Magnolol (MG) is the main active compound of Magnolia officinalis and exerts a wide range of biological activities. In this study, we investigated the effects of MG using tyloxapol (Tylo)-induced (200 mg/kg, i.p.) hyperlipidemia in rats and palmitic acid (PA)-stimulated (0.3 mM) HepG2 cells. Our results showed that Tylo injection significantly increased plasma levels of triglyceride and cholesterol as well as superoxide anion in the livers, whereas MG pretreatment reversed these changes. MG reduced hepatic lipogenesis by attenuating sterol regulatory element-binding protein-1c (SREBP-1c) and fatty acid synthase (FAS) proteins and Srebp-1, Fas, Acc, and Cd36 mRNA expression as well as upregulated the lipolysis-associated genes Hsl, Mgl, and Atgl. Furthermore, MG reduced plasma interleukin-1β (IL-1β) and protein expression of NLR family pyrin domain-containing 3 (NLRP3), apoptosis-associated speck-like protein (ASC), and caspase 1 as well as upregulated nuclear translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) and induction of heme oxygenase-1 (HO-1) in hepatocytes of Tylo-treated rats. Enhanced autophagic flux by elevation of autophagy related protein 5-12 (ATG5-12), ATG7, Beclin1, and microtubule-associated protein light chain 3 B II (LC3BII)/LC3BI ratio, and reduction of sequestosome-1 (SQSTM1/p62) and phosphorylation of mTOR was observed by MG administration. However, autophagy inhibition with 3-methyladenine (3-MA) in HepG2 cells drastically abrogated the MG-mediated suppression of inflammation and lipid metabolism. In conclusion, MG inhibited hepatic steatosis-induced NLRP3 inflammasome activation through the restoration of autophagy to promote HO-1 signaling capable of ameliorating oxidative stress and inflammatory responses.

Loss of ovarian function, as in menopause or after ovariectomy (OVX), is closely associated with obesity and white adipose tissue (WAT) inflammation. Estrogen replacement protects against postmenopausal obesity but increases the risks of carcinogenesis. In the present study, we investigated the effects of long-term treatment of raloxifene (RAL), a selective estrogen receptor modulator, on the features of estrogen deficiency-induced obesity and explored the involvement of canonical and non-canonical Wnt regulation in vivo and in vitro. Adult female rats received bilateral OVX and divided into 5 groups: (1) Sham, (2) OVX, (3) OVX + E2: OVX rats were administered with E2 (50 μg/kg, s.c., 3 times/week), (4) OVX + RAL: OVX rats were treated with RAL (gavage, 1 mg/kg/day) suspended in 0.8% carboxymethylcellulose (CMC), (5) OVX + CMC: 0.8% CMC as vehicle control. All treatments were given for 8 weeks beginning at 1 week after OVX. In 3 T3-L1 cells, the effects of RAL on adipogenesis and lipopolysaccharide (LPS)-induced inflammation were evaluated. Treatment with RAL significantly decreased body weight, visceral fat pad mass, adipocyte size and plasma levels of glucose but increased plasma adiponectin. RAL reduced the elevation of HIF-1α, VEGF-A and proinflammatory cytokines (MCP-1 and TNF-α) expression by inhibition of NF-κB p65 and JNK cascades in retroperitoneal WAT. This anti-inflammatory capacity of RAL may result from upregulation of secreted frizzle-related protein 5 (SFRP5), an adipokine that repressed Wnt5a signaling. Furthermore, RAL inhibited adipogenic factors such as PPAR-γ, C/EBP-α, and FABP4, and preserved canonical Wnt10b/β-catenin protein expression. In 3 T3-L1 adipocytes, RAL (20 μM) diminished lipid accumulation and inhibited adipogenic factors accompanied with the induction of β-catenin, which were effectively reversed by the β-catenin inhibitor IWR-1-endo. In addition, RAL reduced LPS-induced NF-κB p65 and p-IκB expression as well as TNF-α secretion. Suppression of SFRP5 by small interfering RNA significantly abrogated the anti-inflammatory effects of RAL. Distinct activation of canonical β-catenin on inhibition of adipogenesis and non-canonical SFRP5 on suppression of WAT inflammation may contribute to the beneficial effects of RAL. Therefore, this study provides a rationale for the therapeutic potential of RAL for postmenopausal obesity.

Background: Immunoglobulin-related genes are associated with the favorable prognosis of triple-negative breast cancer (TNBC) patients. We aimed to analyze the function and prognostic value of immunoglobulin lambda constant 2 (IGLC2) in TNBC patients.Methods: We knocked down the gene expression of IGLC2 (IGLC2-KD) in MDA-MB-231 cells to evaluate the proliferation, migration, and invasion of tumors via 3-(4,5-Dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, wound healing, and transwell cell migration assay respectively. Relapse-free survival (RFS) and distant metastasis-free survival (DMFS) analyses were conducted using the KM plotter online tool. The GSE76275 data set was used to analyze the association of IGLC2 and clinical characteristics. A pathway enrichment analysis was conducted using the next-generation sequencing data of wild-type and IGLC2-KD MDA-MB-231 cells.Results: The low gene expression of IGLC2 was related to unfavorable RFS, DMFS. The high expression of IGLC2 was exhibited in the basal-like immune-activated (BLIA) TNBC molecular subtype, which was immune-activated and showed excellent response to immune therapy. IGLC2 was positively correlated with programmed death-ligand 1 (PD-L1) as shown by Spearman correlation (r = 0.25, p < 0.0001). IGLC2 had a strong prognostic effect on lymph node-negative TNBC (RFS range: 0.31, q value= 8.2e-05; DMFS = 0.16, q value = 8.2e-05) but had no significance on lymph node-positive ones. The shRNA-mediated silencing of IGLC2 increased the proliferation, migration, and invasion of MDA-MB-231 cells. The results of pathway enrichment analysis showed that IGLC2 is related to the PI3K-Akt signaling pathway, MAPK signaling pathway, and extracellular matrix–receptor interaction. We confirmed that MDA-MB-231 tumor cells expressed IGLC2, subverting the traditional finding of generation by immune cells.Conclusions: IGLC2 linked with the proliferation, migration, and invasion of MDA-MB-231 cells. A high expression of IGLC2 was related to favorable prognosis for TNBC patients. IGLC2 may serve as a biomarker for the identification of TNBC patients who can benefit the most from immune checkpoint blockade treatment.

Background/Objectives: Otitis media (OM), including acute otitis media (AOM) and chronic otitis media (COM), is a common middle ear disease that can lead to significant morbidity if not accurately diagnosed. Otoscopic interpretation remains subjective and operator-dependent, underscoring the need for objective and reproducible diagnostic support. Recent advances in artificial intelligence (AI) offer promising solutions for automated otoscopic image analysis. Methods: We developed an AI-based diagnostic framework consisting of three sequential steps: (1) semi-supervised learning for automatic recognition and semantic segmentation of tympanic membrane structures, (2) region-based feature extraction, and (3) disease classification. A total of 607 clinical otoscopic images were retrospectively collected, including normal ears (n = 220), AOM (n = 157), and COM with tympanic membrane perforation (n = 230). Among these, 485 images were used for training and 122 for independent testing. Semantic segmentation of five anatomically relevant regions was performed using multiple convolutional neural network architectures, including U-Net, PSPNet, HRNet, and DeepLabV3+. Following segmentation, color and texture features were extracted from each region and used to train a neural network-based classifier to differentiate disease states. Results: Among the evaluated segmentation models, U-Net demonstrated superior performance, achieving an overall pixel accuracy of 96.76% and a mean Dice similarity coefficient of 71.68%. The segmented regions enabled reliable extraction of discriminative chromatic and texture features. In the final classification stage, the proposed framework achieved diagnostic accuracies of 100% for normal ears, 100% for AOM, and 91.3% for COM on the independent test set, with an overall accuracy of 96.72%. Conclusions: This study demonstrates that a semi-supervised, segmentation-driven AI pipeline integrating feature extraction and classification can achieve high diagnostic accuracy for otitis media. The proposed framework offers a clinically interpretable and fully automated approach that may enhance diagnostic consistency, support clinical decision-making, and facilitate scalable otoscopic assessment in diverse healthcare screening settings for disease prevention and health education.