Faradarmani Consciousness Field Suppresses Alzheimer’s Disease Development in both in vitro and in vivo Models of the Disease
Bibliographic record
Abstract
Alzheimer’s Disease (AD) is one of the most common causes of dementia, imposing large financial and psychological burdens on nations worldwide. Thus, we direly need new treatment strategies or drugs for this disease. The aim of this study is to investigate the effects of a novel non-pharmacological method in the treatment of Alzheimer’s disease, based on employing Taheri Consciousness Fields. These fields function at the level of cellular and molecular processes. In this study, the effects of Faradarmani Consciousness Field (CF) on the AD mouse model (in vivo) and human neuron cell line in vitro were investigated. In this study, we established a human neuron cell culture as well as a traumatic brain injury (TBI) mouse model. We then measured changes in amyloidopathy, tau protein content, microtubule assembly, neuronal cell survival, and finally behavior of TBI mice in Elevated Plus Maze under treatment of the Faradarmani CF. According to the results, the treatment of human neural cells and a mouse model of Alzheimer's disease by the Faradarmani CF leads to complete survival of neural cell models and elimination of amyloidopathy and tau protein, and remarkable behavioral improvement of the treated TBI mice model in the elevated plus-maze. Based on the results, Faradarmani CF treatment suppresses AD development in laboratory models. In this regard, conducting a human clinical study with the aim of introducing a new global complementary and alternative medicine in AD treatment is highly recommended.
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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 | 0.001 |
| 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".