Repeat length in spinocerebellar ataxia type 4 (SCA4) predicts age at onset and disease severity.

Background: Recently, an exonic GGC repeat expansion (RE) was identified by long-read genome sequencing in the ZFHX3 gen, causing spinocerebellar ataxia type 4 (SCA4), a dominant form of ataxia with sensory neuropathy. However, the analysis of larger cohorts of patients remained demanding, resulting in a challenge to diagnose patients and leaving the question of anticipation in SCA4 unanswered.

Objectives: We aimed to develop a GGC repeat test for clinical SCA4 screening and to apply this test to screen two large German SCA pedigrees and samples of unrelated patients collected over the last 25 years.

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network.

Tetanus is a life-threatening infectious disease, which is still common in low- and middle-income countries, including in Vietnam. This disease is characterized by muscle spasm and in severe cases is complicated by autonomic dysfunction. Ideally continuous vital sign monitoring using bedside monitors allows the prompt detection of the onset of autonomic nervous system dysfunction or avoiding rapid deterioration.

Landmark tracking in 4D ultrasound using generalized representation learning.

Purpose: In this study, we present and validate a novel concept for target tracking in 4D ultrasound. The key idea is to replace image patch similarity metrics by distances in a latent representation. For this, 3D ultrasound patches are mapped into a representation space using sliced-Wasserstein autoencoders.

Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.

Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible.

Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.

Sepsis is associated with high mortality-particularly in low-middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis.

Using Deep Neural Networks to Improve Contact Wrench Estimation of Serial Robotic Manipulators in Static Tasks.

Reliable force-driven robot-interaction requires precise contact wrench measurements. In most robot systems these measurements are severely incorrect and in most manipulation tasks expensive additional force sensors are installed. We follow a learning approach to train the dependencies between joint torques and end-effector contact wrenches.

Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue.

Even though the field of medical imaging advances, there are structures in the human body that are barely assessible with classical image acquisition modalities. One example are the three leaflets of the aortic valve due to their thin structure and high movement. However, with an increasing accuracy of biomechanical simulation, for example of the heart function, and extense computing capabilities available, concise knowledge of the individual morphology of these structures could have a high impact on personalized therapy and intervention planning as well as on clinical research.

Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends.

Purpose Of Review: This review provides an overview of the most recent robotic ultrasound systems that have contemporary emerged over the past five years, highlighting their status and future directions. The systems are categorized based on their level of robot autonomy (LORA).

Recent Findings: Teleoperating systems show the highest level of technical maturity.

Anatomic factors influencing the anterior stability of reverse total shoulder arthroplasty.

Background: Several factors affect the stability of the reverse shoulder arthroplasty. The influence of bony anatomy on anterior stability remains unclear. This study aimed to identify the correlations between bony anatomy and anterior dislocation forces.

The stabilization effect of the conjoint tendon in reverse total shoulder arthroplasty.

Background: Numerous factors determine stability of reverse total shoulder arthroplasty. The effect of the conjoint tendon in relation to stability remains unknown. In this biomechanical study, we evaluated the influence of the conjoint tendon on the anterior stability of reverse total shoulder arthroplasty with a hemispherical glenosphere and a glenosphere with 9 mm lateralisation.

Effect of the humeral neck-shaft angle and glenosphere lateralization on stability of reverse shoulder arthroplasty: a cadaveric study.

Background: Lateralizing the glenosphere and decreasing the humeral neck-shaft angles are implant design parameters that reduce the risk of scapular impingement. The effects of these parameters on joint stability remain unclear. This study evaluated the effect of glenosphere lateralization and humeral neck-shaft angle on joint stability by quantifying the anterior dislocation force in different arm positions.

Personality profiles are different in musician's dystonia and other isolated focal dystonias.

Psychological abnormalities have been reported in patients with musician's dystonia. To further differentiate these abnormalities, we evaluated personality traits in musician's dystonia and compared them to those in other isolated focal dystonias. Therefore patients with musician's dystonia (n = 101) and other isolated focal dystonias (n = 85) underwent the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI).

Neuroimaging abnormalities in individuals exhibiting Parkinson's disease risk markers.

Background: The concept of prodromal Parkinson's disease (PD) involves variable combinations of nonmotor features and subtle motor abnormalities as a result of ongoing neurodegeneration in the brain stem including substantia nigra (SN) and abnormal findings upon transcranial sonography and nuclear imaging. Except for nuclear imaging, the predictive value of risk markers for the conversion to overt PD is low.

Objective: The objective of this study was to determine whether PD risk markers are associated with changes in brain structure and to what extent cognitive changes are risk markers for PD.

Associations of specific psychiatric disorders with isolated focal dystonia, and monogenic and idiopathic Parkinson's disease.

Comorbidity of psychiatric disorders in patients with movement disorders is common. Often, psychiatric symptoms manifest before the onset of the movement disorder, thus not representing a mere reaction to its burden. How the disease mechanisms of psychiatric and movement disorders are related is still poorly understood.

Non-motor symptoms and quality of life in subjects with mild parkinsonian signs.

Background: Mild parkinsonian signs (MPS) are frequent in the elderly population and associated with the presence of risk markers for Parkinson's disease (PD). Both MPS and non-motor signs may be present in prodromal PD and may significantly impair quality of life (QoL).

Objective: To disentangle the contribution of motor impairment and extra-motor manifestations to QoL in subjects with MPS (n=63), manifest PD (n=69), disorders with motor symptoms due to non-neurodegenerative diseases (n=213) and healthy controls (n=258).

Prediction of individual prosthesis size for valve-sparing aortic root reconstruction based on geometric features.

Valve-sparing aortic root reconstruction is an up- and-coming approach for patients suffering from aortic valve insufficiencies which promises to significantly reduce complications. However, the success of the treatment strongly depends on the challenging task of choosing the correct size of the prosthesis, for which, up to now, surgeons solely have to rely on their experience. Here, we present a novel machine learning based approach, which might make it possible to predict the size of the prosthesis from pre-operatively acquired ultrasound images.

A Brief Nonmotor Screen Combined with Transcranial Ultrasound may Improve Diagnostic Accuracy of Parkinson's Disease.

Background: The addition of a simple nonmotor symptom (NMS) screen and transcranial sonography (TCS) to standard clinical assessment may improve the diagnostic accuracy of Parkinson's disease (PD).

Methods: Sixty-nine subjects (23 established PD group, 23 healthy controls, and 23 possible PD) were enrolled. All completed 3 "yes-no" NMS questions (score, 0-3) and had a transcranial ultrasound assessing nigral hyperechogenicity (score, 0-1).

Intertemporal choice in Parkinson's disease and restless legs syndrome.

Background: Impulse control disorders in Parkinson's disease are a potential consequence of dopaminergic therapy. Impulse control problems might be revealed by intertemporal choice tasks which entail to forgo an immediately available reward in favor of a larger but later reward. The steepness of the discounting curve can be quantified by the parameter k.