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Gut microbiome-targeted interventions such as fecal transplant, prebiotics, probiotics, synbiotics, and antibiotic gut depletion are speculated to be of potential use in delaying the onset and progression of Parkinson's disease by rebalancing the gut microbiome in the context of the gut-brain axis. Our study aims to organize recent findings regarding these interventions in Parkinson's disease animal models to identify how they affect neuroinflammation and motor outcomes. A systematic literature search was applied in PubMed, Web of Science, Embase, and SCOPUS for gut microbiome-targeted non-dietary interventions. Studies that investigated gut-targeted interventions by using in vivo murine PD models to follow dopaminergic cell loss, motor tests, and neuroinflammatory markers as outcomes were considered to be eligible. A total of 1335 studies were identified in the databases, out of which 29 were found to be eligible. A narrative systematization of the resulting data was performed, and the effect direction for the outcomes was represented. Quality assessment using the SYRCLE risk of bias tool was also performed. Out of the 29 eligible studies, we found that a significant majority report that the intervention reduced the dopaminergic cell loss (82.76%, 95% CI [64.23%, 94.15%]) produced by the induction of the disease model. Also, most studies reported a reduction in microglial (87.5%, 95% CI [61.65%, 98.45%]) and astrocytic activation (84,62%, 95% CI [54.55%, 98.08%]) caused by the induction of the disease model. These results were also mirrored in the majority (96.4% 95% CI [81.65%, 99.91%]) of the studies reporting an increase in performance in behavioral motor tests. A significant limitation of the study was that insufficient information was found in the studies to assess specific causes of the risk of bias. These results show that non-dietary gut microbiome-targeted interventions can improve neuroinflammatory and motor outcomes in acute Parkinson's disease animal models. Further studies are needed to clarify if these benefits transfer to the long-term pathogenesis of the disease, which is not yet fully understood. The study had no funding source, and the protocol was registered in the PROSPERO database with the ID number CRD42023461495.
RESUMO
Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.
RESUMO
INTRODUCTION: Quantitative electroencephalography (QEEG) has been documented as a helpful tool in the differential diagnosis of Alzheimer's disease (AD) with common forms of dementia. The main objective of the study was to assess the role of QEEG in AD differential diagnosis with other forms of dementia: Lewy body dementia (LBD), Parkinson's disease dementia (PDD), frontotemporal dementia (FTD), and vascular dementia (VaD). METHODS: We searched PubMed, Embase, and PsycNET, for articles in English published in peer-reviewed journals from January 1, 1980 to April 23, 2019 using adapted search strategies containing keywords quantitative EEG and Alzheimer. The risk of bias was assessed by applying the QUADAS tool. The systematic review was conducted in line with the PRISMA methodology. RESULTS: We identified 10 articles showcasing QEEG features used in diagnosing dementia, EEG slowing phenomena in AD and PDD, coherence changes in AD and VaD, the role of LORETA in dementia, and the controversial QEEG pattern in FTD. Results vary significantly in terms of sociodemographic features of the studied population, neuropsychological assessment, signal acquisition and processing, and methods of analysis. DISCUSSION: This article provides a comparative synthesis of existing evidence on the role of QEEG in diagnosing dementia, highlighting some specific features for different types of dementia (eg, the slow-wave activity has been remarked in both AD and PDD, but more pronounced in PDD patients, a diminution in anterior and posterior alpha coherence was noticed in AD, and a lower alpha coherence in the left temporal-parietal-occipital regions was observed in VaD). CONCLUSION: QEEG may be a useful investigation for settling the diagnosis of common forms of dementia. Further research of quantitative analyses is warranted, particularly on the association between QEEG, neuropsychological, and imaging features. In conjunction, these methods may provide superior diagnostic accuracy in the diagnosis of dementia.