Effects of Polysaccharide Extracted from Traditional Chinese Medical Herbs on Lymphocyte Transformation Rate and AI-HI Antibody Titer in Chicks
Bibliographic record
Abstract
[Object]: Detect whether different concentrations of Chinese herbs compound polysaccharides (CPS), astragaluspolysaccharides (APS) and angeulica polysaccharides (ASP), epimedium herb polysaccharides (EPS) have effects onthe immunity function of healthy Roman chicken. [Method]: 260 one-day-old chickens were divided into thirteengroups randomly, 20 birds each group. The physiological saline, Chinese herbs compound polysaccharides, APS, ASPor EPS had been hypodermically injected for seven days continuously, and the blood was drawn on the 7th, 14th, 21st,28th, 35th, 42nd, 49th and 56th day to evaluate the activity of the translation rate of blood lymphocyte and AI-HI antibodytiters in chickens. [Result]: The results of the experiment showed that the translation rate of blood lymphocyte andAI-HI antibody titers increased markedly after the use of Chinese herbs compound polysaccharides, APS, ASP and EPSto the chickens. Chinese herbs compound polysaccharides had more effective function improving the translation rate ofblood lymphocyte and AI-HI antibody titers than others. [Conclusion]: The Chinese herbs compound polysaccharides,APS, ASP and EPS could promote the immunity function of the chickens. The Chinese herbs compoundpolysaccharides were the strangest one among them.
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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".