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After priming in Peyer's patches (PPs) and mesenteric lymph nodes (mLN) T- cells infiltrate the intestine through lymphatic draining and homing through the bloodstream. However, we found that in mouse models of acute graft-versus-host disease (GvHD), a subset of alloreactive T-cells directly migrates from PPs to the adjacent intestinal lamina propria (LP), bypassing the normal lymphatic drainage and vascular trafficking routes. Notably, this direct migration occurred in irradiated and unirradiated GvHD models, indicating that irradiation is not a prerequisite for this observed behavior.
Next, we established a method termed serial intravascular staining (SIVS) in mouse models to systematically investigate the trafficking and migration of donor T- cells in the early stages of acute GvHD initiation. We found that the direct migration of T-cells from PPs to LP resulted in faster recruitment of cells after allogeneic hematopoietic cell transplantation (allo-HCT). These directly migrating T-cells were found to be in an activated and proliferative state, exhibiting a TH1/TH17-like phenotype and producing cytokines such as IFN-γ and TNF-α. Furthermore, we observed that the directly migrating alloreactive T-cells expressed specific integrins (α4+, αE+) and chemokine receptors (CxCR3+, CCR5+, and CCR9+). Surprisingly, blocking these integrins and chemokine-coupled receptors did not hinder the direct migration of T- cells from PPs to LP, suggesting the involvement of alternative mechanisms. Previous experiments ruled out the involvement of S1PR1 and topographical features of macrophages, leading us to hypothesize that mediators of cytoskeleton reorganization, such as Coro1a, Dock2, or Cdc42, may play a role in this unique migration process.
Additionally, we observed that directly migrating T-cells created a local inflammatory microenvironment, which attracts circulating T-cells. Histological analysis confirmed that alloreactive PPs-derived T-cells and bloodborne T-cells colocalized. We employed two experimental approaches, including either photoconversion of T-cells in PPs or direct transfer of activated T-cells into the vasculature, to demonstrate this colocalization. We hypothesize that cytokines released by migrating T-cells, such as IFN-γ and TNF-α, may play a role in recruiting T-cells from the vasculature, as inhibiting chemokine-coupled receptors did not impair recruitment.
Background
Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification.
Methods
We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database.
Results
For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations.
Conclusion
Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.