scholarly journals No correlation between microbiota composition and blood parameters in nesting flatback turtles (Natator depressus)

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
T. Franciscus Scheelings ◽  
Robert J. Moore ◽  
Thi Thu Hao Van ◽  
Marcel Klaassen ◽  
Richard D. Reina
Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 1031 ◽  
Author(s):  
Zuzanna Mikołajczak ◽  
Mateusz Rawski ◽  
Jan Mazurkiewicz ◽  
Bartosz Kierończyk ◽  
Damian Józefiak

The present study is the first introduction of hydrolyzed superworm meal in sea trout nutrition. It was conducted to evaluate the effects of inclusion in the diet of hydrolyzed insect meals as a partial replacement for fishmeal on growth performance, feed utilization, organosomatic indices, serum biochemical parameters, gut histomorphology, and microbiota composition of sea trout (Salmo trutta m. trutta). The experiment was performed on 225 sea trout fingerlings distributed into three groups (3 tanks/treatment, 25 fish/tank). The control diet was fishmeal-based. In the experimental groups, 10% of hydrolyzed mealworm (TMD) and superworm (ZMD) meals were included. The protein efficiency ratio was lower in the TMD and ZMD. Higher organosomatic indices and liver lipid contents were found in the group fed ZMD. The ZMD increased levels of aspartate aminotransferase, and decreased levels of alkaline phosphatase. The Aeromonas spp. and Enterococcus spp. populations decreased in the ZMD. The concentrations of the Carnobacterium spp. decreased in the ZMD and TMD, as did that of the Lactobacillus group in the TMD. In conclusion, insect meals may be an alternative protein source in sea trout nutrition, as they yield satisfying growth performance and have the capability to modulate biochemical blood parameters and microbiota composition.


2019 ◽  
Vol 42 ◽  
Author(s):  
Emily F. Wissel ◽  
Leigh K. Smith

Abstract The target article suggests inter-individual variability is a weakness of microbiota-gut-brain (MGB) research, but we discuss why it is actually a strength. We comment on how accounting for individual differences can help researchers systematically understand the observed variance in microbiota composition, interpret null findings, and potentially improve the efficacy of therapeutic treatments in future clinical microbiome research.


Author(s):  
Habib Yarizadeh ◽  
Leila Setayesh ◽  
Caroline Roberts ◽  
Mir Saeed Yekaninejad ◽  
Khadijeh Mirzaei

Abstract. Objectives: Obesity plays an important role in the development of chronic diseases including cardiovascular disease and diabetes. A low resting metabolic rate (RMR) for a given body size and composition is a risk factor for obesity, however, there is limited evidence available regarding the association of nutrient patterns and RMR. The aim of this study was to determine the association of nutrient patterns and RMR in overweight and obese women. Study design: This cross-sectional study was conducted on 360 women who were overweight or obese. Method: Dietary intake was assessed using a semi-quantitative standard food frequency questionnaire (FFQ). Nutrient patterns were also extracted by principal components analysis (PCA). All participants were evaluated for their body composition, RMR, and blood parameters. Result: Three nutrient patterns explaining 64% of the variance in dietary nutrients consumption were identified as B-complex-mineral, antioxidant, and unsaturated fatty acid and vitamin E (USFA-vit E) respectively. Participants were categorized into two groups based on the nutrient patterns. High scores of USFA-vit E pattern was significantly associated with the increase of RMR (β = 0.13, 95% CI = 0.79 to 68.16, p = 0.04). No significant associations were found among B-complex-mineral pattern (β = −0.00, 95% CI = −49.67 to 46.03, p = 0.94) and antioxidant pattern (β = 0.03, 95% CI −41.42 to 22.59, p = 0.56) with RMR. Conclusion: Our results suggested that the “USFA-vit E” pattern (such as PUFA, oleic, linoleic, vit.E, α-tocopherol and EPA) was associated with increased RMR.


Food Chain ◽  
2019 ◽  
Vol 8 (1-2) ◽  
pp. 58-78
Author(s):  
Bazit Bakare ◽  
Olufemi Onifade ◽  
Victoria Ojo ◽  
Kafayat Adebayo ◽  
Anandan Samireddypalle

2006 ◽  
Vol 19 (2) ◽  
pp. 214-226
Author(s):  
Al-Ali M. F. M ◽  
Japer A. A. and Al-Moktar M. A

2016 ◽  
Vol 29 (1) ◽  
pp. 172-184
Author(s):  
Sami J. Al-Maliki ◽  
Ali A. A. Al-Ali ◽  
Salma S. Abbas

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