Resveratrol in Health and Disease. B. Aggarwal and S. Shishodia, eds. Boca Raton, FL: CRC Press, Taylor & Francis Group, 2006, 712 pp., $199.95, hardcover. ISBN 0-8493-3371-7.
Bibliographic record
Abstract
In little more than a decade, resveratrol has advanced from an obscure constituent of Oriental folk medicine to a wonder drug that, according to the authors of the 27 chapters in this volume, inhibits carcinogenesis as well as actual tumors; protects against cardiovascular, neurodegenerative, and anti-inflammatory diseases as well as radiation damage; and displays antibacterial, antifungal, and antioxidant properties. These beneficial effects are apparently mediated through modulation of gene expression, signal transduction, cell cycle progression, prostaglandin biosynthesis, and angiogenesis. No other plant-based compound, not even quinine or digitalis, comes close to matching resveratrol’s array of benefits. One would have to go back to the birth of penicillin to discover a natural agent to match its potential. Before rushing to the nearest purveyor of herbal medicine for a personal supply, the reader should be warned that most of these reported miracles have taken place in test tubes and cell cultures, that results of in vivo studies have been rather mixed, that human trials are only now beginning, and that there is a large question mark over resveratrol’s bioavailability. It is the paradox of promise and uncertainty surrounding resveratrol that makes this volume so welcome and timely. The editors have explored every nook and cranny of their territory. In doing so, they have assembled an excellent team of contributors whose writing is clear, rarely dull, and often accompanied by exemplary illustrations. Several of the pioneers and leaders of the field are represented: John Pezzuto by a chapter on carcinogenesis that is a masterpiece spanning 150 pages, Barry Gehm by a chapter on the estrogenic effects of resveratrol that is shorter but highly informative, and Alberto Bertelli by the final chapter, describing resveratrol’s pharmacokinetics and metabolism. Even those authors whose work I was reading for the first time impressed me with their grasp of the subject. If there is one thing lacking in this entire volume, it is a healthy dose of skepticism. It is true that the writers of reviews have to describe the literature as it exists, but in doing so it is appropriate to make judgements between good papers and bad papers, between core observations and those that are more likely to be epiphenomena, or worse still, the results of poor science. The tone of the volume is dominated by enthusiasm for its protagonist rather than the cautious exercise of critical thought. It seems to me to be axiomatic that a simple trihydroxy-stilbene cannot be all things to all people. Some of its properties must be much more important than others. A decade from now, we are virtually certain to discover that only a modest proportion of its putative benefits are actually deliverable to suffering humanity; there is no way to tell from this book which of those benefits these are likely to be. However, as a superbly presented account of a volcano of knowledge in the midst of erupting, this book can be thoroughly recommended. The readers of this journal will not find its concepts and vocabulary unfamiliar, even if they will be encountering Polygonum cuspidatum, resveratrol’s most prolific plant source, for the first time.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.020 |
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".