Boosting Cardiac Color Doppler Frame Rates with Deep Learning

Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ventricular filling. Concurrently, deep learning is demonstrating promising outcomes in post-processing of echocardiographic data for various applications. This work explores the use of deep learning models for intracardiac Doppler velocity estimation from a reduced number of filtered I/Q signals. We used a supervised learning approach by simulating patient-based cardiac color Doppler acquisitions and proposed data augmentation strategies to enlarge the training dataset. We implemented architectures based on convolutional neural networks. In particular, we focused on comparing the U-Net model and the recent ConvNeXt models, alongside assessing real-valued versus complex-valued representations. We found that both models outperformed the state-of-the-art autocorrelator method, effectively mitigating aliasing and noise. We did not observe significant differences between the use of real and complex data. Finally, we validated the models on in vitro and in vivo experiments. All models produced quantitatively comparable results to the baseline and were more robust to noise. ConvNeXt emerged as the sole model to achieve high-quality results on in vivo aliased samples. These results demonstrate the interest of supervised deep learning methods for Doppler velocity estimation from a reduced number of acquisitions.

Multi-transcriptomics identifies targets of the endoribonuclease DNE1 and highlights its coordination with decapping

Decapping is a crucial step in mRNA degradation in eucaryotes and requires the formation of a holoenzyme complex between the decapping enzyme DECAPPING 2 (DCP2) and the decapping enhancer DCP1. In Arabidopsis (Arabidopsis thaliana), DCP1-ASSOCIATED NYN ENDORIBONUCLEASE 1 (DNE1) is a direct protein partner of DCP1. The function of both DNE1 and decapping are necessary to maintain phyllotaxis, the regularity of organ emergence in the apex. In this study, we combined in vivo mRNA editing, RNA degradome sequencing, transcriptomics and small RNA-omics to identify targets of DNE1 and study how DNE1 and DCP2 cooperate in controlling mRNA fate. Our data reveal that DNE1 mainly contacts and cleaves mRNAs in the coding sequence and has sequence cleavage preferences. DNE1 targets are also degraded through decapping, and both RNA degradation pathways influence the production of mRNA-derived small interfering RNAs. Finally, we detected mRNA features enriched in DNE1 targets including RNA G-quadruplexes and translated upstream open reading frames. Combining these four complementary high-throughput sequencing strategies greatly expands the range of DNE1 targets and allowed us to build a conceptual framework describing the influence of DNE1 and decapping on mRNA fate. These data will be crucial to unveil the specificity of DNE1 action and understand its importance for developmental patterning.

Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping

Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. When evaluated on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original iVFM algorithm. The efficiency of PINNs is boosted through dual-stage optimization and pre-optimized weights. On the other hand, the nnU-Net method excels in generalizability and real-time capabilities. Notably, nnU-Net shows superior robustness on sparse and truncated Doppler data while maintaining independence from explicit boundary conditions. Overall, our results highlight the effectiveness of these methods in reconstructing intraventricular vector blood flow. The study also suggests potential applications of PINNs in ultrafast color Doppler imaging and the incorporation of fluid dynamics equations to derive biomarkers for cardiovascular diseases based on blood flow.

SIMUS3: An open-source simulator for 3-D ultrasound imaging

BACKGROUND AND OBJECTIVE

Computational Ultrasound Imaging (CUI) has become increasingly popular in the medical ultrasound community, facilitated by free simulation software. These tools enable the design and exploration of transmit sequences, transducer arrays, and signal processing. We recently introduced SIMUS, a frequency-based ultrasound simulator within the open-source MUST toolbox, which offers numerical advantages and allows easy consideration of frequency-dependent factors. In response to the growing interest in simulating ultrasound imaging with 2-D matrix arrays, we present 3-D versions, PFIELD3 and SIMUS3.

METHOD

The linear acoustic equations driving these functions are described, with theoretical assumptions reviewed for user guidance.

RESULTS

Comparative analyses with Field II, using a 32×32 element 3-MHz matrix array, highlight the performance of PFIELD3 and SIMUS3 under various transmission conditions.

CONCLUSIONS

This work extends the capabilities of existing CUI tools and provides researchers with valuable resources for advanced ultrasound simulations.

Heat stress promotes Arabidopsis AGO1 phase separation and association with stress granule components

In Arabidopsis thaliana, ARGONAUTE1 (AGO1) plays a central role in microRNA (miRNA) and small interfering RNA (siRNA)-mediated silencing. AGO1 associates to the rough endoplasmic reticulum to conduct miRNA-mediated translational repression, mRNA cleavage, and biogenesis of phased siRNAs. Here, we show that a 37°C heat stress (HS) promotes AGO1 protein accumulation in cytosolic condensates where it colocalizes with components of siRNA bodies and of stress granules. AGO1 contains a prion-like domain in its poorly characterized N-terminal Poly-Q domain, which is sufficient to undergo phase separation independently of the presence of SGS3. HS only moderately affects the small RNA repertoire, the loading of AGO1 by miRNAs, and the signatures of target cleavage, suggesting that its localization in condensates protects AGO1 rather than promoting or impairing its activity in reprogramming gene expression during stress. Collectively, our work sheds new light on the impact of high temperature on a main effector of RNA silencing in plants.

Efficient Stratified 3-D Scatterer Sampling for Freehand Ultrasound Simulation

Ultrasound image simulation is a well-explored field with the main objective of generating realistic synthetic images, further used as ground truth for computational imaging algorithms or for radiologists' training. Several ultrasound simulators are already available, most of them consisting in similar steps: 1) generate a collection of tissue mimicking individual scatterers with random spatial positions and random amplitudes; 2) model the ultrasound probe and the emission and reception schemes; and 3) generate the radio frequency (RF) signals resulting from the interaction between the scatterers and the propagating ultrasound waves. This article is focused on the first step. To ensure fully developed speckle, a few tens of scatterers by resolution cell are needed, demanding to handle high amounts of data (especially in 3-D) and resulting into important computational time. The objective of this work is to explore new scatterer spatial distributions, with application to multiple coherent 2-D slice simulations from 3-D volumes. More precisely, lazy evaluation of pseudorandom schemes proves them to be highly computationally efficient compared with uniform random distribution commonly used. We also propose an end-to-end method from the 3-D tissue volume to resulting ultrasound images using coherent and 3-D-aware scatterer generation and usage in a real-time context.

Think twice before f-numbering

In a 2021 paper, we delved into the details of delay-sum beamforming (DAS) in high-frame-rate ultrasound for medical imaging [1]. We also proposed a simple and fast method of determining an f-number, which is based on the directivity of the transducer elements. In their comment, Martin F. Schiffner and Georg Schmitz argue that we mistakenly link image quality enhancement to the reduction of measurement noise. They disapprove our proposed f-number, claiming it deteriorates the signal-to-noise ratio (SNR). Based on their previous work [2], they also highlight that the f-number should be derived from the grating lobe angles. In this reply, we explain their error in the SNR argument. We also illustrate the potential drawbacks of exclusively relying on grating lobes to establish an f-number with a DAS, suggesting that alternative approaches might be worthy of consideration.

Ultrafast Cardiac Imaging Using Deep Learning for Speckle-Tracking Echocardiography

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this article, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle-tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e., high-quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on nonsimulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.

1885: First total laryngectomy and artificial larynx in France

This year, 2023, is the 150th anniversary of the first total laryngectomy for cancer, by Theodor Billroth. The authors reconstruct the conditions under which, on March 12, 1885, this operation was then performed for the first time in France, by Leon Labbé, and present the man himself, and also M. Cadier, the inventive genius who designed the first artificial larynx used in this country.

Harmful impact of treatment refusal in T3-4M0 endolaryngeal squamous cell carcinoma candidates for total laryngectomy: A STROBE analysis

PURPOSE

To evaluate the consequences of treatment refusal in total laryngectomy (TL) candidates with T3-4M0 endolaryngeal squamous cell carcinoma (SCC).

MATERIALS AND METHODS

A retrospective observational study was conducted in an inception cohort of 576 isolated T3-4M0 endolaryngeal SCC candidates for TL consecutively managed between 1970 and 2019 in a French university teaching hospital. The main endpoint was survival time and cause of death in 2 groups. Group A, 4.5% of the cohort, consisted of 26 patients who declined any laryngeal treatment. Group B consisted of 550 patients who accepted TL. Accessory endpoints were causes of TL refusal and associated variables. The STROBE guideline was applied. The significance threshold was set at P<0.005.

RESULTS

One-and 3-year actuarial survival estimates increased significantly (P<0.0001) from 39% and 15% in group A, to 83% and 63% in group B, respectively. In group A, 92% of causes of death implicated index SCC progression, whereas in group B intercurrent disease, metachronous second primary, locoregional and/or metastatic SCC progression and postoperative complications accounted for 37%, 31%, 29%, and 2%, respectively. The actuarial survival estimates within group A increased significantly (P=0.0003) from 0% at 1-year in patients managed with isolated supportive care to 56% in patients managed with chemotherapy (reaching 0% at 5years). Reasons for TL refusal were fear of surgery, refusal of tracheostoma, loss of physiologic phonation, and certain comorbidities. Age and chronologic period correlated significantly with TL refusal. Median age decreased (P<0.001) from 69years in group A to 58 years in group B. Percentage TL refusal increased (P<0.0001) from 2% to 11% before and after start 1990, respectively.

CONCLUSION

The current study determined loss of survival with refusal of any laryngeal treatment including TL, noted benefit of chemotherapy associated to supportive care, and discussed the possible contribution of immunotherapy.

An extensive survey of phytoviral RNA 3' uridylation identifies extreme variations and virus-specific patterns

Viral RNAs can be uridylated in eukaryotic hosts. However, our knowledge of uridylation patterns and roles remains rudimentary for phytoviruses. Here, we report global 3' terminal RNA uridylation profiles for representatives of the main families of positive single-stranded RNA phytoviruses. We detected uridylation in all 47 viral RNAs investigated here, revealing its prevalence. Yet, uridylation levels of viral RNAs varied from 0.2% to 90%. Unexpectedly, most poly(A) tails of grapevine fanleaf virus (GFLV) RNAs, including encapsidated tails, were strictly monouridylated, which corresponds to an unidentified type of viral genomic RNA extremity. This monouridylation appears beneficial for GFLV because it became dominant when plants were infected with nonuridylated GFLV transcripts. We found that GFLV RNA monouridylation is independent of the known terminal uridylyltransferases (TUTases) HEN1 SUPPRESSOR 1 (HESO1) and UTP:RNA URIDYLYLTRANSFERASE 1 (URT1) in Arabidopsis (Arabidopsis thaliana). By contrast, both TUTases can uridylate other viral RNAs like turnip crinkle virus (TCV) and turnip mosaic virus (TuMV) RNAs. Interestingly, TCV and TuMV degradation intermediates were differentially uridylated by HESO1 and URT1. Although the lack of both TUTases did not prevent viral infection, we detected degradation intermediates of TCV RNA at higher levels in an Arabidopsis heso1 urt1 mutant, suggesting that uridylation participates in clearing viral RNA. Collectively, our work unveils an extreme diversity of uridylation patterns across phytoviruses and constitutes a valuable resource to further decipher pro- and antiviral roles of uridylation.

Successful 10-year outcomes after supracricoid partial laryngectomy for selected glottic squamous cell carcinoma classified as T3N0M0: A STROBE analysis

PURPOSE

To evaluate long-term oncological outcome for patients with selected glottic squamous cell carcinoma (SCC) classified as T3N0M0 treated by supracricoid partial laryngectomy (SCPL).

MATERIALS AND METHODS

Analysis of an inception cohort of 46 patients with isolated untreated SCC classified as T3N0M0 and minimum 10-year follow-up, consecutively treated by SCPL between 1982 and 2012 in a French university teaching hospital. The main endpoint was 5- and 10-year actuarial survival and local control estimates. Accessory endpoints comprised cause of death, screening for variables decreasing survival and increasing risk of local recurrence, oncologic consequences of local recurrence, and laryngeal preservation rate.

RESULTS

Five- and 10-year actuarial survival was 78.1%, and 53.3%, respectively. The main causes of death were intercurrent disease and metachronous second primary, each in 33.3% of cases. Postoperative mortality (aspiration pneumonia) was 2.1%. There were no significant correlations between survival and any study variables. Five- and 10-year local control was 90.5%. Overall local recurrence varied significantly (P=0.003), from 2.3% with negative margins (R0) to 100% with positive margins (R1) and/or dysplasia. Local recurrence was associated with a significantly (P<0.005) increased risk of nodal failure and distant metastasis, and reduced survival. Overall laryngeal preservation was 89.1%.

CONCLUSION

The present results suggest that SCPL should continue to be taught and that this type of partial laryngeal surgery should be included in the various organ-sparing strategies considered in advanced laryngeal cancer.

First Dark Matter Search Results from the LUX-ZEPLIN (LZ) Experiment

The LUX-ZEPLIN experiment is a dark matter detector centered on a dual-phase xenon time projection chamber operating at the Sanford Underground Research Facility in Lead, South Dakota, USA. This Letter reports results from LUX-ZEPLIN's first search for weakly interacting massive particles (WIMPs) with an exposure of 60 live days using a fiducial mass of 5.5 t. A profile-likelihood ratio analysis shows the data to be consistent with a background-only hypothesis, setting new limits on spin-independent WIMP-nucleon, spin-dependent WIMP-neutron, and spin-dependent WIMP-proton cross sections for WIMP masses above 9  GeV/c^{2}. The most stringent limit is set for spin-independent scattering at 36  GeV/c^{2}, rejecting cross sections above 9.2×10^{-48}  cm at the 90% confidence level.

Phase Unwrapping of Color Doppler Echocardiography using Deep Learning

Color Doppler echocardiography is a widely used non-invasive imaging modality that provides real-time information about the intracardiac blood flow. In an apical long-axis view of the left ventricle, color Doppler is subject to phase wrapping, or aliasing, especially during cardiac filling and ejection. When setting up quantitative methods based on color Doppler, it is necessary to correct this wrapping artifact. We developed an unfolded primal-dual network to unwrap (dealias) color Doppler echocardiographic images and compared its effectiveness against two state-of-the-art segmentation approaches based on nnU-Net and transformer models. We trained and evaluated the performance of each method on an in-house dataset and found that the nnU-Net-based method provided the best dealiased results, followed by the primal-dual approach and the transformer-based technique. Noteworthy, the primal-dual network, which had significantly fewer trainable parameters, performed competitively with respect to the other two methods, demonstrating the high potential of deep unfolding methods. Our results suggest that deep learning-based methods can effectively remove aliasing artifacts in color Doppler echocardiographic images, outperforming DeAN, a state-of-the-art semi-automatic technique. Overall, our results show that deep learning-based methods have the potential to effectively preprocess color Doppler images for downstream quantitative analysis.

Total laryngectomy for laryngeal cancer 150 years after its first description: A boon more than a calamity: A STROBE analysis

OBJECTIVE

To illustrate the boon rather than a calamity that total laryngectomy can be for a patient with laryngeal cancer in the 21st century.

MATERIAL AND METHOD

An observational retrospective analysis using the STROBE guideline compared two cohorts of patients with previously untreated cancer, managed by total laryngectomy: the first consisting of 123 patients collected by Morell Mackenzie during the fifteen years (1873-1887) following the initial description, and the second consisting of 53 patients consecutively treated in a French university otorhinolaryngology department during the fifteen years (2006-2020) preceding the 150th anniversary of the first performance. The main endpoint was the comparison of survival and locoregional control estimates (Kaplan-Meier life table method). Secondary endpoints comprised mortality estimates and causes, adjuvant treatments, and phonation modalities.

RESULTS

The 26.2%, 13.1%, and 13.1% 1-, 3-, and 5-year actuarial survival estimates in the Makenzie cohort increased to 88.6%, 68.4%, and 60.9% in the recent French cohort (P<0.0001). The 50.1%, 40.4%, and 34.7% 1-, 3-, and 5-year actuarial locoregional control estimates in the Mackenzie cohort increased to 83.7% (P<0.0001). The 77.7% overall mortality in the Mackenzie cohort decreased to 37.7% (P<.0001). In the Mackenzie cohort, 97.8% of deaths were related to postoperative complications and locoregional recurrence, compared to 50% in the recent French cohort. Distant metastasis, metachronous second primary tumor and intercurrent diseases, not mentioned in the Mackenzie cohort, generated 45% of deaths in the French cohort. Adjunctive treatment was not used in the Mackenzie cohort, whereas neck dissection and postoperative radiation therapy were associated in respectively 98.1% and 69.8% of cases in the French cohort. Phonation was not documented in the Mackenzie cohort; 50% of survivors in the French cohort used a phonatory implant.

CONCLUSIONS

The 20th century witnessed an incredible turn-around. Total laryngectomy, with limited indications, has transformed the etiology of deaths and no longer leaves patients "in a state of abject misery" as Morell Mackenzie put it in 1888.

Aggression, mini-aggression, and abuse against health care workers during the COVID-19 pandemic

Introduction

COVID-19 pandemic brought multiple negative consequences that go beyond the direct damage caused by the disease and that affect health systems as well. Complaints of attacks against health care workers became frequent and concerning. The objective of this survey was to characterize the frequency and type of violent behavior against front-line professionals in Latin America.

Material and methods

A cross-sectional electronic survey was carried out between January 11th to February 28th, 2022. Different health care workers from Latin America who have been delivering care at least from March 2020, regardless of whether they assist or not patients with COVID-19 were included. A non-probabilistic snowball sampling was performed, and the survey was

Results

The survey was responded by 3,544 participants from 19 countries (Figure 1); 58.5% were women, and the mean age was 41.9±11 years. The 70.8% were doctors, 16% nurses, 3.4% physiotherapists, and the remaining 9.8% had other functions within the health team. About 85.1% of physicians were specialists: 33.9% were cardiologists, 14.4% were intensivists or emergency physicians, 10.9% had some surgical specialty, 7.7% were pediatricians or related subspecialties, and the remaining 33.1% had other specialties. The 36.3% and 28.8% worked in public and private practice respectively, the remaining worked in both. Direct and regular care to COVID-19 patients was provided by 74.7% of all contestants.

Among the participants, 54.8% reported acts of violence: 95.6% suffered verbal violence, 11.1% physical violence, and 19.9% other types. 39.5% of respondents experienced it at least once a week. The acts of violence involved patients' relatives (32%), or patients together with their relatives (35.1%). The victims rated the stress level of these events with an average of 8.2±1.8 points (scale from 1 to 10). Approximately half of the health personnel who suffered an assault experienced psychosomatic symptoms after the traumatic event (Figure 2). Among the victims of violence, 56.2% considered changing their care tasks, and 33.6% abandoning their profession. However, only 23% of the health personnel attacked stated that they had made some type of legal action regarding these acts.

In a logistic regression model, doctors (OR 1.95, p&lt;0.01), nurses (OR 1.77, p=0.001), and administrative staff (OR 3.20, p&lt;0.01) suffered more violence than other health workers. Women more frequently suffered violence (OR 1.56, p&lt;0.01), as well as those who worked with patients with COVID-19 (OR 3.59, p&lt;0.01). Conversely, a lower probability of violence was observed at older ages (OR 0.96, p&lt;0.01).

Conclusion

We detected a high prevalence of violence against health personnel in Latin America during the current pandemic. Those caring for COVID-19 patients, younger staff, and women were found to be more vulnerable. It is imperative to develop strategies to mitigate these acts and their repercussions on the health team.

Funding Acknowledgement

Type of funding sources: None.

Effect of 2-hydroxy-4-(methylthio)butanoate (HMTBa) on milk fat, rumen environment and biohydrogenation, and rumen protozoa in lactating cows fed diets with increased risk for milk fat depression

Biohydrogenation-induced milk fat depression (MFD) is a reduction in milk fat synthesis caused by bioactive fatty acids (FA) produced during altered ruminal microbial metabolism of unsaturated FA. The methionine analog 2-hydroxy-4-(methylthio)butanoate (HMTBa) has been shown to reduce the shift to the alternate biohydrogenation pathway and maintain higher milk fat yield in high-producing cows fed diets lower in fiber and higher in unsaturated FA. The objective of this experiment was to verify the effect of HMTBa on biohydrogenation-induced MFD and investigate associated changes in rumen environment and fermentation. Twenty-two rumen cannulated high-producing Holstein cows [168 ± 66 d in milk; 42 ± 7 kg of milk/d (mean ± standard deviation)] were used in a randomized design performed in 2 blocks (1 = 14 cows, 2 = 8 cows). Treatments were control (corn carrier) and HMTBa (0.1% of diet dry matter). The experiment included a 7-d covariate period followed by 3 phases that fed diets with increasing risk of MFD. The diet during the covariate and low-risk phase (7 d) was 32% neutral detergent fiber with no additional oil. The diet during the moderate-risk phase (17 d) was 29% neutral detergent fiber with 0.75% soybean oil. Soybean oil was increased to 1.5% for the last 4 d. The statistical model included the random effect of block and time course data were analyzed with repeated measures including the random effect of cow and tested the interaction of treatment and time. There was no effect of block or interaction of block and treatment or time. There was no overall effect of treatment or treatment by time interaction for dry matter intake, milk yield, and milk protein concentration and yield. Overall, HMTBa increased milk fat percent (3.2 vs. 3.6%) and yield (1,342 vs. 1,543 g/d) and there was no interaction of treatment and dietary phase. Additionally, HMTBa decreased the concentration of trans-10 18:1 in milk fat and rumen digesta. Average total ruminal concentration of volatile FA across the day and total-tract dry matter and fiber digestibility were not affected by HMTBa, but HMTBa increased average rumen butyrate and decreased propionate concentration and increased total protozoa abundance. Additionally, HMTBa increased the fractional rate of α-linoleic acid clearance from the rumen following a bolus predominantly driven by a difference in the first 30 min. Plasma insulin was decreased by HMTBa. In conclusion, HMTBa prevented the increase in trans FA in milk fat associated with MFD through a mechanism that is independent of total volatile FA concentration, but involves modification of rumen biohydrogenation. Decreased propionate and increased butyrate and ruminal protozoa may also have functional roles in the mechanism.

Motion Estimation by Deep Learning in 2D Echocardiography: Synthetic Dataset and Validation

Motion estimation in echocardiography plays an important role in the characterization of cardiac function, allowing the computation of myocardial deformation indices. However, there exist limitations in clinical practice, particularly with regard to the accuracy and robustness of measurements extracted from images. We therefore propose a novel deep learning solution for motion estimation in echocardiography. Our network corresponds to a modified version of PWC-Net which achieves high performance on ultrasound sequences. In parallel, we designed a novel simulation pipeline allowing the generation of a large amount of realistic B-mode sequences. These synthetic data, together with strategies during training and inference, were used to improve the performance of our deep learning solution, which achieved an average endpoint error of 0.07 ± 0.06 mm per frame and 1.20 ± 0.67 mm between ED and ES on our simulated dataset. The performance of our method was further investigated on 30 patients from a publicly available clinical dataset acquired from a GE system. The method showed promise by achieving a mean absolute error of the global longitudinal strain of 2.5 ± 2.1% and a correlation of 0.77 compared to GLS derived from manual segmentation, much better than one of the most efficient methods in the state-of-the-art (namely the FFT-Xcorr block-matching method). We finally evaluated our method on an auxiliary dataset including 30 patients from another center and acquired with a different system. Comparable results were achieved, illustrating the ability of our method to maintain high performance regardless of the echocardiographic data processed.

Arabidopsis RNA processing body components LSM1 and DCP5 aid in the evasion of translational repression during Cauliflower mosaic virus infection

Viral infections impose extraordinary RNA stress, triggering cellular RNA surveillance pathways such as RNA decapping, nonsense-mediated decay, and RNA silencing. Viruses need to maneuver among these pathways to establish infection and succeed in producing high amounts of viral proteins. Processing bodies (PBs) are integral to RNA triage in eukaryotic cells, with several distinct RNA quality control pathways converging for selective RNA regulation. In this study, we investigated the role of Arabidopsis thaliana PBs during Cauliflower mosaic virus (CaMV) infection. We found that several PB components are co-opted into viral factories that support virus multiplication. This pro-viral role was not associated with RNA decay pathways but instead, we established that PB components are helpers in viral RNA translation. While CaMV is normally resilient to RNA silencing, dysfunctions in PB components expose the virus to this pathway, which is similar to previous observations for transgenes. Transgenes, however, undergo RNA quality control-dependent RNA degradation and transcriptional silencing, whereas CaMV RNA remains stable but becomes translationally repressed through decreased ribosome association, revealing a unique dependence among PBs, RNA silencing, and translational repression. Together, our study shows that PB components are co-opted by the virus to maintain efficient translation, a mechanism not associated with canonical PB functions.

AB0684 Fatigue Assessment in Systemic Sclerosis, Santo Domingo, Dominican Republic

Background

Systemic sclerosis (SSc) is a systemic autoimmune disease.1 Fatigue has been reported in 75% of SSc patients, and is the most problematic symptom due to impact on quality of life. Despite its high prevalence, origin is unknown.2 Some studies associate it with the degree of disease activity and decreased quality of life. Fatigue is defined as a feeling of exhaustion, also as a reduction in physical and mental capacity,3 scales such as FACIT-F (Functional Assessment of Chronic Illness Therapy-Fatigue) are used, which evaluates the last 7 days, with a score of 0-52. For the severity analysis, 4 grades are used: no or mild fatigue (40-52), moderate (27-39), severe (14-26) and extreme (0-13).4,5

Objectives

To evaluate the frequency and degree of fatigue in systemic sclerosis.

Methods

Prospective, longitudinal, observational study of the cohort of patients of the Rheumatology service of the Hospital Docente Padre Billini. Patients were interviewed in November 2021. Inclusion criteria: ≥18 years, diagnosis of SSc according ACR/EULAR 2013 classification criteria. Exclusion criteria: previous diagnosis of fibromyalgia, depression or anxiety, treatment with antidepressants, antihistamines, beta-blockers. Measurement of FACIT-F, HAQ-DI, mRSS scales. Statistical analysis was performed with the Pearson correlation (rp) with p=>0.05. The data was analyzed by SPSS V23.

Results

54 met inclusion criteria. 100% female, mean age 53.3±15.1 years, mean disease duration 11.3 years, SScd 75.9% (41), SScl 24.1% (13), interstitial pneumonia 33.3% (18), gastrointestinal reflux disease 27.8% (15), HAP 20.37% (11). Frequency of fatigue 100% (54): moderate FACIT-F: 29.6% (16), severe FACIT-F 38.8% (21), extreme 31.5% (17). Correlation FACIT-F with mRSS and HAQ-DI: Moderate FACIT-F: mRSS mild 43.8% (7), moderate 12.5% (2), severe 18.8% (3), terminal 25% (4), HAQ-DI mild 25% (4), moderate 37.5% (6), severe 18.8% (3). Severe FACIT-F: mRSS normal 4.8% (1), mild 19% (4), moderate 91% (4), severe 33.3 % (7), terminal 23.8% (5), HAQ-DI mild 4.8% (1), moderate 19% (4), severe 47.6% (10). FACIT-F extreme: mRSS mild 7.61% (3), moderate 29.4% (5), severe 35.3% (6), terminal 17.6% (3), HAQ-DI moderate 11.8% (2), severe 52.9% (9). rp=. 246 p>0.05

Conclusion

The study demonstrated a high frequency of fatigue. The most frequent degree was severe. A statistically significant linear association was observed between skin involvement and the degree of functional limitation.

References

[1]Basta F, Afeltra A, Margiotta DPE. Fatigue in systemic sclerosis: a systematic review. Clin Exp Rheumatol. 2018.

[2]Chernis, J., Buni, M., Kazzaz, S., Ying, J., Lyons, M., Assassi, S. and Mayes, M., 2021. Predictors of Perceived Functional Status in Early Systemic Sclerosis: A Prospective Longitudinal Study of the GENISOS Cohort. Arthritis Care & Research.

[3]Stamm, T., Mosor, E., Omara, M., Ritschl, V., & Murphy, S. L. (2020). How can fatigue be addressed in individuals with systemic sclerosis? The Lancet Rheumatology, 2(3), e128-e129.

[4]Nakayama, A., Tunnicliffe, D., Thakkar, V., Singh-Grewal, D., O’Neill, S., Craig, J. and Tong, A., 2016. Patients’ Perspectives and Experiences Living with Systemic Sclerosis: A Systematic Review and Thematic Synthesis of Qualitative Studies. The Journal of Rheumatology, 43(7), pp.1363-1375.

[5]Acaster, S., Dickerhoof, R., DeBusk, K., Bernard, K., Strauss, W. and Allen, L., 2015. Qualitative and quantitative validation of the FACIT-fatigue scale in iron deficiency anemia. Health and Quality of Life Outcomes, 13(1).

Disclosure of Interests

None declared

SIMUS: An open-source simulator for medical ultrasound imaging. Part II: Comparison with four simulators

BACKGROUND AND OBJECTIVE

Computational ultrasound imaging has become a well-established methodology in the ultrasound community. In the accompanying paper (part I), we described a new ultrasound simulator (SIMUS) for MATLAB, which belongs to the Matlab UltraSound Toolbox (MUST). SIMUS can generate pressure fields and radiofrequency RF signals for simulations in medical ultrasound imaging. It works in a harmonic domain and uses far-field and paraxial linear equations.

METHODS

In this article (part II), we illustrate how SIMUS compares with other ultrasound simulators (Field II, k-Wave, FOCUS, and Verasonics) for a homogeneous medium. We designed different transmit sequences (focused, planar, and diverging wavefronts) and calculated the corresponding 2-D and 3-D (with elevation focusing) RMS pressure fields.

RESULTS

SIMUS produced pressure fields similar to those of Field II, FOCUS, and k-Wave. The acoustic fields provided by the Verasonics simulator were significantly different from those of SIMUS and k-Wave, although the overall appearance remained consistent.

CONCLUSION

Our simulations tend to demonstrate that SIMUS is reliable and can be used for realistic medical ultrasound simulations.