Evidence map›Paper›PMID 37484494›Full record

ReviewComputational and structural biotechnology journal2023

Trends in

Sandhya K Jayasekara, Hriday Dhar Joni, Bhagya Jayantha, Lakshika Dissanayake, Christopher Mandrell, Manuka M S Sinharage, Ryan Molitor, Thushari Jayasekara, Poopalasingam Sivakumar, Lahiru N Jayakody

Abstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
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

10 authors.

Sandhya K JayasekaraSchool of Biological Science, Southern Illinois University Carbondale, Carbondale, IL, USA.
Hriday Dhar JoniSchool of Physics and Applied Physics, Southern Illinois University Carbondale, Carbondale, IL, USA.
Bhagya JayanthaSchool of Biological Science, Southern Illinois University Carbondale, Carbondale, IL, USA.
Lakshika DissanayakeSchool of Biological Science, Southern Illinois University Carbondale, Carbondale, IL, USA.
Christopher MandrellSchool of Physics and Applied Physics, Southern Illinois University Carbondale, Carbondale, IL, USA.
Manuka M S SinharageSchool of Physics and Applied Physics, Southern Illinois University Carbondale, Carbondale, IL, USA.
Ryan MolitorSchool of Physics and Applied Physics, Southern Illinois University Carbondale, Carbondale, IL, USA.
Thushari JayasekaraSchool of Physics and Applied Physics, Southern Illinois University Carbondale, Carbondale, IL, USA.
Poopalasingam SivakumarSchool of Physics and Applied Physics, Southern Illinois University Carbondale, Carbondale, IL, USA.
Lahiru N JayakodySchool of Biological Science, Southern Illinois University Carbondale, Carbondale, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polyethylene terephthalate (PET) is the largest produced polyester globally, and less than 30% of all the PET produced globally (∼6 billion pounds annually) is currently recycled into lower-quality products. The major drawbacks in current recycling methods (mechanical and chemical), have inspired the exploration of potentially efficient and sustainable PET depolymerization using biological approaches. Researchers have discovered efficient PET hydrolyzing enzymes in the plastisphere and have demonstrated the selective degradation of PET to original monomers thus enabling biological recycling or upcycling. However, several significant hurdles such as the less efficiency of the hydrolytic reaction, low thermostability of the enzymes, and the inability of the enzyme to depolymerize crystalline PET must be addressed in order to establish techno-economically feasible commercial-scale biological PET recycling or upcycling processes. Researchers leverage a synthetic biology-based design; build, test, and learn (DBTL) methodology to develop commercially applicable efficient PET hydrolyzing enzymes through 1) high-throughput metagenomic and proteomic approaches to discover new PET hydrolyzing enzymes with superior properties: and, 2) enzyme engineering approaches to modify and optimize PET hydrolyzing properties. Recently,

Indexed as

Machine learningMolecular mechanicsMutagenesisPET bio-recyclingPET hydrolases

Identifiers

PMID37484494
PMCPMC10362282

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.