Analysis of Cell Cycle in Embryonic Fibroblasts and SW480 (Colon Cancer) under the Influence of Taheri Consciousness Fields
Bibliographic record
Abstract
According to Taheri, applying the Faradarmani Consciousness Field (FCF) can lead to the repair and improvement of any system that is placed under the influence of this T-Consciousness Field. Previously, a growth-inducing effect of the FCF on the MCF7 and 4T1 cancer cell lines was observed under in vitro and ex vivo environments respectively. The same cannot be said for in vivo experiment as FCF inhibited the growth of tumor in the body of the cancer mouse models. Overall, the results of previous studies confirmed that cancer cell survival and growth is affected by FCF. The present study aimed to evaluate the reproducibility of the observations in previous studies using in vitro cell cultures of fibroblast cell line under Faradarmani Consciousness Field (FCF) and SW480 cell line under two types of Taheri Consciousness Fields (TCFs). Cell cycle analysis showed that FCF led to a decrease in apoptosis and increase in proliferation of fibroblast cell line. This observation was in accordance with previous studies. Furthermore, according to the MTT assay results, both TCFs 1 and 2 increased survival in the SW480. Cell cycle analysis showed that TCF2 reduced cell survival and the proliferation rate of this cell line. In conclusion, TCFs affected death and survival of these cell lines. Further in vitro and in vivo studies are necessary to fully understand the precise mechanism of these non-material/non-energetic fields.
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) | 0.002 | 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".