The Cognitive-Enhancing Effects of Lion’s Mane in A Rodent Model of Attention-Deficit Hyperactivity Disorder (ADHD): A Research Protocol
Bibliographic record
Abstract
The intersection between mycology and modern medicine has captivated the scientific community, with a growing focus on fungus potential to address symptoms of mental health disorders. Lion’s Mane mushroom, renowned for cognitive enhancement attributed to hericenones and erinacines stimulating the nerve growth factors (NGF), has emerged as a subject of significant interest. Recent studies suggest its potential antidepressant and anxiolytic properties, broadening its relevance to various mental health challenges. Attention-deficit/hyperactivity disorder (ADHD), characterized by executive functioning difficulties, notably in attention and memory, is often linked to working memory deficits. Numerous investigations indicate that a substantial proportion of individuals with ADHD exhibit impaired working memory. Working memory, responsible for temporarily holding and processing information, plays a pivotal role in encoding memories for long-term storage or extinction. This paper suggests that Lion’s Mane may offer therapeutic benefits by enhancing working memory, thereby positively influencing the day-to-day executive functioning of individuals grappling with ADHD. It explores the potential benefits of utilizing Lion’s Mane with a specific focus on working memory, using a spontaneously hypertensive rat (SHR) model, which presents ADHD-like symptoms. The potential implications of such findings underscore the promising role of Lion’s Mane in addressing cognitive challenges associated with ADHD.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".