Dataset Information

A historical review of multiple system atrophy with a critical appraisal of cellular and animal models.

ABSTRACT: Multiple system atrophy (MSA) is a progressive neurodegenerative disorder characterized by striatonigral degeneration (SND), olivopontocerebellar atrophy (OPCA), and dysautonomia with cerebellar ataxia or parkinsonian motor features. Isolated autonomic dysfunction with predominant genitourinary dysfunction and orthostatic hypotension and REM sleep behavior disorder are common characteristics of a prodromal phase, which may occur years prior to motor-symptom onset. MSA is a unique synucleinopathy, in which alpha-synuclein (aSyn) accumulates and forms insoluble inclusions in the cytoplasm of oligodendrocytes, termed glial cytoplasmic inclusions (GCIs). The origin of, and precise mechanism by which aSyn accumulates in MSA are unknown, and, therefore, disease-modifying therapies to halt or slow the progression of MSA are currently unavailable. For these reasons, much focus in the field is concerned with deciphering the complex neuropathological mechanisms by which MSA begins and progresses through the course of the disease. This review focuses on the history, etiopathogenesis, neuropathology, as well as cell and animal models of MSA.

SUBMITTER: Marmion DJ

PROVIDER: S-EPMC8528759 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A historical review of multiple system atrophy with a critical appraisal of cellular and animal models.

Marmion David J DJ Peelaerts Wouter W Kordower Jeffrey H JH

Journal of neural transmission (Vienna, Austria : 1996) 20211006 10

Multiple system atrophy (MSA) is a progressive neurodegenerative disorder characterized by striatonigral degeneration (SND), olivopontocerebellar atrophy (OPCA), and dysautonomia with cerebellar ataxia or parkinsonian motor features. Isolated autonomic dysfunction with predominant genitourinary dysfunction and orthostatic hypotension and REM sleep behavior disorder are common characteristics of a prodromal phase, which may occur years prior to motor-symptom onset. MSA is a unique synucleinopathy ...[more]

PMID: 34613484

Similar Datasets

Project description:Objective: This work aims to systematically identify, describe, and appraise all prognostic models for cervical cancer and provide a reference for clinical practice and future research. Methods: We systematically searched PubMed, EMBASE, and Cochrane library databases up to December 2020 and included studies developing, validating, or updating a prognostic model for cervical cancer. Two reviewers extracted information based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies checklist and assessed the risk of bias using the Prediction model Risk Of Bias ASsessment Tool. Results: Fifty-six eligible articles were identified, describing the development of 77 prognostic models and 27 external validation efforts. The 77 prognostic models focused on three types of cervical cancer patients at different stages, i.e., patients with early-stage cervical cancer (n = 29; 38%), patients with locally advanced cervical cancer (n = 27; 35%), and all-stage cervical cancer patients (n = 21; 27%). Among the 77 models, the most frequently used predictors were lymph node status (n = 57; 74%), the International Federation of Gynecology and Obstetrics stage (n = 42; 55%), histological types (n = 38; 49%), and tumor size (n = 37; 48%). The number of models that applied internal validation, presented a full equation, and assessed model calibration was 52 (68%), 16 (21%), and 45 (58%), respectively. Twenty-four models were externally validated, among which three were validated twice. None of the models were assessed with an overall low risk of bias. The Prediction Model of Failure in Locally Advanced Cervical Cancer model was externally validated twice, with acceptable performance, and seemed to be the most reliable. Conclusions: Methodological details including internal validation, sample size, and handling of missing data need to be emphasized on, and external validation is needed to facilitate the application and generalization of models for cervical cancer.

Project description:BackgroundFalls impose significant health and economic burdens on community-dwelling older persons. Decision modelling can inform commissioning of alternative falls prevention strategies. Several methodological challenges arise when modelling public health interventions including community-based falls prevention. This study aims to conduct a systematic review (SR) to: systematically identify community-based falls prevention economic models; synthesise and critically appraise how the models handled key methodological challenges associated with public health modelling; and suggest areas for further methodological research.MethodsThe SR followed the 2021 PRISMA reporting guideline and covered the period 2003-2020 and 12 academic databases and grey literature. The extracted methodological features of included models were synthesised by their relevance to the following challenges: (1) capturing non-health outcomes and societal intervention costs; (2) considering heterogeneity and dynamic complexity; (3) considering theories of human behaviour and implementation; and (4) considering equity issues. The critical appraisal assessed the prevalence of each feature across models, then appraised the methods used to incorporate the feature. The methodological strengths and limitations stated by the modellers were used as indicators of desirable modelling practice and scope for improvement, respectively. The methods were also compared against those suggested in the broader empirical and methodological literature. Areas of further methodological research were suggested based on appraisal results.Results46 models were identified. Comprehensive incorporation of non-health outcomes and societal intervention costs was infrequent. The assessments of heterogeneity and dynamic complexity were limited; subgroup delineation was confined primarily to demographics and binary disease/physical status. Few models incorporated heterogeneity in intervention implementation level, efficacy and cost. Few dynamic variables other than age and falls history were incorporated to characterise the trajectories of falls risk and general health/frailty. Intervention sustainability was frequently based on assumptions; few models estimated the economic/health returns from improved implementation. Seven models incorporated ethnicity- and severity-based subgroups but did not estimate the equity-efficiency trade-offs. Sixteen methodological research suggestions were made.ConclusionExisting community-based falls prevention models contain methodological limitations spanning four challenge areas relevant for public health modelling. There is scope for further methodological research to inform the development of falls prevention and other public health models.

Project description:ObjectivesImproved ability to predict impairments after critical illness could guide clinical decision-making, inform trial enrollment, and facilitate comprehensive patient recovery. A systematic review of the literature was conducted to investigate whether physical, cognitive, and mental health impairments could be predicted in adult survivors of critical illness.Data sourcesA systematic search of PubMed and the Cochrane Library (Prospective Register of Systematic Reviews ID: CRD42018117255) was undertaken on December 8, 2018, and the final searches updated on January 20, 2019.Study selectionFour independent reviewers assessed titles and abstracts against study eligibility criteria. Studies were eligible if a prediction model was developed, validated, or updated for impairments after critical illness in adult patients. Discrepancies were resolved by consensus or an independent adjudicator.Data extractionData on study characteristics, timing of outcome measurement, candidate predictors, and analytic strategies used were extracted. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool.Data synthesisOf 8,549 screened studies, three studies met inclusion. All three studies focused on the development of a prediction model to predict (1) a mental health composite outcome at 3 months post discharge, (2) return-to-pre-ICU functioning and residence at 6 months post discharge, and (3) physical function 2 months post discharge. Only one model had been externally validated. All studies had a high risk of bias, primarily due to the sample size, and statistical methods used to develop and select the predictors for the prediction published model.ConclusionsWe only found three studies that developed a prediction model of any post-ICU impairment. There are several opportunities for improvement for future prediction model development, including the use of standardized outcomes and time horizons, and improved study design and statistical methodology.

Dataset Information

A historical review of multiple system atrophy with a critical appraisal of cellular and animal models.

Publications

A historical review of multiple system atrophy with a critical appraisal of cellular and animal models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets