Effect of Haishengsu on transplanted K562 and drug-resistant K562/ADM tumors: An experimental study
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of Haishengsu (HSS) on transplanted K562 and drug-resistant K562/ADM tumors. METHODS: Mice were inoculated subcutaneously with K562 and K562/ADM cells, respectively. Tumour-bearing animals were divided into HSS, adriamycin, combination therapy (adriamycin plus HSS) and placebo groups. The anti-tumour effect was assessed by tumour growth curve and tumour inhibitory rate (IR). RESULTS: In animals inoculated with K562 cells, the inhibitory rates of high (1800mg/kg) and medium (900mg/kg ) dose HSS groups were 100% and 96.4%, respectively, which was higher than that in the adriamycin (88.9%) or the combination therapy groups (85.8%, P < 0.05). The inhibitory rate in the low-dose HSS group (53.4%) was lower than in all other groups (P < 0.01). In mice inoculated with K562/ADM cells, the inhibitory rates in the high, medium and low dose HSS groups were 100%, 95.9%, and 44.1%, respectively. In the adriamycin group, the inhibitory rate was 23.07%, which was lower than in the HSS group (P < 0.01). Pathological examination of tumour tissues from HSS-treated animals showed extensive necrosis and bleeding. CONCLUSIONS: Haishengsu inhibits the growth of transplanted K562 tumours in mice. It is also effective in suppressing the growth of drug-resistant K562/ADM tumors in this animal model.
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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) | 0.001 | 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 | 0.001 |
| 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".