Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 368

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Identification of Novel Anti-inflammatory Agents from Ayurvedic Medicine for Prevention of Chronic Diseases: “Reverse Pharmacology” and “Bedside to Bench” Approach

Open access
B. Aggarwal, Bharat, Prasad, Sahdeo, Reuter, Simone, Kannappan, Ramaswamy, R. Yadav, Vivek, Park, Byoungduck, Hye Kim, Ji, C. Gupta, Subash, Phromnoi, Kanokkarn, Sundaram, Chitra, Prasad, Seema, M. Chaturvedi, Madan, Sung, Bokyung·2011·Current Drug Targets

Abstract

Inflammation, although first characterized by Cornelius Celsus, a physician in first Century Rome, it was Rudolf Virchow, a German physician in nineteenth century who suggested a link between inflammation and cancer, cardiovascular diseases, diabetes, pulmonary diseases, neurological diseases and other chronic diseases. Extensive research within last three decades has confirmed these observations and identified the molecular basis for most chronic diseases and for the associated inflammation. The transcription factor, Nuclear Factor-kappaB (NF-kappaB) that controls over 500 different gene products, has emerged as major mediator of inflammation. Thus agents that can inhibit NF-kappaB and diminish chronic inflammation have potential to prevent or delay the onset of the chronic diseases and further even treat them. In an attempt to identify novel anti-inflammatory agents which are safe and effective, in contrast to high throughput screen, we have turned to "reverse pharmacology" or "bed to benchside" approach. We found that Ayurveda, a science of long life, almost 6,000 years old, can serve as a "goldmine" for novel anti-inflammatory agents used for centuries to treat chronic diseases. The current review is an attempt to provide description of various Ayurvedic plants currently used for treatment, their active chemical components, and the inflammatory pathways that they inhibit.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2174/138945011798109464

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Cited by

See all 368

Metadata

Authors
B. Aggarwal, B., Prasad, S., Reuter, S., Kannappan, R., R. Yadav, V., Park, B., Hye Kim, J., C. Gupta, S., Phromnoi, K., Sundaram, C., Prasad, S., M. Chaturvedi, M., & Sung, B.
Venue
Current Drug Targets
Published
2011-10-01
Volume
12
Issue
11
Pages
1595-1653
ISSN
1389-4501 (ISSN-L)
OpenAlex
W2137736893
Cited by
368
References
10
FWCI
36.11
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Identification of Novel Anti-inflammatory Agents from Ayurvedic Medicine for Prevention of Chronic Diseases: “Reverse Pharmacology” and “Bedside to Bench” Approach. https://app.cochat.ai/papers/10.2174/138945011798109464
The paper
B. Aggarwal, B., Prasad, S., Reuter, S., Kannappan, R., R. Yadav, V., Park, B., Hye Kim, J., C. Gupta, S., Phromnoi, K., Sundaram, C., Prasad, S., M. Chaturvedi, M., & Sung, B. (2011). Identification of Novel Anti-inflammatory Agents from Ayurvedic Medicine for Prevention of Chronic Diseases: “Reverse Pharmacology” and “Bedside to Bench” Approach. Current Drug Targets. https://doi.org/10.2174/138945011798109464

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.