Pure akinesia with gait freezing: a clinicopathologic study

(Chemical) Roles of HOCl in Rheumatic Diseases

Chronic rheumatic diseases such as rheumatoid arthritis (RA) are characterized by a dysregulated immune response and persistent inflammation. The large number of neutrophilic granulocytes in the synovial fluid (SF) from RA patients leads to elevated enzyme activities, for example, from myeloperoxidase (MPO) and elastase. Hypochlorous acid (HOCl), as the most important MPO-derived product, is a strong reactive oxygen species (ROS) and known to be involved in the processes of cartilage destruction (particularly regarding the glycosaminoglycans). This review will discuss open questions about the contribution of HOCl in RA in order to improve the understanding of oxidative tissue damaging. First, the (chemical) composition of articular cartilage and SF and the mechanisms of cartilage degradation will be discussed. Afterwards, the products released by neutrophils during inflammation will be summarized and their effects towards the individual, most abundant cartilage compounds (collagen, proteoglycans) and selected cellular components (lipids, DNA) discussed. New developments about neutrophil extracellular traps (NETs) and the use of antioxidants as drugs will be outlined, too. Finally, we will try to estimate the effects induced by these different agents and their contributions in RA.

Intelligent diagnosis system based on artificial intelligence models for predicting freezing of gait in Parkinson's disease

Introduction

Freezing of gait (FoG) is a significant issue for those with Parkinson's disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of life. The underlying apparatus is still not understood; however, it is postulated that it is associated with cognitive disorders, namely impairments in executive and visuospatial functions. During episodes of FoG, patients may experience the risk of falling, which significantly effects their quality of life.

Methods

This research aims to systematically evaluate the effectiveness of machine learning approaches in accurately predicting a FoG event before it occurs. The system was tested using a dataset collected from the Kaggle repository and comprises 3D accelerometer data collected from the lower backs of people who suffer from episodes of FoG, a severe indication frequently realized in persons with Parkinson's disease. Data were acquired by measuring acceleration from 65 patients and 20 healthy senior adults while they engaged in simulated daily life tasks. Of the total participants, 45 exhibited indications of FoG. This research utilizes seven machine learning methods, namely the decision tree, random forest, Knearest neighbors algorithm, LightGBM, and CatBoost models. The Gated Recurrent Unit (GRU)-Transformers and Longterm Recurrent Convolutional Networks (LRCN) models were applied to predict FoG. The construction and model parameters were planned to enhance performance by mitigating computational difficulty and evaluation duration.

Results

The decision tree exhibited exceptional performance, achieving sensitivity rates of 91% in terms of accuracy, precision, recall, and F1- score metrics for the FoG, transition, and normal activity classes, respectively. It has been noted that the system has the capacity to anticipate FoG objectively and precisely. This system will be instrumental in advancing consideration in furthering the comprehension and handling of FoG.

Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease

Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.

Clinical Spectrum of Tauopathies

Tauopathies are both clinical and pathological heterogeneous disorders characterized by neuronal and/or glial accumulation of misfolded tau protein. It is now well understood that every pathologic tauopathy may present with various clinical phenotypes based on the primary site of involvement and the spread and distribution of the pathology in the nervous system making clinicopathological correlation more and more challenging. The clinical spectrum of tauopathies includes syndromes with a strong association with an underlying primary tauopathy, including Richardson syndrome (RS), corticobasal syndrome (CBS), non-fluent agrammatic primary progressive aphasia (nfaPPA)/apraxia of speech, pure akinesia with gait freezing (PAGF), and behavioral variant frontotemporal dementia (bvFTD), or weak association with an underlying primary tauopathy, including Parkinsonian syndrome, late-onset cerebellar ataxia, primary lateral sclerosis, semantic variant PPA (svPPA), and amnestic syndrome. Here, we discuss clinical syndromes associated with various primary tauopathies and their distinguishing clinical features and new biomarkers becoming available to improve in vivo diagnosis. Although the typical phenotypic clinical presentations lead us to suspect specific underlying pathologies, it is still challenging to differentiate pathology accurately based on clinical findings due to large phenotypic overlaps. Larger pathology-confirmed studies to validate the use of different biomarkers and prospective longitudinal cohorts evaluating detailed clinical, biofluid, and imaging protocols in subjects presenting with heterogenous phenotypes reflecting a variety of suspected underlying pathologies are fundamental for a better understanding of the clinicopathological correlations.

An Unusual Presentation of Progressive Supranuclear Palsy

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder with varied manifestations. Progressive gait freezing (PGF) is considered to be a rare and uncommon presentation of PSP. Here we present 2 patients with freezing of gait as the initial manifestation of PSP-PGF. One patient fulfilled the criteria of PSP-PGF, while the second did not. Nevertheless, according to the movement disorders society-PSP criteria, he met the threshold for possible PSP with progressive gait freezing. We emphasize a broad PSP-PGF spectrum of symptoms and sensitize to the fact that freezing of backward gait could indeed represent an unusual manifestation of atypical parkinsonism.

Towards Real-time Prediction of Freezing of Gait in Patients with Parkinsons Disease: A Novel Deep One-class Classifier

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinsons disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm's potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training.

Pallidal degenerations and related disorders: an update

Neurodegenerative disorders involving preferentially the globus pallidus, its efferet and afferent circuits and/or related neuronal systems are rare. They include a variety of both familial and sporadic progressive movement disorders, clinically manifesting as choreoathetosis, dystonia, Parkinsonism, akinesia or myoclonus, often associated with seizures, mental impairment and motor or cerebellar symptoms. Based on the involved neuronal systems, this heterogenous group has been classified into several subgroups: "pure" pallidal atrophy (PPA) and extended forms, pallidonigral and pallidonigrospinal degeneration (PND, PNSD), pallidopyramidal syndrome (PPS), a highly debatable group, pallidopontonigral (PPND), nigrostriatal-pallidal-pyramidal degeneration (NSPPD) (Kufor-Rakeb syndrome /KRS), pallidoluysian degeneration (PLD), pallidoluysionigral degeneration (PLND), pallidoluysiodentate atrophy (PLDA), the more frequent dentatorubral-pallidoluysian atrophy (DRPLA), and other hereditary multisystem disorders affecting these systems, e.g., neuroferritinopathy (NF). Some of these syndromes are sporadic, others show autosomal recessive or dominant heredity, and for some specific gene mutations have been detected, e.g., ATP13A2/PARK9 (KRS), FTL1 or ATP13A2 (neuroferritinopathy), CAG triple expansions in gene ATN1 (DRPLA) or pA152T variant in MAPT gene (PNLD). One of the latter, and both PPND and DRPLA are particular subcortical 4-R tauopathies, related to progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and frontotemporal lobe degeneration-17 (FTLD-17), while others show additional 3-R and 4-R tauopathies or TDP-43 pathologies. The differential diagnosis includes a large variety of neurodegenerations ranging from Huntington and Joseph-Machado disease, tauopathies (PSP), torsion dystonia, multiple system atrophy, neurodegeneration with brain iron accumulation (NBIA), and other extrapyramidal disorders. Neuroimaging data and biological markers have been published for only few syndromes. In the presence of positive family histories, an early genetic counseling may be effective. The etiology of most phenotypes is unknown, and only for some pathogenic mechanisms, like polyglutamine-induced oxidative stress and autophagy in DRPLA, mitochondrial dysfunction induced by oxidative stress in KRS or ferrostasis/toxicity and protein aggregation in NF, have been discussed. Currently no disease-modifying therapy is available, and symptomatic treatment of hypo-, hyperkinetic, spastic or other symptoms may be helpful.

Brain volume and flortaucipir analysis of progressive supranuclear palsy clinical variants

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All PSP variants showed atrophy or flortaucipir uptake in subcortical structures.

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Speech/language, frontal and corticobasal variants showed cortical involvement.

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Dentatorubrothalamic tract involvement was only seen in some variants.

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PSP variants show different patterns of damage to subcortical-cortical circuitry.

Background and purpose

Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that is associated with different clinical variants, including PSP-Richardson's syndrome (PSP-RS), PSP-parkinsonism (PSP-P), PSP-corticobasal syndrome (PSP-CBS), PSP-frontal (PSP-F), PSP-progressive gait freezing (PSP-PGF) and PSP-speech/language (PSP-SL). While PSP-RS has been well-characterized on neuroimaging, the characteristics of the other atypical variants are less well defined and it is unknown how they compare to each other or relate to neuropathology. We aimed to assess and compare regional atrophy on MRI and [¹⁸F]flortaucipir uptake on PET across PSP variants.

Materials and methods

105 PSP patients (53 PSP-RS, 23 PSP-SL, 12 PSP-P, 8 PSP-CBS, 5 PSP-F and 4 PSP-PGF) underwent volumetric MRI, with 59 of these also undergoing flortaucipir PET. Voxel-level and region-level analyses were performed comparing PSP variants to 30 controls and to each other. Semi-quantitative tau burden measurements were also performed in 21 patients with autopsy-confirmed PSP.

Results

All variants showed evidence for atrophy or increased flortaucipir uptake in striatum, globus pallidus and thalamus. Superior cerebellar peduncle volume loss was only observed in PSP-RS, PSP-CBS and PSP-F. Volume loss in the frontal lobes was observed in PSP-SL, PSP-CBS and PSP-F, with these variants also showing highest cortical tau burden at autopsy. The PSP-P and PSP-PGF variants showed more restricted patterns of neurodegeneration predominantly involving striatum, globus pallidus, subthalamic nucleus and thalamus. The PSP-SL variant showed greater volume loss and flortaucipir uptake in supplementary motor area and motor cortex compared to all other variants, but showed less involvement of subthalamic nucleus and midbrain. Compared to PSP-RS, PSP-P had larger midbrain volume and greater flortaucipir uptake in putamen.

Conclusion

The PSP variants have different patterns of involvement of subcortical circuitry, perhaps suggesting different patterns of disease spread through the brain. These findings will be important in the development of appropriate neuroimaging biomarkers for the different PSP variants.

Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: Addressing the Class Imbalance Problem

Freezing of gait (FoG) is a common motor symptom in patients with Parkinson's disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm's potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency.