SAMGAT: structure-aware multilevel graph attention networks for automatic rumor detection.

The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to effectively capture propagation structure features. These methods also often overlook the presence of comments irrelevant to the discussion topic of the source post.

BiMGCL: rumor detection bi-directional multi-level graph contrastive learning.

The rapid development of large language models has significantly reduced the cost of producing rumors, which brings a tremendous challenge to the authenticity of content on social media. Therefore, it has become crucially important to identify and detect rumors. Existing deep learning methods usually require a large amount of labeled data, which leads to poor robustness in dealing with different types of rumor events.

[Effect of B Lymphocyte-Induced Mature Protein-1 Expression in Bone Marrow Mononuclear Cells on Prognosis of Patients with Multiple Myeloma].

Objective: To study the effect of B lymphocyte-induced mature protein-1(Blimp1) expression in bone marrow mononuclear cells on the prognosis of patients with multiple myeloma.

Methods: Forty-eight patients with multiple myeloma from January 2014 to January 2015 were selected. The expression of Blimp1 in the bone marrow of all the patients was measured.

[Value of patient education in the treatment of allergic rhinitis].

Objective: To evaluate the effect of patient education on patients with allergic rhinitis (AR).

Method: From January 2009 to December 2013, 100 cases of allergic rhinitis were treated. The patients were randomly divided into experimental group or control group by Stochastic tables law,50 patients in control group accepted only drug treatment, 50 patients in experimental group accepted both drug treatment and patient education.