LACT) were performed off-line. The related factors were evaluated by multivariate linear regression analysis to find the independent factors. Statistical analysis was conducted using SPSS software.

Results

Study group included 219 patients(age 55.3±13.5 years, 55% females). According to BP patterns patients were divided into two groups: non-dippers(n=53), presenting with <10% reduction in both systolic and diastolic BP at nighttime, and dippers(n=166). Non-dippers were older(61.3±11.3 vs 54.2±13.7 years, p<0.01) and exhibited higher BMI(30.2±4.9 vs 28.6±4.5 kg/m2 p<0.01). There were no differences between groups in office BP(non-dippers 128.3±13.3/72.9±9.5 vs 128.1±14.6/75.8±10.8 mmHg in dippers), neither 24hr BP(124.2±13.3/73.5±9.6 vs 121.4±11.5/74.2±7.1 mmHg, respectively). After adjustments for age and BMI, in the non-dipper group higher PWV(8.3±1.2 vs 7.9±1.2, p=0.05) and LACD(-14.7±5.7 vs -17.0±6.7, p<0.001) were observed, with no-differences in LV GLS(-20.7±3.3 vs -19.8±3.9; NS). Multivariate linear regression analysis revealed that age(p<0.001), office(p<0.001) and nighttime SBP(p=0.008)(but not dipping status, p=0.50), diabetes(p=0.02) and creatinine level(p=0.02) were independently corelated with PWV. LACD was independently corelated with age(p<0.001), LV mass(p=0.04) and LV GLS(p=0.03).

Conclusions

Arterial stiffness and left atrial performance are associated with circadian rhythm of BP. Nocturnal systolic BP but not dipping profile is the independent risk factor of worse arterial and left atrial performance.
Relationship between left atrial sphericity assessed by echocardiography and left atrial fibrosis in patients undergoing ablation for atrial fibrillation
Authors: Pilichowska E , Baran J , Kowalik I , Kulakowski P , Zaborska B
Published: January 2025

Purpose

The left atrial (LA) shape is the novel marker characterizing LA myopathy. Increased LA sphericity measured in magnetic resonance was shown to have relationship with LA fibrosis. Data on LA sphericity measured in echocardiography are sparse. The aim was to assess LA sphericity in relation to degree of LA fibrosis derived from electroanatomical mapping (EAM) in patients with atrial fibrillation (AF).

Methods

Patients (pts) with AF were studied with TTE before first ablation during sinus rhythm.

LA sphericity index (LASi) was calculated as the ratio of LA maximum volume (area-length method) to LA volume of sphere structured by maximum LA length from a 4- and 2-chamber views.

The EAM of LA was performed using Carto System. Low amplitude potentials area (LAPA) was quantitatively analysed and expressed as a percentage of LA surface using the cut-off <0.05 mV to detect sites of fibrosis.

LA parameters were compared between mild (LAPA<10%), moderate (LAPA 10-40%) and extensive (LAPA>40%) degree of LA fibrosis.

Results

Study group consisted of 51 pts (74,5 % males, mean age 56±10 years).

The median [Q1-Q3] LA volume index (LAVi) was 37.1 [28.9 – 52.6] mL/m².

The median [Q1-Q3] LASi was 0.82 [0.71 – 1.06].

The LAPA ranged from 2 to 77 percent of LA surface.

The Spearman’s correlation analysis showed a significant correlation between LASi and LAPA (0.59, p<0.001).

Increased LA sphericity was observed in LAPA≥10% whereas increased LAVi in LAPA≥40% subgroup (table).

Conclusion

The LASi derived from TTE correlates well with the extent of LAPA, thus might be a simple and useful parameter of LA geometric remodeling associated with increased LA fibrosis.

LA geometry in relation to fibrosis
Incremental prognostic value of echocardiography measures of right ventricular systolic function in patients with chronic heart failure
Authors: Gentile F , Chianca M , Bazan L , Sciarrone P , Chubuchny V , Taddei C , Poggianti E , Passino C , Emdin M , Giannoni A
Published: January 2025

Background

Tricuspid annular plane systolic excursion (TAPSE), DTI-derived tricuspid lateral annular systolic velocity wave (S′), and fractional area change (RV-FAC) are the most used echocardiographic measures of right ventricular (RV) systolic function.

Purpose

To compare the prognostic value of TAPSE, S’, and RV-FAC in a large cohort of patients with chronic heart failure (HF).

Methods

Consecutive out-patients with HF and left ventricular ejection fraction (LVEF) <50% on guideline-recommended therapies undergoing echocardiography over a 6-year period were selected and followed-up for the endpoint of cardiac death.

Results

Among the 1,590 patients analyzed (71±12 years, 77% males, LVEF 34±9%), 202 (13%) died from cardiac causes during a median 28-month follow-up (range 14-40 months). According to the recommended cut-points for TAPSE (17 mm), S’ (9.5 cm/s), or RV-FAC (35%), RV systolic function was reduced in 37%, 40%, and 35% of patients. Among these, the attribution of RV dysfunction was discordant in 21%, 31%, and 33% of the cases considering TAPSE and S’, TAPSE and RV-FAC, or S’ and RV-FAC. While, taken alone, TAPSE <17 mm and RV-FAC <35% were more accurate than S’ <9.5 cm/s in predicting the risk of cardiac death (p<0.001), their combination provided incremental prognostic power (p<0.001). No further incremental value was observed by adding S’ to the combination of TAPSE and RV-FAC (p=0.145).

Conclusion

In patients with chronic HF and LVEF <50%, RV-FAC and TAPSE are more accurate than S’ in predicting the risk of cardiac death. The combination of RV-FAC and TAPSE provides incremental prognostic value and should be used in the clinical practice.
Validation of left ventricular filling pressure evaluation by order of tricuspid and mitral valve opening in patients with atrial fibrillation
Authors: Nishino H , Murayama M , Iwano H , Kagiyama N , Nakamura Y , Akama Y , Toki M , Takamatsu S , Okada T , Ishizaka S , Kaga S , Yamashita N , Nagai T , Teshima T , Anzai T
Published: January 2025

Introduction

Accurate assessment of the left ventricular (LV) filling pressure in patients with atrial fibrillation (AF) remains difficult. Novel echocardiographic scoring system, visually assessed time difference between mitral valve (MV) and tricuspid valve (TV) opening (VMT) score based on visual assessment of the time sequence of early-diastolic atrioventricular valves opening, is expected to be applied for these patients.

Purpose

This study aimed to test the diagnostic accuracy of the VMT score in patients with AF.

Methods

We analyzed 119 consecutive AF patients who underwent cardiac catheterization and echocardiography within 2 days as a derivation cohort. External validation was performed in 189 AF patients from 4 Japanese tertile hospitals. Elevated LV filling pressure was defined as mean pulmonary arterial wedge pressure (PAWP) ≥15 mmHg. The time sequence of MV / TV opening was visually assessed and scored (0: TV first, 1: simultaneous, 2: MV first). When the inferior vena cava was dilated, 1 point was added and the VMT score was finally graded as 0 to 3. Conventional Doppler parameters recommended by the guidelines to estimate LV filling pressure in AF were also measured.

Results

PAWP was elevated with increase in the VMT score (0: 10±3, 1: 13±5, 2: 22±7, 3: 27±6 mmHg, P < .001) with a significant increase from VMT 1 to 2. VMT≥2 predicted elevated PAWP with an accuracy of 87%, and the diagnostic accuracy was significantly higher in VMT score than in conventional Doppler parameters (C index: 0.88 vs. 0.54 to 0.69; P < .001). In addition, VMT≥2 showed an incremental predictive value over plasma brain natriuretic peptide levels (C index: 0.79 to 0.93; P < .001). In the external validation cohort, VMT≥2 demonstrated acceptable accuracy (72%) to predict elevated PAWP.

Conclusions

VMT scoring was useful for prediction of elevated LV filling pressure and had an incremental benefit over the practical biomarker in patients with AF.
Role of ai model to predict the hfpef in patient with hypertension
Authors: Tselov N , Hristova K , Marinov R , Tsonev S V , Shahin T , Todorov I , Zlatancheva G
Published: January 2025

Arterial hypertension /AH/ affects around 50% of the population over 55 years and is the risk factor of cardiovascular diseases.

The aim of the study is to examine the feasibility, accuracy and reproducibility of fully automated 2D and 3DE TTE quantification software compared to board certified expert cardiologist and uncertified cardiologist in clinical settings.

Methods

Measurements were performed twice on asymptomatic 68 patients ( mean ages 68± 3.5 y )with AH and LV hypertrophy and with fully automated software using AI and manually using and semi-automated software for measurement and analysis.

Results

Both 2DE and 3DE TTE modalities demonstrated good correlation with excellent intra-observer and inter-observer variability for LV EF, volumes, GLS. 3DE measurements significantly underestimated LVEF and GLS./ p-Anova< 0.001/

2DE fully automated , manual and semi-automated measurements reclassified asymptomatic patients with LVH in 40% with normal EF >54% and normal GLS >-21.3 %, 35% with normal EF and reduced GLS<-19% , 11,3% with decreased EF b/n 50-54% and normal GLS , 8,1% with decreased both EF and GLS, and 5,6% reduced < 49% EF and reduced GLS <- 17%. (p <0,01)

Conclusions

Fully automated 2D and 3D TTE measurements EF , volumes and GLS are feasible and yield results with a close agreement of results performed by expert reader measured manually. Automated GLS provides important predictive value and contribute to better screening for decision making in patients with AH and HFpEF
The interplay between arterial stiffness and left ventricular diastolic dysfunction in systemic lupus erythematosus
Authors: Badea-Lupasteanu I , Delcea C , Vijan A , Cocolea S , Dima A , Buzea A , Badila E , Dan G A
Published: January 2025

Introduction

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in systemic lupus erythematosus (SLE) patients. Both arterial stiffness and left ventricular diastolic dysfunction have predictive value and have been reported as early markers of CVD in SLE. The link between arterial stiffness and diastolic dysfunction has been described in patients with established CVD and heart failure with preserved ejection fraction. Yet, it is underexplored in the setting of SLE.

Purpose

The study aims to investigate the relationship between arterial rigidity measured by cardio-ankle vascular index (CAVI) and left ventricular diastolic dysfunction in patients with SLE.

Methods

This is a prospective cross-sectional study of adult SLE patients from a single center. Clinical, biological, electrocardiographic, transthoracic echocardiographic and CAVI data were collected for each participant. CAVI and vascular age were calculated using a Fukuda Denshi device. Diastolic dysfunction was defined according to the 2016 ASE/EACVI guidelines. The ROC analysis used the Youden index-associated criterion to determine cut-off values.

Results

Our cohort included 70 patients, 97.1% female, with a mean age of 53.6±11.7 years and a disease duration of 15.7±10.9 years. All patients had preserved ejection fraction; 50% had diastolic dysfunction. CAVI and vascular age were inversely correlated with the E/A ratio, septal and lateral e’ and positively correlated with the E/e’ ratio, isovolumetric relaxation time and indexed left atrial volume. CAVI (AUC 0.815, 95%CI 0.714-0.915), vascular age (AUC 0.827, 95%CI 0.728-0.927), age (AUC 0.791, 95%CI 0.690-0.892), disease duration (AUC 0.749, 95%CI 0.633-0.864) and NTproBNP (AUC 0.794, 95%CI 0.676-0.912) were associated with diastolic dysfunction. In the univariate analysis the presence of traditional risk factors (any of the following: obesity, hypertension, dyslipidemia, diabetes, smoking), ischemic heart disease, peripheral arterial disease and valvular heart disease were also associated with diastolic dysfunction. In the multivariable regression analysis disease duration (HR 1.1, 95%CI 1.01-1.19), vascular age (HR 1.1, 95%CI 1.04-1.18) and CAVI (HR 3.79, 95%CI 1.78-8.10) were independently correlated with diastolic dysfunction. Patients with CAVI> 8.1 and those with a vascular age> 59 had a RR of diastolic dysfunction of 4.1 (95%CI 1.8-9.1) and 3.2 (95%CI 1.7-6.4), respectively.

Conclusions

Arterial stiffness, evaluated by CAVI and vascular age, was independently associated with diastolic dysfunction in a cohort of adults with SLE, potentially useful for early detection of functional cardiovascular alterations.
A novel imaging-based method to estimate left ventricular filling pressure
Authors: Fernandes J F , Smiseth O A , Othe N , Wakami K , Remme E , Lamata P
Published: January 2025

Background

There is a clinical need in heart failure diagnostics to have a non-invasive measure of left ventricular (LV) filling pressure. Echocardiographic parameters may be used to differentiate between normal and elevated left ventricular (LV) filling pressure, but do not provide a quantitative estimate of the filling pressure.

Purpose

To establish a non-invasive method for estimating LV filling pressure and construction of the LV diastolic pressure curve.

Methods

Retrospective study in 100 patients with suspected coronary artery disease. Ground truth filling pressure (i.e. LV pre-atrial contraction pressure, which is shown to approximate mean left atrial pressure) was measured by LV micromanometer-tipped catheters. Non-invasive filling pressure was estimated as the sum of minimum LV pressure (minPLV) and the maximum early-diastolic atrio-ventricular pressure difference (∆PA-V), as shown in Figure 1. MinPLV was estimated by a regression model trained in 81 patients, which included an estimate of the time constant of τ (Figure 1) and echocardiographic and demographic parameters. ∆PA-V was computed by a simplified Navier-Stokes momentum conservation equation. Accuracy of filling pressure estimate was evaluated in a test cohort of 19 patients.

Results

The strongest predictor of minPLV was τ (R=0.71) followed by LA reservoir strain (R=0.65), LV systolic pressure (R=0.23) and body mass index (R=0.37), all of them independent predictors. There was good agreement between estimated and measured τ (Bias -0.0069s and limits of agreement <0.0195s). When combining estimated τ and the three other predictors in a regression model, there was good agreement between estimated and measured minPLV: Bias -0.5 mmHg, limits of agreement <1.9 mmHg (±2SD). Estimated LV filling pressure also showed good agreement with measured values: Bias 0.0 mmHg, limits of agreement <3.1 mmHg, shown in Figure 2. The diastolic pressure curve was estimated with good accuracy (diastolic meanPLV had bias and limits of agreement were respectively 0.2 and <3.2mmHg).

Conclusions

The proposed non-invasive method estimated LV filling pressure with good accuracy in a population with coronary artery disease. The method also allowed approximation of the LV diastolic pressure curve. The method needs validation in larger populations and in other phenotypes.

Method to calculate LV filling pressure

LV filling pressure (pre-A) agreement