Influence of Faradarmani Consciousness Field on Spatial Memory and Passive Avoidance Behavior of Scopolamine Model of Alzheimer Disease in Male Wistar Rats
Bibliographic record
Abstract
Alzheimer's disease (AD) is a growing public health concern, affecting millions of patients worldwide and costing billions of dollars annually. There is a pressing need to find effective treatment strategies for AD. In the 1980s, Mohammad Ali Taheri introduced novel fields with a non-material, non-energetic nature, named Taheri Consciousness Fields (TCFs). One of these fields, Faradarmani Consciousness Field (FCF), has been introduced as a complementary medicine, and its effects can be investigated through reproducible laboratory experiments. In this study, we evaluated the influence of FCF on scopolamine-induced memory impairments in male Wistar rats. Rats were divided into four groups (n=10 each). The scopolamine groups received a single injection of scopolamine (SCP) (5 mg/kg) intraperitoneally one hour before the test. Rats in the FCF groups were exposed to this treatment one day before the administration of scopolamine. The passive avoidance and Morris water maze (MWM) tests were conducted to evaluate memory function in the scopolamine-induced rats. The results of passive avoidance and MWM tests revealed that scopolamine induced a decline in spatial memory and cognitive function. Whereas, rats treated by FCF spent more time in target zone and the step through latency was significantly greater than SCP group without FCF. Moreover, rats had lower velocity which may be related to the reduction in stress under FCF. Overall, FCF could significantly ameliorate scopolamine-induced cognitive impairment. Further experiments are required to investigate how exactly this field influence memory at the molecular level.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".