<i>Echinacea</i>: a Miracle Herb against Aging and Cancer? Evidence <i>In vivo</i> in Mice
Bibliographic record
Abstract
Echinacea has been viewed as an immunoenhancing herb since it became commercially available several years ago. Indeed, its medicinal significance is responsible for billions of dollars in worldwide sales annually. Unfortunately, most of the 'evidence' for the purported medicinal efficacy of Echinacea has been anecdotal and, moreover, to this day, there is no formal proof on how to achieve the best results-whether it should be consumed daily throughout life as a prophylactic; consumed by either young or old; or consumed after diseases, such as cancer, have taken hold. Our work over the past 5 years has led to conclusive answers to some of these questions, at least in mice. Our results have shown that daily consumption of Echinacea is indeed prophylactic, extends the life span of aging mice, significantly abates leukemia and extends the life span of leukemic mice. Given that humans are 97% genetically common with mice and that virtually all our basic physiology is identical, it is neither unjustified to extrapolate these observations to humans nor would it be an arduous task to perform many of these studies in humans, thus establishing viable scientific evidence replacing the anecdotal.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".