Lewy body dysphagia

The presence of Lewy bodies (LB) in autonomic structures of the central and peripheral nervous system in Parkinson's disease (PD) is well known and could explain clinical signs of pure autonomic failure (PAF) or dysphagia, frequently associated with the disorder. There are many neuropathological reports in the literature with detailed descriptions of PAF, however, LB dysphagia has thus far only been reported once. In the present study, we describe two cases of isolated dysphagia without extrapyramidal syndrome, diagnosed clinically as progressive supranuclear palsy and amyotrophic lateral sclerosis, where detailed neuropathological examination identified LBs in the dorsal vagal motor nuclei in the medulla. These findings confirm the existence of isolated LB dysphagia and emphasize the importance of detailed neuropathological and immunohistochemical examination in cases of dysphagi

Rotation thromboelastometry (ROTEM®) stability and reproducibility over time

Background: Thromboelastometry is a whole blood assay performed to evaluate the viscoelastic properties during blood clot formation and lysis. Rotation thromboelastography (ROTEM®, Pentapharm GmbH, Munich, Germany) has overcome some of the limitations of classic thromboelastography. So far, no clinical validation on reproducibility (inter- and intra-assay variability) and sample stability over time has been published. Methods: To evaluate the pre-analytic aspects, sample stability over time was assessed in 48 patients in eight age groups. Citrated blood was stored at room temperature. Tests measured every 30min from T 0min up to T 120min on two ROTEM® devices were INTEM (ellagic acid activated intrinsic pathway), EXTEM (tissue factor-triggered extrinsic pathway) and FIBTEM (with platelet inhibitor (cytochalasin D) evaluating the contribution of fibrinogen to clot formation). Precision by intra- and inter-assay variability was evaluated at two points of time in 10 volunteers. Finally, reference intervals and effect of age and sex were evaluated. Results: Blood was stable over 120min and no significant differences in ROTEM® results were found. Maximum clot firmness measurements had a coefficient of variation of ≪3% for EXTEM, ≪5% for INTEM and ≪6% for FIBTEM. For clot formation time, the coefficient of variation was ≪4% for EXTEM and ≪3% for INTEM. Coefficient of variation for angle alpha was ≪3% for EXTEM and ≪6% for INTEM. The coefficient of variation for clotting time was ≪15% for both EXTEM and INTEM. Small but significant differences between ROTEM® devices were found for maximum clot firmness in FIBTEM and INTEM as well as clot formation time and alpha angle in INTEM. Conclusions: ROTEM® yields stable results over 120min with a minimal variability on the same ROTEM® device. However, small but significant differences between ROTEM® devices were observed. Analysis should be performed on the same ROTEM® device if small differences are of importance for treatmen

Flow evaluation software for four-dimensional flow MRI: a reliability and validation study

PURPOSE Four-dimensional time-resolved phase-contrast cardiovascular magnetic resonance imaging (4D flow MRI) enables blood flow quantification in multiple vessels, which is crucial for patients with congenital heart disease (CHD). We investigated net flow volumes in the ascending aorta and pulmonary arteries by four different postprocessing software packages for 4D flow MRI in comparison with 2D cine phase-contrast measurements (2D PC). MATERIAL AND METHODS 4D flow and 2D PC datasets of 47 patients with biventricular CHD (median age 16, range 0.6-52 years) were acquired at 1.5 T. Net flow volumes in the ascending aorta, the main, right, and left pulmonary arteries were measured using four different postprocessing software applications and compared to offset-corrected 2D PC data. Reliability of 4D flow postprocessing software was assessed by Bland-Altman analysis and intraclass correlation coefficient (ICC). Linear regression of internal flow controls was calculated. Interobserver reproducibility was evaluated in 25 patients. RESULTS Correlation and agreement of flow volumes were very good for all software compared to 2D PC (ICC ≥ 0.94; bias ≤ 5%). Internal controls were excellent for 2D PC (r ≥ 0.95, p < 0.001) and 4D flow (r ≥ 0.94, p < 0.001) without significant difference of correlation coefficients between methods. Interobserver reliability was good for all vendors (ICC ≥ 0.94, agreement bias < 8%). CONCLUSION Haemodynamic information from 4D flow in the large thoracic arteries assessed by four commercially available postprocessing applications matches routinely performed 2D PC values. Therefore, we consider 4D flow MRI-derived data ready for clinical use in patients with CHD

Signal Thresholding Segmentation of Ventricular Volumes in Young Patients with Various Diseases—Can We Trust the Numbers?

In many cardiac diseases, right and left ventricular volumes in systole and diastole are diagnostically and prognostically relevant. Measurements are made by segmentation of the myocardial borders on cardiac magnetic resonance (CMR) images. Automatic detection of myocardial contours is possible by signal thresholding techniques, but must be validated before use in clinical settings. Biventricular volumes were measured in end-diastole (EDVi) and in end-systole (ESVi) both manually and with the MassK application, with signal thresholds at 30%, 50%, and 70%. Stroke volumes (SV) and cardiac indices (CI) were calculated from volumetric measurements and from flow measured in the ascending aorta and the main pulmonary artery, and both methods were compared. Reproducibility of volumetric measurements was tested in 20 patients. Measurements were acquired in 94 patients aged 15 ± 9 years referred for various conditions. EDVi and ESVi of both ventricles were largest with manual segmentation and inversely proportional to the MassK threshold. Manual and k30 SV and CI corresponded best to flow measurements. Interobserver variability was low for all volumes manually and with MassK. In conclusion, manual and 30% threshold-based biventricular volume segmentation agree best with two-dimensional, phantom-corrected phase contrast flow measurements in a young cardiac referral population and are well reproducible

Probing speech emotion recognition transformers for linguistic knowledge

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -- and thus, to understand linguistic information. In this work, we investigate the extent in which this information is exploited during SER fine-tuning. Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text. Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance. These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing

Myocardial Deformation in Fontan Patients Assessed by Cardiac Magnetic Resonance Feature Tracking: Correlation with Function, Clinical Course, and Biomarkers

Cardiac MR (CMR) is a standard modality for assessing ventricular function of single ventricles. CMR feature-tracking (CMR-FT) is a novel application enabling strain measurement on cine MR images and is used in patients with congenital heart diseases. We sought to assess the feasibility of CMR-FT in Fontan patients and analyze the correlation between CMR-FT strain values and conventional CMR volumetric parameters, clinical findings, and biomarkers. Global circumferential (GCS) and longitudinal (GLS) strain were retrospectively measured by CMR-FT on Steady-State Free Precession cine images. Data regarding post-operative course at Fontan operation, and medication, exercise capacity, invasive hemodynamics, and blood biomarkers at a time interval ± 6 months from CMR were collected. Forty-seven patients underwent CMR 11 ± 6 years after the Fontan operation; age at CMR was 15 ± 7 years. End-diastolic volume (EDV) of the SV was 93 ± 37 ml/m2, end-systolic volume (ESV) was 46 ± 23 ml/m2, and ejection fraction (EF) was 51 ± 11%. Twenty (42%) patients had a single right ventricle (SRV). In single left ventricle (SLV), GCS was higher (p < 0.001), but GLS was lower (p = 0.04) than in SRV. GCS correlated positively with EDV (p = 0.005), ESV (p < 0.001), and EF (p ≤ 0.0001). GLS correlated positively with EF (p = 0.002), but not with ventricular volumes. Impaired GCS correlated with decreased ventricular function (p = 0.03) and atrioventricular valve regurgitation (p = 0.04) at echocardiography, direct atriopulmonary connection (p = 0.02), post-operative complications (p = 0.05), and presence of a rudimentary ventricle (p = 0.01). A reduced GCS was associated with increased NT-pro-BNP (p = 0.05). Myocardial deformation can be measured by CMR-FT in Fontan patients. SLVs have higher GCS, but lower GLS than SRVs. GCS correlates with ventricular volumes and EF, whereas GLS correlates with EF only. Myocardial deformation shows a relationship with several clinical parameters and NT-pro-BNP. Keywords: Biomarkers; CMR; CMR-FT; Fontan; Strai

Probing Speech Emotion Recognition Transformers for Linguistic Knowledge

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -- and thus, to understand linguistic information. In this work, we investigate the extent in which this information is exploited during SER fine-tuning. Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text. Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance. These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing.Comment: This work has been submitted for publication to Interspeech 202