The effects of banana resistant starch (BRS) on obesity-related metabolic and intestinal flora were investigated in a high-fat diet-induced obesity model. After 6 weeks of intervention, the glucolipid metabolism index [blood glucose (GLU), total cholesterol (TC), triacylglycerol (TG), low density lipoprotein-cholesterol (LDL-C), and high density lipoprotein-cholesterol (HDL-C)], hormone index [leptin (LEP), insulin (INS), ghrelin, adiponectin (ADP), and thyroxine (T4)], and 16S rRNA sequencing analyses were performed for each group to explore the regulating effect of intestinal flora and the mechanism of weight loss in obese rats. The results showed that (1) BRS intervention significantly reduced the levels of GLU, TG, TC, LDL-C, LEP, and INS (p < 0.01) and increased the contents of ghrelin (p < 0.05) and ADP (p < 0.01). (2) BRS could improve the diversity of intestinal flora and regulate the overall structure of intestinal microorganisms, mainly by upregulating the Bacteroides/Firmicutes ratio and the relative abundance of Cyanobacteria and downregulating the relative abundances of Deferribacteres and Tenericutes (at the phylum level). BRS could inhibit the proliferation of Turicibacter, Romboutsia, and Oligella and increase the abundances of Bacteroides, Ruminococcaceae, and Lachnospiraceae (at the genus level). (3) Some significant correlations were observed between the gut microbiota and biomarkers. Turicibacter, Romboutsia, and Oligella were positively correlated with GLU, TG, TC, LEP, and INS and negatively correlated with ghrelin and ADP. Bacteroides, Parabacteroides, and Akkermansia were negatively correlated with GLU, TG, and TC. Conclusion: BRS had promising effects on weight loss, which could be associated with the improvement in host metabolism by regulating intestinal flora.

Cinnamon essential oil (CEO) was extracted by three different methods: steam distillation (SD), ultrasound-assisted steam distillation (UASD) and microwave-assisted steam distillation (MASD). The volatiles in CEO were separated and identified by gas chromatography–mass spectrometry (GC-MS), and the differences in volatiles among the three different methods were further analyzed through principal component analysis. The results showed that 36 individual volatile components were present in the CEO from the three different methods. In general, the numbers of aldehydes, esters, alcohols, terpenes, aromatics and ketones were 6, 3, 7, 17, 2, and 1, respectively. The most abundant volatile component was determined to be cinnamic aldehyde. The content of total cinnamic aldehydes, which determines the price of CEO, was the highest among the three methods in the UASD sample (85.633%). Moreover, the highest yield (8.33‰) of essential oil was extracted by the UASD method. Therefore, UASD was the best way for CEO extraction in this research and was recommended for future industrial applications.

Naringenin (NAR) possesses various pharmacological activities including antioxidant, anti-inflammatory, and hepatoprotective effects. However, its therapeutic efficacy is limited by its hydrophobic and crystalline nature. This study aimed to investigate the therapeutic potential and molecular mechanisms of NAR efficiently loaded into cationic nanoparticles (NP-NAR) for treating metabolic dysfunction-associated steatotic liver disease (MASLD) in a mouse model. The results demonstrated that NP-NAR effectively ameliorated lipid metabolism dysbiosis, oxidative stress, insulin resistance, and inflammation in MASLD mice. Transcriptomic analysis and molecular data revealed that NP-NAR promoted fatty acid oxidation via activation of the PPAR signaling pathway, reduced hepatic lipid uptake and lipogenesis by inhibiting the expressions of key genes including CD36, ACC, and FASN. Moreover, NP-NAR modulated cholesterol metabolism by inhibiting the classical bile acid synthesis pathway. 16 S rDNA gene sequencing revealed a disbalanced gut microbiota in MASLD mice, whereas NP-NAR treatment statistically reversed the abundance changes of several intestinal bacteria at the phylum and genus levels, which partly contributed to the balance in intestinal metabolite production, including short-chain fatty acids. In conclusion, these findings suggest that NP-NAR may be a promising candidate for the treatment of obesity-associated MASLD, offering new insight into the mechanisms underlying NAR’s efficacy against MASLD. Graphical Abstract

Resistant starches were isolated from four banana cultivars: Musa AAA Cavendish, Musa ABB Bluggoe, Musa ABB Pisan Awak, and Musa AA Pisang mas. The structural and physicochemical properties of banana resistant starches were studied. Results showed that the particle size and shape of four banana resistant starches were different. Cavendish and Bluggoe banana resistant starch had a C-type crystalline structure, whereas Pisan Awak and Pisang mas had a B-type. The water-holding capacity of Pisang mas was the maximum. The solubility of Pisan Awak and Pisang mas was higher as compared to Cavendish and Bluggoe. The transparency of Cavendish banana resistant starch was the highest. More amylose was observed in Bluggoe and Pisan Awak banana resistant starch, whereas more amylopectin was observed in Cavendish and Pisang mas banana resistant starch. The initial pasting temperatures of Cavendish and Bluggoe banana resistant starches were higher as compared to Pisan Awak and Pisang mas banana resistant starch. The peak viscosity of Cavendish banana resistant starch was the highest in these four samples. The heat stability of Bluggoe banana resistant starch was the best one in the four banana resistant starches. The retrogradation was hard in the case of Bluggoe and Pisan Awak banana resistant starch. In conclusion, the properties of the four banana resistant starch samples were not the same, indicating that these could be used in different food products.

The rising global burden of chronic diseases highlights the limitations of traditional dietary guidelines. Precision Nutrition (PN) aims to deliver personalized dietary advice to optimize individual health, and the effective implementation of PN fundamentally relies on comprehensive and accurate dietary data. However, conventional dietary assessment methods often suffer from quantification errors and poor adaptability to dynamic changes, leading to inaccurate data and ineffective guidance. Machine learning (ML) offers a powerful suite of tools to address these limitations, enabling a paradigm shift across the nutritional management pipeline. Using dietary data as a thematic thread, this article outlines this transformation and synthesizes recent advances across dietary assessment, in-depth mining, and nutritional intervention. Additionally, current challenges and future trends in this domain are also further discussed. ML is driving a critical shift from a subjective, static mode to an objective, dynamic, and personalized paradigm, enabling a loop nutrition management framework. Precise food recognition and nutrient estimation can be implemented automatically with ML techniques like computer vision (CV) and natural language processing (NLP). Integrating with multiple data sources, ML is conducive to uncovering dietary patterns, assessing nutritional status, and deciphering intricate nutritional mechanisms. It also facilitates the development of personalized dietary intervention strategies tailored to individual needs, while enabling adaptive optimization based on users’ feedback and intervention effectiveness. Although challenges regarding data privacy and model interpretability persist, ML undeniably constitutes the vital technical support for advancing PN into practical reality.