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BackgroundThe development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of patients with inflammatory arthritis. The variation in interpretation of disease activity on US images can affect diagnosis, treatment and outcomes in clinical trials. We, therefore, set out to investigate if we could utilise neural network architecture for the interpretation of disease activity on Doppler US images, using the OESS scoring system.MethodsTwo state-of-the-art neural networks were used to extract information from 1342 Doppler US images from patients with rheumatoid arthritis (RA). One neural network divided images as either healthy (Doppler OESS score 0 or 1) or diseased (Doppler OESS score 2 or 3). The other to score images across all four of the OESS systems Doppler US scores (0–3). The neural networks were hereafter tested on a new set of RA Doppler US images (n=176). Agreement between rheumatologist’s scores and network scores was measured with the kappa statistic.ResultsFor the neural network assessing healthy/diseased score, the highest accuracies compared with an expert rheumatologist were 86.4% and 86.9% with a sensitivity of 0.864 and 0.875 and specificity of 0.864 and 0.864, respectively. The other neural network developed to four class Doppler OESS scoring achieved an average per class accuracy of 75.0% and a quadratically weighted kappa score of 0.84.ConclusionThis study is the first to show that neural network technology can be used in the scoring of disease activity on Doppler US images according to the OESS system.

ObjectivesWe have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheumatoid arthritis (RA) patients, giving scores according to the EULAR-OMERACT grading system. We have now further developed the architecture of this neural network and can here present a new idea applying cascaded convolutional neural network (CNN) design with even better results. We evaluate the generalisability of this method on unseen data, comparing the CNN with an expert rheumatologist.MethodsThe images were graded by an expert rheumatologist according to the EULAR-OMERACT synovitis scoring system. CNNs were systematically trained to find the best configuration. The algorithms were evaluated on a separate test data set and compared with the gradings of an expert rheumatologist on a per-joint basis using a Kappa statistic, and on a per-patient basis using a Wilcoxon signed-rank test.ResultsWith 1678 images available for training and 322 images for testing the model, it achieved an overall four-class accuracy of 83.9%. On a per-patient level, there was no significant difference between the classifications of the model and of a human expert (p=0.85). Our original CNN had a four-class accuracy of 75.0%.ConclusionsUsing a new network architecture we have further enhanced the algorithm and have shown strong agreement with an expert rheumatologist on a per-joint basis and on a per-patient basis. This emphasises the potential of using CNNs with this architecture as a strong assistive tool for the objective assessment of disease activity of RA patients.

Parkinson’s disease involves the accumulation of aggregates of ɑ-synuclein (ɑ-Syn), both as intracellular fibrils and as cytotoxic soluble oligomeric species (ɑSOs). No available nanobodies show exclusive preference for the oligomeric state of ɑ-Syn. Here, we describe two nanobodies NB1 and NB2, obtained by immunizing a llama with αSOs, which bind ɑSOs with nM affinity and do not show any measurable affinity for monomeric ɑ-Syn or ɑ-Syn fibrils. While the nanobodies were not useful for high-throughput screening for therapeutic compounds or high-resolution cryoEM, they retained their ability to discriminate against ɑ-Syn monomers in brain tissue and were able to detect ɑ-Syn aggregates in diseased tissue. In addition, αSO binding affinity was improved by DNA-scaffold-mediated NB1 dimerization compared to scaffolded monomeric NB1. The nanobodies promote the uptake of ɑSOs into HEK93 cells via the Sortilin receptor pathway. Their absolute specificity for oligomeric ɑ-Syn makes them promising reagents to detect oligomeric ɑ-Syn in patient samples.