A novel therapeutic modality using CRISPR-engineered dendritic cells to treat allergies

Feng

2022

Allergy

Allergic rhinitis (AR) is a global health problem with increasing prevalence and association with an enormous medical and socioeconomic burden. New recognition of immune cells such as type 2 innate lymphocytes (ILC2s), T helper (Th2) 2 cells, follicular helper T cells, follicular regulatory T cells, regulatory T cells, B cells, dendritic cells, and epithelial cells in AR pathogenesis has been updated in this review paper. An in‐depth understanding of the mechanisms underlying AR will aid the identification of biomarkers associated with disease and ultimately provide valuable parameters critical to guide personalized targeted therapy. As the only etiological treatment option for AR, allergen‐specific immunotherapy (AIT) has attracted increasing attention, with evidence for effectiveness of AIT recently demonstrated in several randomized controlled trials and long‐term real‐life studies. The exploration of biologics as therapeutic options has only involved anti‐IgE and anti‐type 2 inflammatory agents; however, the cost‐effectiveness of these agents remains to be elucidated precisely. In the midst of the currently on‐going COVID‐19 pandemic, a global life‐threatening disease, although some studies have indicated that AR is not a risk factor for severity and mortality of COVID‐19, this needs to be confirmed in multi‐centre, real‐life studies of AR patients from different parts of the world.

Section: Pathomechanisms In Armentioning

confidence: 71%

Update on pathomechanisms and treatments in allergic rhinitis

Feng

2022

Allergy

“…The common core of DMPRs found in our study affects genes involved in the regulation of Th1 skewed immune response such as Il12b , Ifng and Ifngr2 ( 32 – 34 ). Moreover, it affects genes encoding proinflammatory cytokines like Il1a , Il1b , Il2 , Il18r1 , Il24 , Il33 , Litaf , and Tnf ( 35 – 42 ), MHC-II genes including H2-DMa , H2-Ke6 , H2-Eb2 , and H2-Q5 ( 43 ), antigen transport component Vps37b ( 44 ) and proinflammatory transcription factors such as Maf , Junb , Nr4a3 , and Gata3 ( 45 – 48 ). Tolerance is characterized by the reduction of Th2 responses and deviation towards Th1 response, and by the generation of Treg cells ( 49 ).…”

Section: Discussionmentioning

confidence: 99%

Methylation changes induced by a glycodendropeptide immunotherapy and associated to tolerance in mice

et al. 2022

IntroductionAllergen-specific immunotherapy (AIT) is applied as treatment to rise tolerance in patients with food allergies. Although AIT is thoroughly used, the underlying epigenetic events related to tolerant induction are still unknown. Thus, we aim to investigate epigenetic changes that could be related to tolerance in dendritic cells (DCs) from anaphylactic mice to lipid transfer proteins, Pru p 3, in the context of a sublingual immunotherapy (SLIT) with a glycodendropeptide (D1ManPrup3) that has demonstrated tolerant or desensitization responses depending on the treatment dose.MethodsChanges in DNA methylation in CpG context were determined comparing Sensitized (Antigen-only) animals and two groups receiving SLIT with the D1ManPrup3 nanostructure (D1ManPrup3-SLIT): Tolerant (2nM D1ManPrup3) and Desensitized (5nM D1ManPrup3), against anaphylactic animals. DNA from lymph nodes-DCs were isolated and then, Whole Genome Bisulphite Sequencing was performed to analyze methylation.ResultsMost differentially methylated regions were found on the area of influence of gene promoters (DMPRs). Compared to the Anaphylactic group, the highest value was found in Desensitized mice (n = 7,713 DMPRs), followed by Tolerant (n = 4,091 DMPRs) and Sensitized (n = 3,931 DMPRs) mice. Moreover, many of these epigenetic changes were found in genes involved in immune and tolerance responses (Il1b, Il12b, Il1a, Ifng, and Tnf) as shown by functional enrichment (DCs regulation, B cell-mediated immunity, and effector mechanisms).DiscussionIn conclusion, different doses of D1ManPrup3-SLIT induce different DNA methylation changes, which are reflected in the induction of distinct responses, tolerance, or desensitization.

“…The multimodal features are fed into different modality encoders. The modality encoders extract the representations and are general architectures used in other fields, such as ViT [13] for images and General [34] Coarse-grained Attention CL [40] Coarse-grained Attention None [6], [21] Fine-gained Attention None [30], [27], [57] Combined Attention None [44], [39] User-item Graph + Fine-gained Attention None [56] User-item Graph CL [59] Item-item Graph CL [58], [38] Item-item Graph None [33] Item-item Graph + Fine-gained Attention None [50], [45] Knowledge Graph None [2], [46] Knowledge Graph CL [8] Knowledge Graph + Fine-gained Attention None [43] Knowledge Graph + Filtration (graph) None [63], [55], [31] Filtration (graph) None [49], [4] MLP / Concat DRL [15], [28] Fine-gained Attention DRL [61], [36], [48] None DRL…”

Section: Procedures Of Mrsmentioning

confidence: 99%

Multimodal Recommender Systems: A Survey

Liu¹,

Hu²,

Xiao³

et al. 2023

Preprint

The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short video, news and etc., understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to three categories, i.e., Feature Interaction, Feature Enhancement and Model Optimization. To make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper.