6 research outputs found

    The understanding of Parkinson's disease through genetics and new therapies

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    Introduction: Parkinson's disease is one of the progressive neurodegenerative diseases from which people suffer for years. The mechanism of this disease is associated with a decrease in the number of dopaminergic neurons in the substantia nigra (SN) while Lewy bodies are still present. As a result, both motor-ridity, tremor, and bradykinesia-and non-motor symptoms such as anxiety and depression. Nowadays, it is well known that the cause behind Parkinson's disease is mainly environmental changes, genetic susceptibility, and toxins. Unfortunately, there is no cure for the disease but treatments. The replacement of lost neurons, alpha-synuclein and apomorphine, is currently being studied for new therapies. This article focuses on history, mechanism, factors causing Parkinson's disease as well as future therapies for the cure of the diseases. Methodology: Data were collected from medical journals published on PubMed, The Lancet, Cells, and Nature Reviews Neurology databases with a predefined search strategy. All articles considering new therapies for Parkinson's disease were considered. Results: The pathophysiology of Parkinson's disease is currently reasonably understood. However, there is no definitive cure so all the treatments focus mainly on reducing or limiting the symptoms. Current treatment studies focus on genetics, replacing lost neurons, alpha-synuclein and apomorphine. Conclusion: Parkinson's disease is the most common movement disorder worldwide because of the loss of dopaminergic neurons in the substantia nigra. Its symptoms include motor dysfunctions such as rigidity, tremor, and bradykinesia and non-motor dysfunctions such as anxiety and depression. Through genetics, environmental changes and toxins analysis, it is now known that future new therapies are working on replacing lost neurons, alpha-synuclein and apomorphine

    External validation and recalibration of an incidental meningioma prognostic model - IMPACT: protocol for an international multicentre retrospective cohort study

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    Introduction: Due to the increased use of CT and MRI, the prevalence of incidental findings on brain scans is increasing. Meningioma, the most common primary brain tumour, is a frequently encountered incidental finding, with an estimated prevalence of 3/1000. The management of incidental meningioma varies widely with active clinical-radiological monitoring being the most accepted method by clinicians. Duration of monitoring and time intervals for assessment, however, are not well defined. To this end, we have recently developed a statistical model of progression risk based on single-centre retrospective data. The model Incidental Meningioma: Prognostic Analysis Using Patient Comorbidity and MRI Tests (IMPACT) employs baseline clinical and imaging features to categorise the patient with an incidental meningioma into one of three risk groups: low, medium and high risk with a proposed active monitoring strategy based on the risk and temporal trajectory of progression, accounting for actuarial life expectancy. The primary aim of this study is to assess the external validity of this model. Methods and analysis: IMPACT is a retrospective multicentre study which will aim to include 1500 patients with an incidental intracranial meningioma, powered to detect a 10% progression risk. Adult patients ≥16 years diagnosed with an incidental meningioma between 1 January 2009 and 31 December 2010 will be included. Clinical and radiological data will be collected longitudinally until the patient reaches one of the study endpoints: intervention (surgery, stereotactic radiosurgery or fractionated radiotherapy), mortality or last date of follow-up. Data will be uploaded to an online Research Electronic Data Capture database with no unique identifiers. External validity of IMPACT will be tested using established statistical methods. Ethics and dissemination: Local institutional approval at each participating centre will be required. Results of the study will be reported through peer-reviewed articles and conferences and disseminated to participating centres, patients and the public using social media

    SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

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    Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population

    Effects of pre-operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study

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    Pancreatic surgery outcomes: multicentre prospective snapshot study in 67 countries

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