Evidence map›Paper›PMID 33150123›Full record

Review3 Biotech2020

Pitfalls and promises of raw drug identification techniques in the ayurvedic industry: an overview.

Remya Unnikrishnan, Suma Arun Dev, R Jayaraj

Abstract readReview
In one paragraph

Review in 3 Biotech, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Remya UnnikrishnanForest Genetics and Biotechnology Division, Kerala Forest Research Institute, Peechi, Thrissur, Kerala India.
Suma Arun DevForest Genetics and Biotechnology Division, Kerala Forest Research Institute, Peechi, Thrissur, Kerala India.ORCID 0000-0002-6076-9693
R JayarajForest Ecology and Biodiversity Division, Kerala Forest Research Institute, Peechi, Thrissur, Kerala India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

India, with a rich heritage of floral diversity, is well-known for its medicinal plant wealth and is the largest producer of medicinal herbs in the world. Ethnobiological Survey of Ministry of Environment and Forests (MOEF) could identify 8000 plant species utilized in various systems of medicine with approximately 25,000 effective herbal formulations. The extensive consumption to meet demand-supply ratio exerts a heavy strain on the existing resources. This subsequently led to the adulteration and substitution of medicinal plants with look-alike species. The consumer's faith on herbal medicine is in the phase of decline due to the extremities in adulteration/substitution and ensuing consequences. It is imperative to bring forth universally acceptable standard tools to authenticate raw drugs before being processed further into formulations. A vast array of techniques such as physical, chemical (analytical), biochemical, anatomical, organoleptic, and recently emerged DNA based molecular methods are widely used for plant species authentication. In recent years, DNA barcoding has made remarkable progress in the field of medicinal plants research. DNA metabarcoding is the latest development for qualitative evaluation of the herbal formulations, whereas for quantitative analysis, combination of pharmacognostic, pharmacovigilance and analytical methods are inevitable for authentication. This review addresses the overall strengths and shortcomings of the existing as well as recently emerged techniques in authenticating ayurvedic raw drugs.

Indexed as

Analytical techniquesAyurvedic raw drugsDNA barcodingDNA metabarcodingHerbal drug authenticationHRM

Identifiers

PMID33150123
PMCPMC7599280

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.