Improved physicochemical stability of fish oil nanoemulsion via a dense interfacial layer formed by hyaluronic acid-poly(glyceryl)10-stearate.

This study aimed to synthesize a novel emulsifier, hyaluronic acid-poly(glyceryl)10-stearate (HA-PG10-C18), and employ it for the fabrication of nanoemulsions incorporating deep-sea fish oil to improve their apparent solubility and physicochemical stability. 1H NMR and FT-IR analyses indicated successful synthesis of HA-PG10-C18. Nanoemulsions of deep-sea fish oil loaded with HA-PG10-C18 (HA-PG10-C18@NE) were successfully fabricated by ultrasonic emulsification. The fixed aqueous layer thickness (FALT) of PG10-C18@NE and HA-PG10-C18@NE was determined and the FALT of both nanoemulsions was similar, while the surface density of HA-PG10-C18@NE (4.92 × 10-12 ng/nm2) is 60 % higher than that of PG10-C18@NE (3.07 × 10-12 ng/nm2). Notably, HA-PG10-C18@NE demonstrated an exceptional physicochemical stability when exposed to various stressed environmental conditions, especially its freeze-thaw stability. Moreover, after simulated in vitro digestion, the HA-PG10-C18@NE exhibited a comparatively greater liberation of free fatty acids (94.0 ± 1.7 %) when compared to the release observed in PG10-C18@NE (85.5 ± 2.2 %).

Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network.

A large global health issue is cancer, wherein early diagnosis and treatment have proven to be life-saving. This holds true for oral cancer, thus emphasizing the significance of timely intervention. Deep learning techniques have gained traction in early cancer detection, exhibiting promising outcomes in accurate diagnosis. However, collecting a substantial amount of training data poses a challenge for deep learning models in cancer diagnosis. To address this limitation, this study proposes an oral cancer diagnosis approach based on a few-shot learning framework that circumvents the need for extensive training data. Specifically, a prototypical network is employed to construct a diagnostic model, wherein two feature extractors are utilized to extract prototypical features and query features respectively, departing from the conventional use of a single feature extraction function in prototypical networks. Moreover, a customized loss function is designed for the proposed method. Rigorous experimentation using a histopathological image dataset demonstrates the superior performance of our proposed approach over comparison methods.

Hyaluronic Acid Poly(glyceryl)10-Stearate Derivatives: Novel Emulsifiers for Improving the Gastrointestinal Stability and Bioaccessibility of Coenzyme Q10 Nanoemulsions.

Fish oils are a rich source of polyunsaturated fatty acids, including eicosapentaenoic acid and docosahexaenoic acid, which are reported to exhibit therapeutic effects in a variety of human diseases. However, these oils are highly susceptible to degradation due to oxidation, leading to rancidity and the formation of potentially toxic reaction products. The aim of this study was to synthesize a novel emulsifier (HA-PG10-C18) by esterifying hyaluronic acid with poly(glyceryl)10-stearate (PG10-C18). This emulsifier was then used to formulate nanoemulsion-based delivery systems to co-deliver fish oil and coenzyme Q10 (Q10). Q10-loaded fish oil-in-water nanoemulsions were fabricated, and then their physicochemical properties, digestibility, and bioaccessibility were measured. The results indicated that the environmental stability and antioxidant activity of oil droplets coated with HA-PG10-C18 surpassed those coated with PG10-C18 due to the formation of a denser interfacial layer that blocked metal ions, oxygen, and lipase. Meanwhile, the lipid digestibility and Q10 bioaccessibility of nanoemulsions formulated with HA-PG10-C18 (94.9 and 69.2%) were higher than those formulated with PG10-C18 (86.2 and 57.8%), respectively. These results demonstrated that the novel emulsifier synthesized in this study could be used to protect chemically labile fat-soluble substances from oxidative damage, while still retaining their nutritional value.

SP2509, a Selective Inhibitor of LSD1, Suppresses Retinoblastoma Growth by Downregulating ß-catenin Signaling.

Purpose: To study the role of lysine-specific demethylase 1 (LSD1) in retinoblastoma (RB) growth and to determine whether the LSD1 inhibitor SP2509 can inhibit RB progression. Methods: We detected the levels of LSD1 in 12 RB tissue samples, two RB cell lines (Y79 and Weri-RB1), and a retinal pigment epithelium cell line (ARPE-19). Overexpression or knockdown of LSD1 was performed to examine the role of LSD1 in RB cancer cell survival. In vitro and in vivo experiments were conducted to detect the antitumor effect of SP2509, and the antitumor mechanism of SP2509 was examined by RNA sequencing and Western blot. Results: LSD1 is overexpressed in RB tissues and cells and increases RB cancer cell viability and colony formation ability. The LSD1 inhibitor SP2509 inhibits RB cell proliferation in vitro and in vivo. Treatment with SP2509 increases the levels of dimethylated histone 3 lysine 4 (H3K4me2) and inhibits the expression of ß-catenin signaling pathway-related proteins in RB cells. Conclusions: We demonstrated that LSD1 is overexpressed in RB cells and promotes RB cell survival. The LSD1 inhibitor SP2509 exerted strong growth inhibition in vitro and in vivo, which was at least partially mediated by suppression of the ß-catenin pathway.