Evidence map›Paper›PMID 32793287›Full record

ReviewFrontiers in genetics2020

Genetic Manipulation for Improved Nutritional Quality in Rice.

Priyanka Das, Sanghamitra Adak, Arun Lahiri Majumder

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
10.2field-weighted citation impact, top 2% of its field
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

24 citing papers in PubMed, 65 citations in OpenAlex.

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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 at 1 institution in 1 country.

Priyanka DasDivision of Plant Biology, Bose Institute, Kolkata, India.
Sanghamitra AdakDivision of Plant Biology, Bose Institute, Kolkata, India.
Arun Lahiri MajumderDivision of Plant Biology, Bose Institute, Kolkata, India.
Bose Institute · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food with higher nutritional value is always desired for human health. Rice is the prime staple food in more than thirty developing countries, providing at least 20% of dietary protein, 3% of dietary fat and other essential nutrients. Several factors influence the nutrient content of rice which includes agricultural practices, post-harvest processing, cultivar type as well as manipulations followed by selection through breeding and genetic means. In addition to mutation breeding, genetic engineering approach also contributed significantly for the generation of nutrition added varieties of rice in the last decade or so. In the present review, we summarize the research update on improving the nutritional characteristics of rice by using genetic engineering and mutation breeding approach. We also compare the conventional breeding techniques of rice with modern molecular breeding techniques toward the generation of nutritionally improved rice variety as compared to other cereals in areas of micronutrients and availability of essential nutrients such as folate and iron. In addition to biofortification, our focus will be on the efforts to generate low phytate in seeds, increase in essential fatty acids or addition of vitamins (as in golden rice) all leading to the achievements in rice nutrition science. The superiority of biotechnology over conventional breeding being already established, it is essential to ascertain that there are no serious negative agronomic consequences for consumers with any difference in grain size or color or texture, when a nutritionally improved variety of rice is generated through genetic engineering technology.

Indexed as

biotechnologybreedingmicronutrientnutritionrice

Identifiers

PMID32793287
PMCPMC7393646
OpenAlexW3045215035

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.