Alpha-synuclein research: defining strategic moves in the battle against Parkinson’s disease

With the advent of the genetic era in Parkinson’s disease (PD) research in 1997, α-synuclein was identified as an important player in a complex neurodegenerative disease that affects >10 million people worldwide. PD has been estimated to have an economic impact of $51.9 billion in the US alone. Since the initial association with PD, hundreds of researchers have contributed to elucidating the functions of α-synuclein in normal and pathological states, and these remain critical areas for continued research. With this position paper the authors strive to achieve two goals: first, to succinctly summarize the critical features that define α-synuclein’s varied roles, as they are known today; and second, to identify the most pressing knowledge gaps and delineate a multipronged strategy for future research with the goal of enabling therapies to stop or slow disease progression in PD.

Steroid switching in dystrophinopathy treatment: a US chart review of patient characteristics and clinical outcomes

Aim: To describe reasons for switching from prednisone/prednisolone to deflazacort and associated clinical outcomes among patients with Duchenne and Becker muscular dystrophy (DMD and BMD, respectively) in the USA. Methods: A chart review of patients with DMD (n = 62) or BMD (n = 30) who switched from prednisone to deflazacort (02/2017–12/2018) collected demographic/clinical characteristics, reasons for switching, outcomes and common adverse events. Results: The mean ages at switch were 20.1 (DMD) and 9.2 (BMD) years. The primary physician-reported reasons for switching were ‘to slow disease progression’ (DMD: 83%, BMD: 79%) and ‘tolerability’ (67 and 47%). Switching was ‘very’ or ‘somewhat’ effective at addressing the primary reasons in 90–95% of patients. Conclusion: Physician-reported outcomes were consistent with deflazacort addressing patients' primary reasons for switching.

A Randomized, Double-Blind, Placebo-Controlled, Global Phase 3 Study of Edasalonexent in Pediatric Patients with Duchenne Muscular Dystrophy: Results of the PolarisDMD Trial

Background: Edasalonexent (CAT-1004) is an orally-administered novel small molecule drug designed to inhibit NF-κB and potentially reduce inflammation and fibrosis to improve muscle function and thereby slow disease progression and muscle decline in Duchenne muscular dystrophy (DMD). Objective: This international, randomized 2 : 1, placebo-controlled, phase 3 study in patients ≥4 – < 8 years old with DMD due to any dystrophin mutation examined the effect of edasalonexent (100 mg/kg/day) compared to placebo over 52 weeks. Methods: Endpoints were changes in the North Star Ambulatory Assessment (NSAA; primary) and timed function tests (TFTs; secondary). Assessment of health-related function used the Pediatric Outcomes Data Collection tool (PODCI). Results: One hundred thirty one patients received edasalonexent (n = 81) or placebo (n = 38). At week 52, differences between edasalonexent and placebo for NSAA total score and TFTs were not statistically significant, although there were consistently less functional declines in the edasalonexent group. A pre-specified analysis by age demonstrated that younger patients (≤6.0 years) showed more robust and statistically significant differences between edasalonexent and placebo for some assessments. Treatment was well-tolerated and the majority of adverse events were mild, and most commonly involved the gastrointestinal system (primarily diarrhea). Conclusions: Edasalonexent was generally well tolerated with a manageable safety profile at the dose of 100 mg/kg/day. Although edasalonexent did not achieve statistical significance for improvement in primary and secondary functional endpoints for assessment of DMD, subgroup analysis suggested that edasalonexent may slow disease progression if initiated before 6 years of age. (NCT03703882)

Hyperbaric Oxygen Ameliorates Bleomycin-Induced Pulmonary Fibrosis in Mice

The prevalence of pulmonary fibrosis is increasing with an aging population and its burden is likely to increase following COVID-19, with large financial and medical implications. As approved therapies in pulmonary fibrosis only slow disease progression, there is a significant unmet medical need. Hyperbaric oxygen (HBO) is the inhaling of pure oxygen, under the pressure of greater than one atmosphere absolute, and it has been reported to improve pulmonary function in patients with pulmonary fibrosis. Our recent study suggested that repetitive HBO exposure may affect biological processes in mice lungs such as response to wounding and extracellular matrix. To extend these findings, a bleomycin-induced pulmonary fibrosis mouse model was used to evaluate the effect of repetitive HBO exposure on pulmonary fibrosis. Building on our previous findings, we provide evidence that HBO exposure attenuates bleomycin-induced pulmonary fibrosis in mice. In vitro, HBO exposure could reverse, at least partially, transforming growth factor (TGF)-β–induced fibroblast activation, and this effect may be mediated by downregulating TGF-β–induced expression of hypoxia inducible factor (HIF)-1α. These findings support HBO as a potentially life-changing therapy for patients with pulmonary fibrosis, although further research is needed to fully evaluate this.

Atypical Pantothenate Kinase-Associated Neurodegeneration with Variable Phenotypes in an Egyptian Family

Pantothenate kinase-associated neurodegeneration (PKAN) is a rare hereditary neurodegenerative disease characterized by an accumulation of iron within the brain. In the present report, we describe a family with 4 affected siblings presenting with variable clinical manifestations, e.g., parkinsonian features, dystonia and slow disease progression over 5 years. Exome sequencing revealed a causative variant in the pantothenate kinase 2 gene (PANK2). Variant NM_024960.6:c.710C > T was homozygous in all affected subjects. Our report describes the first genetically confirmed cases of PKAN in the Egyptian population. Studying genetics of neurodegenerative diseases in different ethnicities is very important for determining clinical phenotypes and understanding pathomechanisms of these diseases.

Early Warning and Prediction of Heart Failure by Ensemble Deep Learning and Trend Similarity Measure based on Real Healthcare Data (Preprint)

BACKGROUND In recent years, heart diseases cause more than 18 million deaths every year. Heart failure (HF) prediction is essential to slow disease progression by changing lifestyle and pharmacologic interventions before heart diseases occur. Various researches have been proposed recently to predict heart failure. However, these methods did not combine different data sources with high-dimensional for heart failure prediction. In addition, the existing methods failed to consider the coexisting risk factors for heart failure and the complex relationships among them. OBJECTIVE Our goal is to make early warning and prediction of heart failure, which can offer the opportunity to test and ultimately develop effective lifestyle and pharmacologic interventions. In this paper, both electronic medical records and physiological data are considered, so as to provide enough source information to identify valuable risk factors of heart failure and make HF prediction. METHODS In this paper, an early warning and prediction method for heart failure is proposed using deep learning and trend similarity measure approaches. First, we present the data fusion and feature extraction method to merge different sources of data and get several important risk factors, which contain relevant and valuable information for HF. Second, an ensemble deep learning model for HF prediction is proposed based on gradient algorithms and back propagation techniques. In addition, we present an anomaly detection method to eliminate abnormal data caused by mood changes or environmental factors. Finally, evaluated by the Haar wavelet decomposition strategy, a data sequence trend similarity measure method is proposed aiming at prediction and early warning of heart failure in massive medical data. RESULTS The proposed method is evaluated based on our real research project HeartCarer, which includes risk factor information and physiological data. We combine these datasets from 2015 to 2020 to make a better performance evaluation for the proposed deep learning model and similarity measure method. The combined dataset totally involves 2,976 HF patients, 18,203 family members closely related to patients, and 295,801 healthy people. By comparing with other state-of-the-art methods and our prior work in [2] (90%), the proposed method can obtain a higher accuracy of 98.5% in heart disease prediction. CONCLUSIONS Heart failure (HF) prediction is essential to slow disease progression by changing lifestyle and pharmacologic interventions before heart diseases occur. An early warning and prediction method for heart failure is proposed using deep learning and trend similarity measure approaches in this paper. The proposed method is evaluated based on our real research project HeartCarer and obtain a high accuracy in heart disease prediction.

Plant-based diets in chronic kidney disease: what does the evidence say?

The prevention and treatment of chronic kidney disease can be managed not only with medical therapies, but also with dietary changes. Recent studies have shown that plant-based diets can slow disease progression and improve mortality

Tumour Dissemination in Multiple Myeloma Disease Progression and Relapse: A Potential Therapeutic Target in High-Risk Myeloma

Multiple myeloma (MM) is a plasma cell (PC) malignancy characterised by the presence of MM PCs at multiple sites throughout the bone marrow. Increased numbers of peripheral blood MM PCs are associated with rapid disease progression, shorter time to relapse and are a feature of advanced disease. In this review, the current understanding of the process of MM PC dissemination and the extrinsic and intrinsic factors potentially driving it are addressed through analysis of patient-derived MM PCs and MM cell lines as well as mouse models of homing and dissemination. In addition, we discuss how patient cytogenetic subgroups that present with highly disseminated disease, such as t(4;14), t(14;16) and t(14;20), suggest that intrinsic properties of MM PC influence their ability to disseminate. Finally, we discuss the possibility of using therapeutic targeting of tumour dissemination to slow disease progression and prevent overt relapse.