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- Chernyaeva, Larisa, et al.
(författare)
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Reduced binding of apoE4 to complement factor H promotes amyloid-β oligomerization and neuroinflammation
- 2023
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Ingår i: EMBO Reports. - 1469-221X. ; 24:7
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Tidskriftsartikel (refereegranskat)abstract
- The APOE4 variant of apolipoprotein E (apoE) is the most prevalent genetic risk allele associated with late-onset Alzheimer's disease (AD). ApoE interacts with complement regulator factor H (FH), but the role of this interaction in AD pathogenesis is unknown. Here we elucidate the mechanism by which isoform-specific binding of apoE to FH alters Aβ1-42-mediated neurotoxicity and clearance. Flow cytometry and transcriptomic analysis reveal that apoE and FH reduce binding of Aβ1-42 to complement receptor 3 (CR3) and subsequent phagocytosis by microglia which alters expression of genes involved in AD. Moreover, FH forms complement-resistant oligomers with apoE/Aβ1-42 complexes and the formation of these complexes is isoform specific with apoE2 and apoE3 showing higher affinity to FH than apoE4. These FH/apoE complexes reduce Aβ1-42 oligomerization and toxicity, and colocalize with complement activator C1q deposited on Aβ plaques in the brain. These findings provide an important mechanistic insight into AD pathogenesis and explain how the strongest genetic risk factor for AD predisposes for neuroinflammation in the early stages of the disease pathology.
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- Holm, Niels R, et al.
(författare)
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OCT or Angiography Guidance for PCI in Complex Bifurcation Lesions.
- 2023
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Ingår i: The New England journal of medicine. - 1533-4406. ; 389:16, s. 1477-1487
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Tidskriftsartikel (refereegranskat)abstract
- Imaging-guided percutaneous coronary intervention (PCI) is associated with better clinical outcomes than angiography-guided PCI. Whether routine optical coherence tomography (OCT) guidance in PCI of lesions involving coronary-artery branch points (bifurcations) improves clinical outcomes as compared with angiographic guidance is uncertain.We conducted a multicenter, randomized, open-label trial at 38 centers in Europe. Patients with a clinical indication for PCI and a complex bifurcation lesion identified by means of coronary angiography were randomly assigned in a 1:1 ratio to OCT-guided PCI or angiography-guided PCI. The primary end point was a composite of major adverse cardiac events (MACE), defined as death from a cardiac cause, target-lesion myocardial infarction, or ischemia-driven target-lesion revascularization at a median follow-up of 2 years.We assigned 1201 patients to OCT-guided PCI (600 patients) or angiography-guided PCI (601 patients). A total of 111 patients (18.5%) in the OCT-guided PCI group and 116 (19.3%) in the angiography-guided PCI group had a bifurcation lesion involving the left main coronary artery. At 2 years, a primary end-point event had occurred in 59 patients (10.1%) in the OCT-guided PCI group and in 83 patients (14.1%) in the angiography-guided PCI group (hazard ratio, 0.70; 95% confidence interval, 0.50 to 0.98; P=0.035). Procedure-related complications occurred in 41 patients (6.8%) in the OCT-guided PCI group and 34 patients (5.7%) in the angiography-guided PCI group.Among patients with complex coronary-artery bifurcation lesions, OCT-guided PCI was associated with a lower incidence of MACE at 2 years than angiography-guided PCI. (Funded by Abbott Vascular and others; OCTOBER ClinicalTrials.gov number, NCT03171311.).
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- Huhtanen, JT, et al.
(författare)
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Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs
- 2022
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1, s. 11803-
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Tidskriftsartikel (refereegranskat)abstract
- Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional neural network algorithms in joint effusion classification in pediatric and adult elbow radiographs. This retrospective study consisted of a total of 4423 radiographs in a 3-year period from 2017 to 2020. Data was randomly separated into training (n = 2672), validation (n = 892) and test set (n = 859). Two models using VGG16 as the base architecture were trained with either only lateral projection or with four projections (AP, LAT and Obliques). Three radiologists evaluated joint effusion separately on the test set. Accuracy, precision, recall, specificity, F1 measure, Cohen’s kappa, and two-sided 95% confidence intervals were calculated. Mean patient age was 34.4 years (1–98) and 47% were male patients. Trained deep learning framework showed an AUC of 0.951 (95% CI 0.946–0.955) and 0.906 (95% CI 0.89–0.91) for the lateral and four projection elbow joint images in the test set, respectively. Adult and pediatric patient groups separately showed an AUC of 0.966 and 0.924, respectively. Radiologists showed an average accuracy, sensitivity, specificity, precision, F1 score, and AUC of 92.8%, 91.7%, 93.6%, 91.07%, 91.4%, and 92.6%. There were no statistically significant differences between AUC's of the deep learning model and the radiologists (p value > 0.05). The model on the lateral dataset resulted in higher AUC compared to the model with four projection datasets. Using deep learning it is possible to achieve expert level diagnostic accuracy in elbow joint effusion classification in pediatric and adult radiographs. Deep learning used in this study can classify joint effusion in radiographs and can be used in image interpretation as an aid for radiologists.
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