Objective: The main pathological change of Parkinson’s disease (PD) is progressive degeneration and necrosis of dopaminergic neurons in the midbrain, forming a Lewy body in many of the remaining neurons. Studies have found that in transgenic Drosophila, mutations in the PTEN-inducible kinase 1 (PINK1) gene may cause indirect flight muscle defects in Drosophila, and mitochondrial structural dysfunction as well.
Methods: In this study, Wnt4 gene overexpression and knockdown were performed in PINK1 mutant PD transgenic Drosophila, and the protective effect of Wnt4 gene on PD transgenic Drosophila and its possible mechanism were explored. The Wnt4 gene was screened in the previous experiment; And by using the PD transgenic Drosophila model of the MHC-Gal4/UAS system, the PINK1 gene could be specifically activated in the Drosophila muscle tissue.
Results: In PINK1 mutation transgenic fruit flies, the Wnt4 gene to study its implication on PD transgenic fruit flies’ wing normality and flight ability. We found that overexpression of Wnt4 gene significantly reduced abnormality rate of PD transgenic Drosophila and improved its flight ability, and then, increased ATP concentration, enhanced mitochondrial membrane potential and normalized mitochondrial morphology were found. All of these findings suggested Wnt4 gene may have a protective effect on PD transgenic fruit flies. Furthermore, in Wnt4 gene overexpression PD transgenic Drosophila, down-regulation autophagy and apoptosis-related proteins Ref(2)P, Pro-Caspase3, and up-regulation of Beclin1, Atg8a, Bcl2 protein were confirmed by Western Blotting.
Conclusion: The results imply that the restoring of mitochondrial function though Wnt4 gene overexpression in the PINK1 mutant transgenic Drosophila may be related to autophagy and/or apoptosis. 相似文献
BACKGROUND AND PURPOSE:The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports.MATERIALS AND METHODS:We retrospectively analyzed 32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center, performed from March 1 to April 30 each year from 2017 to 2020, involving 20,414 unique patients. To detect acute or subacute ischemic stroke in free-text reports, we trained a random forest natural language processing classifier using 1987 randomly sampled radiology reports with manual annotation. Natural language processing classifier generalizability was evaluated using 1974 imaging reports from an external dataset.RESULTS:The natural language processing classifier achieved a 5-fold cross-validation classification accuracy of 0.97 and an F1 score of 0.74, with a slight underestimation (−5%) of actual numbers of acute or subacute ischemic strokes in cross-validation. Importantly, cross-validation performance stratified by year was similar. Applying the classifier to the complete study cohort, we found an estimated 24% decrease in patients with acute or subacute ischemic strokes reported on CT or MR imaging from March to April 2020 compared with the average from those months in 2017–2019. Among patients with stroke-related order indications, the estimated proportion who underwent neuroimaging with acute or subacute ischemic stroke detection significantly increased from 16% during 2017–2019 to 21% in 2020 (P = .01). The natural language processing classifier performed worse on external data.CONCLUSIONS:Acute or subacute ischemic stroke cases detected by neuroimaging decreased during the COVID-19 pandemic, though a higher proportion of studies ordered for stroke were positive for acute or subacute ischemic strokes. Natural language processing approaches can help automatically track acute or subacute ischemic stroke numbers for epidemiologic studies, though local classifier training is important due to radiologist reporting style differences.There is much concern regarding the impact of the coronavirus disease 2019 (COVID-19) pandemic on the quality of stroke care, including issues with hospital capacity, clinical resource re-allocation, and the safety of patients and clinicians.1,2 Previous reports have shown that there have been substantial decreases in stroke neuroimaging volume during the pandemic.3,4 In addition, acute ischemic infarcts have been found on neuroimaging studies in many hospitalized patients with COVID-19, though the causal relationship is unclear.5,6 Studies like these and other epidemiologic analyses usually rely on the creation of manually curated databases, in which identification of cases can be time-consuming and difficult to update in real-time. One way to facilitate such research is to use natural language processing (NLP), which has shown utility for automated analysis of radiology report data.7 NLP algorithms have been developed previously for the classification of neuroradiology reports for the presence of ischemic stroke findings and acute ischemic stroke subtypes.8,9 Thus, NLP has the potential to facilitate COVID-19 research.In this study, we developed an NLP machine learning model that classifies radiology reports for the presence or absence of acute or subacute ischemic stroke (ASIS), as opposed to chronic stroke. We used this model to quantify the change in ASIS detected on all CT or MR imaging studies performed at a large comprehensive stroke center during the COVID-19 pandemic in the United States. We also evaluated NLP model generalizability and different training strategies using a sample of radiology reports from a second stroke center. 相似文献
Pure mucinous breast cancer (PMBC) is a rare pathologic type of breast cancer, the prognostic factors of which have not been clearly defined. This study aimed to analyze the prognostic markers and distribution of 21-gene recurrence score (RS) in patients with PMBC.
Patients and Methods
Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, a retrospective analysis of PMBC cases was conducted. Multivariate analyses were used to evaluate the indicators for prognosis and the correlations between RS and traditional clinicopathologic characteristics. Disease was subdivided into 4 molecular phenotypes using estrogen receptor (ER) status and tumor grade.
Results
Of the 8048 patients, most had ER-positive and node-negative tumors. Multivariate analysis revealed that molecular phenotype as well as age, race, tumor size, and lymph node status was an independent prognostic factor for patients with PMBC (P < .05). The 5-year breast cancer–specific survival of patients among different phenotypes was significantly different (97.9% for ER-positive and grade I tumor, 96.9% for ER-positive and grade II-III tumor, 96% for ER-negative and grade I tumor, 90.1% for ER-negative and grade II-III tumors, P < .001). The proportions of patients categorized into low, intermediate, and high RS risk group were 64.9%, 31.9%, and 3.2%, respectively. Grade, progesterone receptor status, and age were identified as independent variables associated with RS.
Conclusion
PMBC had favorable biological features and relatively good prognosis. Molecular phenotype as well as age, race, tumor size, and lymph node status were independent prognostic markers. Furthermore, age, progesterone receptor status, and grade could independently predict RS. 相似文献
The ε4 allele of the APOE gene is thought to increase risk from amnestic mild cognitive impairment (aMCI) to Alzheimer’s disease. Cognitive decline in the condition is increasingly considered to worsen functional disconnections in brain network composed of gray matter and white matter. Nevertheless, Whether APOEε4 targets specific white matter functional connectivity in patients with aMCI remains mostly unexplored, mainly due to the challenges of detecting BOLD signals in white matter. Here, we applied a novel approach to investigate APOEε4-related specific bundles and cortical area alterations in aMCI subjects, in order to characterize white matter-gray matter functional connectivity differences throughout the brain. We analyzed 75 patients with aMCI and 76 demographically matched normal controls. The aMCI APOEε4 carriers showed decreased functional connectivity located at left corticospinal tract, bilateral posterior limb of internal capsule, and right temporopolaris, which was different from the regions of aMCI-related changes. We further found that recognition scores were positively associated with the right temporopolaris in aMCI APOEε4 carriers. Collectively, the data provide new evidence that APOEε4 genotype exerts a negative impact on neural activity in both gray and white matter in aMCI, which potentially contributes to functional disconnection and memory decline. A novel method provides full-scale measuring effect of disease conditions on functional architecture throughout the brain. Trial registration: https://www.ClinicalTrials.gov (Identifier: NCT02225964). Registered January 2014.
Objective:In-stent restenosis (ISR) after stenting for intracranial stenosis is a significant issue. This study aimed to evaluate the usefulness of the 3D T1-SPACE technique in the follow-up of patients after stent implantation.Methods:Fifteen patients with intracranial arterial stenosis were prospectively enrolled 6–8 months after stenting. Digital subtraction angiography (DSA) and 3D T1-SPACE imaging were performed to evaluate the degree of stenosis and the enhancement of the vessel wall. Bland–Altman plots were used to assess the agreement between the two imaging methods, and the Pearson correlation coefficient was calculated as a measure of the linear correlation.Results:Eight Enterprise stents and seven Wingspan stents were used in 15 patients. The follow-up DSA after 6–8 months showed that the degree of stenosis was 40% (range, 30–72%), and ISR occurred in 4 of 15 (26.7%) lesions. The degree of stenosis assessed using the 3D T1-SPACE imaging technique was 35% (range, 30–75%). All four patients with ISR demonstrated significant enhancement. The Pearson correlation coefficient between the two methods was 0.959 (p < 0.05), and the Bland–Altman plot showed that all data points were within the consistency limits ( ± 1.96 s).Conclusion:As a non-invasive imaging modality, 3D T1-SPACE showed great consistency with DSA in measuring the degree of stenosis after intracranial stenting. It may be used as an optional method for detecting ISR.Advances in knowledge:This study evaluated the usefulness of 3D T1-SPACE technique in the follow-up of patients after stent implantation, which could be used as an optional and non-invasive method in detection of in-stent restenosis. 相似文献
