Lipid analysis of meat from Bactrian camel (Camelus bacterianus), beef, and tails of fat-tailed sheep using UPLC-Q-TOF/MS based lipidomics

Biological Significance of the Komodo Dragon's Tail (Varanus komodoensis, Varanidae)

The Komodo dragon is a unique reptile with an elongated tail that exhibits hitherto unknown adaptations and functions. This tail, composed of 60-86 vertebrae, serves diverse ecological and physiological roles. In juveniles, it is essential for an arboreal lifestyle and balance, while in adults, it functions as a tool for defense and offensive actions. It possesses characteristic haemal arches and a dorsal keel, along with well-developed muscles which enable precise tail control, influencing the Komodo dragon's maneuverability and directional changes. The tail stores adipose tissue, providing Komodo dragons with the ability to regulate body temperature and independence from other seasonal variations. The tail adipose tissue impacts numerous biochemical processes and may play a crucial role in the animals' metabolic strategies and reproductive capabilities. Its functions include providing essential mineral compounds for the organism, such as calcium, phosphorus, magnesium, iron, and zinc. Analysing the biochemical composition of tail fat is crucial for understanding the health of Komodo dragons.

Reformulation of Tunisian Sun-Dried Merguez with Camel Meat: Characterization of Physicochemical and Compositional Changes in Organic Acids, Fatty Acids, Volatile Compounds, and Minerals

Traditional sun-dried merguez is an authentic Tunisian dried sausage made with a large number of spices and herbs, which was reformulated in this study with camel meat and hump fat and dried as in the artisanal process. This research studied the physicochemical, microbiological, and chemical compositional changes that occurred in fresh camel merguez (FCM) after 12 days of drying to achieve traditional dried camel merguez (DCM). The results showed significant weight loss (54.1%), as well as significant decreases in pH (5.20-4.97), moisture (60.5-12.3%), and water activity (0.986-0.673). These results and the acceptable microbiological quality of DCM can explain the safety of traditionally practiced long-term storage at room temperature. All chemical compositions increased upon drying. The composition of DCM included several organic acids, mainly lactate (2820 mg.kg-1); diverse unsaturated fatty acids, in particular oleic acid (33.2%); and various minerals, specifically iron (8 mg per 100 g), in addition to volatile compounds impacted by herbs and spices rich in terpenes (56.3%). These results can be useful for investing in indigenous products and promoting the exploitation of camel meat.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.