The Chemical Composition and Health-Promoting Benefits of Vegetable Oils-A Review.

With population and economic development increasing worldwide, the public is increasingly concerned with the health benefits and nutritional properties of vegetable oils (VOs). In this review, the chemical composition and health-promoting benefits of 39 kinds of VOs were selected and summarized using Web of Science TM as the main bibliographic databases. The characteristic chemical compositions were analyzed from fatty acid composition, tocols, phytosterols, squalene, carotenoids, phenolics, and phospholipids. Health benefits including antioxidant activity, prevention of cardiovascular disease (CVD), anti-inflammatory, anti-obesity, anti-cancer, diabetes treatment, and kidney and liver protection were examined according to the key components in representative VOs. Every type of vegetable oil has shown its own unique chemical composition with significant variation in each key component and thereby illustrated their own specific advantages and health effects. Therefore, different types of VOs can be selected to meet individual needs accordingly. For example, to prevent CVD, more unsaturated fatty acids and phytosterols should be supplied by consuming pomegranate seed oil, flaxseed oil, or rice bran oil, while coconut oil or perilla seed oil have higher contents of total phenolics and might be better choices for diabetics. Several oils such as olive oil, corn oil, cress oil, and rice bran oil were recommended for their abundant nutritional ingredients, but the intake of only one type of vegetable oil might have drawbacks. This review increases the comprehensive understanding of the correlation between health effects and the characteristic composition of VOs, and provides future trends towards their utilization for the general public's nutrition, balanced diet, and as a reference for disease prevention. Nevertheless, some VOs are in the early stages of research and lack enough reliable data and long-term or large consumption information of the effect on the human body, therefore further investigations will be needed for their health benefits.

Improving the Cellulose Enzymatic Digestibility of Sugarcane Bagasse by Atmospheric Acetic Acid Pretreatment and Peracetic Acid Post-Treatment.

Pretreatment of sugarcane bagasse (SCB) by aqueous acetic acid (AA), with the addition of sulfuric acid (SA) as a catalyst under mild condition (<110 °C), was investigated. A response surface methodology (central composite design) was employed to study the effects of temperature, AA concentration, time, and SA concentration, as well as their interactive effects, on several response variables. Kinetic modeling was further investigated for AA pretreatment using both Saeman's model and the Potential Degree of Reaction (PDR) model. It was found that Saeman's model showed a great deviation from the experimental results, while the PDR model fitted the experimental data very well, with determination coefficients of 0.95-0.99. However, poor enzymatic digestibility of the AA-pretreated substrates was observed, mainly due to the relatively low degree of delignification and acetylation of cellulose. Post-treatment of the pretreated cellulosic solid well improved the cellulose digestibly by further selectively removing 50-60% of the residual linin and acetyl group. The enzymatic polysaccharide conversion increased from <30% for AA-pretreatment to about 70% for PAA post-treatment.

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study.

OBJECTIVES: To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. METHODS: A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. RESULTS: SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. CONCLUSION: The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. CRITICAL RELEVANCE STATEMENT: Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. KEY POINTS: • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer.