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Record W3194641669 · doi:10.3390/biom11081217

Biomedical Applications of Biomolecules Isolated from Methanotrophic Bacteria in Wastewater Treatment Systems

2021· review· en· W3194641669 on OpenAlexafffund
Rana Salem, Ahmed Eldyasti, Gerald F. Audette

Bibliographic record

VenueBiomolecules · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial metabolism and enzyme function
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiomoleculeBacteriaSewage treatmentWastewaterMicroorganismChemistryBiochemical engineeringEnvironmental chemistryBiologyEnvironmental scienceEnvironmental engineeringBiochemistryEngineering

Abstract

fetched live from OpenAlex

Wastewater treatment plants and other remediation facilities serve important roles, both in public health, but also as dynamic research platforms for acquiring useful resources and biomolecules for various applications. An example of this is methanotrophic bacteria within anaerobic digestion processes in wastewater treatment plants. These bacteria are an important microbial source of many products including ectoine, polyhydroxyalkanoates, and methanobactins, which are invaluable to the fields of biotechnology and biomedicine. Here we provide an overview of the methanotrophs' unique metabolism and the biochemical pathways involved in biomolecule formation. We also discuss the potential biomedical applications of these biomolecules through creation of beneficial biocompatible products including vaccines, prosthetics, electronic devices, drug carriers, and heart stents. We highlight the links between molecular biology, public health, and environmental science in the advancement of biomedical research and industrial applications using methanotrophic bacteria in wastewater treatment systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.022
GPT teacher head0.288
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2021
Admission routes2
Has abstractyes

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