Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests.

This study introduces PDMotion, a mobile application comprising 11 digital tests, including those adapted from the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III and novel assessments, for remote Parkinson's Disease (PD) motor symptoms evaluation. Employing machine learning techniques on data from 50 PD patients and 29 healthy controls, PDMotion achieves accuracies of 0.878 for PD status prediction and 0.715 for severity assessment. A post-hoc explanation model is employed to assess the importance of features and tasks in diagnosis and severity evaluation. Notably, novel tasks that are not adapted from MDS-UPDRS Part III like the circle drawing, coordination test, and alternative tapping test are found to be highly important, suggesting digital assessments for PD can go beyond digitizing existing tests. The alternative tapping test emerges as the most significant task. Using its features alone achieves prediction accuracies comparable to the full task set, underscoring its potential as an independent screening tool. This study addresses a notable research gap by digitalizing a wide array of tests, including novel ones, and conducting a comparative analysis of their feature and task importance. These insights provide guidance for task selection and future development in PD mobile assessments, a field previously lacking such comparative studies.

Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods.

White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.

SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals.

While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-optimal results. There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria. Furthermore, thanks to the significant advance in deep learning techniques, several deep learning-based layout methods were proposed recently. These methods have demonstrated the advantages of deep learning approaches for graph drawing. However, none of these existing methods can be directly applied to optimizing non-differentiable criteria without special accommodation. In this work, we propose a novel Generative Adversarial Network (GAN) based deep learning framework for graph drawing, called, which can optimize different quantitative aesthetic goals, regardless of their differentiability. To demonstrate the effectiveness and efficiency of, we conducted experiments on minimizing stress, minimizing edge crossing, maximizing crossing angle, maximizing shape-based metrics, and a combination of multiple aesthetics. Compared with several popular graph drawing algorithms, the experimental results show that achieves good performance both quantitatively and qualitatively.

Automatic labeling of Parkinson's Disease gait videos with weak supervision.

Motor dysfunction in Parkinson's Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-consuming, expensive, semi-subjective, and may potentially result in conflicting labels across different raters. To address this problem, we propose an automatic, objective, and weakly-supervised method for labeling PD patients' gait videos. The proposed method accepts videos of patients and classifies their gait scores as normal (Gait score in MDS-UPDRS = 0) or PD (MDS-UPDRS ≥ 1). Unlike previous work, the proposed method does not require a priori MDS-UPDRS ratings for training, utilizing only domain-specific knowledge obtained from neurologists. We propose several labeling functions that classify patients' gait and use a generative model to learn the accuracy of each labeling function in a self-supervised manner. Since results depended upon the estimated values of the patients' 3D poses, and existing pre-trained 3D pose estimators did not yield accurate results, we propose a weakly-supervised 3D human pose estimation method for fine-tuning pre-trained models in a clinical setting. Using leave-one-out evaluations, the proposed method obtains an accuracy of 89% on a dataset of 29 PD subjects - a significant improvement compared to previous work by 7%-10% depending upon the dataset. The method obtained state-of-the-art results on the Human3.6M dataset. Our results suggest that the use of labeling functions may provide a robust means to interpret and classify patient-oriented videos involving motor tasks.

MGEL: Multigrained Representation Analysis and Ensemble Learning for Text Moderation.

In this work, we describe our efforts in addressing two typical challenges involved in the popular text classification methods when they are applied to text moderation: the representation of multibyte characters and word obfuscations. Specifically, a multihot byte-level scheme is developed to significantly reduce the dimension of one-hot character-level encoding caused by the multiplicity of instance-scarce non-ASCII characters. In addition, we introduce a simple yet effective weighting approach for fusing n-gram features to empower the classical logistic regression. Surprisingly, it outperforms well-tuned representative neural networks greatly. As a continual effort toward text moderation, we endeavor to analyze the current state-of-the-art (SOTA) algorithm bidirectional encoder representations from transformers (BERT), which works well in context understanding but performs poorly on intentional word obfuscations. To resolve this crux, we then develop an enhanced variant and remedy this drawback by integrating byte and character decomposition. It advances the SOTA performance on the largest abusive language datasets as demonstrated by our comprehensive experiments. Our work offers a feasible and effective framework to tackle word obfuscations.

Pearls & Oy-sters: A Case Report of Holmes Tremor Due to Nigrostriatal Dopamine Disruption That Responded to Dopamine Replacement Therapy.

Holmes tremor (HT), also known as midbrain, rubral, or cerebellar pathway outflow tremor, occurs because of disturbances of the cerebellothalamic pathway. This tremor is usually related to lesions in the midbrain peduncular region involving the superior cerebellar peduncle, the red nucleus, and possibly the nigrostriatal circuitry. Common etiologies resulting in HT include tumor, ischemia, and demyelination. We report a case of progressive left-sided HT in an otherwise healthy man with additional symptoms of parkinsonism, hypoesthesia, right oculomotor nerve palsy, cognitive dysfunction, and hypersomnolence. Imaging investigations revealed a right-sided thalamic and midbrain glioma. Dopamine transport imaging demonstrated significant dopaminergic denervation in the right caudate and putamen. The degree of striatal dopamine transporter deficiency was more severe than expected in a patient with Parkinson disease. A trial of dopaminergic agent resulted in significant improvement of the tremor and associated symptoms. Interruption of the nigrostriatal pathway can occur in cases of HT because of midbrain peduncular lesion. The striatal dopaminergic function imaging may have a role in assessing presynaptic dopamine dysfunction and guiding treatment.

DBS-Edmonton App, a Tool to Manage Patient Expectations of DBS in Parkinson Disease.

OBJECTIVE: After deep brain stimulation (DBS) for Parkinson disease (PD), patients often do not report the level of satisfaction anticipated. This misalignment can relate to patients' expectations for an invasive treatment and insufficient knowledge of DBS's effectiveness in relieving motor and nonmotor symptoms (NMS). Patient satisfaction depends on expectations and goals for treatment. We hypothesized that improving patient education with a patient-centered shared decision-making tool emphasizing autonomy would improve patient satisfaction and clinical outcome. METHODS: We developed a computer application (DBS-Edmonton app), allowing patients with PD to input their symptoms and to learn how effective DBS addresses their prioritized symptoms. Sixty-two volunteers referred for DBS used the DBS-Edmonton app. DBS-related knowledge and patient perceptions of the DBS-Edmonton app were assessed with pre- and post-use questionnaires. Fourteen of 24 patients who proceeded to DBS achieved optimization at 6 months. Perceived functional improvement was assessed and compared with 12 control patients with DBS who did not use the DBS-Edmonton app. RESULTS: All 62 volunteers considered the DBS-Edmonton app helpful and would recommend it to others. There was improved knowledge about how NMS and axial symptoms respond to DBS. Postoperatively, there was no significant difference in symptoms improvement assessed by standard scales between the groups. Volunteers who used the DBS-Edmonton app had greater satisfaction (p = 0.014). CONCLUSION: This interventional study showed that the DBS-Edmonton app improved DBS-related knowledge and patient satisfaction, independent of the objective motor outcome. It may assist patients in deciding to proceed to DBS and can be easily incorporated into practice to improve patient satisfaction post-DBS.

DeepGD: A Deep Learning Framework for Graph Drawing Using GNN.

In the past decades, many graph drawing techniques have been proposed for generating aesthetically pleasing graph layouts. However, it remains a challenging task since different layout methods tend to highlight different characteristics of the graphs. Recently, studies on deep-learning-based graph drawing algorithms have emerged but they are often not generalizable to arbitrary graphs without retraining. In this article, we propose a Convolutional-Graph-Neural-Network-based deep learning framework, DeepGD, which can draw arbitrary graphs once trained. It attempts to generate layouts by compromising among multiple prespecified aesthetics considering a good graph layout usually complies with multiple aesthetics simultaneously. In order to balance the tradeoff, we propose two adaptive training strategies, which adjust the weight factor of each aesthetic dynamically during training. The quantitative and qualitative assessment of DeepGD demonstrates that it is capable of drawing arbitrary graphs effectively, while being flexible at accommodating different aesthetic criteria.

Pheochromocytomas and paragangliomas.

PURPOSE OF REVIEW: Great progress has been made in understanding the genetic and molecular basis of pheochromocytoma and paragangliomas (PPGLs). This review highlights the new standards in the diagnosis and management of pediatric PPGLs. RECENT FINDINGS: The vast majority of pediatric PPGLs have an associated germline mutation, making genetic studies imperative in the work up of these tumors. Somatostatin receptor-based imaging modalities such as 68Ga-DOTATATE and 64Cu-DOTATATE are shown to have the greatest sensitivity in pediatric PPGLs. Peptide receptor radionuclide therapies (PRRTs) such as 177Lu-DOTATATE are shown to have efficacy for treating PPGLs. SUMMARY: Genetics play an important role in pediatric PPGLs. Advances in somatostatin receptor-based technology have led to use of 68Ga-DOTATATE and 64Cu-DOTATATE as preferred imaging modalities. While surgery remains the mainstay for management of PPGLs, PRRT is emerging as a treatment option for PPGLs.

Tongue Flap for Keratinized Soft Tissue Coverage Around Dental Implants: A Case Report and Discussion.

The tongue flap is a hardy flap that is routinely utilized by oral and maxillofacial surgeons to cover intraoral defects. It has not been previously described as a method for keratinized soft tissue coverage in conjunction with dental implant placement. In this article, we describe use of a tongue flap in the closure of a chronic anterior maxillary dehiscence and to provide keratinized soft tissue coverage for anterior dental implants.

Peripheral complement interactions with amyloid ß peptide: Erythrocyte clearance mechanisms.

INTRODUCTION: Although amyloid ß peptide (Aß) is cleared from the brain to cerebrospinal fluid and the peripheral circulation, mechanisms for its removal from blood remain unresolved. Primates have uniquely evolved a highly effective peripheral clearance mechanism for pathogens, immune adherence, in which erythrocyte complement receptor 1 (CR1) plays a major role. METHODS: Multidisciplinary methods were used to demonstrate immune adherence capture of Aß by erythrocytes and its deficiency in Alzheimer's disease (AD). RESULTS: Aß was shown to be subject to immune adherence at every step in the pathway. Aß dose-dependently activated serum complement. Complement-opsonized Aß was captured by erythrocytes via CR1. Erythrocytes, Aß, and hepatic Kupffer cells were colocalized in the human liver. Significant deficits in erythrocyte Aß levels were found in AD and mild cognitive impairment patients. DISCUSSION: CR1 polymorphisms elevate AD risk, and >80% of human CR1 is vested in erythrocytes to subserve immune adherence. The present results suggest that this pathway is pathophysiologically relevant in AD.