Effect of Faradarmani Consciousness Field on the Mice 4T1 Breast Cancer Model
Bibliographic record
Abstract
The use of complementary and alternative medicine along with conventional methods of chemotherapy and radiation therapy, with the aim of cancer prevention and treatment, has been investigated and validated in various preclinical and clinical studies. Meanwhile, in contrast to the widespread use of medicinal plants and other complementary and alternative medicine methods in the preclinical trials with animal models of cancer, as an important step confirming the safe and effective use in humans, similar studies in the field of mind-body modalities are rarely examined. A new treatment method founded and introduced by Mohammad Ali Taheri provides a different type of consciousness (Taheri Consciousness Fields) that is neither matter nor energy. The effectiveness and capability of this new complementary and alternative medicine were examined in this study, and the effectiveness of one of Taheri Consciousness Fields (TCFs) named Faradarmani, was investigated in the 4T1 orthotopic breast cancer spontaneous metastasis Balb/c mouse model. According to the results, the Faradarmani CF treatment, during tumor progression, had a significant effect on inhibiting the growth of cancerous masses and preventing metastasis in the mice animal model under the study. Moreover, this treatment had a reproducible and significant positive effect on survival behavior and natural vital functions of the treated mice in comparison with the untreated control group.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".