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https://www.selleckchem.com/products/azaindole-1.html With limited therapeutic options and associated severe adverse effects, fungal infections are a serious threat to human health. Innate immune response mediated by pattern recognition proteins is integral to host defense against fungi. A soluble pattern recognition protein, Surfactant protein D (SP-D), plays an important role in immune surveillance to detect and eliminate human pathogens. SP-D exerts its immunomodulatory activity via direct interaction with several receptors on the epithelial cells lining the mucosal tracts, as well as on innate and adaptive immune cells. Being a C-type lectin, SP-D shows calcium- and sugar-dependent interactions with several glycosylated ligands present on fungal cell walls. The interactome includes cell wall polysaccharides such as 1,3-β-D-glucan, 1,6-β-D-glucan, Galactosaminogalactan Galactomannan, Glucuronoxylomannan, Mannoprotein 1, and glycosylated proteins such as gp45, gp55, major surface glycoprotein complex (gpA). Recently, binding of a recombinant fragment of human ractions between innate immune humoral such as SP-D and fungal pathogens would facilitate the development of novel therapeutic interventions.Objective To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma. Methods A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT examination were collected from two institutions. All cases divided into training cohort (N = 73) and external validation cohort (N = 30). The CT semantic features were identified by two abdominal radiologists. The preprocessed initial CT images were used for CT radiomic features extraction. The LASSO regression were applied to identify optimal radiomic features and construct the Radscore. A Nomogram model was constructed combining the Radscore and the optimal semantic feature

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