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Background: The second consensus criteria for the diagnosis of multiple system atrophy (MSA) are widely recognized as the reference standard for clinical research, but lack sensitivity to diagnose the disease at early stages. Objective: To develop novel Movement Disorder Society (MDS) criteria for MSA diagnosis using an evidence-based and consensus-based methodology. Methods: We identified shortcomings of the second consensus criteria for MSA diagnosis and conducted a systematic literature review to answer predefined questions on clinical presentation and diagnostic tools relevant for MSA diagnosis. The criteria were developed and later optimized using two Delphi rounds within the MSA Criteria Revision Task Force, a survey for MDS membership, and a virtual Consensus Conference. Results: The criteria for neuropathologically established MSA remain unchanged. For a clinical MSA diagnosis a new category of clinically established MSA is introduced, aiming for maximum specificity with acceptable sensitivity. A category of clinically probable MSA is defined to enhance sensitivity while maintaining specificity. A research category of possible prodromal MSA is designed to capture patients in the earliest stages when symptoms and signs are present, but do not meet the threshold for clinically established or clinically probable MSA. Brain magnetic resonance imaging markers suggestive of MSA are required for the diagnosis of clinically established MSA. The number of research biomarkers that support all clinical diagnostic categories will likely grow. Conclusions: This set of MDS MSA diagnostic criteria aims at improving the diagnostic accuracy, particularly in early disease stages. It requires validation in a prospective clinical and a clinicopathological study. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Author(s): Stankovic, Iva; Fanciulli, Alessandra; Kostic, Vladimir S; Krismer, Florian; Meissner, Wassilios G; Palma, Jose Alberto; Panicker, Jalesh N; Seppi, Klaus; Wenning, Gregor K; MoDiMSA Study Group | Abstract: Background: Neuroimaging has been used to support a diagnosis of possible multiple system atrophy (MSA). Only blood pressure changes upon standing are included in the second consensus criteria but other autonomic function tests (AFT) are also useful to diagnose widespread and progressive autonomic failure typical of MSA. Additional diagnostic tools are of interest to improve accuracy of MSA diagnosis. Objectives: To assess the utility of diagnostic tools beyond brain imaging and AFT in enhancing a laboratory-supported diagnosis of MSA to support the upcoming revision of the consensus criteria. Methods: The International Parkinson and Movement Disorders Society MSA Study Group (MoDiMSA) performed a systematic review of original papers on biomarkers, sleep studies, genetic, neuroendocrine, neurophysiological, neuropsychological and other tests including olfactory testing and acute levodopa challenge test published before August 2019. Results: Evaluation of history of levodopa responsiveness and olfaction is useful in patients in whom MSA-parkinsonian subtype is suspected. Neuropsychological testing is useful to exclude dementia at time of diagnosis. Applicability of sphincter EMG is limited. When MSA-cerebellar subtype is suspected, a screening for the common causes of adult-onset progressive ataxia is useful, including spinocerebellar ataxias in selected patients. Diagnosing stridor and REM sleep behavior disorder is useful in both MSA subtypes. However, none of these tools are validated in large longitudinal cohorts of postmortem confirmed MSA cases. Conclusions: Despite limited evidence, additional laboratory work-up of patients with possible MSA beyond imaging and AFT should be considered to optimize the clinical diagnostic accuracy.

OBJECTIVE\ud \ud To assess whether genetic subgroups in recent amyotrophic lateral sclerosis (ALS) trials responded to treatment with lithium carbonate, but that the treatment effect was lost in a large cohort of nonresponders.\ud \ud METHODS\ud \ud Individual participant data were obtained from 3 randomized trials investigating the efficacy of lithium carbonate. We matched clinical data with data regarding the UNC13A and C9orf72 genotype. Our primary outcome was survival at 12 months. On an exploratory basis, we assessed whether the effect of lithium depended on the genotype.\ud \ud RESULTS\ud \ud Clinical data were available for 518 of the 606 participants. Overall, treatment with lithium carbonate did not improve 12-month survival (hazard ratio [HR] 1.0, 95% confidence interval [CI] 0.7-1.4; p = 0.96). Both the UNC13A and C9orf72 genotype were independent predictors of survival (HR 2.4, 95% CI 1.3-4.3; p = 0.006 and HR 2.5, 95% CI 1.1-5.2; p = 0.032, respectively). The effect of lithium was different for UNC13A carriers (p = 0.027), but not for C9orf72 carriers (p = 0.22). The 12-month survival probability for UNC13A carriers treated with lithium carbonate improved from 40.1% (95% CI 23.2-69.1) to 69.7% (95% CI 50.4-96.3).\ud \ud CONCLUSIONS\ud \ud This study incorporated genetic data into past ALS trials to determine treatment effects in a genetic post hoc analysis. Our results suggest that we should reorient our strategies toward finding treatments for ALS, start focusing on genotype-targeted treatments, and standardize genotyping in order to optimize randomization and analysis for future clinical trials.

Infrastructural defects to determine the sicknesses of the crop utilized within the agricultural quarter improvising special standards and solutions. The diagnosis of the different scenario and cause for diseases had been let to indulge in the current mobile technology suitable for the controlling of the disease using wireless scenario or switches. Our paper imparts on the current existing design technique as SVM, providing the mathematical and functional aspects of the design ensuring to improve the locating diseases using test and train scenarios. The setup for the SVM model is also taken in account for considerations of the different data sets of the images related different crops noting to provide the correct information of the problem scenario. These problems might exist due to natural or man-made for each set of the disease observed and identified. Hence recognition of the diseases would suffice the design criteria ensuring different parametric criteria for each level of training and test set provided. To ensure the novel and more accurate scenario different set of data set have been in consideration for different test and train images providing higher and reliable accuracy for the proposed model as part of CNN applying as Transfer learning. Different scenarios of the plant disease image have been considered as data set of 15617 images under restricted cases improvising a train model on CNN with transfer learning. The accuracy observed from the design model is observed 98.56% on the considered test vectors providing required feasibility. These designs also provide a better and convenient solutions for the people utilizing the current technologies.