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Record W4414205696 · doi:10.1155/are/5959199

Synergistic Effects of Fermented Bile Acids and Cholesterol on Growth Performance, Immune Response, and Intestinal Microbiota of <i>Litopenaeus vannamei</i> in Freshwater Environment

2025· article· en· W4414205696 on OpenAlexaff
Qing Guo, Shuping Pei, Lu Zhao, Wénwén Liú, Maocang Yan, Houfa Zhao, Cuimin Mu, Xuepeng Wang

Bibliographic record

VenueAquaculture Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsMinistry of Agriculture
FundersShandong Agricultural University
KeywordsFermentationImmune systemCholesterolSterolBile acidWeight gainMealAmino acid

Abstract

fetched live from OpenAlex

Cholesterol (CHO) is an expensive essential nutrient for crustaceans. Bile acids (BAs), which function as emulsifiers facilitating lipid absorption in vertebrates, play a crucial role in the growth and sterol metabolism. This study conducted a 2‐month feeding experiment and aimed to investigate both the individual and interactive effects of dietary CHO and fermented BAs (FBAs; a novel type of FBAs) on growth performance, immune response, and intestinal health in Litopenaeus vannamei in freshwater environment. A total of 12 isonitrogenous and isolipidic diets were formulated. These diets were formulated based on a basal diet by separately adding FBAs at a level of 0.04% (A3), CHO at levels of 0.05% (C1), 0.10% (C2), 0.20% (C3), 0.30% (C4), and 0.40% (C5) as well as combinations of FBAs and CHO at levels of A3C1, A3C2, A3C3, A3C4, and A3C5. A control group (N) without the addition of either FBAs or CHO was also included. Considering that the basic diet contained 0.08% CHO (from fish meal and other ingredients) and no detectable FBAs, the actual levels of CHO were adjusted to 0.08% (N), 0.13% (C1), 0.18% (C2), 0.28% (C3), 0.38% (C4), and 0.48% (C5). After 60 days, both FBAs, CHO, and their combination could improve the growth performance of shrimp, as indicated by final weight (FW), weight gain (WG), and specific growth rate (SGR). The best promoting effect was found in A3C2 and A3C3 groups. Two‐way analysis of variance (ANOVA) analysis revealed significant synergistic effects between FBAs and CHO ( p &lt; 0.05). These results suggest that CHO is more efficient than FBAs in promoting growth, but its efficiency can be significantly enhanced when combined with FBAs. Hemolymph biochemical parameters, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), α‐amylase (α‐AMS), triglycerides (TGs), and acid phosphatase, were significant affected by different treatments ( p &lt; 0.05). Gene expression levels in the hepatopancreas showed significantly lower levels of anti‐lipopolysaccharride factor ( ALF ), prophenoloxidase ( proPO ), and alpha‐2‐macroglobulin ( α2M ) and significantly higher levels of alkaline phosphatase (AKP) in different treated groups compared to the control groups ( p &lt; 0.05). The addition of FBAs, CHO, and their combination to the diet increased gut microbiota diversity in L. vannamei . At the phylum level, there was a significant decrease in Proteobacteria abundance and a significant increase in Firmicutes, Tenericutes, and Cyanobacteria compared to the control group. At the genus level, Pseudoalteromonadaceae Vibrio , Vibrionaceae vibrio , Shewanella , and Synechococcus were found to be more abundant in the FBAs and CHO treatment group compared to the control group. In conclusion, a combination of 0.18%–0.28% CHO and 0.04% FBAs into feed formulations demonstrated synergistic effects on L. vannamei under freshwater conditions, significantly enhancing their growth performance, hepatopancreatic and intestinal health, and gut microbiota. This study provides a novel approach for improving the efficiency of L. vannamei breeding in freshwater environments by optimizing the ratio of FBAs and CHO. Furthermore, it provides a potential strategy to reduce dietary CHO content, thereby lowering feeding costs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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