Arrhythmogenic right ventricular dysplasia: MR features

Arrhythmogenic right ventricular dysplasia (ARVD) is a heart disease characterized by a total or partial fat replacement of the myocardium. A total of 30 patients were studied with a suspected diagnosis of ARVD. Clinical criteria used for evaluation of ARVD were: (a) ventricular origin arrhythmias with a left bundle branch block configuration, (b) T-wave inversion in the anterior precordial leads, (c) ventricular kinetic alterations observed using echocardiography and angiography and (d) cardiac failure when there are no pathologies attributable to other heart diseases. All patients had serial EKG and echocardiography tests. One third of patients underwent angiocardiography; 7 of 30 had Holter; 7 of 30 had exercise test just to evaluate the effectiveness of the anti-arrhythmic therapy. All patients underwent MRI examination. The following MRI criteria were used: (a) high-intensity areas indicating the fatty substitution of the myocardium, (b) ectasia of the right ventricular outflow tract, (c) dyskinetic bulges, (d) dilation of the right ventricle and (e) enlargement of the right atrium. The diagnosis of ARVD was classified as highly probable for patients manifesting at least three positive criteria, probable with two positive criteria, dubious with one and negative in the absence of all criteria. Highly probable diagnosis of ARVD was made in 8 patients, probable in 4, dubious in 7 and negative in 11. The MRI technique is very effective in the assessment of ARVD. The MRI criteria may be helpful in the diagnosis of this condition.

Nocardial adrenal abscess: CT and MR findings

To our knowledge, four bacterial adrenal abscesses in adults have already been reported in the international literature, but an adrenal Nocardia abscess has never been described previously. In this report the CT and MR imaging appearances and the differential diagnosis of the entity are discussed. The mass could resemble a malignancy. The observation of a rapid growth and colliquation of the mass helped in distinguishing it from a malignancy. The associated pulmonary infection provided a further clue to the diagnosis. The diagnosis was confirmed by surgery.

Differentiating between common PSP phenotypes using structural MRI: a machine learning study

BACKGROUND

Differentiating Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data.

METHODS

Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes.

RESULTS

MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients.

CONCLUSION

The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials.

How do the FIFA World Cup 2018 and the 2016 UEFA championships impact a pediatric emergency department?

INTRODUCTION

In France, soccer is the most popular sport, which today attracts a huge television audience with millions of spectators in the case of international competitions. During certain games with European top clubs, the attendance of adult emergencies is impacted. However, the impact of international soccer competitions on pediatric emergency department (PED) activity has not been evaluated.

METHODS

We performed a retrospective analysis of attendance in the PED of a tertiary university hospital in Nancy (France) during the UEFA 2016 championship and the FIFA 2018 World Cup. Games were represented by 2.5-h blocks and the tournament period was compared with the same period in the previous year.

RESULTS

Considering all games, we did not observe an impact of PED attendance. The admission rate was significantly lower during the final phase (11.1 patients per match vs. 13.9, P=0.037). We observed a decrease in consultations for trauma (4.9 vs. 6.7, P=0.006). The effects were higher during games involving the national French team, with a decrease in less severe admissions (P=0.034), attendance of older children (P=0.016), and the presence of the father as accompanying adult (P=0.002). During the two final matches, we observed a decrease of 14% in the total activity.

CONCLUSION

We found significant differences in PED attendance during two international soccer tournaments. It would be interesting to study this effect in countries other than France or in countries with different sport habits.

Prevalence, genotype and phenotype of familial hypercholesterolemia in 10,194 Finnish men

Background

Familial hypercholesterolemia (FH) is the most frequent monogenic disease with an estimated prevalence of 1:250. The prevalence of different variants varies widely between the populations. According to previous studies, the prevalence of heterozygous FH in Finland has been reported to be 1:400–650, seven Finnish LDLR founder mutations having been considered to cause 80% of FH.

Purpose

To investigate the genetics of FH in the large Finnish population-based study.

Methods

The whole exome sequencing using Next Generation Sequencing method was performed for 10,194 men from eastern Finland who participated in the METSIM (Metabolic Syndrome in Men) study. The findings were confirmed by Sanger sequencing. The coding regions of the FH causing genes LDLR, APOB and PCSK9 were sequenced. The pathogenicity of the variants was classified according to the ACMG guidelines, and using the Genome Aggregation Database (gnomAD), and the ClinVar archive.

Results

Total 22 individuals of 10,194 carry 8 different pathogenic/likely pathogenic (P/LP) FH variants (7 LDLR variants, 1 APOB variant and 0 PCK9 variants). Seven LDLR gene variants classified as P/LP were detected in 21 individuals. Two of the seven variants are founder mutations (p.Pro309Lysfs*59, p.Arg595Gln), four are other previously reported variants (p.Arg124Trp, p.Glu228*, p.Asp266Asn, p.Asp445Glu) and one is a novel variant (p.Gly396Ala). Of the 21 individuals carrying P/LP LDLR variants, 16 carry founder mutations and five other P/LP LDLR variants. Of individuals carrying any P/LP LDLR variant, 28.6% had premature (men <55 years, women <60 years) atherosclerotic cardiovascular disease (CVD): coronary artery disease (CAD) 28.6%, peripheral artery disease (PAD) 0.0%, and stroke or TIA 0.0%. CVD at any age was diagnosed in 38.1% (CAD 33.3%, PAD 4.8%, stroke or TIA 9.5%). Only one individual from 10,194 carry APOB variant which is classified as P/LP. The detected APOB variant is a previously reported variant (p.Gln4494del). None of the 10,194 individuals carry P/LP PCSK9 variants. Thus, the prevalence of P/LP FH variants in the population from eastern Finland was 0.22%, 1 in 463.4 (LDLR variants 0.21%, 1 in 485; APOB variants 0.01%, 1 in 10 194). Of the P/LP variant carriers, 72.7% carry founder mutation of the LDLR, and 27.3% carry other LDLR and APOB variants.

Conclusions

The prevalence of heterozygous FH in the population-based study of 10,194 men from eastern Finland was 1 in 463.4, which is about the same as previously reported. LDLR variants accounted for the majority of FH cases, and although previously reported Finnish LDLR founder mutations were common, about 30% of the mutations detected were other LDLR and APOB variants. Pathogenic/likely pathogenic APOB variants were, however, rare and PCSK9 variants were not found.

Funding Acknowledgement

Type of funding sources: Public hospital(s). Main funding source(s): Kuopio university hospital

Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor

Introduction

Distinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data.

Methods

We enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients.

Results

Both structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase.

Conclusion

Machine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

Neuroimaging correlates of postural instability in Parkinson's disease

BACKGROUND

Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD), but little is known on its pathophysiological basis.

OBJECTIVE

In this study, we aimed to identify the brain structures associated with PI in PD patients, using different MRI approaches.

METHODS

We consecutively enrolled 142 PD patients and 45 control subjects. PI was assessed using the MDS-UPDRS-III pull-test item (PT). A whole-brain regression analysis identified brain areas where grey matter (GM) volume correlated with the PT score in PD patients. Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) were also used to compare unsteady (PT ≥ 1) and steady (PT = 0) PD patients. Associations between GM volume in regions of interest (ROI) and several clinical features were then investigated using LASSO regression analysis.

RESULTS

PI was present in 44.4% of PD patients. The whole-brain approach identified the bilateral inferior frontal gyrus (IFG) and superior temporal gyrus (STG) as the only regions associated with the presence of postural instability. VBM analysis showed reduced GM volume in fronto-temporal areas (superior, middle, medial and inferior frontal gyrus, and STG) in unsteady compared with steady PD patients, and the GM volume of these regions was selectively associated with the PT score and not with any other motor or non-motor symptom.

CONCLUSIONS

This study demonstrates a significant atrophy of fronto-temporal regions in unsteady PD patients, suggesting that these brain areas may play a role in the pathophysiological mechanisms underlying postural instability in PD. This result paves the way for further studies on postural instability in Parkinsonism.

Corrigendum: RT-ring: a small wearable device for tremulous Parkinson's disease diagnosis in primary care

[This corrects the article DOI: 10.3389/fneur.2025.1534205.].

RT-ring: a small wearable device for tremulous Parkinson's disease diagnosis in primary care

Introduction

Differential diagnosis of rest tremor (RT) disorders is challenging, often requiring 123I-ioflupane single-photon-emission-computed tomography (DaTscan), an expensive technique not available worldwide. In the current study, we investigated the performance of a new wearable mobile device termed "RT-ring" in predicting DaTscan result in patients presenting with RT based on rest tremor inertial features.

Methods

Consecutive RT patients underwent RT-ring tremor analysis, surface electromyography (sEMG), and DaTscan. The RT-ring is a miniaturized mobile device that uses machine learning based on inertial tremor data to estimate the RT pattern. This electrophysiologic tremor feature has proven to accurately predict DaTscan result. The primary outcome was the RT-ring's performance in distinguishing patients with and without striatal dopaminergic deficit.

Results

Sixty-seven RT patients were enrolled, including 42 patients with striatal dopaminergic deficit and 25 with normal DaTscan. The RT-ring showed 85.0% sensitivity, 90.9% specificity, and 87.9% balanced accuracy in predicting DaTscan result, and demonstrated 96.8% agreement with sEMG in RT pattern classification.

Conclusion

The RT-ring is a promising, non-invasive, user-friendly, wearable mobile device for supporting the diagnosis of tremulous Parkinson's disease in primary care settings, especially in low-income countries with limited access to dopamine imaging.

Low Seroprevalence of Torque Teno Virus in HCV positive patients and phylogenetic analysis from Pakistani isolates

The torque teno virus (TTV) has a heterogeneous genome and its role in hepatitis C (HCV) infection is still controversial, therefore the purpose of the present study was to determine if there is any association between Hepatitis C and TTV co-infection and to determine the phylogenetic relationship between existing types in the Pakistani population. A total of 500 individuals (250 HCV positive patients and 250 healthy controls) were selected. DNA was extracted from serum samples and polymerase chain reaction (PCR) of the open reading frame 1 (ORF1) region of TTV was performed. Out of 500 samples 9 HCV positive index cases (3.6%) and 8 healthy samples (3.2%) were found to be positive for TTV. A comparison was made between TTV sequences reported from all over the world with the ones obtained in the present study by sequencing of TTV positive samples followed by phylogenetic analysis using maximum parsimony (MP) method. Our results indicated that the virus was undergoing divergent evolution as very high sequence diversity was found in the ORF1 gene. The study also shows that association between HCV and TTV was not found. Because the virus was found to be affecting both healthy individuals and HCV infected population with almost same frequency. Therefore a thorough screening of TTV virus at the population level is required in order to draw a comprehensive inference.

Neuroimaging correlates of postural instability in Progressive Supranuclear Palsy

OBJECTIVE

We aimed to identify the brain structures associated with postural instability (PI) in Progressive Supranuclear Palsy (PSP).

METHODS

Forty-seven PSP patients and 45 control subjects were enrolled in this study. PI was assessed using the items 27 and 28 of the PSP rating scale (postural instability score, PIS). PSP patients were compared with controls using voxel-based morphometry (VBM). In PSP patients, LASSO regression model was used to investigate associations between VBM-based Region-Of-Interest grey matter (GM) volumes and different categories of the PSP rating scale. A whole-brain multi-regression analysis was also used to identify brain areas where GM volumes correlated with the PIS in PSP patients.

RESULTS

VBM analysis showed widespread GM atrophy (fronto-temporal-parietal-occipital regions, limbic lobes, insula, cerebellum, and basal ganglia) in PSP patients compared with control subjects. In PSP patients, LASSO regression analysis showed associations of the right cerebellar lobules IV-V with ocular motor category score, and the left Rolandic area with bulbar category score, while the right inferior frontal gyrus (IFG) was negatively correlated with the PIS. The whole-brain multi-regression analysis identified the right IFG as the only area significantly associated with the PIS.

CONCLUSIONS

In our study, two different approaches demonstrated that the IFG volume was associated with PIS in PSP patients, suggesting that this area may play a role in the pathophysiological mechanisms underlying PI. Our findings may have important implications for developing optimal Transcranial Magnetic Stimulation protocols targeting IFG in parkinsonism with postural disorders.

Development of a New Wearable Device for the Characterization of Hand Tremor

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.