Development of an anti-tauopathy mucosal vaccine specifically targeting pathologic conformers.

Alzheimer's disease (AD) and related tauopathies are associated with pathological tau protein aggregation, which plays an important role in neurofibrillary degeneration and dementia. Targeted immunotherapy to eliminate pathological tau aggregates is known to improve cognitive deficits in AD animal models. The tau repeat domain (TauRD) plays a pivotal role in tau-microtubule interactions and is critically involved in the aggregation of hyperphosphorylated tau proteins. Because TauRD forms the structural core of tau aggregates, the development of immunotherapies that selectively target TauRD-induced pathological aggregates holds great promise for the modulation of tauopathies. In this study, we generated recombinant TauRD polypeptide that form neurofibrillary tangle-like structures and evaluated TauRD-specific immune responses following intranasal immunization in combination with the mucosal adjuvant FlaB. In BALB/C mice, repeated immunizations at one-week intervals induced robust TauRD-specific antibody responses in a TLR5-dependent manner. Notably, the resulting antiserum recognized only the aggregated form of TauRD, while ignoring monomeric TauRD. The antiserum effectively inhibited TauRD filament formation and promoted the phagocytic degradation of TauRD aggregate fragments by microglia. The antiserum also specifically recognized pathological tau conformers in the human AD brain. Based on these results, we engineered a built-in flagellin-adjuvanted TauRD (FlaB-TauRD) vaccine and tested its efficacy in a P301S transgenic mouse model. Mucosal immunization with FlaB-TauRD improved quality of life, as indicated by the amelioration of memory deficits, and alleviated tauopathy progression. Notably, the survival of the vaccinated mice was dramatically extended. In conclusion, we developed a mucosal vaccine that exclusively targets pathological tau conformers and prevents disease progression.

Kvasir-Capsule, a video capsule endoscopy dataset.

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.