Composition analysis and health risk assessment of the hazardous compounds in cooking fumes emitted from heated soybean oils with different refining levels.

The cooking fumes generated from thermal cooking oils contains various of hazardous components and shows deleterious health effects. The edible oil refining is designed to improve the oil quality and safety. While, there remains unknown about the connections between the characteristics and health risks of the cooking fumes and oils with different refining levels. In this study, the hazardous compounds, including aldehydes, ketones, polycyclic aromatic hydrocarbons (PAHs), and particulate matter (PM) in the fumes emitted from heated soybean oils with different refining levels were characterized, and their health risks were assessed. Results demonstrated that the concentration range of aldehydes and ketones (from 328.06 ± 24.64 to 796.52 ± 29.67 µg/m3), PAHs (from 4.39 ± 0.19 to 7.86 ± 0.51 µg/m3), and PM (from 0.36 ± 0.14 to 5.08 ± 0.15 mg/m3) varied among soybean oil with different refining levels, respectively. The neutralized oil showed the highest concentration of aldehydes and ketones, whereas the refined oil showed the lowest. The highest concentration levels of PAHs and PM were observed in fumes emitted from crude oil. A highly significant (p < 0.001) positive correlation between the acid value of cooking oil and the concentrations of PM was found, suggesting that removing free fatty acids is critical for mitigating PM concentration in cooking fumes. Additionally, the incremental lifetime cancer risk (ILCR) values of PAHs and aldehydes were 5.60 × 10-4 to 8.66 × 10-5 and 5.60 × 10-4 to 8.66 × 10-5, respectively, which were substantially higher than the acceptable levels (1.0 × 10-6) established by US EPA. The present study quantifies the impact of edible oil refining on hazardous compound emissions and provides a theoretical basis for controlling the health risks of cooking fumes via precise edible oil processing.

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

Astragalus polysacharin inhibits hepatocellular carcinoma-like phenotypes in a murine HCC model through repression of M2 polarization of tumour-associated macrophages.

CONTEXT: Astragalus polysaccharin (APS), an extract of Astragalus propinquus Schischk, exerts antitumor effects in hepatocellular carcinoma (HCC). OBJECTIVE: This study investigated the mechanism of action of APS in HCC. MATERIALS AND METHODS: Tumour-associated macrophages (TAMs) were treated with APS (0, 8, 16 mg/mL) for 24 h. APS (16 mg/mL)-treated TAMs were co-cultured with MHCC97H/Huh7 cells for 24 h. Finally, BALB/c nude mice were divided into PBS, APS (50 mg/kg), APS (100 mg/kg), APS (200 mg/kg) groups: mice were inoculated with Huh7 cells to construct tumour xenograft model, followed by administration of APS (50, 100, 200 mg/kg) or PBS daily for 30 days. Cell proliferation, migration, invasion, tumour growth, macrophage markers and proportions were measured. RESULTS: APS 16 mg/mL treatment enhanced the expression of M1 macrophage markers (iNOS, IL-1ß and TNF-α) and M1 macrophage proportions, while reducing the expression of M2 macrophage markers (IL-10, Arg-1) and M2 macrophage proportions in TAMs. Moreover, the APS-mediated M1 phenotype of TAMs significantly repressed cell proliferation, migration and invasion of MHCC97H and Huh7 cells. Moreover, APS (50, 100, 200 mg/kg) enhanced M1 macrophage proportions and reduced M2 macrophage proportions in the tumour tissues, and thus inhibited tumour growth of HCC. DISCUSSION AND CONCLUSIONS: APS inhibits HCC-like phenotypes in a murine HCC model through repression of M2 polarization of TAMs. This work provides a novel theoretical basis for the application of APS in the clinical treatment of HCC.