Biochemical composition of larval feed regulates early myogenesis and growth by inducing expression of <i>GH–IGF</i> , myogenic regulating factors, and <i>Myostatin</i> in <i>Labeo rohita</i>
Bibliographic record
Abstract
Balanced nutrient composition of larval fish diet significantly affects growth and survival. Stunted growth and high mortality are common issues in conventional carp hatcheries. To explore the causes, we analyzed the proximate and biochemical composition of live feed (LF) in nursery ponds and compared it to a formulated nano-diet (FND). Rohu ( Labeo rohita (Hamilton,1822)) larvae 3 days after hatching (DAH) were divided into 10 tanks, with five receiving LF and five FND until 35 DAH. Larvae were periodically analyzed for growth and gene expression at specific intervals (3, 10, 15, 20, 25, 30, and 35 DAH). Significant differences in body composition were observed between the two groups. Certain essential fatty acids (arachidonic acid, EPA, and DHA) and amino acids (methionine, lysine, and phenylalanine) were lower in both LF and LF-fed larvae compared to FND and FND-fed larvae. The FND group showed higher survival, specific growth rate, and net weight gain. Additionally, higher expression of GH, IGF-1, and myogenic regulatory factors in the FND group suggest that nutrient composition influences molecular growth regulation. In contrast, elevated Myostatin expression in LF-fed larvae suggests a potential inhibitory effect on early myogenesis, which might indicate the limited nutrient availability in LF.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".